diff --git a/CITATION.cff b/CITATION.cff index d18beea3..777a1de9 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -2,7 +2,7 @@ cff-version: 1.2.0 message: "If you use this software, please cite it as below." title: "CCC-GPU: A graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses" doi: 10.1093/bioinformatics/btag068 -version: v1.0.0 +version: 0.2.4 date-released: 2026-01-16 authors: - family-names: Zhang @@ -31,7 +31,7 @@ keywords: - gene expression - RNA-seq analysis - nonlinear patterns -license: MIT +license: BSD-2-Clause-Patent repository-code: https://github.com/pivlab/ccc-gpu preferred-citation: type: article diff --git a/CMakeLists.txt b/CMakeLists.txt index 8d3acc08..e32bc1b2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,7 @@ -cmake_minimum_required(VERSION 3.15...3.26) +# Single CMake floor for the project; keep in sync with `cmake.version` in +# pyproject.toml ([tool.scikit-build]). The `...4.0` policy ceiling opts into +# modern CMake behavior up to 4.0. +cmake_minimum_required(VERSION 3.26...4.0) project(${SKBUILD_PROJECT_NAME} LANGUAGES CUDA CXX) # When ON, build the CUDA C++ gtests under tests/cuda_ext/ and register them with diff --git a/README.md b/README.md index ba5cae3a..5bb0b500 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Clustermatch Correlation Coefficient GPU (CCC-GPU) -[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause) -[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) +[![License](https://img.shields.io/badge/License-BSD--2--Clause--Patent-orange.svg)](https://opensource.org/license/bsdpluspatent) +[![Python 3.10+](https://img.shields.io/badge/python-3.10--3.14-blue.svg)](https://www.python.org/downloads/) [![CUDA](https://img.shields.io/badge/CUDA-12.0+-green.svg)](https://developer.nvidia.com/cuda-downloads) [![Documentation](https://img.shields.io/badge/docs-readthedocs-blue.svg)](https://ccc-gpu.readthedocs.io/en/latest/) @@ -25,7 +25,7 @@ Original notebooks and scripts used for the manuscript, as well as other analyse ### Requirements **Hardware:** -- Nvidia GPU with CUDA Compute Capability 8.6 or higher +- Nvidia GPU with CUDA Compute Capability 7.5 or higher (wheels ship native code for 7.5, 8.0, 8.6, 8.9, and 9.0) **Software:** - OS: Linux x86_64 distributions using glibc 2.28 or later, including: @@ -33,8 +33,8 @@ Original notebooks and scripts used for the manuscript, as well as other analyse - Ubuntu 18.10+ - Fedora 29+ - CentOS/RHEL 8+ -- Python 3.10 to 3.13 (3.14 will be supported soon) -- Nvidia driver with CUDA 12.5 or higher (for GPU acceleration) +- Python 3.10 to 3.14 +- Nvidia driver with CUDA 12.0 or higher (for GPU acceleration) > **Note**: You can use command `nvidia-smi` to check your Nvidia driver and CUDA version. @@ -45,7 +45,7 @@ Original notebooks and scripts used for the manuscript, as well as other analyse ```bash # Create conda environment if you want to test it out in a separate environment -# conda create -n ccc-gpu -c conda-forge python=3.10 (or 3.11, 3.12, 3.13) +# conda create -n ccc-gpu -c conda-forge python=3.12 # any of 3.10-3.14 # conda activate ccc-gpu # Install cccgpu from PyPI @@ -168,7 +168,7 @@ ccc( - **`n_jobs`** *(int, default=1)*: Number of CPU cores/threads for internal parallelization. `None` uses all available cores. Negative values use `os.cpu_count() + n_jobs`. Must yield a value >= 1. -- **`pvalue_n_perms`** *(int, optional)*: If provided and > 0, computes p-value using the specified number of permutations. If None or 0, p-values are not computed. +- **`pvalue_n_perms`** *(int, optional)*: If provided and > 0, also estimates a one-sided permutation p-value for each coefficient using the specified number of permutations. If None or 0, p-values are not computed. See [Computing p-values](#computing-p-values) below. - **`partitioning_executor`** *(str, default="thread")*: Executor type for data partitioning. `"thread"` uses ThreadPoolExecutor (less memory), `"process"` uses ProcessPoolExecutor (potentially faster), any other value disables parallelization for partitioning. @@ -193,6 +193,42 @@ Return type varies based on input dimensionality and parameters: - Uses GPU acceleration (CUDA) for coefficient computation - NaN values in input data are not supported +### Computing p-values + +Passing `pvalue_n_perms` estimates a **one-sided permutation p-value** for each +coefficient. One feature's partitions are shuffled `pvalue_n_perms` times and the +CCC is recomputed to build an empirical null distribution; the p-value is + +``` +p = (#{permuted CCC >= observed CCC} + 1) / (pvalue_n_perms + 1) +``` + +```python +import numpy as np +from ccc.coef.impl_gpu import ccc + +np.random.seed(123) +x = np.random.randn(300) +y = x + np.random.randn(300) * 0.5 + +coef, pvalue = ccc(x, y, pvalue_n_perms=1000) +print(f"CCC: {coef:.3f}, p-value: {pvalue:.3f}") +``` + +- **Interpretation:** the test is one-sided — a *smaller* p-value is stronger + evidence of a non-chance association. The smallest resolvable p-value is + `1 / (pvalue_n_perms + 1)` (≈ `1e-3` for 999 permutations), so pick + `pvalue_n_perms` for the resolution you need. +- **Shapes:** a 1d pair returns `(coef, pvalue)`; a 2d input returns a + `(coefficients, p-values)` tuple of aligned arrays. +- **Cost:** for a 2d input a p-value is computed for each of the `n*(n-1)/2` + pairs, each costing `pvalue_n_perms` extra CCC evaluations — this can be far + more expensive than the point estimate, so prefer small inputs or a modest + permutation count and parallelize with `n_jobs`. + +See the [Computing p-values](https://ccc-gpu.readthedocs.io/en/latest/usage.html#computing-p-values) +docs section for the full method and interpretation. + ### Working with Gene Expression Data CCC-GPU is particularly useful for genomics applications: @@ -270,6 +306,17 @@ CCC-GPU provides significant performance improvements over CPU-only implementati *Benchmarks performed on synthetic gene expression data with 1000 fixed samples. Hardware: AMD Ryzen Threadripper 7960X CPU and an NVIDIA RTX 4090 GPU. Git commit on which the benchmark results were collected: 05f129dfa47ad801eff963b4189484c7c64bd28e* +The table above was produced with the bundled `ccc-gpu-bench` CLI (installed with the package): + +```bash +# Fast sanity sweep +ccc-gpu-bench coef --preset smoke +# Reproduce the sweep behind the table (long-running; run on the reference GPU box) +ccc-gpu-bench coef --preset paper --format csv -o coef_paper.csv +``` + +See the [Benchmarking](https://ccc-gpu.readthedocs.io/en/latest/benchmarking.html) docs for all modes and the output schema. + ## Documentation Build and view the full documentation locally: @@ -304,7 +351,7 @@ Contributions are welcome! Please feel free to submit a Pull Request. For major ## License -This project is licensed under the BSD 2-Clause License - see the [LICENSE](LICENSE) file for details. +This project is licensed under the BSD-2-Clause Plus Patent License (SPDX: `BSD-2-Clause-Patent`) - see the [LICENSE](LICENSE) file for details. ## Acknowledgments diff --git a/conda-lock.yml b/conda-lock.yml index 452a4050..023b2405 100644 --- a/conda-lock.yml +++ b/conda-lock.yml @@ -9,14 +9,12 @@ # To update a single package to the latest version compatible with the version constraints in the source: # conda-lock lock --lockfile conda-lock.yml --update PACKAGE # To re-solve the entire environment, e.g. after changing a version constraint in the source file: -# conda-lock -f environment/environment-gpu.yml -f environment/environment-gpu-new.yml --lockfile conda-lock.yml +# conda-lock -f environment/environment-gpu.yml --lockfile conda-lock.yml version: 1 metadata: content_hash: - linux-64: ef13e620f5a168f0d218b88025bcf33e45772813fb930a8176edffcbbc0cb8cd + linux-64: 54fdc379cf255b4872a255a968b8a790655dde756d944d99cf71fa47f66b5e58 channels: - - url: rapidsai - used_env_vars: [] - url: conda-forge used_env_vars: [] - url: nvidia @@ -25,30 +23,18 @@ metadata: - linux-64 sources: - environment/environment-gpu.yml - - environment/environment-gpu-new.yml package: -- name: _libgcc_mutex - version: '0.1' - manager: conda - platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2 - hash: - md5: d7c89558ba9fa0495403155b64376d81 - sha256: fe51de6107f9edc7aa4f786a70f4a883943bc9d39b3bb7307c04c41410990726 - category: main - optional: false - name: _openmp_mutex version: '4.5' manager: conda platform: linux-64 dependencies: - _libgcc_mutex: '0.1' + __glibc: '>=2.17,<3.0.a0' libgomp: '>=7.5.0' - url: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2 + url: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda hash: - md5: 73aaf86a425cc6e73fcf236a5a46396d - sha256: fbe2c5e56a653bebb982eda4876a9178aedfc2b545f25d0ce9c4c0b508253d22 + md5: a9f577daf3de00bca7c3c76c0ecbd1de + sha256: 1dd3fffd892081df9726d7eb7e0dea6198962ba775bd88842135a4ddb4deb3c9 category: main optional: false - name: alabaster @@ -63,459 +49,148 @@ package: sha256: 6c4456a138919dae9edd3ac1a74b6fbe5fd66c05675f54df2f8ab8c8d0cc6cea category: main optional: false -- name: alsa-lib - version: 1.2.13 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.13-hb9d3cd8_0.conda - hash: - md5: ae1370588aa6a5157c34c73e9bbb36a0 - sha256: f507b58f77eabc0cc133723cb7fc45c053d551f234df85e70fb3ede082b0cd53 - category: main - optional: false -- name: asttokens - version: 3.0.0 +- name: annotated-types + version: 0.7.0 manager: conda platform: linux-64 dependencies: python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.0-pyhd8ed1ab_1.conda - hash: - md5: 8f587de4bcf981e26228f268df374a9b - sha256: 93b14414b3b3ed91e286e1cbe4e7a60c4e1b1c730b0814d1e452a8ac4b9af593 - category: main - optional: false -- name: attr - version: 2.5.1 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2 - hash: - md5: d9c69a24ad678ffce24c6543a0176b00 - sha256: 82c13b1772c21fc4a17441734de471d3aabf82b61db9b11f4a1bd04a9c4ac324 - category: main - optional: false -- name: aws-c-auth - version: 0.8.1 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-cal: '>=0.8.1,<0.8.2.0a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - aws-c-http: '>=0.9.2,<0.9.3.0a0' - aws-c-io: '>=0.15.3,<0.15.4.0a0' - aws-c-sdkutils: '>=0.2.2,<0.2.3.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.8.1-h205f482_0.conda - hash: - md5: 9c500858e88df50af3cc883d194de78a - sha256: ebe5e33249f37f6bb481de99581ebdc92dbfcf1b6915609bcf3c9e78661d6352 - category: main - optional: false -- name: aws-c-cal - version: 0.8.1 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - libgcc: '>=13' - openssl: '>=3.3.1,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-cal-0.8.1-h1a47875_3.conda - hash: - md5: 55a8561fdbbbd34f50f57d9be12ed084 - sha256: 095ac824ea9303eff67e04090ae531d9eb33d2bf8f82eaade39b839c421e16e8 - category: main - optional: false -- name: aws-c-common - version: 0.10.6 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.10.6-hb9d3cd8_0.conda - hash: - md5: d7d4680337a14001b0e043e96529409b - sha256: 496e92f2150fdc351eacf6e236015deedb3d0d3114f8e5954341cbf9f3dda257 - category: main - optional: false -- name: aws-c-compression - version: 0.3.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-compression-0.3.0-h4e1184b_5.conda - hash: - md5: 3f4c1197462a6df2be6dc8241828fe93 - sha256: 62ca84da83585e7814a40240a1e750b1563b2680b032a471464eccc001c3309b - category: main - optional: false -- name: aws-c-event-stream - version: 0.5.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - aws-c-io: '>=0.15.3,<0.15.4.0a0' - aws-checksums: '>=0.2.2,<0.2.3.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-event-stream-0.5.0-h7959bf6_11.conda - hash: - md5: 9b3fb60fe57925a92f399bc3fc42eccf - sha256: 10d7240c7db0c941fb1a59c4f8ea6689a434b03309ee7b766fa15a809c553c02 - category: main - optional: false -- name: aws-c-http - version: 0.9.2 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-cal: '>=0.8.1,<0.8.2.0a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - aws-c-compression: '>=0.3.0,<0.3.1.0a0' - aws-c-io: '>=0.15.3,<0.15.4.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-http-0.9.2-hefd7a92_4.conda - hash: - md5: 5ce4df662d32d3123ea8da15571b6f51 - sha256: 4a330206bd51148f6c13ca0b7a4db40f29a46f090642ebacdeb88b8a4abd7f99 - category: main - optional: false -- name: aws-c-io - version: 0.15.3 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-cal: '>=0.8.1,<0.8.2.0a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - libgcc: '>=13' - s2n: '>=1.5.11,<1.5.12.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-io-0.15.3-h173a860_6.conda - hash: - md5: 9a063178f1af0a898526cc24ba7be486 - sha256: 335d822eead0a097ffd23677a288e1f18ea22f47a92d4f877419debb93af0e81 - category: main - optional: false -- name: aws-c-mqtt - version: 0.11.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - aws-c-http: '>=0.9.2,<0.9.3.0a0' - aws-c-io: '>=0.15.3,<0.15.4.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-mqtt-0.11.0-h11f4f37_12.conda - hash: - md5: 96c3e0221fa2da97619ee82faa341a73 - sha256: 512d3969426152d9d5fd886e27b13706122dc3fa90eb08c37b0d51a33d7bb14a - category: main - optional: false -- name: aws-c-s3 - version: 0.7.9 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-auth: '>=0.8.1,<0.8.2.0a0' - aws-c-cal: '>=0.8.1,<0.8.2.0a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - aws-c-http: '>=0.9.2,<0.9.3.0a0' - aws-c-io: '>=0.15.3,<0.15.4.0a0' - aws-checksums: '>=0.2.2,<0.2.3.0a0' - libgcc: '>=13' - openssl: '>=3.4.0,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-s3-0.7.9-he1b24dc_1.conda - hash: - md5: caafc32928a5f7f3f7ef67d287689144 - sha256: 15fbdedc56850f8be5be7a5bcaea1af09c97590e631c024ae089737fc932fc42 - category: main - optional: false -- name: aws-c-sdkutils - version: 0.2.2 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-sdkutils-0.2.2-h4e1184b_0.conda - hash: - md5: dcd498d493818b776a77fbc242fbf8e4 - sha256: 0424e380c435ba03b5948d02e8c958866c4eee50ed29e57f99473a5f795a4cfc - category: main - optional: false -- name: aws-checksums - version: 0.2.2 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-checksums-0.2.2-h4e1184b_4.conda - hash: - md5: 74e8c3e4df4ceae34aa2959df4b28101 - sha256: 1ed9a332d06ad595694907fad2d6d801082916c27cd5076096fda4061e6d24a8 - category: main - optional: false -- name: aws-crt-cpp - version: 0.29.9 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-auth: '>=0.8.1,<0.8.2.0a0' - aws-c-cal: '>=0.8.1,<0.8.2.0a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - aws-c-event-stream: '>=0.5.0,<0.5.1.0a0' - aws-c-http: '>=0.9.2,<0.9.3.0a0' - aws-c-io: '>=0.15.3,<0.15.4.0a0' - aws-c-mqtt: '>=0.11.0,<0.11.1.0a0' - aws-c-s3: '>=0.7.9,<0.7.10.0a0' - aws-c-sdkutils: '>=0.2.2,<0.2.3.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.29.9-he0e7f3f_2.conda - hash: - md5: 8a4e6fc8a3b285536202b5456a74a940 - sha256: c1930569713bd5231d48d885a5e3707ac917b428e8f08189d14064a2bb128adc - category: main - optional: false -- name: aws-sdk-cpp - version: 1.11.489 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-c-common: '>=0.10.6,<0.10.7.0a0' - aws-c-event-stream: '>=0.5.0,<0.5.1.0a0' - aws-checksums: '>=0.2.2,<0.2.3.0a0' - aws-crt-cpp: '>=0.29.9,<0.29.10.0a0' - libcurl: '>=8.11.1,<9.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - openssl: '>=3.4.0,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/aws-sdk-cpp-1.11.489-h4d475cb_0.conda + typing-extensions: '>=4.0.0' + url: https://conda.anaconda.org/conda-forge/noarch/annotated-types-0.7.0-pyhd8ed1ab_1.conda hash: - md5: b775e9f46dfa94b228a81d8e8c6d8b1d - sha256: 08d6b7d2ed17bfcc7deb903c7751278ee434abdb27e3be0dceb561f30f030c75 + md5: 2934f256a8acfe48f6ebb4fce6cde29c + sha256: e0ea1ba78fbb64f17062601edda82097fcf815012cf52bb704150a2668110d48 category: main optional: false -- name: azure-core-cpp - version: 1.14.0 +- name: appdirs + version: 1.4.4 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libcurl: '>=8.10.1,<9.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - openssl: '>=3.3.2,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/azure-core-cpp-1.14.0-h5cfcd09_0.conda + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/appdirs-1.4.4-pyhd8ed1ab_1.conda hash: - md5: 0a8838771cc2e985cd295e01ae83baf1 - sha256: fe07debdb089a3db17f40a7f20d283d75284bb4fc269ef727b8ba6fc93f7cb5a + md5: f4e90937bbfc3a4a92539545a37bb448 + sha256: 5b9ef6d338525b332e17c3ed089ca2f53a5d74b7a7b432747d29c6466e39346d category: main optional: false -- name: azure-identity-cpp - version: 1.10.0 +- name: babel + version: 2.18.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - azure-core-cpp: '>=1.14.0,<1.14.1.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - openssl: '>=3.3.2,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/azure-identity-cpp-1.10.0-h113e628_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/babel-2.18.0-pyhcf101f3_1.conda hash: - md5: 73f73f60854f325a55f1d31459f2ab73 - sha256: 286b31616c191486626cb49e9ceb5920d29394b9e913c23adb7eb637629ba4de + md5: f1976ce927373500cc19d3c0b2c85177 + sha256: a14a9ad02101aab25570543a59c5193043b73dc311a25650134ed9e6cb691770 category: main optional: false -- name: azure-storage-blobs-cpp - version: 12.13.0 +- name: backports + version: '1.0' manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - azure-core-cpp: '>=1.14.0,<1.14.1.0a0' - azure-storage-common-cpp: '>=12.8.0,<12.8.1.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-blobs-cpp-12.13.0-h3cf044e_1.conda + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/backports-1.0-pyhd8ed1ab_5.conda hash: - md5: 7eb66060455c7a47d9dcdbfa9f46579b - sha256: 2606260e5379eed255bcdc6adc39b93fb31477337bcd911c121fc43cd29bf394 + md5: 767d508c1a67e02ae8f50e44cacfadb2 + sha256: e1c3dc8b5aa6e12145423fed262b4754d70fec601339896b9ccf483178f690a6 category: main optional: false -- name: azure-storage-common-cpp - version: 12.8.0 +- name: backports.tarfile + version: 1.2.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - azure-core-cpp: '>=1.14.0,<1.14.1.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - libxml2: '>=2.12.7,<3.0a0' - openssl: '>=3.3.2,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-common-cpp-12.8.0-h736e048_1.conda + backports: '' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/backports.tarfile-1.2.0-pyhcf101f3_2.conda hash: - md5: 13de36be8de3ae3f05ba127631599213 - sha256: 273475f002b091b66ce7366da04bf164c3732c03f8692ab2ee2d23335b6a82ba + md5: bea46844deb274b2cc2a3a941745fa73 + sha256: 25abdb37e186f0d6ac3b774a63c81c5bc4bf554b5096b51343fa5e7c381193b1 category: main optional: false -- name: azure-storage-files-datalake-cpp - version: 12.12.0 +- name: backports.zstd + version: 1.6.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - azure-core-cpp: '>=1.14.0,<1.14.1.0a0' - azure-storage-blobs-cpp: '>=12.13.0,<12.13.1.0a0' - azure-storage-common-cpp: '>=12.8.0,<12.8.1.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/azure-storage-files-datalake-cpp-12.12.0-ha633028_1.conda - hash: - md5: 7c1980f89dd41b097549782121a73490 - sha256: 5371e4f3f920933bb89b926a85a67f24388227419abd6e99f6086481e5e8d5f2 - category: main - optional: false -- name: babel - version: 2.17.0 - manager: conda - platform: linux-64 - dependencies: - python: '>=3.9' - pytz: '>=2015.7' - url: https://conda.anaconda.org/conda-forge/noarch/babel-2.17.0-pyhd8ed1ab_0.conda + libgcc: '>=14' + python: '' + python_abi: 3.12.* + zstd: '>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/backports.zstd-1.6.0-py312h90b7ffd_0.conda hash: - md5: 0a01c169f0ab0f91b26e77a3301fbfe4 - sha256: 1c656a35800b7f57f7371605bc6507c8d3ad60fbaaec65876fce7f73df1fc8ac + md5: 55811da425538da800b89c0c588652fa + sha256: 95b3d6d44c17c4061db703289f39915646e455f75f0c8c9d949bf081d2e61579 category: main optional: false - name: binutils - version: '2.43' + version: 2.46.1 manager: conda platform: linux-64 dependencies: - binutils_impl_linux-64: '>=2.43,<2.44.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.43-h4852527_4.conda + binutils_impl_linux-64: '>=2.46.1,<2.46.2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/binutils-2.46.1-default_h4852527_102.conda hash: - md5: 29782348a527eda3ecfc673109d28e93 - sha256: 99a94eead18e7704225ac43682cce3f316fd33bc483749c093eaadef1d31de75 + md5: e8452fe381cac5fff20563a07722dfa5 + sha256: 659c367ef49df7741749a2ad240b007d7df51b4f210505a78543deafb001e44c category: main optional: false - name: binutils_impl_linux-64 - version: '2.43' + version: 2.46.1 manager: conda platform: linux-64 dependencies: - ld_impl_linux-64: '2.43' + ld_impl_linux-64: 2.46.1 sysroot_linux-64: '' - url: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.43-h4bf12b8_4.conda + zstd: '>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.46.1-default_hfdba357_102.conda hash: - md5: ef67db625ad0d2dce398837102f875ed - sha256: 194d771be287dc973f6057c0747010ce28adf960f38d6e03ce3e828d7b74833e + md5: a0c5e0b7f58c8ceeb08e5bc41251d5a2 + sha256: fb7bf36984a37ce7e4714d1d1da0bd0e3bfc679520f5cdc184afc676fd4b5da2 category: main optional: false - name: binutils_linux-64 - version: '2.43' + version: 2.46.1 manager: conda platform: linux-64 dependencies: - binutils_impl_linux-64: '2.43' - url: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.43-h4852527_4.conda + binutils_impl_linux-64: 2.46.1 + url: https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.46.1-default_h4852527_102.conda hash: - md5: c87e146f5b685672d4aa6b527c6d3b5e - sha256: fe662a038dc14334617940f42ede9ba26d4160771255057cb14fb1a81ee12ac1 + md5: 32fd07abe84eb14f17c7f5cc6fa8df82 + sha256: 08d7238663fc408ba2ab60b02fa3d06a7ca9d872962e03e90c7e0fdecb7ed1d0 category: main optional: false -- name: bokeh - version: 3.6.3 +- name: boltons + version: 26.0.0 manager: conda platform: linux-64 dependencies: - contourpy: '>=1.2' - jinja2: '>=2.9' - numpy: '>=1.16' - packaging: '>=16.8' - pandas: '>=1.2' - pillow: '>=7.1.0' python: '>=3.10' - pyyaml: '>=3.10' - tornado: '>=6.2' - xyzservices: '>=2021.09.1' - url: https://conda.anaconda.org/conda-forge/noarch/bokeh-3.6.3-pyhd8ed1ab_0.conda - hash: - md5: 606498329a91bd9d5c0439fb2815816f - sha256: 6cc6841b1660cd3246890d4f601baf51367526afe6256dfd8a8d9a8f7db651fe - category: main - optional: false -- name: brotli - version: 1.1.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - brotli-bin: 1.1.0 - libbrotlidec: 1.1.0 - libbrotlienc: 1.1.0 - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/brotli-1.1.0-hb9d3cd8_2.conda - hash: - md5: 98514fe74548d768907ce7a13f680e8f - sha256: fcb0b5b28ba7492093e54f3184435144e074dfceab27ac8e6a9457e736565b0b - category: main - optional: false -- name: brotli-bin - version: 1.1.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libbrotlidec: 1.1.0 - libbrotlienc: 1.1.0 - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/brotli-bin-1.1.0-hb9d3cd8_2.conda + url: https://conda.anaconda.org/conda-forge/noarch/boltons-26.0.0-pyhd8ed1ab_0.conda hash: - md5: c63b5e52939e795ba8d26e35d767a843 - sha256: 261364d7445513b9a4debc345650fad13c627029bfc800655a266bf1e375bc65 + md5: 06c2cbdcfd20af2f72a95e09f8747c52 + sha256: d3c8be0524a5223bfed89c4a5be90598bfca28e0c702014f7799b978027eb5e1 category: main optional: false - name: brotli-python - version: 1.1.0 + version: 1.2.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.1.0-py310hf71b8c6_2.conda + libgcc: '>=14' + libstdcxx: '>=14' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/brotli-python-1.2.0-py312hdb49522_1.conda hash: - md5: bf502c169c71e3c6ac0d6175addfacc2 - sha256: 14f1e89d3888d560a553f40ac5ba83e4435a107552fa5b2b2029a7472554c1ef + md5: 64088dffd7413a2dd557ce837b4cbbdb + sha256: 49df13a1bb5e388ca0e4e87022260f9501ed4192656d23dc9d9a1b4bf3787918 category: main optional: false - name: bzip2 @@ -524,61 +199,78 @@ package: platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda hash: - md5: 62ee74e96c5ebb0af99386de58cf9553 - sha256: 5ced96500d945fb286c9c838e54fa759aa04a7129c59800f0846b4335cee770d + md5: d2ffd7602c02f2b316fd921d39876885 + sha256: 0b75d45f0bba3e95dc693336fa51f40ea28c980131fec438afb7ce6118ed05f6 category: main optional: false - name: c-ares - version: 1.34.4 + version: 1.34.8 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.4-hb9d3cd8_0.conda + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.8-hb03c661_0.conda hash: - md5: e2775acf57efd5af15b8e3d1d74d72d3 - sha256: d4f28d87b6339b94f74762c0076e29c8ef8ddfff51a564a92da2843573c18320 + md5: 6130ad6705adc993b5d8482b7f66e01f + sha256: dee93a82d045f9aa8277829aa465224afa3c7a6ac18388de66099b2be7f705e7 category: main optional: false - name: c-compiler - version: 1.9.0 + version: 1.7.0 manager: conda platform: linux-64 dependencies: binutils: '' gcc: '' - gcc_linux-64: 13.* - url: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.9.0-h2b85faf_0.conda + gcc_linux-64: 12.* + url: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.7.0-hd590300_1.conda hash: - md5: 3cb814f83f1f71ac1985013697f80cc1 - sha256: 1e4b86b0f3d4ce9f3787b8f62e9f2c5683287f19593131640eed01cbdad38168 + md5: e9dffe1056994133616378309f932d77 + sha256: 4213b6cbaed673c07f8b79c089f3487afdd56de944f21c4861ead862b7657eb4 category: main optional: false - name: ca-certificates - version: 2025.1.31 + version: 2026.6.17 manager: conda platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2025.1.31-hbcca054_0.conda + dependencies: + __unix: '' + url: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2026.6.17-hbd8a1cb_0.conda hash: - md5: 19f3a56f68d2fd06c516076bff482c52 - sha256: bf832198976d559ab44d6cdb315642655547e26d826e34da67cbee6624cda189 + md5: a9965dd99f683c5f444428f896635716 + sha256: f8e3c730fa14ee3f170493779f06522c4acf89169f43db4f039727709b6419cf category: main optional: false -- name: cachetools - version: 5.5.2 +- name: cachecontrol + version: 0.14.4 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/cachetools-5.5.2-pyhd8ed1ab_0.conda + msgpack-python: '>=0.5.2,<2.0.0' + python: '>=3.10' + requests: '>=2.16.0' + url: https://conda.anaconda.org/conda-forge/noarch/cachecontrol-0.14.4-pyha770c72_0.conda + hash: + md5: 13bdbb9b693b29134c56a9a00c23de41 + sha256: cca3a26282a5bc37a10afb1aa2006a21c45033cbc4ff012f9501f56f2a115c12 + category: main + optional: false +- name: cachecontrol-with-filecache + version: 0.14.4 + manager: conda + platform: linux-64 + dependencies: + cachecontrol: 0.14.4 + filelock: '>=3.8.0' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/cachecontrol-with-filecache-0.14.4-pyhd8ed1ab_0.conda hash: - md5: bf9c1698e819fab31f67dbab4256f7ba - sha256: 1823dc939b2c2b5354b6add5921434f9b873209a99569b3a2f24dca6c596c0d6 + md5: 41a7966424a8733b11410216d9a47227 + sha256: becb375d64ebda9f9ed030d529b22da21030401d0dbaee62a208e60fca9c4587 category: main optional: false - name: cccl @@ -593,104 +285,105 @@ package: category: main optional: false - name: certifi - version: 2025.1.31 + version: 2026.6.17 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/certifi-2025.1.31-pyhd8ed1ab_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/certifi-2026.6.17-pyhd8ed1ab_0.conda hash: - md5: c207fa5ac7ea99b149344385a9c0880d - sha256: 42a78446da06a2568cb13e69be3355169fbd0ea424b00fc80b7d840f5baaacf3 + md5: c13824fedced67005d3832c152fe9c2f + sha256: 6c13620e458ba43278379d0cdacc30c497336bddfda81681fd50d114a65c702f category: main optional: false - name: cffi - version: 1.17.1 + version: 2.1.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libffi: '>=3.4,<4.0a0' - libgcc: '>=13' + libffi: '>=3.5.2,<3.6.0a0' + libgcc: '>=14' pycparser: '' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/cffi-1.17.1-py310h8deb56e_0.conda + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/cffi-2.1.0-py312h460c074_0.conda hash: - md5: 1fc24a3196ad5ede2a68148be61894f4 - sha256: 1b389293670268ab80c3b8735bc61bc71366862953e000efbb82204d00e41b6c + md5: 0e336c897248bad80548df3414599254 + sha256: bdbc5810ea52e38b6b95e84826d818194ccfbe401a48c0d3829b8b7bb9093af5 category: main optional: false - name: cfgv - version: 3.3.1 + version: 3.5.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.3.1-pyhd8ed1ab_1.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/cfgv-3.5.0-pyhd8ed1ab_0.conda hash: - md5: 57df494053e17dce2ac3a0b33e1b2a2e - sha256: d5696636733b3c301054b948cdd793f118efacce361d9bd4afb57d5980a9064f + md5: 381bd45fb7aa032691f3063aff47e3a1 + sha256: aa589352e61bb221351a79e5946d56916e3c595783994884accdb3b97fe9d449 category: main optional: false - name: charset-normalizer - version: 3.4.1 + version: 3.4.9 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.1-pyhd8ed1ab_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/charset-normalizer-3.4.9-pyhd8ed1ab_0.conda hash: - md5: e83a31202d1c0a000fce3e9cf3825875 - sha256: 4e0ee91b97e5de3e74567bdacea27f0139709fceca4db8adffbe24deffccb09b + md5: d154b40b109e503430979e8a8d099eaf + sha256: 8d8813ef655b4e75e4fb897abd83ad548882efad7b4e836b021b797f42780799 category: main optional: false - name: click - version: 8.1.8 + version: 8.4.2 manager: conda platform: linux-64 dependencies: __unix: '' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/click-8.1.8-pyh707e725_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/click-8.4.2-pyhc90fa1f_0.conda hash: - md5: f22f4d4970e09d68a10b922cbb0408d3 - sha256: c920d23cd1fcf565031c679adb62d848af60d6fbb0edc2d50ba475cea4f0d8ab + md5: 2c4bd6aeb90bb157456841c3270a0d92 + sha256: ccc4787f511964f9a1f2d2d2859c91c5d571fb60f7f09d4c4e092c9b7a94e671 category: main optional: false -- name: cloudpickle - version: 3.1.1 +- name: click-default-group + version: 1.2.4 manager: conda platform: linux-64 dependencies: + click: '' python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/cloudpickle-3.1.1-pyhd8ed1ab_0.conda + url: https://conda.anaconda.org/conda-forge/noarch/click-default-group-1.2.4-pyhd8ed1ab_1.conda hash: - md5: 364ba6c9fb03886ac979b482f39ebb92 - sha256: 21ecead7268241007bf65691610cd7314da68c1f88113092af690203b5780db5 + md5: 7cd83dd6831b61ad9624a694e4afd7dc + sha256: cb7279eecddbd35ea78fd0e189a9a7db8b84c2c0e3b1271cf26251615f75dc4d category: main optional: false - name: cmake - version: 3.31.6 + version: 4.4.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' bzip2: '>=1.0.8,<2.0a0' - libcurl: '>=8.12.1,<9.0a0' - libexpat: '>=2.6.4,<3.0a0' - libgcc: '>=13' - liblzma: '>=5.6.4,<6.0a0' - libstdcxx: '>=13' - libuv: '>=1.50.0,<2.0a0' - libzlib: '>=1.3.1,<2.0a0' - ncurses: '>=6.5,<7.0a0' - rhash: '>=1.4.5,<2.0a0' + libcurl: '>=8.21.0,<9.0a0' + libexpat: '>=2.8.1,<3.0a0' + libgcc: '>=14' + liblzma: '>=5.8.3,<6.0a0' + libstdcxx: '>=14' + libuv: '>=1.52.1,<2.0a0' + libzlib: '>=1.3.2,<2.0a0' + ncurses: '>=6.6,<7.0a0' + rhash: '>=1.4.6,<2.0a0' zstd: '>=1.5.7,<1.6.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/cmake-3.31.6-h74e3db0_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/cmake-4.4.0-hc85cc9f_0.conda hash: - md5: d6e0e094315ee3e99ca153663e7fa669 - sha256: 82372b404995a92fecfef38e9f1cb4977e71b785a728db5a9ed6f1aec49d547c + md5: d76e52d46d803df5080a220ed0a9d730 + sha256: 673384f97c558a083e0944bf4d14627bd7ce29c09efd33965e4ab7b609d04402 category: main optional: false - name: colorama @@ -705,54 +398,107 @@ package: sha256: ab29d57dc70786c1269633ba3dff20288b81664d3ff8d21af995742e2bb03287 category: main optional: false -- name: contourpy +- name: conda-lock + version: 4.0.2 + manager: conda + platform: linux-64 + dependencies: + __linux: '' + boltons: '>=23.0.0' + cachecontrol: '>=0.14.0,<0.15.0' + cachecontrol-with-filecache: '' + charset-normalizer: '' + click: '>=8.0' + click-default-group: '' + crashtest: '>=0.4.1,<0.5.0' + dulwich: '>=0.22.6,<0.25.0' + ensureconda: '>=1.4.7' + filelock: '' + gitpython: '>=3.1.30' + jinja2: '' + keyring: '>=25.1.0,<26.0.0' + packaging: '>=24.0' + pkginfo: '>=1.12,<2.0' + platformdirs: '>=3.10.0,<5.0.0' + pydantic: '>=2' + pyproject_hooks: '>=1.0.0,<2.0.0' + python: '>=3.10' + python-build: '>=1.2.1,<2.0.0' + python-fastjsonschema: '>=2.18.0,<3.0.0' + python-installer: '>=1.0.1,<2.0.0' + pyyaml: '>=5.1' + requests: '>=2.26,<3.0' + requests-toolbelt: '>=1.0.0,<2.0.0' + ruamel.yaml: '' + semver: '>=3,<4' + setuptools: '' + shellingham: '>=1.5,<2.0' + tomli: '>=2.0.1,<3.0.0' + tomlkit: '>=0.11.4,<1.0.0' + trove-classifiers: '>=2022.5.19' + typing_extensions: '>=4.6.1' + virtualenv: '>=20.26.6,<21.0.0' + zstandard: '>=0.15' + url: https://conda.anaconda.org/conda-forge/noarch/conda-lock-4.0.2-pyha191276_0.conda + hash: + md5: f0968c69a409ba1144232b8c8f83b8fc + sha256: cf7844edd9b70b4bd24bebb892d1f81f792245c8fa8039e7d0725127215c515c + category: main + optional: false +- name: conda-package-streaming + version: 0.13.0 + manager: conda + platform: linux-64 + dependencies: + backports.zstd: '' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/conda-package-streaming-0.13.0-pyhd8ed1ab_0.conda + hash: + md5: b4e3dcace82b486f69bc693f30120205 + sha256: af3b666ea6043a146901cd151d6201126bbe19af8307b58fb175015e3710064c + category: main + optional: false +- name: cpp-expected version: 1.3.1 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - numpy: '>=1.23' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/contourpy-1.3.1-py310h3788b33_0.conda + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/cpp-expected-1.3.1-h171cf75_0.conda hash: - md5: f993b13665fc2bb262b30217c815d137 - sha256: 1b18ebb72fb20b9ece47c582c6112b1d4f0f7deebaa056eada99e1f994e8a81f + md5: d17488e343e4c5c0bd0db18b3934d517 + sha256: 0d9405d9f2de5d4b15d746609d87807aac10e269072d6408b769159762ed113d category: main optional: false -- name: cuda - version: 12.5.1 +- name: crashtest + version: 0.4.1 manager: conda platform: linux-64 dependencies: - __linux: '' - cuda-runtime: 12.5.1.* - cuda-toolkit: 12.5.1.* - url: https://conda.anaconda.org/conda-forge/noarch/cuda-12.5.1-ha804496_0.conda + python: '>=3.9,<4.0' + url: https://conda.anaconda.org/conda-forge/noarch/crashtest-0.4.1-pyhd8ed1ab_1.conda hash: - md5: 5d3d832371e12741bdf7a9d7fbd00a73 - sha256: d20862199c62029dfd977938a39d0de782d4442590020bf72cb93cf91b22ada5 + md5: e036e2f76d9c9aebc12510ed23352b6c + sha256: af1622b15f8c7411d9c14b8adf970cec16fec8a28b98069fdf42b1cd2259ccc9 category: main optional: false -- name: cuda-bindings - version: 12.8.0 +- name: cryptography + version: 49.0.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - cuda-nvrtc: '>=12,<13.0a0' - cuda-version: '>=12,<13.0a0' - libgcc: '>=13' - libnvjitlink: '>=12.3' - libstdcxx: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/cuda-bindings-12.8.0-py310h958ebd6_0.conda + cffi: '>=2.0' + libgcc: '>=14' + openssl: '>=3.5.7,<4.0a0' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/cryptography-49.0.0-py312ha4b625e_0.conda hash: - md5: 07b04fbe9f8e281c0da9dba1405023eb - sha256: d27be645054e094f79752dde1d06fe478299f072a5f8f772d815fe27d0569f0b + md5: 46142c21318ee17d5beecf0de1fc5d47 + sha256: c12e00af17f1507dbc186c9c663b44ca58b9b2209f59506be05547bd730346e5 category: main optional: false - name: cuda-cccl @@ -787,7 +533,6 @@ package: platform: linux-64 dependencies: cuda-cupti-dev: 12.5.82.* - cuda-gdb: 12.5.82.* cuda-nvdisasm: 12.5.39.* cuda-nvprof: 12.5.82.* cuda-nvtx: 12.5.82.* @@ -1019,24 +764,8 @@ package: sha256: 5bffb9a54b075904378dee4e22997f647c2d8d84f9eda05913654488ca3145ec category: main optional: false -- name: cuda-gdb - version: 12.5.82 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - gmp: '>=6.3.0,<7.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/cuda-gdb-12.5.82-hda18ab6_0.conda - hash: - md5: 4e0ce86268e831cab8034d138606c584 - sha256: e8de794e1094d5ccb4667f0e15cfc50d3bcaef3d7b3d261ebe4d13fc72ef2731 - category: main - optional: false -- name: cuda-libraries - version: 12.5.1 +- name: cuda-libraries + version: 12.5.1 manager: conda platform: linux-64 dependencies: @@ -1376,6 +1105,19 @@ package: sha256: d2475f2ec5ba230ab0a1ec89dbd62dd8664ba2fd85abd299c663149d03955a0a category: main optional: false +- name: cuda-pathfinder + version: 1.5.6 + manager: conda + platform: linux-64 + dependencies: + cuda-version: '>=12.0,<14' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/cuda-pathfinder-1.5.6-pyhc364b38_0.conda + hash: + md5: f9b46e920d4929d099565a251b4902db + sha256: aa6b13a1f13e8ee9f4e48d0d1bfe8505d1b40f4c547eea5fba7a69f1ca3ae508 + category: main + optional: false - name: cuda-profiler-api version: 12.5.39 manager: conda @@ -1389,33 +1131,6 @@ package: sha256: d2b109678867e3a573e78c0cee5aad3d672563e0e78a56366fc85fdbd3c06298 category: main optional: false -- name: cuda-python - version: 12.8.0 - manager: conda - platform: linux-64 - dependencies: - cuda-bindings: '>=12.8.0,<12.9.0a0' - cuda-version: '>=12.0,<13.0a0' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/cuda-python-12.8.0-pyh624ad82_1.conda - hash: - md5: 49c0e5774c0f5be4fe180d3e6fcddcdc - sha256: fe7251f2a35cd415cdaaaff52420b873f2fb7b3f9bd31b51a4a458f426f992b1 - category: main - optional: false -- name: cuda-runtime - version: 12.5.1 - manager: conda - platform: linux-64 - dependencies: - __linux: '' - cuda-libraries: 12.5.1.* - url: https://conda.anaconda.org/conda-forge/noarch/cuda-runtime-12.5.1-ha804496_0.conda - hash: - md5: b2000e96585a6e9d143403e7a4cc4d55 - sha256: fcbe8afdf0426ebc7dab2802fdecfe853ea9df996d583c0ceb7f3eea099ce584 - category: main - optional: false - name: cuda-sanitizer-api version: 12.5.81 manager: conda @@ -1489,4679 +1204,2938 @@ package: sha256: f47a338a41db09754458a31f4fccdffedea361e7c45cfd3e35a7f770364ff3d6 category: main optional: false -- name: cudf - version: 25.02.00 +- name: cupy + version: 14.1.1 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.28,<3.0.a0' - cachetools: '' - cuda-cudart: '' - cuda-nvcc-impl: '' + cuda-cudart-dev_linux-64: '' cuda-nvrtc: '' - cuda-python: '>=12.6.2,<13.0a0' cuda-version: '>=12,<13.0a0' - cupy: '>=12.0.0' - fsspec: '>=0.6.0' - libcudf: '>=25.2.0,<25.3.0a0' - libcufile: '' - libgcc: '>=13' - libstdcxx: '>=13' - numba: '>=0.59.1,<0.61.0a0' - numba-cuda: '>=0.2.0,<0.3.0a0' - numpy: '>=1.23,<3.0a0' - nvtx: '>=0.2.1' - packaging: '' - pandas: '>=2.0,<2.2.4dev0' - pyarrow: '>=14.0.0,<20.0.0a0' - pylibcudf: 25.02.00.* - pynvjitlink: '' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - rich: '' - rmm: '>=25.2.0,<25.3.0a0' - typing_extensions: '>=4.0.0' - url: https://conda.anaconda.org/rapidsai/linux-64/cudf-25.02.00-cuda12_py310_250213_g50b2d0faff_0.conda + cupy-core: 14.1.1 + libcublas: '' + libcufft: '' + libcurand: '' + libcusolver: '' + libcusparse: '' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/cupy-14.1.1-py312h53aca36_0.conda hash: - md5: 5b6d36b503f01fb150ba04dd564c4c15 - sha256: 21588b17664b74f02de1018c7678d1f1cc73a67362cb1ec549c7ed1d80c134f2 + md5: 7da0de2f5134adb58428dd058e8687b2 + sha256: 31bca442aa1731226acbf43293803eedfc3a4c4a18930aeec46f1f82faaeff2b category: main optional: false -- name: cuml - version: 25.02.00 +- name: cupy-core + version: 14.1.1 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-python: '>=12.6.2,<13.0a0' - cuda-version: '>=12,<13.0a0' - cudf: 25.02.* - cupy: '>=12.0.0' - dask-cudf: 25.02.* - joblib: '>=0.11' - libcuml: 25.02.00.* - libcumlprims: 25.02.* - libgcc: '>=13' - libstdcxx: '>=13' - numpy: '>=1.23,<3.0a0' - pylibraft: 25.02.* - python: '>=3.10,<3.11.0a0' - raft-dask: 25.02.* - rapids-dask-dependency: 25.02.* - treelite: 4.4.1.* - url: https://conda.anaconda.org/rapidsai/linux-64/cuml-25.02.00-cuda12_py310_250213_ga3a131a8b_0.conda + __glibc: '>=2.17,<3.0.a0' + cuda-pathfinder: '>=1.3.4,<2.0a0' + libgcc: '>=14' + libstdcxx: '>=14' + numpy: '>=1.23,<3,>=2.0' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/cupy-core-14.1.1-py312h16a6543_0.conda hash: - md5: d6bb2aa3cc8b97a04db60c9a71c194bf - sha256: 5c97265456e63f38ff5323c32700a29d8c2e25982e1f1244a83e2a6e4d5e9715 + md5: 794c6c5a4654f45265339382cdc6d28d + sha256: 0933f9fb3f542799ec06efdb1c6c925800969cc4fe76574df6cdf18d360d4ed2 category: main optional: false -- name: cupy - version: 13.3.0 +- name: cxx-compiler + version: 1.7.0 manager: conda platform: linux-64 dependencies: - cuda-cudart-dev_linux-64: '' - cuda-nvrtc: '' - cuda-version: '>=12.0,<13.0a0' - cupy-core: 13.3.0 - libcublas: '' - libcufft: '' - libcurand: '' - libcusolver: '' - libcusparse: '' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/cupy-13.3.0-py310h1b77274_2.conda + c-compiler: 1.7.0 + gxx: '' + gxx_linux-64: 12.* + url: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.7.0-h00ab1b0_1.conda hash: - md5: 39e67291f815da99637a734876dfc77e - sha256: 70e709d551f6e361f30db07095ee1ad100a88856576f1c756fe5b70db85ff2b6 + md5: 28de2e073db9ca9b72858bee9fb6f571 + sha256: cf895938292cfd4cfa2a06c6d57aa25c33cc974d4ffe52e704ffb67f5577b93f category: main optional: false -- name: cupy-core - version: 13.3.0 +- name: dbus + version: 1.16.2 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - fastrlock: '>=0.8.2,<0.9.0a0' - libgcc: '>=12' - libstdcxx: '>=12' - numpy: '>=1.22,<3.0.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/cupy-core-13.3.0-py310h8de46e0_2.conda + libexpat: '>=2.7.3,<3.0a0' + libgcc: '>=14' + libglib: '>=2.86.2,<3.0a0' + libstdcxx: '>=14' + libzlib: '>=1.3.1,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.16.2-h24cb091_1.conda hash: - md5: 9f9f87f2744573abc3e371960374eea0 - sha256: 45f452ba11cd88c3375493b2b9d75bb412320b30d1871f4d817608cf8ec97497 + md5: ce96f2f470d39bd96ce03945af92e280 + sha256: 8bb557af1b2b7983cf56292336a1a1853f26555d9c6cecf1e5b2b96838c9da87 category: main optional: false -- name: cxx-compiler - version: 1.9.0 +- name: distlib + version: 0.4.3 manager: conda platform: linux-64 dependencies: - c-compiler: 1.9.0 - gxx: '' - gxx_linux-64: 13.* - url: https://conda.anaconda.org/conda-forge/linux-64/cxx-compiler-1.9.0-h1a2810e_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/distlib-0.4.3-pyhcf101f3_0.conda hash: - md5: 1ce8b218d359d9ed0ab481f2a3f3c512 - sha256: 5efc51b8e7d87fc5380f00ace9f9c758142eade520a63d3631d2616d1c1b25f9 + md5: 58638f77697c4f6726753eb8be34818b + sha256: e2753997b8bd34205f42be01b8bab8037423dc30c02a1ec12de23e5b4c0b0a2e category: main optional: false -- name: cycler - version: 0.12.1 +- name: docutils + version: 0.21.2 manager: conda platform: linux-64 dependencies: python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda + url: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda hash: - md5: 44600c4667a319d67dbe0681fc0bc833 - sha256: 9827efa891e507a91a8a2acf64e210d2aff394e1cde432ad08e1f8c66b12293c + md5: 24c1ca34138ee57de72a943237cde4cc + sha256: fa5966bb1718bbf6967a85075e30e4547901410cc7cb7b16daf68942e9a94823 category: main optional: false -- name: cytoolz - version: 1.0.1 +- name: dulwich + version: 0.24.10 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - toolz: '>=0.10.0' - url: https://conda.anaconda.org/conda-forge/linux-64/cytoolz-1.0.1-py310ha75aee5_0.conda + libgcc: '>=14' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + setuptools: '' + urllib3: '>=2.2.2' + url: https://conda.anaconda.org/conda-forge/linux-64/dulwich-0.24.10-py312h0ccc70a_1.conda hash: - md5: d0be1adaa04a03aed745f3d02afb59ce - sha256: b427689dfc24a6a297363122ce10d502ea00ddb3c43af6cff175ff563cc94eea + md5: 382d3d92694d52244034c6a8a51fa54b + sha256: ec9e5935d6dc120cde22c266edf0471a26600ee333fe1adb411d95f3c13e019f category: main optional: false -- name: dask - version: 2024.12.1 +- name: ensureconda + version: 1.6.0 manager: conda platform: linux-64 dependencies: - bokeh: '>=3.1.0' - cytoolz: '>=0.11.0' - dask-core: '>=2024.12.1,<2024.12.2.0a0' - dask-expr: '>=1.1,<1.2' - distributed: '>=2024.12.1,<2024.12.2.0a0' - jinja2: '>=2.10.3' - lz4: '>=4.3.2' - numpy: '>=1.24' - pandas: '>=2.0' - pyarrow: '>=14.0.1' + appdirs: '' + click: '>=5.1' + conda-package-streaming: '' + filelock: '' + packaging: '' python: '>=3.10' - url: https://conda.anaconda.org/conda-forge/noarch/dask-2024.12.1-pyhd8ed1ab_0.conda + requests: '>=2' + url: https://conda.anaconda.org/conda-forge/noarch/ensureconda-1.6.0-pyhcf101f3_0.conda hash: - md5: f3134df9565c4d4415ff0e61f3aa28d0 - sha256: 4caae23bb33892998bee07024ddf1eec346400556c7bb7d45d1cee148af060d1 + md5: fcc02785e4da41e5f19f740e017b6ffb + sha256: e3c35c7cee4e6db53de4ad1ca6d35b7aced263d21a6ee2dcdffe401135646ab2 category: main optional: false -- name: dask-core - version: 2024.12.1 +- name: exceptiongroup + version: 1.3.1 manager: conda platform: linux-64 dependencies: - click: '>=8.1' - cloudpickle: '>=3.0.0' - fsspec: '>=2021.09.0' - importlib-metadata: '>=4.13.0' - packaging: '>=20.0' - partd: '>=1.4.0' python: '>=3.10' - pyyaml: '>=5.3.1' - toolz: '>=0.10.0' - url: https://conda.anaconda.org/conda-forge/noarch/dask-core-2024.12.1-pyhd8ed1ab_0.conda + typing_extensions: '>=4.6.0' + url: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.1-pyhd8ed1ab_0.conda hash: - md5: 48060c395f1e87a80330c0adaad332f7 - sha256: a2dfdb73143ddc75ee7ca25b0a8c714ecaedafb45c6a4684883b7648924e2ea3 + md5: 8e662bd460bda79b1ea39194e3c4c9ab + sha256: ee6cf346d017d954255bbcbdb424cddea4d14e4ed7e9813e429db1d795d01144 category: main optional: false -- name: dask-cuda - version: 25.02.00 +- name: expat + version: 2.8.1 manager: conda platform: linux-64 dependencies: - click: '>=8.1' - numba: '>=0.59.1,<0.61.0a0' - numpy: '>=1.23,<3.0a0' - pandas: '>=1.3' - pynvml: '>=12.0.0,<13.0.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - rapids-dask-dependency: 25.2.* - zict: '>=2.0.0' - url: https://conda.anaconda.org/rapidsai/linux-64/dask-cuda-25.02.00-py310_250213_g4c663f5_0.conda + __glibc: '>=2.17,<3.0.a0' + libexpat: 2.8.1 + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/expat-2.8.1-hecca717_1.conda hash: - md5: b7052772e8223382225a6bad49553e0e - sha256: 3017d049adfad1584d1e075b10a702366ac75813c4f348d98c05b44984b5c568 + md5: e211a78613b9d50a096644b97cede8f7 + sha256: bc1e8177a4ebbbfcda98b6f4a1575f3337e69f0d2f1025a84570533ca27b89eb category: main optional: false -- name: dask-cudf - version: 25.02.00 +- name: filelock + version: 3.30.1 manager: conda platform: linux-64 dependencies: - cuda-version: '>=12,<13.0a0' - cudf: 25.02.00.* - pynvml: '>=12.0.0,<13.0.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - rapids-dask-dependency: 25.02.* - url: https://conda.anaconda.org/rapidsai/linux-64/dask-cudf-25.02.00-cuda12_py310_250213_g50b2d0faff_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/filelock-3.30.1-pyhd8ed1ab_0.conda hash: - md5: d04d8ddfca6ffe52c6e5beeacd0e35e0 - sha256: 0f5436245a57c6a6d8c13018c6ef719f6def51006aa8c58e63fb8ebc225179f6 + md5: f42a3e2650fa91ed1d9637b9c734fbbb + sha256: c6b9e32a6b34d589011946fba5a2b3b4e2ba329749b32f96f2d92e1750fc4077 category: main optional: false -- name: dask-expr - version: 1.1.21 +- name: fmt + version: 12.1.0 manager: conda platform: linux-64 dependencies: - dask-core: 2024.12.1 - pandas: '>=2' - pyarrow: '>=14.0.1' - python: '>=3.10' - url: https://conda.anaconda.org/conda-forge/noarch/dask-expr-1.1.21-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/fmt-12.1.0-hff5e90c_0.conda hash: - md5: e72a014dbbd35545dcfba4de9c92fb1d - sha256: 5aceb0fb2ba39a3fa30f5b8fe7b0d9d832aacdc76dd2b01bd88d92893eabc50f + md5: f7d7a4104082b39e3b3473fbd4a38229 + sha256: d4e92ba7a7b4965341dc0fca57ec72d01d111b53c12d11396473115585a9ead6 category: main optional: false -- name: dbus - version: 1.13.6 +- name: fontconfig + version: 2.18.1 manager: conda platform: linux-64 dependencies: - expat: '>=2.4.2,<3.0a0' - libgcc-ng: '>=9.4.0' - libglib: '>=2.70.2,<3.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2 + __glibc: '>=2.17,<3.0.a0' + libexpat: '>=2.8.1,<3.0a0' + libfreetype: '>=2.14.3' + libfreetype6: '>=2.14.3' + libgcc: '>=14' + libuuid: '>=2.42.1,<3.0a0' + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.18.1-h27c8c51_0.conda hash: - md5: ecfff944ba3960ecb334b9a2663d708d - sha256: 8f5f995699a2d9dbdd62c61385bfeeb57c82a681a7c8c5313c395aa0ccab68a5 + md5: e0e050cfa9fa85fe39632ab11cb7f3e0 + sha256: 2e50bdcebdf70a865b81f2456bbc586386451ec601c60f2b6cd22b8c40a2d384 category: main optional: false -- name: decorator - version: 5.2.1 +- name: freetype + version: 2.14.3 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda + libfreetype: 2.14.3 + libfreetype6: 2.14.3 + url: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.14.3-ha770c72_0.conda hash: - md5: 9ce473d1d1be1cc3810856a48b3fab32 - sha256: c17c6b9937c08ad63cb20a26f403a3234088e57d4455600974a0ce865cb14017 + md5: 8462b5322567212beeb025f3519fb3e2 + sha256: c934c385889c7836f034039b43b05ccfa98f53c900db03d8411189892ced090b category: main optional: false -- name: distlib - version: 0.3.9 +- name: gcc + version: 12.4.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.9-pyhd8ed1ab_1.conda + gcc_impl_linux-64: 12.4.0.* + url: https://conda.anaconda.org/conda-forge/linux-64/gcc-12.4.0-h236703b_2.conda hash: - md5: 8d88f4a2242e6b96f9ecff9a6a05b2f1 - sha256: 0e160c21776bd881b79ce70053e59736f51036784fa43a50da10a04f0c1b9c45 + md5: ec54d965fd9d276c256ae3cf1d3aface + sha256: ebe2dabb0a6f0ef05039d3a26b9c6b0aa050d7e791c6ab77ee91653b2098cdc3 category: main optional: false -- name: distributed - version: 2024.12.1 +- name: gcc_impl_linux-64 + version: 12.4.0 manager: conda platform: linux-64 dependencies: - click: '>=8.0' - cloudpickle: '>=3.0.0' - cytoolz: '>=0.11.2' - dask-core: '>=2024.12.1,<2024.12.2.0a0' - jinja2: '>=2.10.3' - locket: '>=1.0.0' - msgpack-python: '>=1.0.2' - packaging: '>=20.0' - psutil: '>=5.8.0' - python: '>=3.10' - pyyaml: '>=5.4.1' - sortedcontainers: '>=2.0.5' - tblib: '>=1.6.0' - toolz: '>=0.11.2' - tornado: '>=6.2.0' - urllib3: '>=1.26.5' - zict: '>=3.0.0' - url: https://conda.anaconda.org/conda-forge/noarch/distributed-2024.12.1-pyhd8ed1ab_0.conda + binutils_impl_linux-64: '>=2.40' + libgcc: '>=12.4.0' + libgcc-devel_linux-64: 12.4.0 + libgomp: '>=12.4.0' + libsanitizer: 12.4.0 + libstdcxx: '>=12.4.0' + sysroot_linux-64: '' + url: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-12.4.0-h26ba24d_2.conda hash: - md5: 58df114d7649ddb3f68c9b9adc6fbabe - sha256: 0cdd52fd0654428eb5bc6f460747ac484d07cca8434e361078f20e2c3258bb1e + md5: f091c5ea6c862ab1796c82465a7c2364 + sha256: 635cd3d70ca6f4c3ad3f4b5837b5badb058f2416392592bd5914aa805f0bc28e category: main optional: false -- name: distributed-ucxx - version: 0.42.00 +- name: gcc_linux-64 + version: 12.4.0 manager: conda platform: linux-64 dependencies: - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - rapids-dask-dependency: 25.2.* - ucxx: '>=0.42.0,<0.43.0a0' - url: https://conda.anaconda.org/rapidsai/linux-64/distributed-ucxx-0.42.00-py3.10_250213_g762a6ed_0.conda + binutils_linux-64: '' + gcc_impl_linux-64: 12.4.0.* + sysroot_linux-64: '' + url: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-12.4.0-h6b7512a_10.conda hash: - md5: c1db3e4da3d985c3d8f27999ee15a26f - sha256: 60c075c7fd27762d37fe535ff002520a74c32ea6f53ac93d9add3e5d1e8d9e4c + md5: 18432a261dca2bb05b45e60adee37d77 + sha256: 004d2ed6a3fc79452dec4c6cac556d0b26cf2457d33c4ace95beed4e6e832b55 category: main optional: false -- name: dlpack - version: '0.8' +- name: gds-tools + version: 1.10.1.7 manager: conda platform: linux-64 dependencies: + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libcufile: '>=1.10.1.7,<2.0a0' libgcc-ng: '>=12' libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/dlpack-0.8-h59595ed_3.conda + url: https://conda.anaconda.org/conda-forge/linux-64/gds-tools-1.10.1.7-he02047a_0.conda hash: - md5: ee290dba0b7497f2d357c057aaea123f - sha256: 5884a5e18a779586a2179c0187ef75caa148568dd576c4dbc278deead77cf4b1 + md5: 75a4f9a6b8aa210ef0a5e319a2d0846a + sha256: 97c86f98ddcbbdc3c58892a3b443a2e94de398e471fe6a31fc4a1b50958f1bcf category: main optional: false -- name: docutils - version: 0.21.2 +- name: gitdb + version: 4.0.12 manager: conda platform: linux-64 dependencies: python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/docutils-0.21.2-pyhd8ed1ab_1.conda + smmap: '>=3.0.1,<6' + url: https://conda.anaconda.org/conda-forge/noarch/gitdb-4.0.12-pyhd8ed1ab_0.conda hash: - md5: 24c1ca34138ee57de72a943237cde4cc - sha256: fa5966bb1718bbf6967a85075e30e4547901410cc7cb7b16daf68942e9a94823 + md5: 7c14f3706e099f8fcd47af2d494616cc + sha256: dbbec21a369872c8ebe23cb9a3b9d63638479ee30face165aa0fccc96e93eec3 category: main optional: false -- name: exceptiongroup - version: 1.2.2 +- name: gitpython + version: 3.1.52 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda + gitdb: '>=4.0.1,<5' + python: '>=3.10' + typing_extensions: '>=3.10.0.2' + url: https://conda.anaconda.org/conda-forge/noarch/gitpython-3.1.52-pyhd8ed1ab_0.conda hash: - md5: a16662747cdeb9abbac74d0057cc976e - sha256: cbde2c64ec317118fc06b223c5fd87c8a680255e7348dd60e7b292d2e103e701 + md5: f3f5111c75b5d06a9d8abc849180f2fe + sha256: d3b01f5ba797df68dd680cea943a0aad0dc23b2a5b53a60e5b2b816a1d17c047 category: main optional: false -- name: executing - version: 2.1.0 +- name: gxx + version: 12.4.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/executing-2.1.0-pyhd8ed1ab_1.conda + gcc: 12.4.0.* + gxx_impl_linux-64: 12.4.0.* + url: https://conda.anaconda.org/conda-forge/linux-64/gxx-12.4.0-h236703b_2.conda hash: - md5: ef8b5fca76806159fc25b4f48d8737eb - sha256: 28d25ea375ebab4bf7479228f8430db20986187b04999136ff5c722ebd32eb60 + md5: 5735863174438abb776bd1fefccec00a + sha256: 6c3ea9877dc6babf064bafacd9e67280072b676864c26e90cbfec52eaa32a60e category: main optional: false -- name: expat - version: 2.6.4 +- name: gxx_impl_linux-64 + version: 12.4.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libexpat: 2.6.4 - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/expat-2.6.4-h5888daf_0.conda + gcc_impl_linux-64: 12.4.0 + libstdcxx-devel_linux-64: 12.4.0 + sysroot_linux-64: '' + tzdata: '' + url: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-12.4.0-h3ff227c_2.conda hash: - md5: 1d6afef758879ef5ee78127eb4cd2c4a - sha256: 1848c7db9e264e3b8036ee133d570dd880422983cd20dd9585a505289606d276 + md5: 5f8ae076e514514aeeb0eb52dac2d55d + sha256: 548987d77c5d6d648c1166e9a1eb810032f25fb1d61692a0a5a072db126e5f3f category: main optional: false -- name: fastrlock - version: 0.8.3 +- name: gxx_linux-64 + version: 12.4.0 manager: conda platform: linux-64 dependencies: - python: '' - libstdcxx: '>=13' - libgcc: '>=13' - __glibc: '>=2.17,<3.0.a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/fastrlock-0.8.3-py310h8c668a6_1.conda + binutils_linux-64: '' + gcc_linux-64: 12.4.0 + gxx_impl_linux-64: 12.4.0.* + sysroot_linux-64: '' + url: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-12.4.0-h8489865_10.conda hash: - md5: 4c9c2d9a2754460d342a84703b64c96b - sha256: 25c6927ff29307a937ab3d549665adfd69070c4eccc850b6dc7fb401fd4f118c + md5: f01962bad75d6d68802a1eb56bb70478 + sha256: 6ea7b3957ace8960347069f032851a66755b785a5e34cd845c1b6b1e649b686e category: main optional: false -- name: filelock - version: 3.17.0 +- name: h2 + version: 4.3.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/filelock-3.17.0-pyhd8ed1ab_0.conda + hpack: '>=4.1,<5' + hyperframe: '>=6.1,<7' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/h2-4.3.0-pyhcf101f3_0.conda hash: - md5: 7f402b4a1007ee355bc50ce4d24d4a57 - sha256: 006d7e5a0c17a6973596dd86bfc80d74ce541144d2aee2d22d46fd41df560a63 + md5: 164fc43f0b53b6e3a7bc7dce5e4f1dc9 + sha256: 84c64443368f84b600bfecc529a1194a3b14c3656ee2e832d15a20e0329b6da3 category: main optional: false -- name: fmt - version: 11.0.2 +- name: hpack + version: 4.2.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/fmt-11.0.2-h434a139_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/hpack-4.2.0-pyhd8ed1ab_0.conda hash: - md5: 995f7e13598497691c1dc476d889bc04 - sha256: c620e2ab084948985ae9b8848d841f603e8055655513340e04b6cf129099b5ca + md5: b395909221b9bd1df066e5930e18855b + sha256: fdcea5d7cb314485d3907192ef024c704311548c5b0cbeb390cd1951051e29d2 category: main optional: false -- name: font-ttf-dejavu-sans-mono - version: '2.37' +- name: hyperframe + version: 6.1.0 manager: conda platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2 + dependencies: + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda hash: - md5: 0c96522c6bdaed4b1566d11387caaf45 - sha256: 58d7f40d2940dd0a8aa28651239adbf5613254df0f75789919c4e6762054403b + md5: 8e6923fc12f1fe8f8c4e5c9f343256ac + sha256: 77af6f5fe8b62ca07d09ac60127a30d9069fdc3c68d6b256754d0ffb1f7779f8 category: main optional: false -- name: font-ttf-inconsolata - version: '3.000' +- name: icu + version: '78.3' manager: conda platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2 + dependencies: + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/icu-78.3-h33c6efd_0.conda hash: - md5: 34893075a5c9e55cdafac56607368fc6 - sha256: c52a29fdac682c20d252facc50f01e7c2e7ceac52aa9817aaf0bb83f7559ec5c + md5: c80d8a3b84358cb967fa81e7075fbc8a + sha256: fbf86c4a59c2ed05bbffb2ba25c7ed94f6185ec30ecb691615d42342baa1a16a category: main optional: false -- name: font-ttf-source-code-pro - version: '2.038' +- name: identify + version: 2.6.19 manager: conda platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2 + dependencies: + python: '>=3.10' + ukkonen: '' + url: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.19-pyhd8ed1ab_0.conda hash: - md5: 4d59c254e01d9cde7957100457e2d5fb - sha256: 00925c8c055a2275614b4d983e1df637245e19058d79fc7dd1a93b8d9fb4b139 + md5: 84a3233b709a289a4ddd7a2fd27dd988 + sha256: 381cedccf0866babfc135d65ee40b778bd20e927d2a5ec810f750c5860a7c5b8 category: main optional: false -- name: font-ttf-ubuntu - version: '0.83' +- name: idna + version: '3.18' manager: conda platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_3.conda + dependencies: + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/idna-3.18-pyhcf101f3_0.conda hash: - md5: 49023d73832ef61042f6a237cb2687e7 - sha256: 2821ec1dc454bd8b9a31d0ed22a7ce22422c0aef163c59f49dfdf915d0f0ca14 + md5: 577b04680ae422adb86fc60d7b940659 + sha256: c75632ea624aa450a394f570749420c5a2e0997d0216bc29d5d45b0f39df0426 category: main optional: false -- name: fontconfig - version: 2.15.0 +- name: imagesize + version: 2.0.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - freetype: '>=2.12.1,<3.0a0' - libexpat: '>=2.6.3,<3.0a0' - libgcc: '>=13' - libuuid: '>=2.38.1,<3.0a0' - libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/imagesize-2.0.0-pyhd8ed1ab_0.conda hash: - md5: 8f5b0b297b59e1ac160ad4beec99dbee - sha256: 7093aa19d6df5ccb6ca50329ef8510c6acb6b0d8001191909397368b65b02113 + md5: 92617c2ba2847cca7a6ed813b6f4ab79 + sha256: 5a047f9eac290e679b4e6f6f4cbfcc5acdfbf031a4f06824d4ddb590cdbb850b category: main optional: false -- name: fonts-conda-ecosystem - version: '1' +- name: importlib-metadata + version: 9.0.0 manager: conda platform: linux-64 dependencies: - fonts-conda-forge: '' - url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2 + python: '>=3.10' + zipp: '>=3.20' + url: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-9.0.0-pyhcf101f3_0.conda hash: - md5: fee5683a3f04bd15cbd8318b096a27ab - sha256: a997f2f1921bb9c9d76e6fa2f6b408b7fa549edd349a77639c9fe7a23ea93e61 + md5: ffc17e785d64e12fc311af9184221839 + sha256: 43e2a5497cad1598ff88a3e69f69bc88b7b8f141fa63c60eab5db296317318b8 category: main optional: false -- name: fonts-conda-forge - version: '1' +- name: importlib-resources + version: 7.1.0 manager: conda platform: linux-64 dependencies: - font-ttf-dejavu-sans-mono: '' - font-ttf-inconsolata: '' - font-ttf-source-code-pro: '' - font-ttf-ubuntu: '' - url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2 + importlib_resources: '>=7.1.0,<7.1.1.0a0' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/importlib-resources-7.1.0-pyhd8ed1ab_0.conda hash: - md5: f766549260d6815b0c52253f1fb1bb29 - sha256: 53f23a3319466053818540bcdf2091f253cbdbab1e0e9ae7b9e509dcaa2a5e38 + md5: e3bffa82b874f8b9a2631bddb3869529 + sha256: 6a2f86ef0965605d742b5b94229bf8b829258d0a9f640e3651901cc72ef9a0a5 category: main optional: false -- name: fonttools - version: 4.56.0 +- name: importlib_resources + version: 7.1.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - brotli: '' - libgcc: '>=13' - munkres: '' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - unicodedata2: '>=15.1.0' - url: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.56.0-py310h89163eb_0.conda + python: '>=3.10' + zipp: '>=3.1.0' + url: https://conda.anaconda.org/conda-forge/noarch/importlib_resources-7.1.0-pyhd8ed1ab_0.conda hash: - md5: cd3125e1924bd8699dac9989652bca74 - sha256: 751599162ba980477e9267b67d82e116465d0cf69efc52e016e8f72e7f9cdfcd + md5: 0ba6225c279baf7ea9473a62ea0ec9ae + sha256: a563a51aa522998172838e867e6dedcf630bc45796e8612f5a1f6d73e9c8125a category: main optional: false -- name: freetype - version: 2.12.1 +- name: iniconfig + version: 2.3.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libpng: '>=1.6.39,<1.7.0a0' - libzlib: '>=1.2.13,<2.0.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.12.1-h267a509_2.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.3.0-pyhd8ed1ab_0.conda hash: - md5: 9ae35c3d96db2c94ce0cef86efdfa2cb - sha256: b2e3c449ec9d907dd4656cb0dc93e140f447175b125a3824b31368b06c666bb6 + md5: 9614359868482abba1bd15ce465e3c42 + sha256: e1a9e3b1c8fe62dc3932a616c284b5d8cbe3124bbfbedcf4ce5c828cb166ee19 category: main optional: false -- name: fsspec - version: 2025.2.0 +- name: jaraco.classes + version: 3.4.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/fsspec-2025.2.0-pyhd8ed1ab_0.conda + more-itertools: '' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/jaraco.classes-3.4.0-pyhcf101f3_3.conda hash: - md5: d9ea16b71920b03beafc17fcca16df90 - sha256: 7433b8469074985b651693778ec6f03d2a23fad9919a515e3b8545996b5e721a + md5: d59568bad316413c89831456e691de29 + sha256: 3cc991f0f09dfd00d2626e745ba68da03e4f1dcbb7b36dd20f7a7373643cd5d5 category: main optional: false -- name: gcc - version: 13.3.0 +- name: jaraco.context + version: 6.1.2 manager: conda platform: linux-64 dependencies: - gcc_impl_linux-64: 13.3.0.* - url: https://conda.anaconda.org/conda-forge/linux-64/gcc-13.3.0-h9576a4e_2.conda + backports.tarfile: '' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/jaraco.context-6.1.2-pyhcf101f3_0.conda hash: - md5: d92e51bf4b6bdbfe45e5884fb0755afe - sha256: 300f077029e7626d69cc250a69acd6018c1fced3f5bf76adf37854f3370d2c45 + md5: b9d2b1ffa307961f6bb7d83c8ba8a8be + sha256: 108b3919db8ef81b5585b38a3fedbe9eeccdf8fa576c250a13559a1188f597cc category: main optional: false -- name: gcc_impl_linux-64 - version: 13.3.0 +- name: jaraco.functools + version: 4.6.0 manager: conda platform: linux-64 dependencies: - binutils_impl_linux-64: '>=2.40' - libgcc: '>=13.3.0' - libgcc-devel_linux-64: 13.3.0 - libgomp: '>=13.3.0' - libsanitizer: 13.3.0 - libstdcxx: '>=13.3.0' - sysroot_linux-64: '' - url: https://conda.anaconda.org/conda-forge/linux-64/gcc_impl_linux-64-13.3.0-h1e990d8_2.conda + more-itertools: '' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/jaraco.functools-4.6.0-pyhcf101f3_0.conda hash: - md5: f46cf0acdcb6019397d37df1e407ab91 - sha256: c3e9f243ea8292eecad78bb200d8f5b590e0f82bf7e7452a3a7c8df4eea6f774 + md5: 8665b873062a31a90b563bf493f9fb2c + sha256: d23b9e8e8d5968699ec2bc9f8d182598e86e494dbe8a3a71b4a53b1be9a3cb16 category: main optional: false -- name: gcc_linux-64 - version: 13.3.0 +- name: jeepney + version: 0.9.0 manager: conda platform: linux-64 dependencies: - binutils_linux-64: '' - gcc_impl_linux-64: 13.3.0.* - sysroot_linux-64: '' - url: https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-13.3.0-hc28eda2_7.conda + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/jeepney-0.9.0-pyhd8ed1ab_0.conda hash: - md5: ac23afbf5805389eb771e2ad3b476f75 - sha256: 1e5ac50580a68fdc7d2f5722abcf1a87898c24b1ab6eb5ecd322634742d93645 + md5: b4b91eb14fbe2f850dd2c5fc20676c0d + sha256: 00d37d85ca856431c67c8f6e890251e7cc9e5ef3724a0302b8d4a101f22aa27f category: main optional: false -- name: gds-tools - version: 1.10.1.7 +- name: jinja2 + version: 3.1.6 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libcufile: '>=1.10.1.7,<2.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/gds-tools-1.10.1.7-he02047a_0.conda + markupsafe: '>=2.0' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.6-pyhcf101f3_1.conda hash: - md5: 75a4f9a6b8aa210ef0a5e319a2d0846a - sha256: 97c86f98ddcbbdc3c58892a3b443a2e94de398e471fe6a31fc4a1b50958f1bcf + md5: 04558c96691bed63104678757beb4f8d + sha256: fc9ca7348a4f25fed2079f2153ecdcf5f9cf2a0bc36c4172420ca09e1849df7b category: main optional: false -- name: gflags - version: 2.2.2 +- name: joblib + version: 1.5.3 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-h5888daf_1005.conda + python: '>=3.10' + setuptools: '' + url: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.3-pyhd8ed1ab_0.conda hash: - md5: d411fc29e338efb48c5fd4576d71d881 - sha256: 6c33bf0c4d8f418546ba9c250db4e4221040936aef8956353bc764d4877bc39a + md5: 615de2a4d97af50c350e5cf160149e77 + sha256: 301539229d7be6420c084490b8145583291123f0ce6b92f56be5948a2c83a379 category: main optional: false -- name: glog - version: 0.7.1 +- name: kernel-headers_linux-64 + version: 4.18.0 manager: conda platform: linux-64 - dependencies: - gflags: '>=2.2.2,<2.3.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/glog-0.7.1-hbabe93e_0.conda + dependencies: {} + url: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-4.18.0-he073ed8_9.conda hash: - md5: ff862eebdfeb2fd048ae9dc92510baca - sha256: dc824dc1d0aa358e28da2ecbbb9f03d932d976c8dca11214aa1dcdfcbd054ba2 + md5: 86d9cba083cd041bfbf242a01a7a1999 + sha256: 41557eeadf641de6aeae49486cef30d02a6912d8da98585d687894afd65b356a category: main optional: false -- name: gmp - version: 6.3.0 +- name: keyring + version: 25.7.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/gmp-6.3.0-hac33072_2.conda + __linux: '' + importlib-metadata: '>=4.11.4' + importlib_resources: '' + jaraco.classes: '' + jaraco.context: '' + jaraco.functools: '' + jeepney: '>=0.4.2' + python: '>=3.10' + secretstorage: '>=3.2' + url: https://conda.anaconda.org/conda-forge/noarch/keyring-25.7.0-pyha804496_0.conda hash: - md5: c94a5994ef49749880a8139cf9afcbe1 - sha256: 309cf4f04fec0c31b6771a5809a1909b4b3154a2208f52351e1ada006f4c750c + md5: 9eeb0eaf04fa934808d3e070eebbe630 + sha256: 010718b1b1a35ce72782d38e6d6b9495d8d7d0dbea9a3e42901d030ff2189545 category: main optional: false -- name: gxx - version: 13.3.0 +- name: keyutils + version: 1.6.3 manager: conda platform: linux-64 dependencies: - gcc: 13.3.0.* - gxx_impl_linux-64: 13.3.0.* - url: https://conda.anaconda.org/conda-forge/linux-64/gxx-13.3.0-h9576a4e_2.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=13' + url: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.3-hb9d3cd8_0.conda hash: - md5: 07e8df00b7cd3084ad3ef598ce32a71c - sha256: fa9d0171c17e4c4203a4199fcc35571a25c1f16c0ad992080d4f0ced53bf5aa5 + md5: b38117a3c920364aff79f870c984b4a3 + sha256: 0960d06048a7185d3542d850986d807c6e37ca2e644342dd0c72feefcf26c2a4 category: main optional: false -- name: gxx_impl_linux-64 - version: 13.3.0 +- name: krb5 + version: 1.22.2 manager: conda platform: linux-64 dependencies: - gcc_impl_linux-64: 13.3.0 - libstdcxx-devel_linux-64: 13.3.0 - sysroot_linux-64: '' - tzdata: '' - url: https://conda.anaconda.org/conda-forge/linux-64/gxx_impl_linux-64-13.3.0-hae580e1_2.conda + __glibc: '>=2.17,<3.0.a0' + keyutils: '>=1.6.3,<2.0a0' + libedit: '>=3.1.20250104,<3.2.0a0,>=3.1.20250104,<4.0a0' + libgcc: '>=14' + libstdcxx: '>=14' + openssl: '>=3.5.7,<4.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.22.2-hbde042b_1.conda hash: - md5: b55f02540605c322a47719029f8404cc - sha256: 7cb36526a5c3e75ae07452aee5c9b6219f62fad9f85cc6d1dab5b21d1c4cc996 + md5: 54157a1c8c0bb70f62dd0b17fba7e7f2 + sha256: 9b07046870772f28740e3f6149f09ff222843733087a33c5540b169c6289652d category: main optional: false -- name: gxx_linux-64 - version: 13.3.0 +- name: ld_impl_linux-64 + version: 2.46.1 manager: conda platform: linux-64 dependencies: - binutils_linux-64: '' - gcc_linux-64: 13.3.0 - gxx_impl_linux-64: 13.3.0.* - sysroot_linux-64: '' - url: https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-13.3.0-h6834431_7.conda + __glibc: '>=2.17,<3.0.a0' + zstd: '>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.46.1-default_hbd61a6d_102.conda hash: - md5: 7c82ca9bda609b6f72f670e4219d3787 - sha256: a9b1ffea76f2cc5aedeead4793fcded7a687cce9d5e3f4fe93629f1b1d5043a6 + md5: 449500f2c089da11c40f5c21312e3e07 + sha256: 27d83f1188cd19bcb7754a078b3fa7f4cfb8527f8eb2fde54dd01fc529d1adec category: main optional: false -- name: h2 - version: 4.2.0 +- name: libarchive + version: 3.8.8 manager: conda platform: linux-64 dependencies: - hpack: '>=4.1,<5' - hyperframe: '>=6.1,<7' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + bzip2: '>=1.0.8,<2.0a0' + libgcc: '>=14' + liblzma: '>=5.8.3,<6.0a0' + libxml2: '' + libxml2-16: '>=2.14.6' + libzlib: '>=1.3.2,<2.0a0' + lz4-c: '>=1.10.0,<1.11.0a0' + lzo: '>=2.10,<3.0a0' + openssl: '>=3.5.7,<4.0a0' + zstd: '>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libarchive-3.8.8-gpl_hc2c16d8_100.conda hash: - md5: b4754fb1bdcb70c8fd54f918301582c6 - sha256: 0aa1cdc67a9fe75ea95b5644b734a756200d6ec9d0dff66530aec3d1c1e9df75 + md5: 44652e646cb623f486ea72e7e7479222 + sha256: f916b51f55f51a9bb2d902e0a5f029490f7b745aee549c349751c1787ddc26b8 category: main optional: false -- name: hpack - version: 4.1.0 +- name: libblas + version: 3.11.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda + libopenblas: '>=0.3.33,<0.3.34.0a0,>=0.3.33,<1.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.11.0-8_h4a7cf45_openblas.conda hash: - md5: 0a802cb9888dd14eeefc611f05c40b6e - sha256: 6ad78a180576c706aabeb5b4c8ceb97c0cb25f1e112d76495bff23e3779948ba + md5: 00fc660ab1b2f5ca07e92b4900d10c79 + sha256: b2da6bfd72a1c9cb143ccf64bf5b28790cb4eb58bd1cb978f6537b2322f7d48b category: main optional: false -- name: hyperframe - version: 6.1.0 +- name: libcblas + version: 3.11.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/hyperframe-6.1.0-pyhd8ed1ab_0.conda + libblas: 3.11.0 + url: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.11.0-8_h0358290_openblas.conda hash: - md5: 8e6923fc12f1fe8f8c4e5c9f343256ac - sha256: 77af6f5fe8b62ca07d09ac60127a30d9069fdc3c68d6b256754d0ffb1f7779f8 + md5: 33a413f1095f8325e5c30fde3b0d2445 + sha256: 1a2bc77bb26520255904a3d9b1f40e6bf0bf9d8d3405c7709dd162282820915a category: main optional: false -- name: icu - version: '75.1' +- name: libcublas + version: 12.5.3.2 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' + cuda-nvrtc: '' + cuda-version: '>=12.5,<12.6.0a0' libgcc-ng: '>=12' libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/icu-75.1-he02047a_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/libcublas-12.5.3.2-he02047a_0.conda hash: - md5: 8b189310083baabfb622af68fd9d3ae3 - sha256: 71e750d509f5fa3421087ba88ef9a7b9be11c53174af3aa4d06aff4c18b38e8e + md5: 0a90d9671f8ca4cd6801130b35e1ecbd + sha256: 3c20901ef2d7c737120d889dd10690604850a1b5ea56e9f92c1a7b00453769b9 category: main optional: false -- name: identify - version: 2.6.8 +- name: libcublas-dev + version: 12.5.3.2 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - ukkonen: '' - url: https://conda.anaconda.org/conda-forge/noarch/identify-2.6.8-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libcublas: 12.5.3.2 + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcublas-dev-12.5.3.2-he02047a_0.conda hash: - md5: 153a6ad50ad9db7bb4e042ee52a56f87 - sha256: 26347a71ff3bf9d3d775b6764a85782d4b9238a8e3a5c16a548325724dccbdea + md5: f59f34aa2068c1cf2307e0c8309a63e3 + sha256: 85203a05763992b980a3c8667d93ef22ee40f3e8e33a4f68eeddae393031829c category: main optional: false -- name: idna - version: '3.10' +- name: libcufft + version: 11.2.3.61 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/idna-3.10-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcufft-11.2.3.61-he02047a_0.conda hash: - md5: 39a4f67be3286c86d696df570b1201b7 - sha256: d7a472c9fd479e2e8dcb83fb8d433fce971ea369d704ece380e876f9c3494e87 + md5: f24ecbfdac382e5f4117ad1182d55ae4 + sha256: d5c476cabecf0c0da8a81ea1bb7ccf6fb5eab5316ac7aa1c9f8aad671e55fc81 category: main optional: false -- name: imagesize - version: 1.4.1 +- name: libcufft-dev + version: 11.2.3.61 manager: conda platform: linux-64 dependencies: - python: '>=3.4' - url: https://conda.anaconda.org/conda-forge/noarch/imagesize-1.4.1-pyhd8ed1ab_0.tar.bz2 + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libcufft: 11.2.3.61 + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcufft-dev-11.2.3.61-he02047a_0.conda hash: - md5: 7de5386c8fea29e76b303f37dde4c352 - sha256: c2bfd7043e0c4c12d8b5593de666c1e81d67b83c474a0a79282cc5c4ef845460 + md5: 591254d342058b68078c104eee6a4e1e + sha256: 4d69d62e8211265ef32418303c13c1cfd52ea9811c0b863753796fcbecfe8632 category: main optional: false -- name: importlib-metadata - version: 8.6.1 +- name: libcufile + version: 1.10.1.7 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - zipp: '>=0.5' - url: https://conda.anaconda.org/conda-forge/noarch/importlib-metadata-8.6.1-pyha770c72_0.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.10.1.7-he02047a_0.conda hash: - md5: f4b39bf00c69f56ac01e020ebfac066c - sha256: 598951ebdb23e25e4cec4bbff0ae369cec65ead80b50bc08b441d8e54de5cf03 + md5: 2e68ad67fce5223e17722149094dafcf + sha256: b2c9c247410e2bdba7ca9f627d32cd4387c292e8a7ad46a16b1f552f639c6879 category: main optional: false -- name: iniconfig - version: 2.0.0 +- name: libcufile-dev + version: 1.10.1.7 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/iniconfig-2.0.0-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libcufile: 1.10.1.7 + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcufile-dev-1.10.1.7-he02047a_0.conda hash: - md5: 6837f3eff7dcea42ecd714ce1ac2b108 - sha256: 0ec8f4d02053cd03b0f3e63168316530949484f80e16f5e2fb199a1d117a89ca + md5: fe4cd204981cdc1b7c5467248e2afb5a + sha256: d20774438407b92a81a570f8f5252f33e841bd36b16f0cf526fcbd556372c6c3 category: main optional: false -- name: ipython - version: 8.32.0 +- name: libcurand + version: 10.3.6.82 manager: conda platform: linux-64 dependencies: - __unix: '' - pexpect: '>4.3' - python: '' - decorator: '' - exceptiongroup: '' - jedi: '>=0.16' - matplotlib-inline: '' - pickleshare: '' - prompt-toolkit: '>=3.0.41,<3.1.0' - pygments: '>=2.4.0' - stack_data: '' - traitlets: '>=5.13.0' - typing_extensions: '>=4.6' - url: https://conda.anaconda.org/conda-forge/noarch/ipython-8.32.0-pyh907856f_0.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcurand-10.3.6.82-he02047a_0.conda hash: - md5: 9de86472b8f207fb098c69daaad50e67 - sha256: b1b940cfe85d5f0aaed83ef8c9f07ee80daa68acb05feeb5142d620472b01e0d + md5: d1287d0b8348cbe15ed1cd0d53764781 + sha256: 9232af3728d29246e555a6f9c636bb21fb92920db276b34f0041a093f7bbb801 category: main optional: false -- name: jedi - version: 0.19.2 +- name: libcurand-dev + version: 10.3.6.82 manager: conda platform: linux-64 dependencies: - parso: '>=0.8.3,<0.9.0' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/jedi-0.19.2-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libcurand: 10.3.6.82 + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcurand-dev-10.3.6.82-he02047a_0.conda hash: - md5: a4f4c5dc9b80bc50e0d3dc4e6e8f1bd9 - sha256: 92c4d217e2dc68983f724aa983cca5464dcb929c566627b26a2511159667dba8 + md5: cfc1bf658e7cd830a784cb0b798a4071 + sha256: 4baa86b9123cdef6efd3e5ec6cdb5ae5a02b4dd7452aa24a78fc7258a1d1f8d4 category: main optional: false -- name: jinja2 - version: 3.1.5 +- name: libcurl + version: 8.21.0 manager: conda platform: linux-64 dependencies: - markupsafe: '>=2.0' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/jinja2-3.1.5-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + krb5: '>=1.22.2,<1.23.0a0' + libgcc: '>=14' + libnghttp2: '>=1.68.1,<2.0a0' + libpsl: '>=0.22.0,<0.23.0a0' + libssh2: '>=1.11.1,<2.0a0' + libzlib: '>=1.3.2,<2.0a0' + openssl: '>=3.5.7,<4.0a0' + zstd: '>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.21.0-hae6b9f4_2.conda hash: - md5: 2752a6ed44105bfb18c9bef1177d9dcd - sha256: 98977694b9ecaa3218662f843425f39501f81973c450f995eec68f1803ed71c3 + md5: f9c59d277a16ec8f272b2d5dd2ec3335 + sha256: ba8354084b34fce56d5697982fce4a384c148e972e15c0ab891187617fe7ec29 category: main optional: false -- name: joblib - version: 1.4.2 +- name: libcusolver + version: 11.6.3.83 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - setuptools: '' - url: https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_1.conda - hash: - md5: bf8243ee348f3a10a14ed0cae323e0c1 - sha256: 51cc2dc491668af0c4d9299b0ab750f16ccf413ec5e2391b924108c1fbacae9b - category: main - optional: false -- name: kernel-headers_linux-64 - version: 3.10.0 - manager: conda - platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/noarch/kernel-headers_linux-64-3.10.0-he073ed8_18.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libcublas: '>=12.5.3.2,<12.6.0a0' + libcusparse: '>=12.5.1.3,<12.6.0a0' + libgcc-ng: '>=12' + libnvjitlink: '>=12.5.82,<13.0.0a0' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcusolver-11.6.3.83-he02047a_0.conda hash: - md5: ad8527bf134a90e1c9ed35fa0b64318c - sha256: a922841ad80bd7b222502e65c07ecb67e4176c4fa5b03678a005f39fcc98be4b + md5: d09f318068654b26f8fa4c716e32b797 + sha256: e884091e81205362fe8939b6a4dda6369043e1d5e86ca27e7c0fb9b85ed6251c category: main optional: false -- name: keyutils - version: 1.6.1 +- name: libcusolver-dev + version: 11.6.3.83 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=10.3.0' - url: https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2 + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libcusolver: 11.6.3.83 + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcusolver-dev-11.6.3.83-he02047a_0.conda hash: - md5: 30186d27e2c9fa62b45fb1476b7200e3 - sha256: 150c05a6e538610ca7c43beb3a40d65c90537497a4f6a5f4d15ec0451b6f5ebb + md5: 5b57530c4b29443792f8288f8d68c39e + sha256: 21cf9a127822d4643d41c3dfcc47a6c6cd8938d749ce589552f74ddef4c42aac category: main optional: false -- name: kiwisolver - version: 1.4.7 +- name: libcusparse + version: 12.5.1.3 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/kiwisolver-1.4.7-py310h3788b33_0.conda + cuda-version: '>=12.5,<12.6.0a0' + libgcc-ng: '>=12' + libnvjitlink: '>=12.5.82,<13.0.0a0' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libcusparse-12.5.1.3-he02047a_0.conda hash: - md5: 4186d9b4d004b0fe0de6aa62496fb48a - sha256: d97a9894803674e4f8155a5e98a49337d28bdee77dfd87e1614a824d190cd086 + md5: 39501c7f687f65be6246d4fdefabfd27 + sha256: de1e49e649a82164986864340de75db8190fabb388f59a38cb9b1d1084c4e11b category: main optional: false -- name: krb5 - version: 1.21.3 +- name: libcusparse-dev + version: 12.5.1.3 manager: conda platform: linux-64 dependencies: - keyutils: '>=1.6.1,<2.0a0' - libedit: '>=3.1.20191231,<4.0a0' + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libcusparse: 12.5.1.3 libgcc-ng: '>=12' + libnvjitlink: '>=12.5.82,<13.0.0a0' libstdcxx-ng: '>=12' - openssl: '>=3.3.1,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/krb5-1.21.3-h659f571_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/libcusparse-dev-12.5.1.3-he02047a_0.conda hash: - md5: 3f43953b7d3fb3aaa1d0d0723d91e368 - sha256: 99df692f7a8a5c27cd14b5fb1374ee55e756631b9c3d659ed3ee60830249b238 + md5: 927c7ba9aeaf2ff46dac087c7e9a4b3a + sha256: 616f71647b8da9bdae826bd712810929b1cd134b6804d54b290d65e1c823d53a category: main optional: false -- name: lcms2 - version: '2.17' +- name: libedit + version: 3.1.20250104 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' libgcc: '>=13' - libjpeg-turbo: '>=3.0.0,<4.0a0' - libtiff: '>=4.7.0,<4.8.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/lcms2-2.17-h717163a_0.conda + ncurses: '>=6.5,<7.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda hash: - md5: 000e85703f0fd9594c81710dd5066471 - sha256: d6a61830a354da022eae93fa896d0991385a875c6bba53c82263a289deda9db8 + md5: c277e0a4d549b03ac1e9d6cbbe3d017b + sha256: d789471216e7aba3c184cd054ed61ce3f6dac6f87a50ec69291b9297f8c18724 category: main optional: false -- name: ld_impl_linux-64 - version: '2.43' +- name: libev + version: '4.33' manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - url: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda hash: - md5: 01f8d123c96816249efd255a31ad7712 - sha256: db73f38155d901a610b2320525b9dd3b31e4949215c870685fd92ea61b5ce472 + md5: 172bf1cd1ff8629f2b1179945ed45055 + sha256: 1cd6048169fa0395af74ed5d8f1716e22c19a81a8a36f934c110ca3ad4dd27b4 category: main optional: false -- name: lerc - version: 4.0.0 +- name: libexpat + version: 2.8.1 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2 + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.8.1-hecca717_1.conda hash: - md5: 76bbff344f0134279f225174e9064c8f - sha256: cb55f36dcd898203927133280ae1dc643368af041a48bcf7c026acb7c47b0c12 + md5: b24d3c612f71e7aa74158d92106318b2 + sha256: 16feffd9ddbbe5b718515d38ee376c685ba95491cd901244e24671d20b952a77 category: main optional: false -- name: libabseil - version: '20240722.0' +- name: libffi + version: 3.5.2 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libabseil-20240722.0-cxx17_hbbce691_4.conda + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.5.2-h3435931_0.conda hash: - md5: 488f260ccda0afaf08acb286db439c2f - sha256: 143a586aa67d50622ef703de57b9d43f44945836d6568e0e7aa174bd8c45e0d4 + md5: a360c33a5abe61c07959e449fa1453eb + sha256: 31f19b6a88ce40ebc0d5a992c131f57d919f73c0b92cd1617a5bec83f6e961e6 category: main optional: false -- name: libarrow - version: 19.0.1 +- name: libfreetype + version: 2.14.3 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - aws-crt-cpp: '>=0.29.9,<0.29.10.0a0' - aws-sdk-cpp: '>=1.11.489,<1.11.490.0a0' - azure-core-cpp: '>=1.14.0,<1.14.1.0a0' - azure-identity-cpp: '>=1.10.0,<1.10.1.0a0' - azure-storage-blobs-cpp: '>=12.13.0,<12.13.1.0a0' - azure-storage-files-datalake-cpp: '>=12.12.0,<12.12.1.0a0' - bzip2: '>=1.0.8,<2.0a0' - glog: '>=0.7.1,<0.8.0a0' - libabseil: '>=20240722.0,<20240723.0a0' - libbrotlidec: '>=1.1.0,<1.2.0a0' - libbrotlienc: '>=1.1.0,<1.2.0a0' - libgcc: '>=13' - libgoogle-cloud: '>=2.35.0,<2.36.0a0' - libgoogle-cloud-storage: '>=2.35.0,<2.36.0a0' - libopentelemetry-cpp: '>=1.18.0,<1.19.0a0' - libprotobuf: '>=5.28.3,<5.28.4.0a0' - libre2-11: '>=2024.7.2' - libstdcxx: '>=13' - libutf8proc: '>=2.10.0,<2.11.0a0' - libzlib: '>=1.3.1,<2.0a0' - lz4-c: '>=1.10.0,<1.11.0a0' - orc: '>=2.0.3,<2.0.4.0a0' - re2: '' - snappy: '>=1.2.1,<1.3.0a0' - zstd: '>=1.5.6,<1.6.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-19.0.1-hfa2a6e7_0_cpu.conda + libfreetype6: '>=2.14.3' + url: https://conda.anaconda.org/conda-forge/linux-64/libfreetype-2.14.3-ha770c72_0.conda hash: - md5: 11b712ed1316c98592f6bae7ccfaa86c - sha256: 7b1f61045b37266989023a007d6331875062bb658068a6e6ab49720495ca3543 + md5: e289f3d17880e44b633ba911d57a321b + sha256: 38f014a7129e644636e46064ecd6b1945e729c2140e21d75bb476af39e692db2 category: main optional: false -- name: libarrow-acero - version: 19.0.1 +- name: libfreetype6 + version: 2.14.3 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libarrow: 19.0.1 - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-acero-19.0.1-hcb10f89_0_cpu.conda + libgcc: '>=14' + libpng: '>=1.6.55,<1.7.0a0' + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libfreetype6-2.14.3-h73754d4_0.conda hash: - md5: 0d63e2dea06c44c9d2c8be3e7e38eea9 - sha256: 9a3c38a8f1516fe5b7801d0407ff704efd53955ebd63f7fbc439ec3b563d19cc + md5: fb16b4b69e3f1dcfe79d80db8fd0c55d + sha256: 16f020f96da79db1863fcdd8f2b8f4f7d52f177dd4c58601e38e9182e91adf1d category: main optional: false -- name: libarrow-dataset - version: 19.0.1 +- name: libgcc + version: 15.2.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libarrow: 19.0.1 - libarrow-acero: 19.0.1 - libgcc: '>=13' - libparquet: 19.0.1 - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-dataset-19.0.1-hcb10f89_0_cpu.conda + _openmp_mutex: '>=4.5' + url: https://conda.anaconda.org/conda-forge/linux-64/libgcc-15.2.0-he0feb66_19.conda hash: - md5: ec52b3b990be399f4267a9acabb73070 - sha256: f756208d787db50b6be68210cb9eec3644b8291a8a353bb2071ea4451bfc1412 + md5: 57736f29cc2b0ec0b6c2952d3f101b6a + sha256: 8e0a3b5e41272e5678499b5dfc4cddb673f9e935de01eb0767ce857001229f46 category: main optional: false -- name: libarrow-substrait - version: 19.0.1 +- name: libgcc-devel_linux-64 + version: 12.4.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libabseil: '>=20240722.0,<20240723.0a0' - libarrow: 19.0.1 - libarrow-acero: 19.0.1 - libarrow-dataset: 19.0.1 - libgcc: '>=13' - libprotobuf: '>=5.28.3,<5.28.4.0a0' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libarrow-substrait-19.0.1-h08228c5_0_cpu.conda + __unix: '' + url: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-12.4.0-h1762d19_102.conda hash: - md5: 792e2359bb93513324326cbe3ee4ebdd - sha256: e0b3ed06ce74c6a083dab59fb3059fdbc40fc71ff94ce470ca0a7c7ffe8d0317 + md5: 5c9ee54252cddf9f83dc48f6ceef0ba4 + sha256: 4f8486faaa5696a4115a621100acda0f64b49631f2c4bc6046e0f72496348d76 category: main optional: false -- name: libblas - version: 3.9.0 +- name: libgcc-ng + version: 15.2.0 manager: conda platform: linux-64 dependencies: - libopenblas: '>=0.3.29,<1.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openblas.conda + libgcc: 15.2.0 + url: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-15.2.0-h69a702a_19.conda hash: - md5: 728dbebd0f7a20337218beacffd37916 - sha256: 9839fc4ac0cbb0aa3b9eea520adfb57311838959222654804e58f6f2d1771db5 + md5: 331ee9b72b9dff570d56b1302c5ab37d + sha256: 9dcf54adfaa5e861123c2da4f2f0451a685464ea7e5a41ad91cf67b31d658d98 category: main optional: false -- name: libbrotlicommon - version: 1.1.0 +- name: libgfortran + version: 15.2.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hb9d3cd8_2.conda + libgfortran5: 15.2.0 + url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-15.2.0-h69a702a_19.conda hash: - md5: 41b599ed2b02abcfdd84302bff174b23 - sha256: d9db2de60ea917298e658143354a530e9ca5f9c63471c65cf47ab39fd2f429e3 + md5: 42bf7eca1a951735fa06c0e3c0d5c8e6 + sha256: 561a42758ef25b9ce308c4e2cf56daee4f06138385a17e29a492cd928e00be6f category: main optional: false -- name: libbrotlidec - version: 1.1.0 +- name: libgfortran5 + version: 15.2.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libbrotlicommon: 1.1.0 - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libbrotlidec-1.1.0-hb9d3cd8_2.conda + libgcc: '>=15.2.0' + url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-15.2.0-h68bc16d_19.conda hash: - md5: 9566f0bd264fbd463002e759b8a82401 - sha256: 2892d512cad096cb03f1b66361deeab58b64e15ba525d6592bb6d609e7045edf + md5: 85072b0ad177c966294f129b7c04a2d5 + sha256: 057978bb69fea29ed715a9b98adf71015c31baecc4aeb2bfc20d4fd5d83579d4 category: main optional: false -- name: libbrotlienc - version: 1.1.0 +- name: libglib + version: 2.88.2 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libbrotlicommon: 1.1.0 - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libbrotlienc-1.1.0-hb9d3cd8_2.conda + libffi: '>=3.5.2,<3.6.0a0' + libgcc: '>=14' + libiconv: '>=1.18,<2.0a0' + libzlib: '>=1.3.2,<2.0a0' + pcre2: '>=10.47,<10.48.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.88.2-h0d30a3d_0.conda hash: - md5: 06f70867945ea6a84d35836af780f1de - sha256: 779f58174e99de3600e939fa46eddb453ec5d3c60bb46cdaa8b4c127224dbf29 + md5: 889febc66cd9e4190f80ef9718fa239b + sha256: 4bee10e62796f01e4fa2b5849135b1cc061337fe9cf5eb9bd79e9664922ae0e4 category: main optional: false -- name: libcap - version: '2.71' +- name: libgomp + version: 15.2.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - attr: '>=2.5.1,<2.6.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libcap-2.71-h39aace5_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/libgomp-15.2.0-he0feb66_19.conda hash: - md5: dd19e4e3043f6948bd7454b946ee0983 - sha256: 2bbefac94f4ab8ff7c64dc843238b6c8edcc9ff1f2b5a0a48407a904dc7ccfb2 + md5: faac990cb7aedc7f3a2224f2c9b0c26c + sha256: 5abe4ab9d93f6c9757d654f1969ae2267d4505315c1f2f8fe705fd60af084f1b category: main optional: false -- name: libcblas - version: 3.9.0 +- name: libiconv + version: '1.18' manager: conda platform: linux-64 dependencies: - libblas: 3.9.0 - url: https://conda.anaconda.org/conda-forge/linux-64/libcblas-3.9.0-31_he106b2a_openblas.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h3b78370_2.conda hash: - md5: abb32c727da370c481a1c206f5159ce9 - sha256: ede8545011f5b208b151fe3e883eb4e31d495ab925ab7b9ce394edca846e0c0d + md5: 915f5995e94f60e9a4826e0b0920ee88 + sha256: c467851a7312765447155e071752d7bf9bf44d610a5687e32706f480aad2833f category: main optional: false -- name: libcrc32c - version: 1.1.2 +- name: liblapack + version: 3.11.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=9.4.0' - libstdcxx-ng: '>=9.4.0' - url: https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2 + libblas: 3.11.0 + url: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.11.0-8_h47877c9_openblas.conda hash: - md5: c965a5aa0d5c1c37ffc62dff36e28400 - sha256: fd1d153962764433fe6233f34a72cdeed5dcf8a883a85769e8295ce940b5b0c5 + md5: 809be8ba8712c77bc7d44c2d99390dc4 + sha256: 168e327d737059553e15cc6ec36d76b9bbb3931c2a7721555fd68b4c9348b247 category: main optional: false -- name: libcublas - version: 12.5.3.2 +- name: liblzma + version: 5.8.3 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - cuda-nvrtc: '' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcublas-12.5.3.2-he02047a_0.conda + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.8.3-hb03c661_0.conda hash: - md5: 0a90d9671f8ca4cd6801130b35e1ecbd - sha256: 3c20901ef2d7c737120d889dd10690604850a1b5ea56e9f92c1a7b00453769b9 + md5: b88d90cad08e6bc8ad540cb310a761fb + sha256: ec30e52a3c1bf7d0425380a189d209a52baa03f22fb66dd3eb587acaa765bd6d category: main optional: false -- name: libcublas-dev - version: 12.5.3.2 +- name: libmamba + version: 2.8.1 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libcublas: 12.5.3.2 - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcublas-dev-12.5.3.2-he02047a_0.conda + cpp-expected: '>=1.3.1,<1.3.2.0a0' + fmt: '>=12.1.0,<12.2.0a0' + libarchive: '>=3.8.7,<3.9.0a0' + libcurl: '>=8.20.0,<9.0a0' + libgcc: '>=14' + libmsgpack-c: '>=6.1.0,<7.0a0' + libsolv: '>=0.7.39,<0.8.0a0' + libstdcxx: '>=14' + nlohmann_json-abi: 3.12.0 + openssl: '>=3.5.6,<4.0a0' + reproc: '>=14.2,<15.0a0' + reproc-cpp: '>=14.2,<15.0a0' + simdjson: '>=4.6.4,<4.7.0a0' + spdlog: '>=1.17.0,<1.18.0a0' + yaml-cpp: '>=0.8.0,<0.9.0a0' + zstd: '>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libmamba-2.8.1-hd28c85e_0.conda hash: - md5: f59f34aa2068c1cf2307e0c8309a63e3 - sha256: 85203a05763992b980a3c8667d93ef22ee40f3e8e33a4f68eeddae393031829c + md5: aeed88a1185f15ae486744897a8020cf + sha256: 24fe39602b6fc637dc79b973bc49cd884226d7b02aea7e7b3e831ee8cde7135b category: main optional: false -- name: libcudf - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - cuda-nvrtc: '' - cuda-version: '>=12,<13.0a0' - dlpack: '>=0.8,<1.0' - libcufile: '' - libkvikio: 25.02.* - librmm: 25.02.* - nvcomp: 4.1.0.6.* - url: https://conda.anaconda.org/rapidsai/linux-64/libcudf-25.02.00-cuda12_250213_g50b2d0faff_0.conda - hash: - md5: 4aaf0404950dbb0ae49d11121197c6c4 - sha256: 59f004725103b0e29be004bb623f5e60abc8ba7f9384a06e6b376d24b1c15da5 - category: main - optional: false -- name: libcufft - version: 11.2.3.61 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcufft-11.2.3.61-he02047a_0.conda - hash: - md5: f24ecbfdac382e5f4117ad1182d55ae4 - sha256: d5c476cabecf0c0da8a81ea1bb7ccf6fb5eab5316ac7aa1c9f8aad671e55fc81 - category: main - optional: false -- name: libcufft-dev - version: 11.2.3.61 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libcufft: 11.2.3.61 - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcufft-dev-11.2.3.61-he02047a_0.conda - hash: - md5: 591254d342058b68078c104eee6a4e1e - sha256: 4d69d62e8211265ef32418303c13c1cfd52ea9811c0b863753796fcbecfe8632 - category: main - optional: false -- name: libcufile - version: 1.10.1.7 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcufile-1.10.1.7-he02047a_0.conda - hash: - md5: 2e68ad67fce5223e17722149094dafcf - sha256: b2c9c247410e2bdba7ca9f627d32cd4387c292e8a7ad46a16b1f552f639c6879 - category: main - optional: false -- name: libcufile-dev - version: 1.10.1.7 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libcufile: 1.10.1.7 - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcufile-dev-1.10.1.7-he02047a_0.conda - hash: - md5: fe4cd204981cdc1b7c5467248e2afb5a - sha256: d20774438407b92a81a570f8f5252f33e841bd36b16f0cf526fcbd556372c6c3 - category: main - optional: false -- name: libcuml - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - cuda-cudart: '' - cuda-version: '>=12,<13.0a0' - libcublas: '' - libcufft: '' - libcumlprims: 25.02.* - libcurand: '' - libcusolver: '' - libcusparse: '' - libcuvs: 25.02.* - librmm: 25.02.* - treelite: 4.4.1.* - url: https://conda.anaconda.org/rapidsai/linux-64/libcuml-25.02.00-cuda12_250213_ga3a131a8b_0.conda - hash: - md5: 05463f41dd8f9f593165f474a74d175d - sha256: d6deac892d4b7ef072bdf985bb0a5aabd23516e12bcdd1655e92ed37d9d84a63 - category: main - optional: false -- name: libcumlprims - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-version: '>=12,<13.0a0' - libcublas: '' - libcurand: '' - libcusolver: '' - libcusparse: '' - libgcc: '>=13' - librmm: '>=25.2.0,<25.3.0a0' - libstdcxx: '>=13' - nccl: '>=2.25.1.1,<3.0a0' - url: https://conda.anaconda.org/rapidsai/linux-64/libcumlprims-25.02.00-cuda12_250213_g5eeba45_0.conda - hash: - md5: 52214919cb664e539c0a15481ec1cccb - sha256: edc34c9a6ec11cb376868c223fa995fcc7234af652feb409c1b05dc52740fb8f - category: main - optional: false -- name: libcurand - version: 10.3.6.82 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcurand-10.3.6.82-he02047a_0.conda - hash: - md5: d1287d0b8348cbe15ed1cd0d53764781 - sha256: 9232af3728d29246e555a6f9c636bb21fb92920db276b34f0041a093f7bbb801 - category: main - optional: false -- name: libcurand-dev - version: 10.3.6.82 +- name: libmsgpack-c + version: 6.1.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libcurand: 10.3.6.82 - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcurand-dev-10.3.6.82-he02047a_0.conda + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/libmsgpack-c-6.1.0-h54a6638_6.conda hash: - md5: cfc1bf658e7cd830a784cb0b798a4071 - sha256: 4baa86b9123cdef6efd3e5ec6cdb5ae5a02b4dd7452aa24a78fc7258a1d1f8d4 + md5: fde05e07c2a9c96ad09216f0d5fda9a1 + sha256: 79a75422873cb4107fb731d3794402eda063fcc9a809f4bb0b5738a6b3d46dee category: main optional: false -- name: libcurl - version: 8.12.1 +- name: libnghttp2 + version: 1.68.1 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - krb5: '>=1.21.3,<1.22.0a0' - libgcc: '>=13' - libnghttp2: '>=1.64.0,<2.0a0' - libssh2: '>=1.11.1,<2.0a0' + c-ares: '>=1.34.6,<2.0a0' + libev: '>=4.33,<4.34.0a0,>=4.33,<5.0a0' + libgcc: '>=14' + libstdcxx: '>=14' libzlib: '>=1.3.1,<2.0a0' - openssl: '>=3.4.1,<4.0a0' - zstd: '>=1.5.6,<1.6.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libcurl-8.12.1-h332b0f4_0.conda - hash: - md5: 45e9dc4e7b25e2841deb392be085500e - sha256: 2ebc3039af29269e4cdb858fca36265e5e400c1125a4bcd84ae73a596e0e76ca - category: main - optional: false -- name: libcusolver - version: 11.6.3.83 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libcublas: '>=12.5.3.2,<12.6.0a0' - libcusparse: '>=12.5.1.3,<12.6.0a0' - libgcc-ng: '>=12' - libnvjitlink: '>=12.5.82,<12.6.0a0' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcusolver-11.6.3.83-he02047a_0.conda - hash: - md5: d09f318068654b26f8fa4c716e32b797 - sha256: e884091e81205362fe8939b6a4dda6369043e1d5e86ca27e7c0fb9b85ed6251c - category: main - optional: false -- name: libcusolver-dev - version: 11.6.3.83 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libcusolver: 11.6.3.83 - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcusolver-dev-11.6.3.83-he02047a_0.conda + openssl: '>=3.5.5,<4.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.68.1-h877daf1_0.conda hash: - md5: 5b57530c4b29443792f8288f8d68c39e - sha256: 21cf9a127822d4643d41c3dfcc47a6c6cd8938d749ce589552f74ddef4c42aac + md5: 2a45e7f8af083626f009645a6481f12d + sha256: 663444d77a42f2265f54fb8b48c5450bfff4388d9c0f8253dd7855f0d993153f category: main optional: false -- name: libcusparse - version: 12.5.1.3 +- name: libnpp + version: 12.3.0.159 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' cuda-version: '>=12.5,<12.6.0a0' libgcc-ng: '>=12' - libnvjitlink: '>=12.5.82,<12.6.0a0' libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcusparse-12.5.1.3-he02047a_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/libnpp-12.3.0.159-he02047a_0.conda hash: - md5: 39501c7f687f65be6246d4fdefabfd27 - sha256: de1e49e649a82164986864340de75db8190fabb388f59a38cb9b1d1084c4e11b + md5: e41f67d7c89019904daed95aeed1311f + sha256: 9b0cd19f70e95ce06d24d6d4b03d1c34fc5623287da920348e6451913d54ad7a category: main optional: false -- name: libcusparse-dev - version: 12.5.1.3 +- name: libnpp-dev + version: 12.3.0.159 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' cuda-version: '>=12.5,<12.6.0a0' - libcusparse: 12.5.1.3 libgcc-ng: '>=12' - libnvjitlink: '>=12.5.82,<12.6.0a0' + libnpp: 12.3.0.159 libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libcusparse-dev-12.5.1.3-he02047a_0.conda - hash: - md5: 927c7ba9aeaf2ff46dac087c7e9a4b3a - sha256: 616f71647b8da9bdae826bd712810929b1cd134b6804d54b290d65e1c823d53a - category: main - optional: false -- name: libcuvs - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-version: '>=12,<13.0a0' - libcublas: '' - libcurand: '' - libcusolver: '' - libcusparse: '' - libgcc: '>=13' - libraft-headers: 25.02.* - librmm: '>=25.2.0,<25.3.0a0' - libstdcxx: '>=13' - nccl: '>=2.25.1.1,<3.0a0' - url: https://conda.anaconda.org/rapidsai/linux-64/libcuvs-25.02.00-cuda12_250213_g09c9f19_0.conda - hash: - md5: 377aa998c08cb97ff91925d14ee003a0 - sha256: a863c84eff003948ce1edfbb3fadd973041ea3201b31fb9a50b20f0e88f9ecc7 - category: main - optional: false -- name: libdeflate - version: '1.23' - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.23-h4ddbbb0_0.conda - hash: - md5: 8dfae1d2e74767e9ce36d5fa0d8605db - sha256: 511d801626d02f4247a04fff957cc6e9ec4cc7e8622bd9acd076bcdc5de5fe66 - category: main - optional: false -- name: libedit - version: 3.1.20250104 - manager: conda - platform: linux-64 - dependencies: - ncurses: '>=6.5,<7.0a0' - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20250104-pl5321h7949ede_0.conda - hash: - md5: c277e0a4d549b03ac1e9d6cbbe3d017b - sha256: d789471216e7aba3c184cd054ed61ce3f6dac6f87a50ec69291b9297f8c18724 - category: main - optional: false -- name: libev - version: '4.33' - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda - hash: - md5: 172bf1cd1ff8629f2b1179945ed45055 - sha256: 1cd6048169fa0395af74ed5d8f1716e22c19a81a8a36f934c110ca3ad4dd27b4 - category: main - optional: false -- name: libevent - version: 2.1.12 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - openssl: '>=3.1.1,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libevent-2.1.12-hf998b51_1.conda - hash: - md5: a1cfcc585f0c42bf8d5546bb1dfb668d - sha256: 2e14399d81fb348e9d231a82ca4d816bf855206923759b69ad006ba482764131 - category: main - optional: false -- name: libexpat - version: 2.6.4 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.4-h5888daf_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/libnpp-dev-12.3.0.159-he02047a_0.conda hash: - md5: db833e03127376d461e1e13e76f09b6c - sha256: 56541b98447b58e52d824bd59d6382d609e11de1f8adf20b23143e353d2b8d26 + md5: 20b6a97acf3286fd6fb979e919938f2a + sha256: 78e2dfc7d10970c4a59e3235350a9fcda453a43e74986a400c48ede7acc6acaf category: main optional: false -- name: libffi - version: 3.4.6 +- name: libnsl + version: 2.0.1 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_0.conda - hash: - md5: e3eb7806380bc8bcecba6d749ad5f026 - sha256: 67a6c95e33ebc763c1adc3455b9a9ecde901850eb2fceb8e646cc05ef3a663da - category: main - optional: false -- name: libgcc - version: 14.2.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - _openmp_mutex: '>=4.5' - url: https://conda.anaconda.org/conda-forge/linux-64/libgcc-14.2.0-h767d61c_2.conda + url: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hb9d3cd8_1.conda hash: - md5: ef504d1acbd74b7cc6849ef8af47dd03 - sha256: 3a572d031cb86deb541d15c1875aaa097baefc0c580b54dc61f5edab99215792 - category: main - optional: false -- name: libgcc-devel_linux-64 - version: 13.3.0 - manager: conda - platform: linux-64 - dependencies: - __unix: '' - url: https://conda.anaconda.org/conda-forge/noarch/libgcc-devel_linux-64-13.3.0-hc03c837_102.conda - hash: - md5: 4c1d6961a6a54f602ae510d9bf31fa60 - sha256: 538544a2e0651bfeb0348ca6469b6b608606f6080a0b5a531af3a3852fec0215 - category: main - optional: false -- name: libgcc-ng - version: 14.2.0 - manager: conda - platform: linux-64 - dependencies: - libgcc: 14.2.0 - url: https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.2.0-h69a702a_2.conda - hash: - md5: a2222a6ada71fb478682efe483ce0f92 - sha256: fb7558c328b38b2f9d2e412c48da7890e7721ba018d733ebdfea57280df01904 - category: main - optional: false -- name: libgcrypt-lib - version: 1.11.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libgpg-error: '>=1.51,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libgcrypt-lib-1.11.0-hb9d3cd8_2.conda - hash: - md5: e55712ff40a054134d51b89afca57dbc - sha256: ffc3602f9298da248786f46b00d0594d26a18feeb1b07ce88f3d7d61075e39e6 - category: main - optional: false -- name: libgfortran - version: 14.2.0 - manager: conda - platform: linux-64 - dependencies: - libgfortran5: 14.2.0 - url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran-14.2.0-h69a702a_2.conda - hash: - md5: fb54c4ea68b460c278d26eea89cfbcc3 - sha256: e05263e8960da03c341650f2a3ffa4ccae4e111cb198e8933a2908125459e5a6 - category: main - optional: false -- name: libgfortran5 - version: 14.2.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=14.2.0' - url: https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.2.0-hf1ad2bd_2.conda - hash: - md5: 556a4fdfac7287d349b8f09aba899693 - sha256: c17b7cf3073a1f4e1f34d50872934fa326346e104d3c445abc1e62481ad6085c - category: main - optional: false -- name: libglib - version: 2.82.2 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libffi: '>=3.4,<4.0a0' - libgcc: '>=13' - libiconv: '>=1.17,<2.0a0' - libzlib: '>=1.3.1,<2.0a0' - pcre2: '>=10.44,<10.45.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libglib-2.82.2-h2ff4ddf_1.conda - hash: - md5: 37d1af619d999ee8f1f73cf5a06f4e2f - sha256: f0804a9e46ae7b32ca698d26c1c95aa82a91f71b6051883d4a46bea725be9ea4 - category: main - optional: false -- name: libgomp - version: 14.2.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libgomp-14.2.0-h767d61c_2.conda - hash: - md5: 06d02030237f4d5b3d9a7e7d348fe3c6 - sha256: 1a3130e0b9267e781b89399580f3163632d59fe5b0142900d63052ab1a53490e - category: main - optional: false -- name: libgoogle-cloud - version: 2.35.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libabseil: '>=20240722.0,<20240723.0a0' - libcurl: '>=8.11.1,<9.0a0' - libgcc: '>=13' - libgrpc: '>=1.67.1,<1.68.0a0' - libprotobuf: '>=5.28.3,<5.28.4.0a0' - libstdcxx: '>=13' - openssl: '>=3.4.0,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-2.35.0-h2b5623c_0.conda - hash: - md5: 1040ab07d7af9f23cf2466ffe4e58db1 - sha256: d747d14c69da512d8993a995dc2df90e857778b0a8542f12fb751544128af685 - category: main - optional: false -- name: libgoogle-cloud-storage - version: 2.35.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libabseil: '' - libcrc32c: '>=1.1.2,<1.2.0a0' - libcurl: '' - libgcc: '>=13' - libgoogle-cloud: 2.35.0 - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - openssl: '' - url: https://conda.anaconda.org/conda-forge/linux-64/libgoogle-cloud-storage-2.35.0-h0121fbd_0.conda - hash: - md5: 34e2243e0428aac6b3e903ef99b6d57d - sha256: cb1ef70e55d2c1defbfd8413dbe85b5550782470dda4f8d393f28d41b6d9b007 - category: main - optional: false -- name: libgpg-error - version: '1.51' - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libgpg-error-1.51-hbd13f7d_1.conda - hash: - md5: 168cc19c031482f83b23c4eebbb94e26 - sha256: 9e0c09c1faf2151ade3ccb64e52d3c1f2dde85c00e37c6a3e6a8bced2aba68be - category: main - optional: false -- name: libgrpc - version: 1.67.1 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - c-ares: '>=1.34.4,<2.0a0' - libabseil: '>=20240722.0,<20240723.0a0' - libgcc: '>=13' - libprotobuf: '>=5.28.3,<5.28.4.0a0' - libre2-11: '>=2024.7.2' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - openssl: '>=3.4.0,<4.0a0' - re2: '' - url: https://conda.anaconda.org/conda-forge/linux-64/libgrpc-1.67.1-h25350d4_1.conda - hash: - md5: 0c6497a760b99a926c7c12b74951a39c - sha256: 014627485b3cf0ea18e04c0bab07be7fb98722a3aeeb58477acc7e1c3d2f911e - category: main - optional: false -- name: libiconv - version: '1.18' - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.18-h4ce23a2_1.conda - hash: - md5: e796ff8ddc598affdf7c173d6145f087 - sha256: 18a4afe14f731bfb9cf388659994263904d20111e42f841e9eea1bb6f91f4ab4 - category: main - optional: false -- name: libjpeg-turbo - version: 3.0.0 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda - hash: - md5: ea25936bb4080d843790b586850f82b8 - sha256: b954e09b7e49c2f2433d6f3bb73868eda5e378278b0f8c1dd10a7ef090e14f2f - category: main - optional: false -- name: libkvikio - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - cuda-version: '>=12,<13.0a0' - libcufile-dev: '' - libcurl: '>=8.5.0,<9.0a0' - url: https://conda.anaconda.org/rapidsai/linux-64/libkvikio-25.02.00-cuda12_250213_gdf0fe28_0.conda - hash: - md5: c5a5efd49fe12633c443bfb06158bd05 - sha256: 2e7d9114b0f593e8bfcecdfee784d3c9e8ccf8eebc0b63af560fe6e3736f9379 - category: main - optional: false -- name: liblapack - version: 3.9.0 - manager: conda - platform: linux-64 - dependencies: - libblas: 3.9.0 - url: https://conda.anaconda.org/conda-forge/linux-64/liblapack-3.9.0-31_h7ac8fdf_openblas.conda - hash: - md5: 452b98eafe050ecff932f0ec832dd03f - sha256: f583661921456e798aba10972a8abbd9d33571c655c1f66eff450edc9cbefcf3 - category: main - optional: false -- name: libllvm14 - version: 14.0.6 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - libzlib: '>=1.2.13,<2.0.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libllvm14-14.0.6-hcd5def8_4.conda - hash: - md5: 73301c133ded2bf71906aa2104edae8b - sha256: 225cc7c3b20ac1db1bdb37fa18c95bf8aecef4388e984ab2f7540a9f4382106a - category: main - optional: false -- name: liblzma - version: 5.6.4 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/liblzma-5.6.4-hb9d3cd8_0.conda - hash: - md5: 42d5b6a0f30d3c10cd88cb8584fda1cb - sha256: cad52e10319ca4585bc37f0bc7cce99ec7c15dc9168e42ccb96b741b0a27db3f - category: main - optional: false -- name: libnghttp2 - version: 1.64.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - c-ares: '>=1.32.3,<2.0a0' - libev: '>=4.33,<5.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - openssl: '>=3.3.2,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libnghttp2-1.64.0-h161d5f1_0.conda - hash: - md5: 19e57602824042dfd0446292ef90488b - sha256: b0f2b3695b13a989f75d8fd7f4778e1c7aabe3b36db83f0fe80b2cd812c0e975 - category: main - optional: false -- name: libnl - version: 3.11.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libnl-3.11.0-hb9d3cd8_0.conda - hash: - md5: db63358239cbe1ff86242406d440e44a - sha256: ba7c5d294e3d80f08ac5a39564217702d1a752e352e486210faff794ac5001b4 - category: main - optional: false -- name: libnpp - version: 12.3.0.159 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libnpp-12.3.0.159-he02047a_0.conda - hash: - md5: e41f67d7c89019904daed95aeed1311f - sha256: 9b0cd19f70e95ce06d24d6d4b03d1c34fc5623287da920348e6451913d54ad7a - category: main - optional: false -- name: libnpp-dev - version: 12.3.0.159 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libnpp: 12.3.0.159 - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libnpp-dev-12.3.0.159-he02047a_0.conda - hash: - md5: 20b6a97acf3286fd6fb979e919938f2a - sha256: 78e2dfc7d10970c4a59e3235350a9fcda453a43e74986a400c48ede7acc6acaf - category: main - optional: false -- name: libnsl - version: 2.0.1 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda - hash: - md5: 30fd6e37fe21f86f4bd26d6ee73eeec7 - sha256: 26d77a3bb4dceeedc2a41bd688564fe71bf2d149fdcf117049970bc02ff1add6 + md5: d864d34357c3b65a4b731f78c0801dc4 + sha256: 927fe72b054277cde6cb82597d0fcf6baf127dcbce2e0a9d8925a68f1265eef5 category: main optional: false - name: libnvfatbin - version: 12.5.82 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libnvfatbin-12.5.82-he02047a_0.conda - hash: - md5: 2cf4281d62f02ee11c26029a6b68e300 - sha256: 53708c5d90fb27edda8131f802f4b8423aaebf873480acc00af34670b60f555a - category: main - optional: false -- name: libnvfatbin-dev - version: 12.5.82 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libnvfatbin: 12.5.82 - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libnvfatbin-dev-12.5.82-he02047a_0.conda - hash: - md5: dbe4c03d6ce20280b0b266e5004efa47 - sha256: 0783038db939e1d0b67c87d8a1412e6d87993df36c1f6e4149bb20e4b6744073 - category: main - optional: false -- name: libnvjitlink - version: 12.5.82 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libnvjitlink-12.5.82-he02047a_0.conda - hash: - md5: e5e58d150028643731c5cbf83e5ba249 - sha256: 39bebb36643994c14d638eae8bded7c98f07893656de836e2a52d1522b063137 - category: main - optional: false -- name: libnvjitlink-dev - version: 12.5.82 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libnvjitlink: 12.5.82 - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libnvjitlink-dev-12.5.82-he02047a_0.conda - hash: - md5: e14f1a78ecd13e2504b0a0e60fad072e - sha256: 59d5dfc1cc7b6c96fc2c30d6469af245a85d4d591b07e1a022e6e226b5fd8924 - category: main - optional: false -- name: libnvjpeg - version: 12.3.2.81 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12.5,<12.6.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libnvjpeg-12.3.2.81-he02047a_0.conda - hash: - md5: 91d1b39af9202b46cadf31c89d521bef - sha256: 48b512996142fd849ca4653da238ede33312939fc78903b8eb80cee16e5184f3 - category: main - optional: false -- name: libnvjpeg-dev - version: 12.3.2.81 - manager: conda - platform: linux-64 - dependencies: - cuda-cudart-dev: '' - cuda-version: '>=12.5,<12.6.0a0' - libnvjpeg: 12.3.2.81 - url: https://conda.anaconda.org/conda-forge/linux-64/libnvjpeg-dev-12.3.2.81-ha770c72_0.conda - hash: - md5: 8c19a53db5344e45aa4596099b3dbeeb - sha256: bc04ef074d2623fda565ec8ce17c69c4097e8993630bf89b854393a84ed0f30e - category: main - optional: false -- name: libopenblas - version: 0.3.29 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=14' - libgfortran: '' - libgfortran5: '>=14.2.0' - url: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.29-pthreads_h94d23a6_0.conda - hash: - md5: 0a4d0252248ef9a0f88f2ba8b8a08e12 - sha256: cc5389ea254f111ef17a53df75e8e5209ef2ea6117e3f8aced88b5a8e51f11c4 - category: main - optional: false -- name: libopentelemetry-cpp - version: 1.18.0 - manager: conda - platform: linux-64 - dependencies: - libabseil: '>=20240722.0,<20240723.0a0' - libcurl: '>=8.11.1,<9.0a0' - libgrpc: '>=1.67.1,<1.68.0a0' - libopentelemetry-cpp-headers: 1.18.0 - libprotobuf: '>=5.28.3,<5.28.4.0a0' - libzlib: '>=1.3.1,<2.0a0' - nlohmann_json: '' - prometheus-cpp: '>=1.3.0,<1.4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-1.18.0-hfcad708_1.conda - hash: - md5: 1f5a5d66e77a39dc5bd639ec953705cf - sha256: 4ea235e08676f16b0d3c3380befe1478c0fa0141512ee709b011005c55c9619f - category: main - optional: false -- name: libopentelemetry-cpp-headers - version: 1.18.0 - manager: conda - platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/linux-64/libopentelemetry-cpp-headers-1.18.0-ha770c72_1.conda - hash: - md5: 4fb055f57404920a43b147031471e03b - sha256: aa1f7dea79ea8513ff77339ba7c6e9cf10dfa537143e7718b1cfb3af52b649f2 - category: main - optional: false -- name: libparquet - version: 19.0.1 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libarrow: 19.0.1 - libgcc: '>=13' - libstdcxx: '>=13' - libthrift: '>=0.21.0,<0.21.1.0a0' - openssl: '>=3.4.1,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libparquet-19.0.1-h081d1f1_0_cpu.conda - hash: - md5: 8b58c378d65b213c001f04a174a2a70e - sha256: e9c4a07e79886963bfcd05894a15b5d4c7137c1122273de68845315c35d6505d - category: main - optional: false -- name: libpng - version: 1.6.47 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.47-h943b412_0.conda - hash: - md5: 55199e2ae2c3651f6f9b2a447b47bdc9 - sha256: 23367d71da58c9a61c8cbd963fcffb92768d4ae5ffbef9a47cdf1f54f98c5c36 - category: main - optional: false -- name: libprotobuf - version: 5.28.3 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libabseil: '>=20240722.0,<20240723.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libprotobuf-5.28.3-h6128344_1.conda - hash: - md5: d8703f1ffe5a06356f06467f1d0b9464 - sha256: 51125ebb8b7152e4a4e69fd2398489c4ec8473195c27cde3cbdf1cb6d18c5493 - category: main - optional: false -- name: libraft - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-version: '>=12,<13.0a0' - libcublas: '' - libcurand: '' - libcusolver: '' - libcusparse: '' - libgcc: '>=13' - libraft-headers: 25.02.00 - libstdcxx: '>=13' - url: https://conda.anaconda.org/rapidsai/linux-64/libraft-25.02.00-cuda12_250213_gbdcc918d_0.conda - hash: - md5: 9a617a046bc76436ddd0cfcc61040d71 - sha256: 585d0689aa77063c74b2e93a282bce52dc2684ea847c4c6c7ee9595e2d29f346 - category: main - optional: false -- name: libraft-headers - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - cuda-cudart-dev: '' - cuda-profiler-api: '' - cuda-version: '>=12,<13.0a0' - libcublas-dev: '' - libcurand-dev: '' - libcusolver-dev: '' - libcusparse-dev: '' - libraft-headers-only: 25.02.00 - librmm: 25.02.* - url: https://conda.anaconda.org/rapidsai/linux-64/libraft-headers-25.02.00-cuda12_250213_gbdcc918d_0.conda - hash: - md5: 267d53d6c3bcd05852bd0c8ce063d472 - sha256: 025d03d391464aa387f14885250d4f855b5ddff6e9d4f410d37a8b8b26d5514b - category: main - optional: false -- name: libraft-headers-only - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-version: '>=12,<13.0a0' - fmt: '>=11.0.2,<12' - libgcc: '>=13' - librmm: 25.02.* - libstdcxx: '>=13' - spdlog: '>=1.14.1,<1.15' - url: https://conda.anaconda.org/rapidsai/linux-64/libraft-headers-only-25.02.00-cuda12_250213_gbdcc918d_0.conda - hash: - md5: 80ded4d58d10e7dbce2fdfc711e467a9 - sha256: 85f17288d7604d784a0a6f0d8f0b31ccede24464bb672ef2f0cbc74a2503df6f - category: main - optional: false -- name: libre2-11 - version: 2024.07.02 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libabseil: '>=20240722.0,<20240723.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libre2-11-2024.07.02-hbbce691_2.conda - hash: - md5: b2fede24428726dd867611664fb372e8 - sha256: 4420f8362c71251892ba1eeb957c5e445e4e1596c0c651c28d0d8b415fe120c7 - category: main - optional: false -- name: librmm - version: 25.02.00 - manager: conda - platform: linux-64 - dependencies: - cuda-version: '>=12,<13.0a0' - fmt: '>=11.0.2,<12' - spdlog: '>=1.14.1,<1.15' - url: https://conda.anaconda.org/rapidsai/linux-64/librmm-25.02.00-cuda12_250213_g39b4ccdb_0.conda - hash: - md5: 00e12f8ead1a7c1b71f9960c684aebab - sha256: ad676251ffcbbcc9aa64ad04dfdbcc5a1d67899bd1a272942b68ab6925e10904 - category: main - optional: false -- name: libsanitizer - version: 13.3.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13.3.0' - libstdcxx: '>=13.3.0' - url: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-13.3.0-he8ea267_2.conda - hash: - md5: 2b6cdf7bb95d3d10ef4e38ce0bc95dba - sha256: 27c4c8bf8e2dd60182d47274389be7c70446df6ed5344206266321ee749158b4 - category: main - optional: false -- name: libsqlite - version: 3.49.1 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.49.1-hee588c1_1.conda - hash: - md5: 73cea06049cc4174578b432320a003b8 - sha256: 7a09eef804ef7cf4d88215c2297eabb72af8ad0bd5b012060111c289f14bbe7d - category: main - optional: false -- name: libssh2 - version: 1.11.1 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libzlib: '>=1.3.1,<2.0a0' - openssl: '>=3.4.0,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hf672d98_0.conda - hash: - md5: be2de152d8073ef1c01b7728475f2fe7 - sha256: 0407ac9fda2bb67e11e357066eff144c845801d00b5f664efbc48813af1e7bb9 - category: main - optional: false -- name: libstdcxx - version: 14.2.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: 14.2.0 - url: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-14.2.0-h8f9b012_2.conda - hash: - md5: a78c856b6dc6bf4ea8daeb9beaaa3fb0 - sha256: 8f5bd92e4a24e1d35ba015c5252e8f818898478cb3bc50bd8b12ab54707dc4da - category: main - optional: false -- name: libstdcxx-devel_linux-64 - version: 13.3.0 - manager: conda - platform: linux-64 - dependencies: - __unix: '' - url: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.3.0-hc03c837_102.conda - hash: - md5: aa38de2738c5f4a72a880e3d31ffe8b4 - sha256: abc89056d4ca7debe938504b3b6d9ccc6d7a0f0b528fe3409230636a21e81002 - category: main - optional: false -- name: libstdcxx-ng - version: 14.2.0 - manager: conda - platform: linux-64 - dependencies: - libstdcxx: 14.2.0 - url: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.2.0-h4852527_2.conda - hash: - md5: c75da67f045c2627f59e6fcb5f4e3a9b - sha256: e86f38b007cf97cc2c67cd519f2de12a313c4ee3f5ef11652ad08932a5e34189 - category: main - optional: false -- name: libsystemd0 - version: '257.3' - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libcap: '>=2.71,<2.72.0a0' - libgcc: '>=13' - libgcrypt-lib: '>=1.11.0,<2.0a0' - liblzma: '>=5.6.4,<6.0a0' - lz4-c: '>=1.10.0,<1.11.0a0' - zstd: '>=1.5.6,<1.6.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libsystemd0-257.3-h3dc2cb9_0.conda - hash: - md5: df057752e83bd254f6d65646eb67cd2e - sha256: dd566e2ef4a83b27d2b26d988cbbed50456294892744639f30f19954d2ee3287 - category: main - optional: false -- name: libthrift - version: 0.21.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libevent: '>=2.1.12,<2.1.13.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - openssl: '>=3.3.2,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libthrift-0.21.0-h0e7cc3e_0.conda - hash: - md5: dcb95c0a98ba9ff737f7ae482aef7833 - sha256: ebb395232973c18745b86c9a399a4725b2c39293c9a91b8e59251be013db42f0 - category: main - optional: false -- name: libtiff - version: 4.7.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - lerc: '>=4.0.0,<5.0a0' - libdeflate: '>=1.23,<1.24.0a0' - libgcc: '>=13' - libjpeg-turbo: '>=3.0.0,<4.0a0' - liblzma: '>=5.6.3,<6.0a0' - libstdcxx: '>=13' - libwebp-base: '>=1.4.0,<2.0a0' - libzlib: '>=1.3.1,<2.0a0' - zstd: '>=1.5.6,<1.6.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.7.0-hd9ff511_3.conda - hash: - md5: 0ea6510969e1296cc19966fad481f6de - sha256: b224e16b88d76ea95e4af56e2bc638c603bd26a770b98d117d04541d3aafa002 - category: main - optional: false -- name: libucxx - version: 0.42.00 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-version: '>=12,<13.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - ucx: '>=1.15.0,<1.19.0' - url: https://conda.anaconda.org/rapidsai/linux-64/libucxx-0.42.00-cuda12_250213_g762a6ed_0.conda - hash: - md5: 2e704d18be477c0aad2847e0f716a092 - sha256: c4cd86d6870e4c7ae233afa42ed1949e05e2f672c922b6422de6f678918f066e - category: main - optional: false -- name: libudev1 - version: '257.3' - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libcap: '>=2.71,<2.72.0a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libudev1-257.3-h9a4d06a_0.conda - hash: - md5: e7817c912b25f7599a50eba270e1a463 - sha256: 35bdafc4b02f61a327f82bb11263c31466367e50b4e5efab3d413509315cb0a7 - category: main - optional: false -- name: libutf8proc - version: 2.10.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.10.0-h4c51ac1_0.conda - hash: - md5: aeccfff2806ae38430638ffbb4be9610 - sha256: 8e41563ee963bf8ded06da45f4e70bf42f913cb3c2e79364eb3218deffa3cd74 - category: main - optional: false -- name: libuuid - version: 2.38.1 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda - hash: - md5: 40b61aab5c7ba9ff276c41cfffe6b80b - sha256: 787eb542f055a2b3de553614b25f09eefb0a0931b0c87dbcce6efdfd92f04f18 - category: main - optional: false -- name: libuv - version: 1.50.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.50.0-hb9d3cd8_0.conda - hash: - md5: 771ee65e13bc599b0b62af5359d80169 - sha256: b4a8890023902aef9f1f33e3e35603ad9c2f16c21fdb58e968fa6c1bd3e94c0b - category: main - optional: false -- name: libwebp-base - version: 1.5.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.5.0-h851e524_0.conda - hash: - md5: 63f790534398730f59e1b899c3644d4a - sha256: c45283fd3e90df5f0bd3dbcd31f59cdd2b001d424cf30a07223655413b158eaf - category: main - optional: false -- name: libxcb - version: 1.17.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - pthread-stubs: '' - xorg-libxau: '>=1.0.11,<2.0a0' - xorg-libxdmcp: '' - url: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda - hash: - md5: 92ed62436b625154323d40d5f2f11dd7 - sha256: 666c0c431b23c6cec6e492840b176dde533d48b7e6fb8883f5071223433776aa - category: main - optional: false -- name: libxcrypt - version: 4.4.36 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda - hash: - md5: 5aa797f8787fe7a17d1b0821485b5adc - sha256: 6ae68e0b86423ef188196fff6207ed0c8195dd84273cb5623b85aa08033a410c - category: main - optional: false -- name: libxkbcommon - version: 1.8.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - libxcb: '>=1.17.0,<2.0a0' - libxml2: '>=2.13.5,<3.0a0' - xkeyboard-config: '' - xorg-libxau: '>=1.0.12,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.8.0-hc4a0caf_0.conda - hash: - md5: f1656760dbf05f47f962bfdc59fc3416 - sha256: 583203155abcfb03938d8473afbf129156b5b30301a0f796c8ecca8c5b7b2ed2 - category: main - optional: false -- name: libxkbfile - version: 1.1.0 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/libxkbfile-1.1.0-h166bdaf_1.conda - hash: - md5: c6274d38be57ac8b059b8e33d406aaf6 - sha256: f7c9f4d42c9e87dcf7055c33e9957db4e319735de9bdd5d9ba5633c27b853ae1 - category: main - optional: false -- name: libxml2 - version: 2.13.6 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - icu: '>=75.1,<76.0a0' - libgcc: '>=13' - libiconv: '>=1.18,<2.0a0' - liblzma: '>=5.6.4,<6.0a0' - libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.13.6-h8d12d68_0.conda - hash: - md5: 328382c0e0ca648e5c189d5ec336c604 - sha256: db8af71ea9c0ae95b7cb4a0f59319522ed2243942437a1200ceb391493018d85 - category: main - optional: false -- name: libzlib - version: 1.3.1 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-hb9d3cd8_2.conda - hash: - md5: edb0dca6bc32e4f4789199455a1dbeb8 - sha256: d4bfe88d7cb447768e31650f06257995601f89076080e76df55e3112d4e47dc4 - category: main - optional: false -- name: llvmlite - version: 0.43.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libllvm14: '>=14.0.6,<14.1.0a0' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.43.0-py310h1a6248f_1.conda - hash: - md5: 8153f0ba820cca5bae3101d1bc178d95 - sha256: 071ce1a0fed522a19990b1cb49cba01d5b03f0e851a1ea0c364622267e32bca1 - category: main - optional: false -- name: locket - version: 1.0.0 - manager: conda - platform: linux-64 - dependencies: - python: '>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*' - url: https://conda.anaconda.org/conda-forge/noarch/locket-1.0.0-pyhd8ed1ab_0.tar.bz2 - hash: - md5: 91e27ef3d05cc772ce627e51cff111c4 - sha256: 9afe0b5cfa418e8bdb30d8917c5a6cec10372b037924916f1f85b9f4899a67a6 - category: main - optional: false -- name: lz4 - version: 4.3.3 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - lz4-c: '>=1.10.0,<1.11.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/lz4-4.3.3-py310h80b8a69_2.conda - hash: - md5: 2b8aa03bc9deca99d7e5d26ce27bb93d - sha256: 7a1807e906846b633e0e2aeba720edf4f98df8d6bb886ddcc091fa0e3a622139 - category: main - optional: false -- name: lz4-c - version: 1.10.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda - hash: - md5: 9de5350a85c4a20c685259b889aa6393 - sha256: 47326f811392a5fd3055f0f773036c392d26fdb32e4d8e7a8197eed951489346 - category: main - optional: false -- name: markdown-it-py - version: 3.0.0 - manager: conda - platform: linux-64 - dependencies: - mdurl: '>=0.1,<1' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/markdown-it-py-3.0.0-pyhd8ed1ab_1.conda - hash: - md5: fee3164ac23dfca50cfcc8b85ddefb81 - sha256: 0fbacdfb31e55964152b24d5567e9a9996e1e7902fb08eb7d91b5fd6ce60803a - category: main - optional: false -- name: markupsafe - version: 3.0.2 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.2-py310h89163eb_1.conda - hash: - md5: 8ce3f0332fd6de0d737e2911d329523f - sha256: 0bed20ec27dcbcaf04f02b2345358e1161fb338f8423a4ada1cf0f4d46918741 - category: main - optional: false -- name: matplotlib-base - version: 3.10.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - contourpy: '>=1.0.1' - cycler: '>=0.10' - fonttools: '>=4.22.0' - freetype: '>=2.12.1,<3.0a0' - kiwisolver: '>=1.3.1' - libgcc: '>=13' - libstdcxx: '>=13' - numpy: '>=1.23' - packaging: '>=20.0' - pillow: '>=8' - pyparsing: '>=2.3.1' - python: '>=3.10,<3.11.0a0' - python-dateutil: '>=2.7' - python_abi: 3.10.* - qhull: '>=2020.2,<2020.3.0a0' - tk: '>=8.6.13,<8.7.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/matplotlib-base-3.10.0-py310h68603db_0.conda - hash: - md5: 409498230a11a71578ed49d006165249 - sha256: f903cbe3522e1874f36d5dd108ec85f0f88a800dcebcc78c1e69bebfcd289e70 - category: main - optional: false -- name: matplotlib-inline - version: 0.1.7 - manager: conda - platform: linux-64 - dependencies: - python: '>=3.9' - traitlets: '' - url: https://conda.anaconda.org/conda-forge/noarch/matplotlib-inline-0.1.7-pyhd8ed1ab_1.conda - hash: - md5: af6ab708897df59bd6e7283ceab1b56b - sha256: 69b7dc7131703d3d60da9b0faa6dd8acbf6f6c396224cf6aef3e855b8c0c41c6 - category: main - optional: false -- name: mdurl - version: 0.1.2 - manager: conda - platform: linux-64 - dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/mdurl-0.1.2-pyhd8ed1ab_1.conda - hash: - md5: 592132998493b3ff25fd7479396e8351 - sha256: 78c1bbe1723449c52b7a9df1af2ee5f005209f67e40b6e1d3c7619127c43b1c7 - category: main - optional: false -- name: minepy - version: 1.2.6 - manager: conda - platform: linux-64 - dependencies: - libgcc-ng: '>=10.3.0' - numpy: '>=1.3.0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/minepy-1.2.6-py310h5764c6d_2.tar.bz2 - hash: - md5: 20feeddbfade77a241aae4e2cfc043c7 - sha256: 33bb20d1549fd94a9ab794bd80f82f123a80b43560af2cd2544c9ad2fd5b6129 - category: main - optional: false -- name: msgpack-python - version: 1.1.0 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.1.0-py310h3788b33_0.conda - hash: - md5: 6b586fb03d84e5bfbb1a8a3d9e2c9b60 - sha256: 73ca5f0c7d0727a57dcc3c402823ce3aa159ca075210be83078fcc485971e259 - category: main - optional: false -- name: munkres - version: 1.1.4 - manager: conda - platform: linux-64 - dependencies: - python: '' - url: https://conda.anaconda.org/conda-forge/noarch/munkres-1.1.4-pyh9f0ad1d_0.tar.bz2 - hash: - md5: 2ba8498c1018c1e9c61eb99b973dfe19 - sha256: f86fb22b58e93d04b6f25e0d811b56797689d598788b59dcb47f59045b568306 - category: main - optional: false -- name: nccl - version: 2.25.1.1 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12,<13.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/nccl-2.25.1.1-ha44e49d_0.conda - hash: - md5: 24f6e4b7fff53c8a1c01a20518b8b971 - sha256: 5f6ed4e6fa067e15f3e60ceeb08d543d46fa8780e09f6774571ea0c3a64cc85a - category: main - optional: false -- name: ncurses - version: '6.5' - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda - hash: - md5: 47e340acb35de30501a76c7c799c41d7 - sha256: 3fde293232fa3fca98635e1167de6b7c7fda83caf24b9d6c91ec9eefb4f4d586 - category: main - optional: false -- name: nlohmann_json - version: 3.11.3 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/nlohmann_json-3.11.3-he02047a_1.conda - hash: - md5: e46f7ac4917215b49df2ea09a694a3fa - sha256: ce4bcced4f8eea71b7cac8bc3daac097abf7a5792f278cd811dedada199500c1 - category: main - optional: false -- name: nodeenv - version: 1.9.1 - manager: conda - platform: linux-64 - dependencies: - python: '>=3.9' - setuptools: '' - url: https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.9.1-pyhd8ed1ab_1.conda - hash: - md5: 7ba3f09fceae6a120d664217e58fe686 - sha256: 3636eec0e60466a00069b47ce94b6d88b01419b6577d8e393da44bb5bc8d3468 - category: main - optional: false -- name: nsight-compute - version: 2024.2.1.2 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - alsa-lib: '>=1.2.12,<1.3.0a0' - cuda-version: '>=12.5,<12.6.0a0' - dbus: '>=1.13.6,<2.0a0' - fontconfig: '>=2.14.2,<3.0a0' - fonts-conda-ecosystem: '' - freetype: '>=2.12.1,<3.0a0' - krb5: '>=1.21.3,<1.22.0a0' - libexpat: '>=2.6.2,<3.0a0' - libgcc-ng: '>=12' - libglib: '>=2.80.3,<3.0a0' - libstdcxx-ng: '>=12' - libxcb: '>=1.16,<2.0.0a0' - libxkbcommon: '>=1.7.0,<2.0a0' - libxkbfile: '>=1.1.0,<1.2.0a0' - libzlib: '>=1.3.1,<2.0a0' - nspr: '>=4.35,<5.0a0' - nss: '>=3.102,<4.0a0' - wayland: '>=1.23.0,<2.0a0' - xcb-util-cursor: '>=0.1.4,<0.2.0a0' - xcb-util-image: '>=0.4.0,<0.5.0a0' - xcb-util-keysyms: '>=0.4.1,<0.5.0a0' - xcb-util-renderutil: '>=0.3.10,<0.4.0a0' - xcb-util-wm: '>=0.4.2,<0.5.0a0' - xorg-libice: '>=1.1.1,<2.0a0' - xorg-libsm: '>=1.2.4,<2.0a0' - xorg-libx11: '>=1.8.9,<2.0a0' - xorg-libxcomposite: '>=0.4.6,<0.5.0a0' - xorg-libxdamage: '>=1.1.5,<1.2.0a0' - xorg-libxext: '>=1.3.4,<2.0a0' - xorg-libxfixes: '>=5.0.3,<5.1.0a0' - xorg-libxrandr: '>=1.5.2,<1.6.0a0' - xorg-libxrender: '>=0.9.11,<0.10.0a0' - xorg-libxtst: '>=1.2.3,<1.3.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/nsight-compute-2024.2.1.2-he3ecb2e_0.conda - hash: - md5: d3f28b12614255ae2221c6b8244e54e1 - sha256: edc1d420f17fce340b48983887d59c5a87a707bd608719a8f248059b34a48b2e - category: main - optional: false -- name: nspr - version: '4.36' - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/nspr-4.36-h5888daf_0.conda - hash: - md5: de9cd5bca9e4918527b9b72b6e2e1409 - sha256: a87471d9265a7c02a98c20debac8b13afd80963968ed7b1c1c2ac7b80955ce31 - category: main - optional: false -- name: nss - version: '3.108' - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libsqlite: '>=3.48.0,<4.0a0' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - nspr: '>=4.36,<5.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/nss-3.108-h159eef7_0.conda - hash: - md5: 3c872a5aa802ee5c645e09d4c5d38585 - sha256: 32c1dad692c37978378bbdd6fbca7a1c3bbac576240cf0001fb1e210e1a4e77f - category: main - optional: false -- name: numba - version: 0.60.0 - manager: conda - platform: linux-64 - dependencies: - _openmp_mutex: '>=4.5' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - llvmlite: '>=0.43.0,<0.44.0a0' - numpy: '>=1.22.3,<2.1' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/numba-0.60.0-py310h5dc88bb_0.conda - hash: - md5: 73e2e2c0ffad216572ce01952ff0099c - sha256: c76c5baa087c2be3374bdb5eee37caf0c70f390c02a48aeb5e4337b600e5e319 - category: main - optional: false -- name: numba-cuda - version: 0.2.0 - manager: conda - platform: linux-64 - dependencies: - python: '' - numba: '>=0.59.1' - url: https://conda.anaconda.org/conda-forge/noarch/numba-cuda-0.2.0-pyh267e887_0.conda - hash: - md5: 2d0b457e1cab8ef79354b80dbee43657 - sha256: 6f61ba00deecfb1d01d51341fd44aef35522ac6bcc118802babf19333194d15c - category: main - optional: false -- name: numpy - version: 1.26.4 - manager: conda - platform: linux-64 - dependencies: - libblas: '>=3.9.0,<4.0a0' - libcblas: '>=3.9.0,<4.0a0' - libgcc-ng: '>=12' - liblapack: '>=3.9.0,<4.0a0' - libstdcxx-ng: '>=12' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py310hb13e2d6_0.conda - hash: - md5: 6593de64c935768b6bad3e19b3e978be - sha256: 028fe2ea8e915a0a032b75165f11747770326f3d767e642880540c60a3256425 - category: main - optional: false -- name: nvcomp - version: 4.1.0.6 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - cuda-version: '>=12,<13.0a0' - libgcc: '>=12' - libstdcxx: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/nvcomp-4.1.0.6-h66a0f98_0.conda - hash: - md5: 068b64a493153b94220d76dc231c328d - sha256: 9aa57919763aacb246b4c80953ca820be1f81a407043036ebd69bfa4ea06c5ac - category: main - optional: false -- name: nvidia-ml-py - version: 12.570.86 - manager: conda - platform: linux-64 - dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/nvidia-ml-py-12.570.86-pyhd8ed1ab_0.conda - hash: - md5: bc4b4177052f661da6d56838dfe57a52 - sha256: 6fd70c3d5b509ef1c30cb13df877f14b04bb0e53da032950921b31d95aa5971d - category: main - optional: false -- name: nvtx - version: 0.2.11 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/nvtx-0.2.11-py310ha75aee5_0.conda - hash: - md5: 7c26aeb2ad27b19b37e4e988a9585bcc - sha256: 949ca999cac4cf399bb73fa477edcc11032da6f21ebf0b8d938b55944004defd - category: main - optional: false -- name: ocl-icd - version: 2.3.2 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - opencl-headers: '>=2024.10.24' - url: https://conda.anaconda.org/conda-forge/linux-64/ocl-icd-2.3.2-hb9d3cd8_2.conda - hash: - md5: 2e8d2b469559d6b2cb6fd4b34f9c8d7f - sha256: 96ddd13054032fabd54636f634d50bc74d10d8578bc946405c429b2d895db6f2 - category: main - optional: false -- name: opencl-headers - version: 2024.10.24 - manager: conda - platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/opencl-headers-2024.10.24-h5888daf_0.conda - hash: - md5: 3ba02cce423fdac1a8582bd6bb189359 - sha256: 7e1d3ad55d4ad3ddf826e205d4603b9ed40c5e655a9dfd66b56f459d7ba14db3 - category: main - optional: false -- name: openjpeg - version: 2.5.3 + version: 12.5.82 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libpng: '>=1.6.44,<1.7.0a0' - libstdcxx: '>=13' - libtiff: '>=4.7.0,<4.8.0a0' - libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.3-h5fbd93e_0.conda + cuda-version: '>=12.5,<12.6.0a0' + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libnvfatbin-12.5.82-he02047a_0.conda hash: - md5: 9e5816bc95d285c115a3ebc2f8563564 - sha256: 5bee706ea5ba453ed7fd9da7da8380dd88b865c8d30b5aaec14d2b6dd32dbc39 + md5: 2cf4281d62f02ee11c26029a6b68e300 + sha256: 53708c5d90fb27edda8131f802f4b8423aaebf873480acc00af34670b60f555a category: main optional: false -- name: openssl - version: 3.4.1 +- name: libnvfatbin-dev + version: 12.5.82 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - ca-certificates: '' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.4.1-h7b32b05_0.conda + cuda-version: '>=12.5,<12.6.0a0' + libgcc-ng: '>=12' + libnvfatbin: 12.5.82 + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libnvfatbin-dev-12.5.82-he02047a_0.conda hash: - md5: 41adf927e746dc75ecf0ef841c454e48 - sha256: cbf62df3c79a5c2d113247ddea5658e9ff3697b6e741c210656e239ecaf1768f + md5: dbe4c03d6ce20280b0b266e5004efa47 + sha256: 0783038db939e1d0b67c87d8a1412e6d87993df36c1f6e4149bb20e4b6744073 category: main optional: false -- name: orc - version: 2.0.3 +- name: libnvjitlink + version: 12.5.82 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libprotobuf: '>=5.28.3,<5.28.4.0a0' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - lz4-c: '>=1.10.0,<1.11.0a0' - snappy: '>=1.2.1,<1.3.0a0' - tzdata: '' - zstd: '>=1.5.6,<1.6.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.3-h12ee42a_2.conda + cuda-version: '>=12.0.0,<12.6.0a0' + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libnvjitlink-12.5.82-he02047a_0.conda hash: - md5: 4f6f9f3f80354ad185e276c120eac3f0 - sha256: dff5cc8023905782c86b3459055f26d4b97890e403b0698477c9fed15d8669cc + md5: e5e58d150028643731c5cbf83e5ba249 + sha256: 39bebb36643994c14d638eae8bded7c98f07893656de836e2a52d1522b063137 category: main optional: false -- name: packaging - version: '24.2' +- name: libnvjitlink-dev + version: 12.5.82 manager: conda platform: linux-64 dependencies: - python: '>=3.8' - url: https://conda.anaconda.org/conda-forge/noarch/packaging-24.2-pyhd8ed1ab_2.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + libgcc-ng: '>=12' + libnvjitlink: 12.5.82 + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libnvjitlink-dev-12.5.82-he02047a_0.conda hash: - md5: 3bfed7e6228ebf2f7b9eaa47f1b4e2aa - sha256: da157b19bcd398b9804c5c52fc000fcb8ab0525bdb9c70f95beaa0bb42f85af1 + md5: e14f1a78ecd13e2504b0a0e60fad072e + sha256: 59d5dfc1cc7b6c96fc2c30d6469af245a85d4d591b07e1a022e6e226b5fd8924 category: main optional: false -- name: pandas - version: 2.2.3 +- name: libnvjpeg + version: 12.3.2.81 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - numpy: '>=1.22.4' - python: '>=3.10,<3.11.0a0' - python-dateutil: '>=2.8.1' - python-tzdata: '>=2022a' - python_abi: 3.10.* - pytz: '>=2020.1,<2024.2' - url: https://conda.anaconda.org/conda-forge/linux-64/pandas-2.2.3-py310h5eaa309_1.conda + cuda-version: '>=12.5,<12.6.0a0' + libgcc-ng: '>=12' + libstdcxx-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libnvjpeg-12.3.2.81-he02047a_0.conda hash: - md5: e67778e1cac3bca3b3300f6164f7ffb9 - sha256: d772223fd1ca882717ec6db55a13a6be9439c64ca3532231855ce7834599b8a5 + md5: 91d1b39af9202b46cadf31c89d521bef + sha256: 48b512996142fd849ca4653da238ede33312939fc78903b8eb80cee16e5184f3 category: main optional: false -- name: parso - version: 0.8.4 +- name: libnvjpeg-dev + version: 12.3.2.81 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/parso-0.8.4-pyhd8ed1ab_1.conda + cuda-cudart-dev: '' + cuda-version: '>=12.5,<12.6.0a0' + libnvjpeg: 12.3.2.81 + url: https://conda.anaconda.org/conda-forge/linux-64/libnvjpeg-dev-12.3.2.81-ha770c72_0.conda hash: - md5: 5c092057b6badd30f75b06244ecd01c9 - sha256: 17131120c10401a99205fc6fe436e7903c0fa092f1b3e80452927ab377239bcc + md5: 8c19a53db5344e45aa4596099b3dbeeb + sha256: bc04ef074d2623fda565ec8ce17c69c4097e8993630bf89b854393a84ed0f30e category: main optional: false -- name: partd - version: 1.4.2 +- name: libopenblas + version: 0.3.33 manager: conda platform: linux-64 dependencies: - locket: '' - python: '>=3.9' - toolz: '' - url: https://conda.anaconda.org/conda-forge/noarch/partd-1.4.2-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libgfortran: '' + libgfortran5: '>=14.3.0' + url: https://conda.anaconda.org/conda-forge/linux-64/libopenblas-0.3.33-pthreads_h94d23a6_0.conda hash: - md5: 0badf9c54e24cecfb0ad2f99d680c163 - sha256: 472fc587c63ec4f6eba0cc0b06008a6371e0a08a5986de3cf4e8024a47b4fe6c + md5: 2d3278b721e40468295ca755c3b84070 + sha256: 3d9aa85648e5e18a6d66db98b8c4317cc426721ad7a220aa86330d1ccedc8903 category: main optional: false -- name: patsy - version: 1.0.1 +- name: libpng + version: 1.6.58 manager: conda platform: linux-64 dependencies: - numpy: '>=1.4.0' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/patsy-1.0.1-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.58-h421ea60_0.conda hash: - md5: ee23fabfd0a8c6b8d6f3729b47b2859d - sha256: ab52916f056b435757d46d4ce0a93fd73af47df9c11fd72b74cc4b7e1caca563 + md5: eba48a68a1a2b9d3c0d9511548db85db + sha256: 377cfe037f3eeb3b1bf3ad333f724a64d32f315ee1958581fc671891d63d3f89 category: main optional: false -- name: pcre2 - version: '10.44' +- name: libpsl + version: 0.22.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - bzip2: '>=1.0.8,<2.0a0' - libgcc-ng: '>=12' - libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hba22ea6_2.conda + icu: '>=78.3,<79.0a0' + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/libpsl-0.22.0-h49b2146_1.conda hash: - md5: df359c09c41cd186fffb93a2d87aa6f5 - sha256: 1087716b399dab91cc9511d6499036ccdc53eb29a288bebcb19cf465c51d7c0d + md5: af5ddfb52ad25d833c70c7511478d4eb + sha256: 71118885feab91c61bea4e9532ec46e0653b24f02e563681f822915aee8435bb category: main optional: false -- name: pexpect - version: 4.9.0 +- name: libsanitizer + version: 12.4.0 manager: conda platform: linux-64 dependencies: - ptyprocess: '>=0.5' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/pexpect-4.9.0-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=12.4.0' + libstdcxx: '>=12.4.0' + url: https://conda.anaconda.org/conda-forge/linux-64/libsanitizer-12.4.0-ha732cd4_2.conda hash: - md5: d0d408b1f18883a944376da5cf8101ea - sha256: 202af1de83b585d36445dc1fda94266697341994d1a3328fabde4989e1b3d07a + md5: e729f335fee31fd68429187c9e0f97c2 + sha256: d9a23eee55fc2a901e67565c328c37e7c2336ca805d985ad4a67b7837fb4e40a category: main optional: false -- name: pickleshare - version: 0.7.5 +- name: libsolv + version: 0.7.39 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/pickleshare-0.7.5-pyhd8ed1ab_1004.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libsolv-0.7.39-h9463b59_0.conda hash: - md5: 11a9d1d09a3615fc07c3faf79bc0b943 - sha256: e2ac3d66c367dada209fc6da43e645672364b9fd5f9d28b9f016e24b81af475b + md5: 3a1136dda53febf7693145db51db3dcb + sha256: 65e49d51607307ca47542de815ef9d1698cfd067350e03d3d82fde61ba5ecf6f category: main optional: false -- name: pillow - version: 11.1.0 +- name: libsqlite + version: 3.53.3 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - freetype: '>=2.12.1,<3.0a0' - lcms2: '>=2.16,<3.0a0' - libgcc: '>=13' - libjpeg-turbo: '>=3.0.0,<4.0a0' - libtiff: '>=4.7.0,<4.8.0a0' - libwebp-base: '>=1.5.0,<2.0a0' - libxcb: '>=1.17.0,<2.0a0' - libzlib: '>=1.3.1,<2.0a0' - openjpeg: '>=2.5.3,<3.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - tk: '>=8.6.13,<8.7.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/pillow-11.1.0-py310h7e6dc6c_0.conda + libgcc: '>=14' + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libsqlite-3.53.3-h0c1763c_0.conda hash: - md5: 14d300b9e1504748e70cc6499a7b4d25 - sha256: e11d694b7c12b6a76624e8c3e48892924668a97ec26f353ce37b0648bd12ad87 + md5: 4aed8e657e9ff156bdbe849b4df44389 + sha256: 365376f4815e5e80def2b3462a2419708b7c292da0da85278386c2618621fff4 category: main optional: false -- name: pip - version: 25.0.1 +- name: libssh2 + version: 1.11.1 manager: conda platform: linux-64 dependencies: - python: '>=3.9,<3.13.0a0' - setuptools: '' - wheel: '' - url: https://conda.anaconda.org/conda-forge/noarch/pip-25.0.1-pyh8b19718_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=13' + libzlib: '>=1.3.1,<2.0a0' + openssl: '>=3.5.0,<4.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libssh2-1.11.1-hcf80075_0.conda hash: - md5: 79b5c1440aedc5010f687048d9103628 - sha256: 585940f09d87787f79f73ff5dff8eb2af8a67e5bec5eebf2f553cd26c840ba69 + md5: eecce068c7e4eddeb169591baac20ac4 + sha256: fa39bfd69228a13e553bd24601332b7cfeb30ca11a3ca50bb028108fe90a7661 category: main optional: false -- name: platformdirs - version: 4.3.6 +- name: libstdcxx + version: 15.2.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.3.6-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: 15.2.0 + url: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-15.2.0-h934c35e_19.conda hash: - md5: 577852c7e53901ddccc7e6a9959ddebe - sha256: bb50f6499e8bc1d1a26f17716c97984671121608dc0c3ecd34858112bce59a27 + md5: 5794b3bdc38177caf969dabd3af08549 + sha256: dff1058c76ec6b8759e41cefa2508162d00e4a5e6721aa68ec3fd10094e702dc category: main optional: false -- name: pluggy - version: 1.5.0 +- name: libstdcxx-devel_linux-64 + version: 12.4.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.5.0-pyhd8ed1ab_1.conda + __unix: '' + url: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-12.4.0-h1762d19_102.conda hash: - md5: e9dcbce5f45f9ee500e728ae58b605b6 - sha256: 122433fc5318816b8c69283aaf267c73d87aa2d09ce39f64c9805c9a3b264819 + md5: aa2ae7befd3d165f3cfc4d3b39cebeb5 + sha256: 5e86d884d6877ce428d90a484cdc66d5968bf81dc189393239c43fe9b831da7d category: main optional: false -- name: pre-commit - version: 4.1.0 +- name: libstdcxx-ng + version: 15.2.0 manager: conda platform: linux-64 dependencies: - cfgv: '>=2.0.0' - identify: '>=1.0.0' - nodeenv: '>=0.11.1' - python: '>=3.9' - pyyaml: '>=5.1' - virtualenv: '>=20.10.0' - url: https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.1.0-pyha770c72_0.conda + libstdcxx: 15.2.0 + url: https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-15.2.0-hdf11a46_19.conda hash: - md5: 5353f5eb201a9415b12385e35ed1148d - sha256: b260b4b47956b654232f698be1b757935268830a808040aff2006d08953e9e32 + md5: e5ce228e579726c07255dbf90dc62101 + sha256: 0672b6b6e1791c92e8eccad58081a99d614fcf82bca5841f9dfa3c3e658f83b9 category: main optional: false -- name: prometheus-cpp - version: 1.3.0 +- name: libuuid + version: 2.42.2 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libcurl: '>=8.10.1,<9.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - zlib: '' - url: https://conda.anaconda.org/conda-forge/linux-64/prometheus-cpp-1.3.0-ha5d0236_0.conda + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.42.2-h5347b49_0.conda hash: - md5: a83f6a2fdc079e643237887a37460668 - sha256: 013669433eb447548f21c3c6b16b2ed64356f726b5f77c1b39d5ba17a8a4b8bc + md5: 01bb81d12c957de066ea7362007df642 + sha256: 9b1bdce27a7e31f7d241aeecff67a1f3101d52a2b1e33ccc2cdf2613072bf81f category: main optional: false -- name: prompt-toolkit - version: 3.0.50 +- name: libuv + version: 1.52.1 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - wcwidth: '' - url: https://conda.anaconda.org/conda-forge/noarch/prompt-toolkit-3.0.50-pyha770c72_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/libuv-1.52.1-h280c20c_0.conda hash: - md5: 7d823138f550b14ecae927a5ff3286de - sha256: 0749c49a349bf55b8539ce5addce559b77592165da622944a51c630e94d97889 + md5: 4e33d49bf4fc853855a3b00643aa5484 + sha256: e28e4519223f78b3163599ca89c3f2d80bfb53e907e7fc74e806e60d1efa578b category: main optional: false -- name: psutil - version: 6.1.1 +- name: libxcb + version: 1.17.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' libgcc: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/psutil-6.1.1-py310ha75aee5_0.conda + pthread-stubs: '' + xorg-libxau: '>=1.0.11,<2.0a0' + xorg-libxdmcp: '' + url: https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.17.0-h8a09558_0.conda hash: - md5: 00838ea1d4e87b1e6e2552bba98cc899 - sha256: a643a57e5338fb3a154c5d57fdc72d80170cf7868f20acbbeedde014195f0d92 + md5: 92ed62436b625154323d40d5f2f11dd7 + sha256: 666c0c431b23c6cec6e492840b176dde533d48b7e6fb8883f5071223433776aa category: main optional: false -- name: pthread-stubs - version: '0.4' +- name: libxcrypt + version: 4.4.36 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda + libgcc-ng: '>=12' + url: https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda hash: - md5: b3c17d95b5a10c6e64a21fa17573e70e - sha256: 9c88f8c64590e9567c6c80823f0328e58d3b1efb0e1c539c0315ceca764e0973 + md5: 5aa797f8787fe7a17d1b0821485b5adc + sha256: 6ae68e0b86423ef188196fff6207ed0c8195dd84273cb5623b85aa08033a410c category: main optional: false -- name: ptyprocess - version: 0.7.0 +- name: libxkbcommon + version: 1.13.2 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/ptyprocess-0.7.0-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + libxcb: '>=1.17.0,<2.0a0' + libxml2: '' + libxml2-16: '>=2.14.6' + xkeyboard-config: '' + xorg-libxau: '>=1.0.12,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libxkbcommon-1.13.2-hca5e8e5_0.conda hash: - md5: 7d9daffbb8d8e0af0f769dbbcd173a54 - sha256: a7713dfe30faf17508ec359e0bc7e0983f5d94682492469bd462cdaae9c64d83 + md5: dc8b067e22b414172bedd8e3f03f3c95 + sha256: 046f2ff4acebd8729fac03e99c8c307dfb48b6a32894ba8c11576e78f6e76e43 category: main optional: false -- name: pure_eval - version: 0.2.3 +- name: libxml2 + version: 2.15.3 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/pure_eval-0.2.3-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + icu: '>=78.3,<79.0a0' + libgcc: '>=14' + libiconv: '>=1.18,<2.0a0' + liblzma: '>=5.8.3,<6.0a0' + libxml2-16: 2.15.3 + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.15.3-h49c6c72_0.conda hash: - md5: 3bfdfb8dbcdc4af1ae3f9a8eb3948f04 - sha256: 71bd24600d14bb171a6321d523486f6a06f855e75e547fa0cb2a0953b02047f0 + md5: 995d8c8bad2a3cc8db14675a153dec2b + sha256: 3bc5551720c58591f6ea1146f7d1539c734ed1c40e7b9f5cb8cb7e900c509aba category: main optional: false -- name: pyarrow - version: 19.0.1 +- name: libxml2-16 + version: 2.15.3 manager: conda platform: linux-64 dependencies: - libarrow-acero: 19.0.1.* - libarrow-dataset: 19.0.1.* - libarrow-substrait: 19.0.1.* - libparquet: 19.0.1.* - pyarrow-core: 19.0.1 - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-19.0.1-py310hff52083_0.conda + __glibc: '>=2.17,<3.0.a0' + icu: '>=78.3,<79.0a0' + libgcc: '>=14' + libiconv: '>=1.18,<2.0a0' + liblzma: '>=5.8.3,<6.0a0' + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/libxml2-16-2.15.3-hca6bf5a_0.conda hash: - md5: 96aab335d44df02cd3aaba0c7dd1a645 - sha256: 0e9fcf42e2a2563eb071d8b6a6809fa4067cceec5dd3989787a7b354bf3e1a62 + md5: e79d2c2f24b027aa8d5ab1b1ba3061e7 + sha256: 3d44f737c5ae52d5af32682cc1530df433f401f8e58a7533926536244127572a category: main optional: false -- name: pyarrow-core - version: 19.0.1 +- name: libzlib + version: 1.3.2 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libarrow: 19.0.1.* - libgcc: '>=13' - libstdcxx: '>=13' - libzlib: '>=1.3.1,<2.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/pyarrow-core-19.0.1-py310hac404ae_0_cpu.conda + url: https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.2-h25fd6f3_2.conda hash: - md5: 08bfbf49d206e2fbcccd7b92d2526a2a - sha256: b5c63e67ebc1ae151e728759f96fc01b818f6b7de0ee62526448bdd9d85caa47 + md5: d87ff7921124eccd67248aa483c23fec + sha256: 55044c403570f0dc26e6364de4dc5368e5f3fc7ff103e867c487e2b5ab2bcda9 category: main optional: false -- name: pybind11 - version: 2.13.6 +- name: llvmlite + version: 0.48.0 manager: conda platform: linux-64 dependencies: - pybind11-global: 2.13.6 + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + libzlib: '>=1.3.2,<2.0a0' python: '' - url: https://conda.anaconda.org/conda-forge/noarch/pybind11-2.13.6-pyh1ec8472_2.conda + python_abi: 3.12.* + zstd: '>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/llvmlite-0.48.0-py312ha7fb451_1.conda hash: - md5: 8088a5e7b2888c780738c3130f2a969d - sha256: 27f888492af3d5ab19553f263b0015bf3766a334668b5b3a79c7dc0416e603c1 + md5: 765eece0b00be7efd8fa9d10789c806f + sha256: 83d276b2105eaed8bec7bcd28406acbc02c88aaf9eca4394abadd0051bebdfac category: main optional: false -- name: pybind11-global - version: 2.13.6 +- name: lz4-c + version: 1.10.0 manager: conda platform: linux-64 dependencies: - __unix: '' - python: '' - url: https://conda.anaconda.org/conda-forge/noarch/pybind11-global-2.13.6-pyh415d2e4_2.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=13' + libstdcxx: '>=13' + url: https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.10.0-h5888daf_1.conda hash: - md5: 120541563e520d12d8e39abd7de9092c - sha256: 9ff0d61d86878f81779bdb7e47656a75feaab539893462cff29b8ec353026d81 + md5: 9de5350a85c4a20c685259b889aa6393 + sha256: 47326f811392a5fd3055f0f773036c392d26fdb32e4d8e7a8197eed951489346 category: main optional: false -- name: pycparser - version: '2.22' +- name: lzo + version: '2.10' manager: conda platform: linux-64 dependencies: - python: '' - url: https://conda.anaconda.org/conda-forge/noarch/pycparser-2.22-pyh29332c3_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/lzo-2.10-h280c20c_1002.conda hash: - md5: 12c566707c80111f9799308d9e265aef - sha256: 79db7928d13fab2d892592223d7570f5061c192f27b9febd1a418427b719acc6 + md5: 45161d96307e3a447cc3eb5896cf6f8c + sha256: 5c6bbeec116e29f08e3dad3d0524e9bc5527098e12fc432c0e5ca53ea16337d4 category: main optional: false -- name: pygments - version: 2.19.1 +- name: mamba + version: 2.8.1 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/pygments-2.19.1-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + libmamba: ==2.8.1,>=2.8.1,<2.9.0a0 + reproc: '>=14.2,<15.0a0' + reproc-cpp: '>=14.2,<15.0a0' + zstd: '>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/mamba-2.8.1-h73a5c8a_0.conda hash: - md5: 232fb4577b6687b2d503ef8e254270c9 - sha256: 28a3e3161390a9d23bc02b4419448f8d27679d9e2c250e29849e37749c8de86b + md5: c84aafceeb82e5ffefc546c3c40b2ee9 + sha256: 4fdc7d543c546f0a25f1a336f927cce7e1a0daaa843a2ecbc4b6a7fbf8083fdf category: main optional: false -- name: pylibcudf - version: 25.02.00 +- name: markupsafe + version: 3.0.3 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-python: '>=12.6.2,<13.0a0' - fsspec: '>=0.6.0' - libcudf: '>=25.2.0,<25.3.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - numpy: '>=1.23,<3.0a0' - nvtx: '>=0.2.1' - packaging: '' - pandas: '>=2.0,<2.2.4dev0' - pyarrow: '>=14.0.0,<20.0.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - rmm: '>=25.2.0,<25.3.0a0' - typing_extensions: '>=4.0.0' - url: https://conda.anaconda.org/rapidsai/linux-64/pylibcudf-25.02.00-cuda12_py310_250213_g50b2d0faff_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/markupsafe-3.0.3-py312h8a5da7c_1.conda hash: - md5: 5308085d620614c0b837998f64a2087a - sha256: f3c855b77cea2ead47007f4cfa0ac96805517a82fc55df425919052114ee8b4b + md5: 93a4752d42b12943a355b682ee43285b + sha256: 5f3aad1f3a685ed0b591faad335957dbdb1b73abfd6fc731a0d42718e0653b33 category: main optional: false -- name: pylibraft - version: 25.02.00 +- name: more-itertools + version: 11.1.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-python: '>=12.6.2,<13.0a0' - cuda-version: '>=12,<13.0a0' - libgcc: '>=13' - libraft: 25.02.00.* - libraft-headers: 25.02.00.* - libstdcxx: '>=13' - numpy: '>=1.23,<3.0a0' - python: '>=3.10,<3.11.0a0' - rmm: 25.02.* - url: https://conda.anaconda.org/rapidsai/linux-64/pylibraft-25.02.00-cuda12_py310_250213_gbdcc918d_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/more-itertools-11.1.0-pyhcf101f3_0.conda hash: - md5: 7a8b0e03030bc8240f8788834015aa64 - sha256: 8527892b0160eae421c3d19e16af934f0a6d93dc9e1ee58d2e9c2981efad7fd0 + md5: 0e694257dbf916540b749c3ffc808efe + sha256: 6725c1c8db9d025db763e1bba1acdcff2eb2ae20a7766a71512ea84081033288 category: main optional: false -- name: pynvjitlink - version: 0.5.0 +- name: msgpack-python + version: 1.2.1 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-version: '>=12,<12.9.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - numba: '>=0.58' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/rapidsai/linux-64/pynvjitlink-0.5.0-py310hf640dd1_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + python: '' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/msgpack-python-1.2.1-py312h0a2e395_1.conda hash: - md5: 8e64de027b1e340dbbc9222c2160d270 - sha256: 2590086f54701a14e8430661f73c0b17a300a9a26ecc592757d97434cc387853 + md5: a142257c1b69e37cfefc66dc228a4d93 + sha256: c9764c77dd7f9c581c1d8a24c3024edf777cacfb5a4180de5badc6638dc8590f category: main optional: false -- name: pynvml - version: 12.0.0 +- name: narwhals + version: 2.24.0 manager: conda platform: linux-64 dependencies: - nvidia-ml-py: ~=12.0 - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/pynvml-12.0.0-pyhd8ed1ab_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/narwhals-2.24.0-pyhcf101f3_0.conda hash: - md5: 27a6f747d629815308c2d7e4f17aca4c - sha256: 51e937b70daea9e9f9230b32166955c21e30f26a3dff28981c3d23ad5e6b2ec6 + md5: 42ef6cbb3e1d0e6689b9dd160f560eae + sha256: 57f525a1c55b08c3204fab05d2c74a3e9c2172d7f08f1ecaa07e19865cc1d7cf category: main optional: false -- name: pyparsing - version: 3.2.1 +- name: ncurses + version: '6.6' manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/pyparsing-3.2.1-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.6-hdb14827_0.conda hash: - md5: 285e237b8f351e85e7574a2c7bfa6d46 - sha256: f513fed4001fd228d3bf386269237b4ca6bff732c99ffc11fcbad8529b35407c + md5: fc21868a1a5aacc937e7a18747acb8a5 + sha256: fc89f74bbe362fb29fa3c037697a89bec140b346a2469a90f7936d1d7ea4d8a3 category: main optional: false -- name: pysocks - version: 1.7.1 +- name: ninja + version: 1.13.2 manager: conda platform: linux-64 dependencies: - __unix: '' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/ninja-1.13.2-h171cf75_0.conda hash: - md5: 461219d1a5bd61342293efa2c0c90eac - sha256: ba3b032fa52709ce0d9fd388f63d330a026754587a2f461117cac9ab73d8d0d8 + md5: b518e9e92493721281a60fa975bddc65 + sha256: 6f7d59dbec0a7b00bf5d103a4306e8886678b796ff2151b62452d4582b2a53fb category: main optional: false -- name: pytest - version: 8.3.4 +- name: nlohmann_json-abi + version: 3.12.0 manager: conda platform: linux-64 - dependencies: - colorama: '' - exceptiongroup: '>=1.0.0rc8' - iniconfig: '' - packaging: '' - pluggy: <2,>=1.5 - python: '>=3.9' - tomli: '>=1' - url: https://conda.anaconda.org/conda-forge/noarch/pytest-8.3.4-pyhd8ed1ab_1.conda + dependencies: {} + url: https://conda.anaconda.org/conda-forge/noarch/nlohmann_json-abi-3.12.0-h0f90c79_1.conda hash: - md5: 799ed216dc6af62520f32aa39bc1c2bb - sha256: 75245ca9d0cbd6d38bb45ec02430189a9d4c21c055c5259739d738a2298d61b3 + md5: 59659d0213082bc13be8500bab80c002 + sha256: 2a909594ca78843258e4bda36e43d165cda844743329838a29402823c8f20dec category: main optional: false -- name: python - version: 3.10.16 +- name: nodeenv + version: 1.10.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - bzip2: '>=1.0.8,<2.0a0' - ld_impl_linux-64: '>=2.36.1' - libffi: '>=3.4,<4.0a0' - libgcc: '>=13' - liblzma: '>=5.6.3,<6.0a0' - libnsl: '>=2.0.1,<2.1.0a0' - libsqlite: '>=3.47.0,<4.0a0' - libuuid: '>=2.38.1,<3.0a0' - libxcrypt: '>=4.4.36' - libzlib: '>=1.3.1,<2.0a0' - ncurses: '>=6.5,<7.0a0' - openssl: '>=3.4.0,<4.0a0' - readline: '>=8.2,<9.0a0' - tk: '>=8.6.13,<8.7.0a0' - tzdata: '' - url: https://conda.anaconda.org/conda-forge/linux-64/python-3.10.16-he725a3c_1_cpython.conda + python: '>=3.10' + setuptools: '' + url: https://conda.anaconda.org/conda-forge/noarch/nodeenv-1.10.0-pyhd8ed1ab_0.conda hash: - md5: b887811a901b3aa622a92caf03bc8917 - sha256: 3f90a2d5062a73cd2dd8a0027718aee1db93f7975b9cfe529e2c9aeec2db262e + md5: eb52d14a901e23c39e9e7b4a1a5c015f + sha256: 4fa40e3e13fc6ea0a93f67dfc76c96190afd7ea4ffc1bac2612d954b42cdc3ee category: main optional: false -- name: python-dateutil - version: 2.9.0.post0 +- name: nsight-compute + version: 2024.2.1.2 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - six: '>=1.5' - url: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhff2d567_1.conda + __glibc: '>=2.17,<3.0.a0' + cuda-version: '>=12.5,<12.6.0a0' + dbus: '>=1.13.18,<2.0a0' + expat: '>=2.6.2,<3.0a0' + fontconfig: '>=2.14.1,<3.0a0' + freetype: '>=2.12.1,<3.0a0' + libgcc-ng: '>=11.2.0' + libglib: '>=2.78.4,<3.0a0' + libstdcxx-ng: '>=11.2.0' + libxcb: '>=1.15,<2.0a0' + libxkbcommon: '>=1.0.1,<2.0a0' + nspr: '>=4.35,<5.0a0' + nss: '>=3.89.1,<4.0a0' + url: https://conda.anaconda.org/nvidia/linux-64/nsight-compute-2024.2.1.2-2.tar.bz2 hash: - md5: 5ba79d7c71f03c678c8ead841f347d6e - sha256: a50052536f1ef8516ed11a844f9413661829aa083304dc624c5925298d078d79 + md5: b7158cb8e9cc4f191e0a255d54be860e + sha256: 6b978729397fa232bb1f562e6144ce04a98e8f4c622e92a052ef0162d119e88c category: main optional: false -- name: python-tzdata - version: '2025.1' +- name: nspr + version: '4.38' manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/python-tzdata-2025.1-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/nspr-4.38-h29cc59b_0.conda hash: - md5: 392c91c42edd569a7ec99ed8648f597a - sha256: 1597d6055d34e709ab8915091973552a0b8764c8032ede07c4e99670da029629 + md5: e235d5566c9cc8970eb2798dd4ecf62f + sha256: e3664264bd936c357523b55c71ed5a30263c6ba278d726a75b1eb112e6fb0b64 category: main optional: false -- name: python_abi - version: '3.10' +- name: nss + version: '3.118' manager: conda platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.10-5_cp310.conda + dependencies: + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libsqlite: '>=3.51.0,<4.0a0' + libstdcxx: '>=14' + libzlib: '>=1.3.1,<2.0a0' + nspr: '>=4.38,<5.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/nss-3.118-h445c969_0.conda hash: - md5: 2921c34715e74b3587b4cff4d36844f9 - sha256: 074d2f0b31f0333b7e553042b17ea54714b74263f8adda9a68a4bd8c7e219971 + md5: 567fbeed956c200c1db5782a424e58ee + sha256: 44dd98ffeac859d84a6dcba79a2096193a42fc10b29b28a5115687a680dd6aea category: main optional: false -- name: pytz - version: '2024.1' +- name: numba + version: 0.66.0 manager: conda platform: linux-64 dependencies: - python: '>=3.7' - url: https://conda.anaconda.org/conda-forge/noarch/pytz-2024.1-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + _openmp_mutex: '>=4.5' + libgcc: '>=14' + libstdcxx: '>=14' + llvmlite: '>=0.48.0,<0.49.0a0' + numpy: '>=1.22.3,<2.5,>=1.23,<3' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/numba-0.66.0-py312h6d1259f_0.conda hash: - md5: 3eeeeb9e4827ace8c0c1419c85d590ad - sha256: 1a7d6b233f7e6e3bbcbad054c8fd51e690a67b129a899a056a5e45dd9f00cb41 + md5: 7194ca60421426dc149b8f63b789f01a + sha256: ec76898bf8f524428b92c11e0920fb7362f70b59466cbcb7b6cced9bd3156d2e category: main optional: false -- name: pyyaml - version: 6.0.2 +- name: numpy + version: 2.4.6 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - yaml: '>=0.2.5,<0.3.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.2-py310h89163eb_2.conda + libblas: '>=3.9.0,<4.0a0' + libcblas: '>=3.9.0,<4.0a0' + libgcc: '>=14' + liblapack: '>=3.9.0,<4.0a0' + libstdcxx: '>=14' + python: '' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.4.6-py312h33ff503_0.conda hash: - md5: fd343408e64cf1e273ab7c710da374db - sha256: 5fba7f5babcac872c72f6509c25331bcfac4f8f5031f0102530a41b41336fce6 + md5: 6e31d55ee1110fda83b4f4045f4d73ff + sha256: dfcbeadb3e7ad0da7a55a0525884ca34c19584154e13cc4159396b305d1bd445 category: main optional: false -- name: qhull - version: '2020.2' +- name: ocl-icd + version: 2.3.4 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/qhull-2020.2-h434a139_5.conda + libgcc: '>=14' + opencl-headers: '>=2025.6.13' + url: https://conda.anaconda.org/conda-forge/linux-64/ocl-icd-2.3.4-hb03c661_1.conda hash: - md5: 353823361b1d27eb3960efb076dfcaf6 - sha256: 776363493bad83308ba30bcb88c2552632581b143e8ee25b1982c8c743e73abc + md5: c2871ba95727fd1382c05db66048b64c + sha256: 75f3bf733523a338f73d6c276c4a26634877cd970edb558f2769d9fa52b100a9 category: main optional: false -- name: raft-dask - version: 25.02.00 +- name: opencl-headers + version: 2025.06.13 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-python: '>=12.6.2,<13.0a0' - cuda-version: '>=12,<13.0a0' - dask-cuda: 25.02.* - distributed-ucxx: 0.42.* - libgcc: '>=13' - libstdcxx: '>=13' - nccl: '>=2.25.1.1,<3.0a0' - pylibraft: 25.02.00.* - python: '>=3.10,<3.11.0a0' - rapids-dask-dependency: 25.02.* - rmm: 25.02.* - ucx-py: 0.42.* - url: https://conda.anaconda.org/rapidsai/linux-64/raft-dask-25.02.00-cuda12_py310_250213_gbdcc918d_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/opencl-headers-2025.06.13-hecca717_0.conda hash: - md5: 0dc30830b96f1685a920d60653569d7c - sha256: be4554f022fe7abb3a8fc59ce86686b26aee08ba6e2e8544b3efec1d0f12dc29 + md5: c930c8052d780caa41216af7de472226 + sha256: 8de2f0cd8a659b01abf86e7fbb8cea4f28ada62fd288429a2bbc040db1b98dd0 category: main optional: false -- name: rapids-dask-dependency - version: 25.02.00 +- name: openssl + version: 3.6.3 manager: conda platform: linux-64 dependencies: - dask: 2024.12.1 - dask-core: 2024.12.1 - dask-expr: 1.1.21 - distributed: 2024.12.1 - python: '' - url: https://conda.anaconda.org/rapidsai/noarch/rapids-dask-dependency-25.02.00-py_0.conda + __glibc: '>=2.17,<3.0.a0' + ca-certificates: '' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.6.3-h35e630c_0.conda hash: - md5: ce5ea2e507d699c28d15422bc4dd3461 - sha256: ff0dfd0f12ed57e2e700675e661a597b33b245b63bf928a40e9034c5585f0294 + md5: 79dd2074b5cd5c5c6b2930514a11e22d + sha256: d48f5c22b9897c01e4dff3680f1f57ceb02711ab9c62f74339b080419dfad34b category: main optional: false -- name: rdma-core - version: '56.0' +- name: packaging + version: '26.2' manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libnl: '>=3.11.0,<4.0a0' - libstdcxx: '>=13' - libsystemd0: '>=257.2' - libudev1: '>=257.2' - url: https://conda.anaconda.org/conda-forge/linux-64/rdma-core-56.0-h5888daf_0.conda + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/packaging-26.2-pyhc364b38_0.conda hash: - md5: a73b3f6d529417fa78d64e8af82444b1 - sha256: 24cc8c5e8a88a81931c73b8255a4af038a0a72cd1575ec5e507def2ea3f238bb + md5: 4c06a92e74452cfa53623a81592e8934 + sha256: 3906abfb6511a3bb309e39b9b1b7bc38f50a723971de2395489fd1f379255890 category: main optional: false -- name: re2 - version: 2024.07.02 +- name: pandas + version: 3.0.3 manager: conda platform: linux-64 dependencies: - libre2-11: 2024.07.02 - url: https://conda.anaconda.org/conda-forge/linux-64/re2-2024.07.02-h9925aae_2.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + numpy: '>=1.23,<3,>=1.26.0' + python: '' + python-dateutil: '>=2.8.2' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/pandas-3.0.3-py312h8ecdadd_0.conda hash: - md5: e84ddf12bde691e8ec894b00ea829ddf - sha256: d213c44958d49ce7e0d4d5b81afec23640cce5016685dbb2d23571a99caa4474 + md5: 15c437bfa4cbddd379b95357c9aa4150 + sha256: 009408dcfdc789b8a1748d6a63fd2134ea2edc8474231ea7beba0ac3ad772a37 category: main optional: false -- name: readline - version: '8.2' +- name: pathspec + version: 1.1.1 manager: conda platform: linux-64 dependencies: - libgcc: '>=13' - ncurses: '>=6.5,<7.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/readline-8.2-h8c095d6_2.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/pathspec-1.1.1-pyhd8ed1ab_0.conda hash: - md5: 283b96675859b20a825f8fa30f311446 - sha256: 2d6d0c026902561ed77cd646b5021aef2d4db22e57a5b0178dfc669231e06d2c + md5: 11adc78451c998c0fd162584abfa3559 + sha256: 6eaee417d33f298db79bc7185ab1208604c0e6cf51dade34cd513c6f9db9c6f3 category: main optional: false -- name: requests - version: 2.32.3 +- name: pcre2 + version: '10.47' manager: conda platform: linux-64 dependencies: - certifi: '>=2017.4.17' - charset-normalizer: '>=2,<4' - idna: '>=2.5,<4' - python: '>=3.9' - urllib3: '>=1.21.1,<3' - url: https://conda.anaconda.org/conda-forge/noarch/requests-2.32.3-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + bzip2: '>=1.0.8,<2.0a0' + libgcc: '>=14' + libzlib: '>=1.3.1,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.47-haa7fec5_0.conda hash: - md5: a9b9368f3701a417eac9edbcae7cb737 - sha256: d701ca1136197aa121bbbe0e8c18db6b5c94acbd041c2b43c70e5ae104e1d8ad + md5: 7a3bff861a6583f1889021facefc08b1 + sha256: 5e6f7d161356fefd981948bea5139c5aa0436767751a6930cb1ca801ebb113ff category: main optional: false -- name: rhash - version: 1.4.5 +- name: pip + version: 26.1.2 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/rhash-1.4.5-hb9d3cd8_0.conda + python: '>=3.10,<3.13.0a0' + setuptools: '' + wheel: '' + url: https://conda.anaconda.org/conda-forge/noarch/pip-26.1.2-pyh8b19718_0.conda hash: - md5: 9af0e7981755f09c81421946c4bcea04 - sha256: 04677caac29ec64a5d41d0cca8dbec5f60fa166d5458ff5a4393e4dc08a4799e + md5: 511fbc2c63d2c73650ad1755e4d357ba + sha256: 29b7d75bf81ad11645a8e320b369abdc90a92b93f2a9178e853d9dddf82e5106 category: main optional: false -- name: rich - version: 13.9.4 +- name: pkginfo + version: 1.12.1.2 manager: conda platform: linux-64 dependencies: - markdown-it-py: '>=2.2.0' - pygments: '>=2.13.0,<3.0.0' python: '>=3.9' - typing_extensions: '>=4.0.0,<5.0.0' - url: https://conda.anaconda.org/conda-forge/noarch/rich-13.9.4-pyhd8ed1ab_1.conda + url: https://conda.anaconda.org/conda-forge/noarch/pkginfo-1.12.1.2-pyhd8ed1ab_0.conda hash: - md5: 7aed65d4ff222bfb7335997aa40b7da5 - sha256: 06a760c5ae572e72e865d5a87e9fe3cc171e1a9c996e63daf3db52ff1a0b4457 + md5: dc702b2fae7ebe770aff3c83adb16b63 + sha256: 353fd5a2c3ce31811a6272cd328874eb0d327b1eafd32a1e19001c4ad137ad3a category: main optional: false -- name: rmm - version: 25.02.00 +- name: platformdirs + version: 4.10.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-python: '>=12.6.2,<13.0a0' - cuda-version: '>=12,<13.0a0' - libgcc: '>=13' - librmm: '>=25.2.0,<25.3.0a0' - libstdcxx: '>=13' - numba: '>=0.59.1,<0.61.0a0' - numpy: '>=1.23,<3.0a0' - python: '>=3.10,<3.11.0a0' - url: https://conda.anaconda.org/rapidsai/linux-64/rmm-25.02.00-cuda12_py310_250213_g39b4ccdb_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/platformdirs-4.10.0-pyhcf101f3_0.conda hash: - md5: c8b82d67ef416c6c82263b92c4d66cf1 - sha256: c551d776890378308a52ca28ad8c49ceedd221c42ed44f6c33fca4255a626a89 + md5: 2c5ef45db85d34799771629bd5860fd7 + sha256: 9e5e1fd3506ccfc4d444fc4d2d39b0ed097d5d0e3bd3d4bdf6bcc81aaf66860d category: main optional: false -- name: s2n - version: 1.5.11 +- name: pluggy + version: 1.6.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - openssl: '>=3.4.0,<4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/s2n-1.5.11-h072c03f_0.conda + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/pluggy-1.6.0-pyhf9edf01_1.conda hash: - md5: 5e8060d52f676a40edef0006a75c718f - sha256: cfdd98c8f9a1e5b6f9abce5dac6d590cc9fe541a08466c9e4a26f90e00b569e3 + md5: d7585b6550ad04c8c5e21097ada2888e + sha256: e14aafa63efa0528ca99ba568eaf506eb55a0371d12e6250aaaa61718d2eb62e category: main optional: false -- name: scikit-learn - version: 1.6.1 +- name: pre-commit + version: 4.6.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - _openmp_mutex: '>=4.5' - joblib: '>=1.2.0' - libgcc: '>=13' - libstdcxx: '>=13' - numpy: '>=1.19,<3' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - scipy: '' - threadpoolctl: '>=3.1.0' - url: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.6.1-py310h27f47ee_0.conda + cfgv: '>=2.0.0' + identify: '>=1.0.0' + nodeenv: '>=0.11.1' + python: '>=3.10' + pyyaml: '>=5.1' + virtualenv: '>=20.10.0' + url: https://conda.anaconda.org/conda-forge/noarch/pre-commit-4.6.0-pyha770c72_0.conda hash: - md5: 618ec5a8500fb53e8e52785e06d239f4 - sha256: 5c865487412b900d0abeb934907e5357c4a6cad19093316701ffd575980d0c54 + md5: 7859736b4f8ebe6c8481bf48d91c9a1e + sha256: 716960bf0a9eb334458a26b3bdcb17b8d0786062138a4f48c7f335c8418c5d0b category: main optional: false -- name: scipy - version: 1.15.2 +- name: pthread-stubs + version: '0.4' manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libblas: '>=3.9.0,<4.0a0' - libcblas: '>=3.9.0,<4.0a0' libgcc: '>=13' - libgfortran: '' - libgfortran5: '>=13.3.0' - liblapack: '>=3.9.0,<4.0a0' - libstdcxx: '>=13' - numpy: '>=1.23.5' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.15.2-py310h1d65ade_0.conda + url: https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-hb9d3cd8_1002.conda hash: - md5: 8c29cd33b64b2eb78597fa28b5595c8d - sha256: 4cb98641f870666d365594013701d5691205a0fe81ac3ba7778a23b1cc2caa8e + md5: b3c17d95b5a10c6e64a21fa17573e70e + sha256: 9c88f8c64590e9567c6c80823f0328e58d3b1efb0e1c539c0315ceca764e0973 category: main optional: false -- name: seaborn - version: 0.13.2 +- name: pybind11 + version: 3.0.3 manager: conda platform: linux-64 dependencies: - seaborn-base: 0.13.2 - statsmodels: '>=0.12' - url: https://conda.anaconda.org/conda-forge/noarch/seaborn-0.13.2-hd8ed1ab_3.conda + pybind11-global: ==3.0.3 + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/pybind11-3.0.3-pyhfe8187e_0.conda hash: - md5: 62afb877ca2c2b4b6f9ecb37320085b6 - sha256: ea29a69b14dd6be5cdeeaa551bf50d78cafeaf0351e271e358f9b820fcab4cb0 + md5: e0f4549ccb507d4af8ed5c5345210673 + sha256: 71a9524f44d6ac6304feae71e2bbe8d8ce0816f0be7a0271c15681ad1040965d category: main optional: false -- name: seaborn-base - version: 0.13.2 +- name: pybind11-global + version: 3.0.3 manager: conda platform: linux-64 dependencies: - matplotlib-base: '>=3.4,!=3.6.1' - numpy: '>=1.20,!=1.24.0' - pandas: '>=1.2' - python: '>=3.9' - scipy: '>=1.7' - url: https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.13.2-pyhd8ed1ab_3.conda + __unix: '' + python: '>=3.8' + url: https://conda.anaconda.org/conda-forge/noarch/pybind11-global-3.0.3-pyh648e204_0.conda hash: - md5: fd96da444e81f9e6fcaac38590f3dd42 - sha256: f209c9c18187570b85ec06283c72d64b8738f825b1b82178f194f4866877f8aa + md5: 7fdc3e18c14b862ae5f064c1ea8e2636 + sha256: 97a0fbd2a81d95e90d714e5c628fe860b29a3caad53abcfb90add1965ad85bef category: main optional: false -- name: setuptools - version: 75.8.0 +- name: pycparser + version: '3.0' manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/setuptools-75.8.0-pyhff2d567_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/pycparser-3.0-pyhcf101f3_0.conda hash: - md5: 8f28e299c11afdd79e0ec1e279dcdc52 - sha256: e0778e4f276e9a81b51c56f51ec22a27b4d8fc955abc0be77ad09ca9bea06bb9 + md5: 9c5491066224083c41b6d5635ed7107b + sha256: e27e0473fc6723311a0bd48b89b616fa1b996a2f7a2b555338cbbcfb9c640568 category: main optional: false -- name: six - version: 1.17.0 +- name: pydantic + version: 2.13.4 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhd8ed1ab_0.conda + annotated-types: '>=0.6.0' + pydantic-core: 2.46.4 + python: '>=3.10' + typing-inspection: '>=0.4.2' + typing_extensions: '>=4.14.1' + url: https://conda.anaconda.org/conda-forge/noarch/pydantic-2.13.4-pyhcf101f3_0.conda hash: - md5: a451d576819089b0d672f18768be0f65 - sha256: 41db0180680cc67c3fa76544ffd48d6a5679d96f4b71d7498a759e94edc9a2db + md5: 729843edafc0899b3348bd3f19525b9d + sha256: 69700e31165df070e9716315e042196aa92525dae5deb5107785847ab9f4189f category: main optional: false -- name: snappy - version: 1.2.1 +- name: pydantic-core + version: 2.46.4 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/snappy-1.2.1-h8bd8927_1.conda + libgcc: '>=14' + python: '' + python_abi: 3.12.* + typing-extensions: '>=4.6.0,!=4.7.0' + url: https://conda.anaconda.org/conda-forge/linux-64/pydantic-core-2.46.4-py312h868fb18_0.conda hash: - md5: 3b3e64af585eadfb52bb90b553db5edf - sha256: ec91e86eeb2c6bbf09d51351b851e945185d70661d2ada67204c9a6419d282d3 + md5: dfb9a57535eb8c35c6744da7043063f0 + sha256: b8260660d064fb947f4b573ec4a782696bc8b19042452eaa4e9bb1152b540555 category: main optional: false -- name: snowballstemmer - version: 2.2.0 +- name: pygments + version: 2.20.0 manager: conda platform: linux-64 dependencies: - python: '>=2' - url: https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-2.2.0-pyhd8ed1ab_0.tar.bz2 + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/pygments-2.20.0-pyhd8ed1ab_0.conda hash: - md5: 4d22a9315e78c6827f806065957d566e - sha256: a0fd916633252d99efb6223b1050202841fa8d2d53dacca564b0ed77249d3228 + md5: 16c18772b340887160c79a6acc022db0 + sha256: cf70b2f5ad9ae472b71235e5c8a736c9316df3705746de419b59d442e8348e86 category: main optional: false -- name: sortedcontainers - version: 2.4.0 +- name: pyproject_hooks + version: 1.2.0 manager: conda platform: linux-64 dependencies: python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/sortedcontainers-2.4.0-pyhd8ed1ab_1.conda + tomli: '>=1.1.0' + url: https://conda.anaconda.org/conda-forge/noarch/pyproject_hooks-1.2.0-pyhd8ed1ab_1.conda hash: - md5: 0401a17ae845fa72c7210e206ec5647d - sha256: d1e3e06b5cf26093047e63c8cc77b70d970411c5cbc0cb1fad461a8a8df599f7 + md5: d4582021af437c931d7d77ec39007845 + sha256: 065ac44591da9abf1ff740feb25929554b920b00d09287a551fcced2c9791092 category: main optional: false -- name: spdlog - version: 1.14.1 +- name: pysocks + version: 1.7.1 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - fmt: '>=11.0.1,<12.0a0' - libgcc-ng: '>=12' - libstdcxx-ng: '>=12' - url: https://conda.anaconda.org/conda-forge/linux-64/spdlog-1.14.1-hed91bc2_1.conda + __unix: '' + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/pysocks-1.7.1-pyha55dd90_7.conda hash: - md5: 909188c8979846bac8e586908cf1ca6a - sha256: 0c604fe3f78ddb2b612841722bd9b5db24d0484e30ced89fac78c0a3f524dfd6 + md5: 461219d1a5bd61342293efa2c0c90eac + sha256: ba3b032fa52709ce0d9fd388f63d330a026754587a2f461117cac9ab73d8d0d8 category: main optional: false -- name: sphinx - version: 8.1.3 +- name: pytest + version: 8.4.2 manager: conda platform: linux-64 dependencies: - alabaster: '>=0.7.14' - babel: '>=2.13' - colorama: '>=0.4.6' - docutils: '>=0.20,<0.22' - imagesize: '>=1.3' - jinja2: '>=3.1' - packaging: '>=23.0' - pygments: '>=2.17' + colorama: '>=0.4' + exceptiongroup: '>=1' + iniconfig: '>=1' + packaging: '>=20' + pluggy: '>=1.5,<2' + pygments: '>=2.7.2' python: '>=3.10' - requests: '>=2.30.0' - snowballstemmer: '>=2.2' - sphinxcontrib-applehelp: '>=1.0.7' - sphinxcontrib-devhelp: '>=1.0.6' - sphinxcontrib-htmlhelp: '>=2.0.6' - sphinxcontrib-jsmath: '>=1.0.1' - sphinxcontrib-qthelp: '>=1.0.6' - sphinxcontrib-serializinghtml: '>=1.1.9' - tomli: '>=2.0' - url: https://conda.anaconda.org/conda-forge/noarch/sphinx-8.1.3-pyhd8ed1ab_1.conda + tomli: '>=1' + url: https://conda.anaconda.org/conda-forge/noarch/pytest-8.4.2-pyhcf101f3_1.conda hash: - md5: 1a3281a0dc355c02b5506d87db2d78ac - sha256: 3228eb332ce159f031d4b7d2e08117df973b0ba3ddcb8f5dbb7f429f71d27ea1 + md5: da0c42269086f5170e2b296878ec13a6 + sha256: 39f41a52eb6f927caf5cd42a2ff98a09bb850ce9758b432869374b6253826962 category: main optional: false -- name: sphinxcontrib-applehelp - version: 2.0.0 +- name: python + version: 3.12.13 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - sphinx: '>=5' - url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-2.0.0-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + bzip2: '>=1.0.8,<2.0a0' + ld_impl_linux-64: '>=2.36.1' + libexpat: '>=2.7.4,<3.0a0' + libffi: '>=3.5.2,<3.6.0a0' + libgcc: '>=14' + liblzma: '>=5.8.2,<6.0a0' + libnsl: '>=2.0.1,<2.1.0a0' + libsqlite: '>=3.51.2,<4.0a0' + libuuid: '>=2.41.3,<3.0a0' + libxcrypt: '>=4.4.36' + libzlib: '>=1.3.1,<2.0a0' + ncurses: '>=6.5,<7.0a0' + openssl: '>=3.5.5,<4.0a0' + pip: '' + readline: '>=8.3,<9.0a0' + tk: '>=8.6.13,<8.7.0a0' + tzdata: '' + url: https://conda.anaconda.org/conda-forge/linux-64/python-3.12.13-hd63d673_0_cpython.conda hash: - md5: 16e3f039c0aa6446513e94ab18a8784b - sha256: d7433a344a9ad32a680b881c81b0034bc61618d12c39dd6e3309abeffa9577ba + md5: 7eccb41177e15cc672e1babe9056018e + sha256: a44655c1c3e1d43ed8704890a91e12afd68130414ea2c0872e154e5633a13d7e category: main optional: false -- name: sphinxcontrib-devhelp - version: 2.0.0 +- name: python-build + version: 1.5.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - sphinx: '>=5' - url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_1.conda + colorama: '' + importlib-metadata: '>=4.6' + packaging: '>=24.0' + pyproject_hooks: '' + python: '>=3.10' + tomli: '>=1.1.0' + url: https://conda.anaconda.org/conda-forge/noarch/python-build-1.5.0-pyhc364b38_0.conda hash: - md5: 910f28a05c178feba832f842155cbfff - sha256: 55d5076005d20b84b20bee7844e686b7e60eb9f683af04492e598a622b12d53d + md5: fa587158c0d768127faa1a3b4df01e5d + sha256: eb8d5e44fddee9033eb7cfdd5ea584b7594b50e31c7602bef553af0fd4ee9266 category: main optional: false -- name: sphinxcontrib-htmlhelp - version: 2.1.0 +- name: python-dateutil + version: 2.9.0.post0 manager: conda platform: linux-64 dependencies: python: '>=3.9' - sphinx: '>=5' - url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_1.conda + six: '>=1.5' + url: https://conda.anaconda.org/conda-forge/noarch/python-dateutil-2.9.0.post0-pyhe01879c_2.conda hash: - md5: e9fb3fe8a5b758b4aff187d434f94f03 - sha256: c1492c0262ccf16694bdcd3bb62aa4627878ea8782d5cd3876614ffeb62b3996 + md5: 5b8d21249ff20967101ffa321cab24e8 + sha256: d6a17ece93bbd5139e02d2bd7dbfa80bee1a4261dced63f65f679121686bf664 category: main optional: false -- name: sphinxcontrib-jsmath - version: 1.0.1 +- name: python-fastjsonschema + version: 2.21.2 manager: conda platform: linux-64 dependencies: python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_1.conda + url: https://conda.anaconda.org/conda-forge/noarch/python-fastjsonschema-2.21.2-pyhe01879c_0.conda hash: - md5: fa839b5ff59e192f411ccc7dae6588bb - sha256: 578bef5ec630e5b2b8810d898bbbf79b9ae66d49b7938bcc3efc364e679f2a62 + md5: 23029aae904a2ba587daba708208012f + sha256: df9aa74e9e28e8d1309274648aac08ec447a92512c33f61a8de0afa9ce32ebe8 category: main optional: false -- name: sphinxcontrib-qthelp - version: 2.0.0 +- name: python-installer + version: 1.0.1 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - sphinx: '>=5' - url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/python-installer-1.0.1-pyh332efcf_0.conda hash: - md5: 00534ebcc0375929b45c3039b5ba7636 - sha256: c664fefae4acdb5fae973bdde25836faf451f41d04342b64a358f9a7753c92ca + md5: 5cc7e2e78962dc902cae9e29c2aa4b2f + sha256: 9c18fa1ebd0c839b6ea12e99d510a0968c2288cc423185cb34bf7332b97684e9 category: main optional: false -- name: sphinxcontrib-serializinghtml - version: 1.1.10 +- name: python_abi + version: '3.12' manager: conda platform: linux-64 - dependencies: - python: '>=3.9' - sphinx: '>=5' - url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-1.1.10-pyhd8ed1ab_1.conda + dependencies: {} + url: https://conda.anaconda.org/conda-forge/noarch/python_abi-3.12-8_cp312.conda hash: - md5: 3bc61f7161d28137797e038263c04c54 - sha256: 64d89ecc0264347486971a94487cb8d7c65bfc0176750cf7502b8a272f4ab557 + md5: c3efd25ac4d74b1584d2f7a57195ddf1 + sha256: 80677180dd3c22deb7426ca89d6203f1c7f1f256f2d5a94dc210f6e758229809 category: main optional: false -- name: stack_data - version: 0.6.3 +- name: pyyaml + version: 6.0.3 manager: conda platform: linux-64 dependencies: - asttokens: '' - executing: '' - pure_eval: '' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.3-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + yaml: '>=0.2.5,<0.3.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/pyyaml-6.0.3-py312h8a5da7c_1.conda hash: - md5: b1b505328da7a6b246787df4b5a49fbc - sha256: 570da295d421661af487f1595045760526964f41471021056e993e73089e9c41 + md5: 15878599a87992e44c059731771591cb + sha256: cb142bfd92f6e55749365ddc244294fa7b64db6d08c45b018ff1c658907bfcbf category: main optional: false -- name: statsmodels - version: 0.14.4 +- name: readline + version: '8.3' manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - numpy: '>=1.19,<3' - packaging: '>=21.3' - pandas: '!=2.1.0,>=1.4' - patsy: '>=0.5.6' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - scipy: '!=1.9.2,>=1.8' - url: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.4-py310hf462985_0.conda + libgcc: '>=14' + ncurses: '>=6.5,<7.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/readline-8.3-h853b02a_0.conda hash: - md5: 636d3c500d8a851e377360e88ec95372 - sha256: a060f9b7e9bff3cae075a00e278089893c20cc0663ced09f9c4d92522ce76a21 + md5: d7d95fc8287ea7bf33e0e7116d2b95ec + sha256: 12ffde5a6f958e285aa22c191ca01bbd3d6e710aa852e00618fa6ddc59149002 category: main optional: false -- name: sysroot_linux-64 - version: '2.17' +- name: reproc + version: 14.2.7.post0 manager: conda platform: linux-64 dependencies: - kernel-headers_linux-64: 3.10.0 - tzdata: '' - url: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.17-h0157908_18.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/reproc-14.2.7.post0-hb03c661_1.conda hash: - md5: 460eba7851277ec1fd80a1a24080787a - sha256: 69ab5804bdd2e8e493d5709eebff382a72fab3e9af6adf93a237ccf8f7dbd624 + md5: 2e1edbf819157ca726ec65afb4eb2d5d + sha256: b47cbe954b1522f944c1c17fae964d362dfcf7ef7516fda1c368971426a8e933 category: main optional: false -- name: tblib - version: 3.0.0 +- name: reproc-cpp + version: 14.2.7.post0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/tblib-3.0.0-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + reproc: 14.2.7.post0 + url: https://conda.anaconda.org/conda-forge/linux-64/reproc-cpp-14.2.7.post0-hecca717_1.conda hash: - md5: 60ce69f73f3e75b21f1c27b1b471320c - sha256: 6869cd2e043426d30c84d0ff6619f176b39728f9c75dc95dca89db994548bb8a + md5: a0ef2594b3ab25a933ee8e7a1ac1e21b + sha256: f29ba41cc71a01f1dd7eeba573b1535f0feec72e9ab6b4a390b4e71230d61ade category: main optional: false -- name: threadpoolctl - version: 3.5.0 +- name: requests + version: 2.34.2 manager: conda platform: linux-64 dependencies: - python: '>=3.8' - url: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.5.0-pyhc1e730c_0.conda + certifi: '>=2023.5.7' + charset-normalizer: '>=2,<4' + idna: '>=2.5,<4' + python: '>=3.10' + urllib3: '>=1.26,<3' + url: https://conda.anaconda.org/conda-forge/noarch/requests-2.34.2-pyhcf101f3_0.conda hash: - md5: df68d78237980a159bd7149f33c0e8fd - sha256: 45e402941f6bed094022c5726a2ca494e6224b85180d2367fb6ddd9aea68079d + md5: 4a85203c1d80c1059086ae860836ffb9 + sha256: 1715246b19c9f85ee022933b4845f2fc14ac9184981b7b7d9b728bec8e9588da category: main optional: false -- name: tk - version: 8.6.13 +- name: requests-toolbelt + version: 1.0.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libzlib: '>=1.2.13,<2.0.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda + python: '>=3.9' + requests: '>=2.0.1,<3.0.0' + url: https://conda.anaconda.org/conda-forge/noarch/requests-toolbelt-1.0.0-pyhd8ed1ab_1.conda hash: - md5: d453b98d9c83e71da0741bb0ff4d76bc - sha256: e0569c9caa68bf476bead1bed3d79650bb080b532c64a4af7d8ca286c08dea4e + md5: 66de8645e324fda0ea6ef28c2f99a2ab + sha256: c0b815e72bb3f08b67d60d5e02251bbb0164905b5f72942ff5b6d2a339640630 category: main optional: false -- name: tomli - version: 2.2.1 +- name: rhash + version: 1.4.6 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=13' + url: https://conda.anaconda.org/conda-forge/linux-64/rhash-1.4.6-hb9d3cd8_1.conda hash: - md5: ac944244f1fed2eb49bae07193ae8215 - sha256: 18636339a79656962723077df9a56c0ac7b8a864329eb8f847ee3d38495b863e + md5: c1c9b02933fdb2cfb791d936c20e887e + sha256: d5c73079c1dd2c2a313c3bfd81c73dbd066b7eb08d213778c8bff520091ae894 category: main optional: false -- name: toolz - version: 1.0.0 +- name: roman-numerals + version: 4.1.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/toolz-1.0.0-pyhd8ed1ab_1.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/roman-numerals-4.1.0-pyhd8ed1ab_0.conda hash: - md5: 40d0ed782a8aaa16ef248e68c06c168d - sha256: eda38f423c33c2eaeca49ed946a8d3bf466cc3364970e083a65eb2fd85258d87 + md5: 0dc48b4b570931adc8641e55c6c17fe4 + sha256: 30f3c04fcfb64c44d821d392a4a0b8915650dbd900c8befc20ade8fde8ec6aa2 category: main optional: false -- name: tornado - version: 6.4.2 +- name: roman-numerals-py + version: 4.1.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/tornado-6.4.2-py310ha75aee5_0.conda + python: '>=3.10' + roman-numerals: 4.1.0 + url: https://conda.anaconda.org/conda-forge/noarch/roman-numerals-py-4.1.0-pyhd8ed1ab_0.conda hash: - md5: 166d59aab40b9c607b4cc21c03924e9d - sha256: 9c2b86d4e58c8b0e7d13a7f4c440f34e2201bae9cfc1d7e1d30a5bc7ffb1d4c8 + md5: 28687768633154993d521aecfa4a56ac + sha256: ce21b50a412b87b388db9e8dfbf8eb16fc436c23750b29bf612ee1a74dd0beb2 category: main optional: false -- name: traitlets - version: 5.14.3 +- name: ruamel.yaml + version: 0.19.1 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/traitlets-5.14.3-pyhd8ed1ab_1.conda + python: '>=3.10' + ruamel.yaml.clib: '>=0.2.15' + url: https://conda.anaconda.org/conda-forge/noarch/ruamel.yaml-0.19.1-pyhcf101f3_0.conda hash: - md5: 019a7385be9af33791c989871317e1ed - sha256: f39a5620c6e8e9e98357507262a7869de2ae8cc07da8b7f84e517c9fd6c2b959 + md5: 06ad944772941d5dae1e0d09848d8e49 + sha256: b48bebe297a63ae60f52e50be328262e880702db4d9b4e86731473ada459c2a1 category: main optional: false -- name: treelite - version: 4.4.1 +- name: ruamel.yaml.clib + version: 0.2.15 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - _openmp_mutex: '>=4.5' - libgcc: '>=13' - libstdcxx: '>=13' - numpy: '' - packaging: '' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - scipy: '' - url: https://conda.anaconda.org/conda-forge/linux-64/treelite-4.4.1-py310hebdfe98_1.conda + libgcc: '>=14' + python: '' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/ruamel.yaml.clib-0.2.15-py312h5253ce2_1.conda hash: - md5: fe36ce3c7f9b4c1f7d01644268bd7530 - sha256: 115dc5bda94afe7d6daf28e78bc58627f077fd4025b48dc8cd917bae536088c2 + md5: 84aa470567e2211a2f8e5c8491cdd78c + sha256: dc520329bdfd356e2f464393f8ad9b8450fd5a269699907b2b8d629300c2c068 category: main optional: false -- name: typing_extensions - version: 4.12.2 +- name: scikit-build-core + version: 1.0.3 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.12.2-pyha770c72_1.conda + exceptiongroup: '>=1.0' + importlib-resources: '>=1.3' + packaging: '>=23.2' + pathspec: '>=0.12.0' + python: '>=3.8' + tomli: '>=1.2.2' + typing_extensions: '>=4' + url: https://conda.anaconda.org/conda-forge/noarch/scikit-build-core-1.0.3-pyh04d0eab_0.conda hash: - md5: d17f13df8b65464ca316cbc000a3cb64 - sha256: 337be7af5af8b2817f115b3b68870208b30c31d3439bec07bfb2d8f4823e3568 + md5: 5d9f7061c53e8f854b161c3dd89bf135 + sha256: 8c01a200a00e60f7ba69d8370ac6572fe85356da7b8cec539349499d86db4315 category: main optional: false -- name: tzdata - version: 2025a +- name: scikit-learn + version: 1.9.0 manager: conda platform: linux-64 - dependencies: {} - url: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025a-h78e105d_0.conda + dependencies: + __glibc: '>=2.17,<3.0.a0' + _openmp_mutex: '>=4.5' + joblib: '>=1.4.0' + libgcc: '>=14' + libstdcxx: '>=14' + narwhals: '>=2.0.1' + numpy: '>=1.23,<3,>=1.24.1' + python: '' + python_abi: 3.12.* + scipy: '>=1.10.0' + threadpoolctl: '>=3.5.0' + url: https://conda.anaconda.org/conda-forge/linux-64/scikit-learn-1.9.0-np2py312h3226591_0.conda hash: - md5: dbcace4706afdfb7eb891f7b37d07c04 - sha256: c4b1ae8a2931fe9b274c44af29c5475a85b37693999f8c792dad0f8c6734b1de + md5: e6e9b5795bb495325c3b4ebd451519aa + sha256: 8c0cd0326b5a17ddcf189fc4f119bf6871b7853595c088075847c484a3ed567e category: main optional: false -- name: ucx - version: 1.18.0 +- name: scipy + version: 1.16.3 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - _openmp_mutex: '>=4.5' - libgcc: '>=13' - libstdcxx: '>=13' - rdma-core: '>=55.0' - url: https://conda.anaconda.org/conda-forge/linux-64/ucx-1.18.0-h8887438_1.conda + libblas: '>=3.9.0,<4.0a0' + libcblas: '>=3.9.0,<4.0a0' + libgcc: '>=14' + libgfortran: '' + libgfortran5: '>=14.3.0' + liblapack: '>=3.9.0,<4.0a0' + libstdcxx: '>=14' + numpy: <2.6,>=1.23,<3,>=1.25.2 + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/scipy-1.16.3-py312h54fa4ab_2.conda hash: - md5: 79ee7788f4930ff0546eca984fee59f5 - sha256: e0cba51d6866f99de6569e0b20f03e8fc7ae339b6a8776c1ca21193a23bddfe2 + md5: e82683871cbc4bb257b7694f31a91327 + sha256: 2f73242ca3164b4f305becb535a8245ff25839a42d4e62b222f866a5bf58b989 category: main optional: false -- name: ucx-py - version: 0.42.00 +- name: secretstorage + version: 3.5.0 manager: conda platform: linux-64 dependencies: - libgcc: '' - libgcc-ng: '>=12' - libstdcxx: '' - libstdcxx-ng: '>=12' - numpy: '>=1.23,<3.0a0' - pynvml: '>=12.0.0,<13.0.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - ucx: '>=1.15.0,<1.19.0' - url: https://conda.anaconda.org/rapidsai/linux-64/ucx-py-0.42.00-py310_250213_gad9cfc2_0.conda + cryptography: '>=2.0' + dbus: '' + jeepney: '>=0.6' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/secretstorage-3.5.0-py312h7900ff3_0.conda hash: - md5: 1cae41c3ea160b2e795ddafe8cf72f48 - sha256: b3c8451ca6f5d3cd46919c9ec36240376a55ff1b94524125b216b630613cfd6c + md5: a2a3a7d918348b77688e2ed98519a9d5 + sha256: 0d07e47cf5de6035e724b64d7f315473b8f7b7dbe8297f4b8d563c8710ea6dd8 category: main optional: false -- name: ucxx - version: 0.42.00 +- name: semver + version: 3.0.4 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.28,<3.0.a0' - cuda-cudart: '' - cuda-version: '>=12,<13.0a0' - libgcc: '>=13' - libstdcxx: '>=13' - libucxx: 0.42.00 - numpy: '>=1.23,<3.0a0' - pynvml: '>=12.0.0,<13.0.0a0' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - rmm: '>=25.2.0,<25.3.0a0' - ucx: '>=1.15.0,<1.19.0' - url: https://conda.anaconda.org/rapidsai/linux-64/ucxx-0.42.00-cuda12_py3.10_250213_g762a6ed_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/semver-3.0.4-pyhcf101f3_1.conda hash: - md5: 83ae1e7cae39ad648f4dbac854278dc0 - sha256: 7467792ef462c18f34ed537eefa55616d54332ccc073dd5f14e5876066dea803 + md5: 8e7be844ccb9706a999a337e056606ab + sha256: bea67173ed67c73cf16691ef72e58059492ac1ed1c880cfbeb6f1295c5add7d6 category: main optional: false -- name: ukkonen - version: 1.0.1 +- name: setuptools + version: 83.0.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - cffi: '' - libgcc: '>=13' - libstdcxx: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.0.1-py310h3788b33_5.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/setuptools-83.0.0-pyh332efcf_0.conda hash: - md5: e05b0475166b68c9dc4d7937e0315654 - sha256: d491c87088b7c430e9b77acc03307a4ad58bc6cdd686353710c3178977712df6 + md5: 6bf6acbab2499830180ec88c3aff2fa4 + sha256: 48a9f96016505debadfc67f06de7ac548decbc38d327409b24b0432ef6f16335 category: main optional: false -- name: unicodedata2 - version: 16.0.0 +- name: shellingham + version: 1.5.4 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/unicodedata2-16.0.0-py310ha75aee5_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/shellingham-1.5.4-pyhd8ed1ab_2.conda hash: - md5: 1d7a4b9202cdd10d56ecdd7f6c347190 - sha256: 0468c864c60190fdb94b4705bca618e77589d5cb9fa096de47caccd1f22b0b54 + md5: 83ea3a2ddb7a75c1b09cea582aa4f106 + sha256: 1d6534df8e7924d9087bd388fbac5bd868c5bf8971c36885f9f016da0657d22b category: main optional: false -- name: upsetplot - version: 0.9.0 +- name: simdjson + version: 4.6.4 manager: conda platform: linux-64 dependencies: - matplotlib-base: '>=2.0' - pandas: '' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/upsetplot-0.9.0-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/simdjson-4.6.4-hb700be7_0.conda hash: - md5: 69e4f1eef254434392c10896c23e6801 - sha256: 884c4c325f99b5e3bb01de14a51ff4220fd5dc57355e999fddf03db564c45b5d + md5: a0a159ba93ca0018f3d3e333470b045f + sha256: 7a1f81823ec7d5ad659024de878ec34ea90205743cd685790621d7c013cc0a75 category: main optional: false -- name: urllib3 - version: 2.3.0 +- name: six + version: 1.17.0 manager: conda platform: linux-64 dependencies: - brotli-python: '>=1.0.9' - h2: '>=4,<5' - pysocks: '>=1.5.6,<2.0,!=1.5.7' python: '>=3.9' - zstandard: '>=0.18.0' - url: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.3.0-pyhd8ed1ab_0.conda + url: https://conda.anaconda.org/conda-forge/noarch/six-1.17.0-pyhe01879c_1.conda hash: - md5: 32674f8dbfb7b26410ed580dd3c10a29 - sha256: 114919ffa80c328127dab9c8e7a38f9d563c617691fb81fccb11c1e86763727e + md5: 3339e3b65d58accf4ca4fb8748ab16b3 + sha256: 458227f759d5e3fcec5d9b7acce54e10c9e1f4f4b7ec978f3bfd54ce4ee9853d category: main optional: false -- name: virtualenv - version: 20.29.2 +- name: smmap + version: 5.0.3 manager: conda platform: linux-64 dependencies: - distlib: '>=0.3.7,<1' - filelock: '>=3.12.2,<4' - platformdirs: '>=3.9.1,<5' - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.29.2-pyhd8ed1ab_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/smmap-5.0.3-pyhcf101f3_1.conda hash: - md5: d8a3ee355d5ecc9ee2565cafba1d3573 - sha256: c50a4ab0f5f1164230d42a29f12f61ece9c7b102f57ed1c607d2cd7c77e107b5 + md5: b899f1195be56d8215ed1973a8f409c3 + sha256: bc86861086db65c9b56dd6a1605755f41a9875b94fc3b69e137f686700d91aa1 category: main optional: false -- name: wayland - version: 1.23.1 +- name: snowballstemmer + version: 3.1.1 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libexpat: '>=2.6.2,<3.0a0' - libffi: '>=3.4,<4.0a0' - libgcc-ng: '>=13' - libstdcxx-ng: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/wayland-1.23.1-h3e06ad9_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/snowballstemmer-3.1.1-pyhd8ed1ab_0.conda hash: - md5: 0a732427643ae5e0486a727927791da1 - sha256: 0884b2023a32d2620192cf2e2fc6784b8d1e31cf9f137e49e00802d4daf7d1c1 + md5: 46b6abe31482f6bca064b965696ae807 + sha256: ad89284ea94821c20ff87e64b948e4afc690cf5202d14c009355b0594cf23aea category: main optional: false -- name: wcwidth - version: 0.2.13 +- name: spdlog + version: 1.17.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/wcwidth-0.2.13-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + fmt: '>=12.1.0,<12.2.0a0' + libgcc: '>=14' + libstdcxx: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/spdlog-1.17.0-hab81395_1.conda hash: - md5: b68980f2495d096e71c7fd9d7ccf63e6 - sha256: f21e63e8f7346f9074fd00ca3b079bd3d2fa4d71f1f89d5b6934bf31446dc2a5 + md5: a3b0e874fa56f72bc54e5c595712a333 + sha256: c650f3df027afde77a5fbf58600ec4ed81a9edddf81f323cfb3e260f6dc19f56 category: main optional: false -- name: wheel - version: 0.45.1 +- name: sphinx + version: 8.2.3 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/wheel-0.45.1-pyhd8ed1ab_1.conda + alabaster: '>=0.7.14' + babel: '>=2.13' + colorama: '>=0.4.6' + docutils: '>=0.20,<0.22' + imagesize: '>=1.3' + jinja2: '>=3.1' + packaging: '>=23.0' + pygments: '>=2.17' + python: '>=3.11' + requests: '>=2.30.0' + roman-numerals-py: '>=1.0.0' + snowballstemmer: '>=2.2' + sphinxcontrib-applehelp: '>=1.0.7' + sphinxcontrib-devhelp: '>=1.0.6' + sphinxcontrib-htmlhelp: '>=2.0.6' + sphinxcontrib-jsmath: '>=1.0.1' + sphinxcontrib-qthelp: '>=1.0.6' + sphinxcontrib-serializinghtml: '>=1.1.9' + url: https://conda.anaconda.org/conda-forge/noarch/sphinx-8.2.3-pyhd8ed1ab_0.conda hash: - md5: 75cb7132eb58d97896e173ef12ac9986 - sha256: 1b34021e815ff89a4d902d879c3bd2040bc1bd6169b32e9427497fa05c55f1ce + md5: f7af826063ed569bb13f7207d6f949b0 + sha256: 995f58c662db0197d681fa345522fd9e7ac5f05330d3dff095ab2f102e260ab0 category: main optional: false -- name: xcb-util - version: 0.4.1 +- name: sphinx-rtd-theme + version: 3.1.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libxcb: '>=1.16,<2.0.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-0.4.1-hb711507_2.conda + sphinx_rtd_theme: 3.1.0 + url: https://conda.anaconda.org/conda-forge/noarch/sphinx-rtd-theme-3.1.0-hd8ed1ab_0.conda hash: - md5: 8637c3e5821654d0edf97e2b0404b443 - sha256: 416aa55d946ce4ab173ab338796564893a2f820e80e04e098ff00c25fb981263 + md5: 3b1a32d3d5c2064822203f2a6f3f1173 + sha256: ae5e8e514f21e6f62b63c13f684939ba007168bfd7742e8cb573fce69fbf62da category: main optional: false -- name: xcb-util-cursor - version: 0.1.5 +- name: sphinx_rtd_theme + version: 3.1.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libxcb: '>=1.16,<2.0.0a0' - xcb-util-image: '>=0.4.0,<0.5.0a0' - xcb-util-renderutil: '>=0.3.10,<0.4.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-cursor-0.1.5-hb9d3cd8_0.conda + docutils: '>0.18,<0.22' + python: '>=3.8' + sphinx: '>=6,<9' + sphinxcontrib-jquery: '>=4,<5' + url: https://conda.anaconda.org/conda-forge/noarch/sphinx_rtd_theme-3.1.0-pyha770c72_0.conda hash: - md5: eb44b3b6deb1cab08d72cb61686fe64c - sha256: c7b35db96f6e32a9e5346f97adc968ef2f33948e3d7084295baebc0e33abdd5b + md5: cede6bc99a0253fa676f03cfdc666d57 + sha256: 1d57a0cd74ecc0e5dc006f6591145d1abb6658464919d4aeb163d3db714f80e6 category: main optional: false -- name: xcb-util-image - version: 0.4.0 +- name: sphinxcontrib-applehelp + version: 2.0.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libxcb: '>=1.16,<2.0.0a0' - xcb-util: '>=0.4.1,<0.5.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-image-0.4.0-hb711507_2.conda + python: '>=3.9' + sphinx: '>=5' + url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-applehelp-2.0.0-pyhd8ed1ab_1.conda hash: - md5: a0901183f08b6c7107aab109733a3c91 - sha256: 94b12ff8b30260d9de4fd7a28cca12e028e572cbc504fd42aa2646ec4a5bded7 + md5: 16e3f039c0aa6446513e94ab18a8784b + sha256: d7433a344a9ad32a680b881c81b0034bc61618d12c39dd6e3309abeffa9577ba category: main optional: false -- name: xcb-util-keysyms - version: 0.4.1 +- name: sphinxcontrib-devhelp + version: 2.0.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libxcb: '>=1.16,<2.0.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-keysyms-0.4.1-hb711507_0.conda + python: '>=3.9' + sphinx: '>=5' + url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-devhelp-2.0.0-pyhd8ed1ab_1.conda hash: - md5: ad748ccca349aec3e91743e08b5e2b50 - sha256: 546e3ee01e95a4c884b6401284bb22da449a2f4daf508d038fdfa0712fe4cc69 + md5: 910f28a05c178feba832f842155cbfff + sha256: 55d5076005d20b84b20bee7844e686b7e60eb9f683af04492e598a622b12d53d category: main optional: false -- name: xcb-util-renderutil - version: 0.3.10 +- name: sphinxcontrib-htmlhelp + version: 2.1.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libxcb: '>=1.16,<2.0.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-renderutil-0.3.10-hb711507_0.conda + python: '>=3.9' + sphinx: '>=5' + url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-htmlhelp-2.1.0-pyhd8ed1ab_1.conda hash: - md5: 0e0cbe0564d03a99afd5fd7b362feecd - sha256: 2d401dadc43855971ce008344a4b5bd804aca9487d8ebd83328592217daca3df + md5: e9fb3fe8a5b758b4aff187d434f94f03 + sha256: c1492c0262ccf16694bdcd3bb62aa4627878ea8782d5cd3876614ffeb62b3996 category: main optional: false -- name: xcb-util-wm - version: 0.4.2 +- name: sphinxcontrib-jquery + version: '4.1' manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - libxcb: '>=1.16,<2.0.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xcb-util-wm-0.4.2-hb711507_0.conda + python: '>=3.9' + sphinx: '>=1.8' + url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jquery-4.1-pyhd8ed1ab_1.conda hash: - md5: 608e0ef8256b81d04456e8d211eee3e8 - sha256: 31d44f297ad87a1e6510895740325a635dd204556aa7e079194a0034cdd7e66a + md5: 403185829255321ea427333f7773dd1f + sha256: 69c08d18663b57ebc8e4187c64c8d29b10996bb465a515cd288d87b6f2f52a5e category: main optional: false -- name: xkeyboard-config - version: '2.43' +- name: sphinxcontrib-jsmath + version: 1.0.1 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - xorg-libx11: '>=1.8.10,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.43-hb9d3cd8_0.conda + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-jsmath-1.0.1-pyhd8ed1ab_1.conda hash: - md5: f725c7425d6d7c15e31f3b99a88ea02f - sha256: 0d89b5873515a1f05d311f37ea4e087bbccc0418afa38f2f6189e97280db3179 + md5: fa839b5ff59e192f411ccc7dae6588bb + sha256: 578bef5ec630e5b2b8810d898bbbf79b9ae66d49b7938bcc3efc364e679f2a62 category: main optional: false -- name: xorg-compositeproto - version: 0.4.2 +- name: sphinxcontrib-qthelp + version: 2.0.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-compositeproto-0.4.2-hb9d3cd8_1002.conda + python: '>=3.9' + sphinx: '>=5' + url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-qthelp-2.0.0-pyhd8ed1ab_1.conda hash: - md5: 317d35860cab6c16d91a81f67303023d - sha256: b800b09a090e4acef0b8653bcb1a4811e8f44559d4eff050886770fdfa77857b + md5: 00534ebcc0375929b45c3039b5ba7636 + sha256: c664fefae4acdb5fae973bdde25836faf451f41d04342b64a358f9a7753c92ca category: main optional: false -- name: xorg-damageproto - version: 1.2.1 +- name: sphinxcontrib-serializinghtml + version: 2.0.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-damageproto-1.2.1-hb9d3cd8_1003.conda + python: '>=3.10' + sphinx: '>=5' + url: https://conda.anaconda.org/conda-forge/noarch/sphinxcontrib-serializinghtml-2.0.0-pyhd8ed1ab_0.conda hash: - md5: 5135d24c55235da88c2fe48a8662927a - sha256: 424a9202255359a75770eaca534e4e8b07464242f0146871915e1bb76fb3ffae + md5: f77df1fcf9af03b7287342638befca77 + sha256: 20b49741065fd7d3fabf98caf6d19b6436badb06b6d41f66b58f1fc2b52f37a1 category: main optional: false -- name: xorg-inputproto - version: 2.3.2 +- name: sysroot_linux-64 + version: '2.28' manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-inputproto-2.3.2-hb9d3cd8_1003.conda + __glibc: '>=2.28' + kernel-headers_linux-64: 4.18.0 + tzdata: '' + url: https://conda.anaconda.org/conda-forge/noarch/sysroot_linux-64-2.28-h4ee821c_9.conda hash: - md5: 32623b33f2047dbc9ae2f2e8fd3880e9 - sha256: 77eea289f9d3fa753a290f988533c842694b826fe1900abd6d7b142c528512ba + md5: 13dc3adbc692664cd3beabd216434749 + sha256: c47299fe37aebb0fcf674b3be588e67e4afb86225be4b0d452c7eb75c086b851 category: main optional: false -- name: xorg-libice - version: 1.1.2 +- name: threadpoolctl + version: 3.6.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.1.2-hb9d3cd8_0.conda + python: '>=3.9' + url: https://conda.anaconda.org/conda-forge/noarch/threadpoolctl-3.6.0-pyhecae5ae_0.conda hash: - md5: fb901ff28063514abb6046c9ec2c4a45 - sha256: c12396aabb21244c212e488bbdc4abcdef0b7404b15761d9329f5a4a39113c4b + md5: 9d64911b31d57ca443e9f1e36b04385f + sha256: 6016672e0e72c4cf23c0cf7b1986283bd86a9c17e8d319212d78d8e9ae42fdfd category: main optional: false -- name: xorg-libsm - version: 1.2.5 +- name: tk + version: 8.6.13 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libuuid: '>=2.38.1,<3.0a0' - xorg-libice: '>=1.1.2,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.5-he73a12e_0.conda + libgcc: '>=14' + libzlib: '>=1.3.2,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_hd70dff1_3.conda hash: - md5: 4c3e9fab69804ec6077697922d70c6e2 - sha256: 760f43df6c2ce8cbbbcb8f2f3b7fc0f306716c011e28d1d340f3dfa8ccf29185 + md5: 48a1049e710857572fc2a832aa394d9f + sha256: 43624eab22f5f29df7d6ffe914cf442f28fd559b55b290906255492826e636e8 category: main optional: false -- name: xorg-libx11 - version: 1.8.11 +- name: tomli + version: 2.4.1 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libxcb: '>=1.17.0,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.11-h4f16b4b_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/tomli-2.4.1-pyhcf101f3_0.conda hash: - md5: b6eb6d0cb323179af168df8fe16fb0a1 - sha256: a0e7fca9e341dc2455b20cd320fc1655e011f7f5f28367ecf8617cccd4bb2821 + md5: b5325cf06a000c5b14970462ff5e4d58 + sha256: 91cafdb64268e43e0e10d30bd1bef5af392e69f00edd34dfaf909f69ab2da6bd category: main optional: false -- name: xorg-libxau - version: 1.0.12 +- name: tomlkit + version: 0.15.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb9d3cd8_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/tomlkit-0.15.0-pyha770c72_0.conda hash: - md5: f6ebe2cb3f82ba6c057dde5d9debe4f7 - sha256: ed10c9283974d311855ae08a16dfd7e56241fac632aec3b92e3cfe73cff31038 + md5: 42ef10a8f7f5d55a2e267c0d5daa6387 + sha256: 1cd52f9ccb4854c4d731438afe0e833b6b71edaf5ede661152aa98efb3a7cc70 category: main optional: false -- name: xorg-libxcomposite - version: 0.4.6 +- name: trove-classifiers + version: 2026.6.1.19 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=12' - xorg-compositeproto: '' - xorg-libx11: '>=1.7.2,<2.0a0' - xorg-libxfixes: '' - xorg-xproto: '' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxcomposite-0.4.6-h0b41bf4_1.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/trove-classifiers-2026.6.1.19-pyhcf101f3_0.conda hash: - md5: ada6777364a0ea2407a1894e54779cc4 - sha256: 7c5806a8de1ce0d4e0c7aae8d29565f11fba6c6da4a787c3e09f1fcc428725a4 + md5: f23d5a5855f09bcb5026938486a9750d + sha256: 74dabeb087f26116a262a7580a78622f2bf109798a7e154d4d9b48234ed72b11 category: main optional: false -- name: xorg-libxdamage - version: 1.1.5 +- name: typing-extensions + version: 4.16.0 manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=9.4.0' - xorg-damageproto: '' - xorg-libx11: '>=1.7.2,<2.0a0' - xorg-libxext: 1.3.* - xorg-libxfixes: '' - xorg-util-macros: '' - xorg-xproto: '' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdamage-1.1.5-h7f98852_1.tar.bz2 + typing_extensions: ==4.16.0 + url: https://conda.anaconda.org/conda-forge/noarch/typing-extensions-4.16.0-h69aa097_0.conda hash: - md5: bebd3814ec2355fab6a474b07ed73093 - sha256: 4cab878855e48669b64dd7522a518433ac83bb56fa79743d12db316326e2e39e + md5: c680b5747e8c4c8f23dca0bb7042a8fc + sha256: b141933ece3518f6d7b75dfb59451e2f26b405a44c18e2518a83e9a02e09315c category: main optional: false -- name: xorg-libxdmcp - version: 1.1.5 +- name: typing-inspection + version: 0.4.2 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb9d3cd8_0.conda + python: '>=3.10' + typing_extensions: '>=4.12.0' + url: https://conda.anaconda.org/conda-forge/noarch/typing-inspection-0.4.2-pyhcf101f3_2.conda hash: - md5: 8035c64cb77ed555e3f150b7b3972480 - sha256: 6b250f3e59db07c2514057944a3ea2044d6a8cdde8a47b6497c254520fade1ee + md5: 53f5409c5cfd6c5a66417d68e3f0a864 + sha256: 8b90d2f19f9458b8c58a55e1fcdc1d90c1603a847a47654d8a454549413ba60a category: main optional: false -- name: xorg-libxext - version: 1.3.6 +- name: typing_extensions + version: 4.16.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - xorg-libx11: '>=1.8.10,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.6-hb9d3cd8_0.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/typing_extensions-4.16.0-pyhcf101f3_0.conda hash: - md5: febbab7d15033c913d53c7a2c102309d - sha256: da5dc921c017c05f38a38bd75245017463104457b63a1ce633ed41f214159c14 + md5: c70ad746c22219b9700931707482992c + sha256: 2d888f90af0686044882c74193ec80a90ec1943145d94a7b1b048958acda1848 category: main optional: false -- name: xorg-libxfixes - version: 5.0.3 +- name: tzdata + version: 2026b manager: conda platform: linux-64 - dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - xorg-libx11: '>=1.8.10,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxfixes-5.0.3-hb9d3cd8_1005.conda + dependencies: {} + url: https://conda.anaconda.org/conda-forge/noarch/tzdata-2026b-h151e31d_0.conda hash: - md5: 94bf5ea07b072bfd3f0a9fc45e6302c0 - sha256: 7817d62ff0b037b3deda4a1439eaa438edfd71ba5312368d1b2c041c27a6f125 + md5: fbfbcd26e5d2d28cfc9c389fce8c3f60 + sha256: ddd51761d40cae39586a9e10f57c1f306059743eff1c70c26549d275ed08ea87 category: main optional: false -- name: xorg-libxi - version: 1.7.10 +- name: ukkonen + version: 1.1.0 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc-ng: '>=12' - xorg-inputproto: '' - xorg-libx11: '>=1.8.9,<2.0a0' - xorg-libxext: '>=1.3.4,<2.0a0' - xorg-libxfixes: 5.0.* - xorg-xextproto: '>=7.3.0,<8.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxi-1.7.10-h4bc722e_1.conda + cffi: '' + libgcc: '>=14' + libstdcxx: '>=14' + python: '>=3.12,<3.13.0a0' + python_abi: 3.12.* + url: https://conda.anaconda.org/conda-forge/linux-64/ukkonen-1.1.0-py312hd9148b4_0.conda hash: - md5: 749baebe7e2ff3360630e069175e528b - sha256: e1416eb435e3d903bc658e3c637f0e87efd2dca290fe70daf29738b3a3d1f8ff + md5: 55fd03988b1b1bc6faabbfb5b481ecd7 + sha256: c975070ac28fe23a5bbb2b8aeca5976b06630eb2de2dc149782f74018bf07ae8 category: main optional: false -- name: xorg-libxrandr - version: 1.5.4 +- name: urllib3 + version: 2.7.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - xorg-libx11: '>=1.8.10,<2.0a0' - xorg-libxext: '>=1.3.6,<2.0a0' - xorg-libxrender: '>=0.9.11,<0.10.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrandr-1.5.4-hb9d3cd8_0.conda + backports.zstd: '>=1.0.0' + brotli-python: '>=1.2.0' + h2: '>=4,<5' + pysocks: '>=1.5.6,<2.0,!=1.5.7' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/urllib3-2.7.0-pyhd8ed1ab_0.conda hash: - md5: 2de7f99d6581a4a7adbff607b5c278ca - sha256: ac0f037e0791a620a69980914a77cb6bb40308e26db11698029d6708f5aa8e0d + md5: cbb88288f74dbe6ada1c6c7d0a97223e + sha256: feff959a816f7988a0893201aa9727bbb7ee1e9cec2c4f0428269b489eb93fb4 category: main optional: false -- name: xorg-libxrender - version: 0.9.12 +- name: virtualenv + version: 20.39.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - xorg-libx11: '>=1.8.10,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.12-hb9d3cd8_0.conda + distlib: '>=0.3.7,<1' + filelock: '>=3.24.2,<4' + importlib-metadata: '>=6.6' + platformdirs: '>=3.9.1,<5' + python: '>=3.10' + typing_extensions: '>=4.13.2' + url: https://conda.anaconda.org/conda-forge/noarch/virtualenv-20.39.0-pyhcf101f3_0.conda hash: - md5: 96d57aba173e878a2089d5638016dc5e - sha256: 044c7b3153c224c6cedd4484dd91b389d2d7fd9c776ad0f4a34f099b3389f4a1 + md5: e73db224203e56b25e040446fa1584db + sha256: de93eed364f14f08f78ff41994dfe22ff018521c4702e432630d10c0eb0eff6b category: main optional: false -- name: xorg-libxtst - version: 1.2.5 +- name: wheel + version: 0.47.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - xorg-libx11: '>=1.8.10,<2.0a0' - xorg-libxext: '>=1.3.6,<2.0a0' - xorg-libxi: '>=1.7.10,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxtst-1.2.5-hb9d3cd8_3.conda + packaging: '>=24.0' + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/wheel-0.47.0-pyhd8ed1ab_0.conda hash: - md5: 7bbe9a0cc0df0ac5f5a8ad6d6a11af2f - sha256: 752fdaac5d58ed863bbf685bb6f98092fe1a488ea8ebb7ed7b606ccfce08637a + md5: d0e3b2f0030cf4fca58bde71d246e94c + sha256: 9e156ffaefb8463437144326ada4b85d1de17961b9997ac5f1cbbaf747bd8bed category: main optional: false -- name: xorg-util-macros - version: 1.20.2 +- name: xkeyboard-config + version: '2.48' manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-util-macros-1.20.2-hb9d3cd8_0.conda + libgcc: '>=14' + xorg-libx11: '>=1.8.13,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/xkeyboard-config-2.48-h280c20c_0.conda hash: - md5: 7958d1f0a3e1dd6419891e1e8e64614c - sha256: c45a8af346e129f335f27634da8c89b70e6c380026366901ae9e5e479019ce82 + md5: b233b41be0bf210989d57160ed39b394 + sha256: 3b04afd5d1a65d2d27ac2d49a63b01ab8bcd875776779ec63e337370ed38afdc category: main optional: false -- name: xorg-xextproto - version: 7.3.0 +- name: xorg-libx11 + version: 1.8.13 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-hb9d3cd8_1004.conda + libgcc: '>=14' + libxcb: '>=1.17.0,<2.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.8.13-he1eb515_0.conda hash: - md5: bc4cd53a083b6720d61a1519a1900878 - sha256: f302a3f6284ee9ad3b39e45251d7ed15167896564dc33e006077a896fd3458a6 + md5: 861fb6ccbc677bb9a9fb2468430b9c6a + sha256: 516d4060139dbb4de49a4dcdc6317a9353fb39ebd47789c14e6fe52de0deee42 category: main optional: false -- name: xorg-xproto - version: 7.0.31 +- name: xorg-libxau + version: 1.0.12 manager: conda platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - url: https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-hb9d3cd8_1008.conda + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.12-hb03c661_1.conda hash: - md5: a63f5b66876bb1ec734ab4bdc4d11e86 - sha256: ea02425c898d6694167952794e9a865e02e14e9c844efb067374f90b9ce8ce33 + md5: b2895afaf55bf96a8c8282a2e47a5de0 + sha256: 6bc6ab7a90a5d8ac94c7e300cc10beb0500eeba4b99822768ca2f2ef356f731b category: main optional: false -- name: xyzservices - version: 2025.1.0 +- name: xorg-libxdmcp + version: 1.1.5 manager: conda platform: linux-64 dependencies: - python: '>=3.8' - url: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2025.1.0-pyhd8ed1ab_0.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.5-hb03c661_1.conda hash: - md5: fdf07e281a9e5e10fc75b2dd444136e9 - sha256: 9978c22319e85026d5a4134944f73bac820c948ca6b6c32af6b6985b5221cd8a + md5: 1dafce8548e38671bea82e3f5c6ce22f + sha256: 25d255fb2eef929d21ff660a0c687d38a6d2ccfbcbf0cc6aa738b12af6e9d142 category: main optional: false - name: yaml @@ -6169,64 +4143,55 @@ package: manager: conda platform: linux-64 dependencies: - libgcc-ng: '>=9.4.0' - url: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h7f98852_2.tar.bz2 + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=14' + url: https://conda.anaconda.org/conda-forge/linux-64/yaml-0.2.5-h280c20c_3.conda hash: - md5: 4cb3ad778ec2d5a7acbdf254eb1c42ae - sha256: a4e34c710eeb26945bdbdaba82d3d74f60a78f54a874ec10d373811a5d217535 + md5: a77f85f77be52ff59391544bfe73390a + sha256: 6d9ea2f731e284e9316d95fa61869fe7bbba33df7929f82693c121022810f4ad category: main optional: false -- name: zict - version: 3.0.0 +- name: yaml-cpp + version: 0.8.0 manager: conda platform: linux-64 dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/zict-3.0.0-pyhd8ed1ab_1.conda + __glibc: '>=2.17,<3.0.a0' + libgcc: '>=13' + libstdcxx: '>=13' + url: https://conda.anaconda.org/conda-forge/linux-64/yaml-cpp-0.8.0-h3f2d84a_0.conda hash: - md5: e52c2ef711ccf31bb7f70ca87d144b9e - sha256: 5488542dceeb9f2874e726646548ecc5608060934d6f9ceaa7c6a48c61f9cc8d + md5: 92b90f5f7a322e74468bb4909c7354b5 + sha256: 4b0b713a4308864a59d5f0b66ac61b7960151c8022511cdc914c0c0458375eca category: main optional: false - name: zipp - version: 3.21.0 - manager: conda - platform: linux-64 - dependencies: - python: '>=3.9' - url: https://conda.anaconda.org/conda-forge/noarch/zipp-3.21.0-pyhd8ed1ab_1.conda - hash: - md5: 0c3cc595284c5e8f0f9900a9b228a332 - sha256: 567c04f124525c97a096b65769834b7acb047db24b15a56888a322bf3966c3e1 - category: main - optional: false -- name: zlib - version: 1.3.1 + version: 4.1.0 manager: conda platform: linux-64 dependencies: - __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libzlib: 1.3.1 - url: https://conda.anaconda.org/conda-forge/linux-64/zlib-1.3.1-hb9d3cd8_2.conda + python: '>=3.10' + url: https://conda.anaconda.org/conda-forge/noarch/zipp-4.1.0-pyhcf101f3_0.conda hash: - md5: c9f075ab2f33b3bbee9e62d4ad0a6cd8 - sha256: 5d7c0e5f0005f74112a34a7425179f4eb6e73c92f5d109e6af4ddeca407c92ab + md5: ba3dcdc8584155c97c648ae9c044b7a3 + sha256: 210bd31c22bb88f5e2a167df24c95bb5f152b2ada7502f9b8c49d1f5366db423 category: main optional: false - name: zstandard - version: 0.19.0 + version: 0.25.0 manager: conda platform: linux-64 dependencies: - cffi: '>=1.8' - libgcc-ng: '>=12' - python: '>=3.10,<3.11.0a0' - python_abi: 3.10.* - url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.19.0-py310h5764c6d_0.tar.bz2 + __glibc: '>=2.17,<3.0.a0' + cffi: '>=1.11' + libgcc: '>=14' + python: '' + python_abi: 3.12.* + zstd: '>=1.5.7,<1.5.8.0a0,>=1.5.7,<1.6.0a0' + url: https://conda.anaconda.org/conda-forge/linux-64/zstandard-0.25.0-py312h5253ce2_1.conda hash: - md5: 74ea667169b1296fb31bb86f13abfa49 - sha256: 958e22d2b24204e08ca0d64db55d63520583db99852cecc82d22c1a3832b23a2 + md5: 02738ff9855946075cbd1b5274399a41 + sha256: c2bcb8aa930d6ea3c9c7a64fc4fab58ad7bcac483a9a45de294f67d2f447f413 category: main optional: false - name: zstd @@ -6235,12 +4200,10 @@ package: platform: linux-64 dependencies: __glibc: '>=2.17,<3.0.a0' - libgcc: '>=13' - libstdcxx: '>=13' libzlib: '>=1.3.1,<2.0a0' - url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_1.conda + url: https://conda.anaconda.org/conda-forge/linux-64/zstd-1.5.7-hb78ec9c_6.conda hash: - md5: 02e4e2fa41a6528afba2e54cbc4280ff - sha256: 532d3623961e34c53aba98db2ad0a33b7a52ff90d6960e505fb2d2efc06bb7da + md5: 4a13eeac0b5c8e5b8ab496e6c4ddd829 + sha256: 68f0206ca6e98fea941e5717cec780ed2873ffabc0e1ed34428c061e2c6268c7 category: main optional: false diff --git a/docs/PUBLISHING.md b/docs/PUBLISHING.md index 5aae7522..cf7c1a15 100644 --- a/docs/PUBLISHING.md +++ b/docs/PUBLISHING.md @@ -1,182 +1,11 @@ # Publishing CCC-GPU to PyPI -This document describes how to build and publish the cccgpu package to PyPI and test PyPI. +The publishing guide now lives in the Sphinx documentation, as the single +source of truth: -## Prerequisites +- Source: [`docs/source/development/package_publishing.rst`](source/development/package_publishing.rst) +- Rendered: the **Development → Package Publishing** page at + -1. **Conda environment**: Ensure you have the `ccc-gpu` conda environment set up -2. **PyPI account**: Register at [PyPI](https://pypi.org/) and [test PyPI](https://test.pypi.org/) -3. **API tokens**: Generate API tokens for authentication (recommended over passwords) - -## Setup - -### 1. Configure PyPI credentials - -Copy the example configuration and update with your credentials: - -```bash -cp .pypirc.example ~/.pypirc -chmod 600 ~/.pypirc # Protect your credentials -``` - -Edit `~/.pypirc` and add your API tokens: -- Get test PyPI token from: https://test.pypi.org/manage/account/token/ -- Get PyPI token from: https://pypi.org/manage/account/token/ - -### 2. Make scripts executable - -```bash -chmod +x scripts/pypi/00-build_package.sh -chmod +x scripts/pypi/10-upload_to_test_pypi.sh -``` - -## Building the Package - -The package uses `scikit-build-core` to build C++/CUDA extensions along with the Python package. - -### Build command: - -```bash -./scripts/pypi/00-build_package.sh -``` - -This script will: -1. Activate the `ccc-gpu` conda environment -2. Clean previous builds -3. Install/update build dependencies (`build`, `twine`, `setuptools`, `wheel`, `auditwheel`) -4. Build both source distribution (`.tar.gz`) and wheel (`.whl`) -5. Fix wheel platform tags for PyPI compatibility (convert `linux_x86_64` to `manylinux_2_17_x86_64`) -6. Place built packages in `dist/` directory - -### Manual build (if needed): - -```bash -mamba activate ccc-gpu -python -m pip install --upgrade build auditwheel -python -m build -# Fix wheel tags if needed -auditwheel repair dist/*.whl --plat-tag manylinux_2_17_x86_64 --wheel-dir dist/ -``` - -## Publishing to Test PyPI - -Test PyPI is a separate instance for testing package uploads without affecting the main index. - -### Upload to test PyPI: - -```bash -./scripts/pypi/10-upload_to_test_pypi.sh -``` - -This script will: -1. Activate the conda environment -2. Check package integrity with `twine check` -3. Upload to test PyPI -4. Provide installation instructions - -### Manual upload (if needed): - -```bash -mamba activate ccc-gpu -python -m twine upload --repository testpypi dist/* -``` - -### Installing from test PyPI: - -```bash -pip install --index-url https://test.pypi.org/simple/ \ - --extra-index-url https://pypi.org/simple/ \ - cccgpu -``` - -Note: The `--extra-index-url` is needed to install dependencies from the main PyPI. - -## Publishing to Production PyPI - -Once tested, publish to the main PyPI: - -```bash -mamba activate ccc-gpu -python -m twine upload dist/* -``` - -### Installing from PyPI: - -```bash -pip install cccgpu -``` - -## Version Management - -Before building a new release: - -1. Update version in `pyproject.toml`: - ```toml - [project] - version = "0.2.1" # Increment as needed - ``` - -2. Tag the release: - ```bash - git tag -a v0.2.1 -m "Release version 0.2.1" - git push origin v0.2.1 - ``` - -## Troubleshooting - -### CUDA/GPU Dependencies - -The package requires CUDA toolkit for building. Users installing from PyPI need: -- CUDA toolkit installed -- Compatible GPU -- Appropriate CUDA version matching the build - -### Build Errors - -If build fails with CUDA errors: -1. Ensure CUDA toolkit is installed and accessible -2. Check `CUDAToolkit_ROOT` environment variable -3. Verify GPU and CUDA compatibility - -### Authentication Issues - -If upload fails with authentication errors: -1. Verify API tokens in `~/.pypirc` -2. Use `__token__` as username with API tokens -3. Ensure tokens have upload permissions - -### Package Already Exists - -If version already exists: -1. Increment version in `pyproject.toml` -2. Rebuild the package -3. Upload the new version - -## CI/CD Integration - -For automated publishing, set these secrets in your CI/CD system: -- `TWINE_USERNAME`: Set to `__token__` -- `TWINE_PASSWORD`: Your PyPI API token -- `TWINE_REPOSITORY_URL`: https://test.pypi.org/legacy/ (for test) - -Example GitHub Actions workflow: - -```yaml -- name: Build and publish - env: - TWINE_USERNAME: __token__ - TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} - run: | - python -m build - python -m twine upload dist/* -``` - -## Best Practices - -1. **Always test on test PyPI first** before publishing to production -2. **Use API tokens** instead of passwords for security -3. **Semantic versioning**: Follow MAJOR.MINOR.PATCH convention -4. **Check package**: Run `twine check dist/*` before uploading -5. **Clean builds**: Remove old builds before creating new ones -6. **Document changes**: Update CHANGELOG for each release -7. **Test installation**: Verify package installs correctly after publishing \ No newline at end of file +Build the docs locally with `cd docs && make html` and open +`build/html/index.html`. diff --git a/docs/requirements.txt b/docs/requirements.txt index 24d8b68a..13fb262f 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,12 @@ # Documentation build dependencies for Read the Docs sphinx>=7.1.0 sphinx-rtd-theme>=2.0.0 + +# Runtime deps needed for autodoc to import the `ccc` package on Read the Docs +# (the compiled CUDA extension and CUDA-only libs are mocked in conf.py instead). +numpy +scipy +numba +pandas +scikit-learn +pyyaml diff --git a/docs/source/_static/.gitkeep b/docs/source/_static/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/docs/source/api.rst b/docs/source/api.rst new file mode 100644 index 00000000..86e35c23 --- /dev/null +++ b/docs/source/api.rst @@ -0,0 +1,23 @@ +API Reference +============= + +The public entry point is the ``ccc()`` function. Two implementations share the +same signature and return contract: + +- :func:`ccc.coef.impl_gpu.ccc` -- the GPU-accelerated implementation (recommended + for large inputs; coefficient values are computed in float32). +- :func:`ccc.coef.impl.ccc` -- the reference CPU implementation (float64). + +Both accept a ``pvalue_n_perms`` argument to estimate a one-sided permutation +p-value alongside each coefficient. See :doc:`usage` (the "Computing p-values" +section) for the method, interpretation, and cost. + +GPU implementation +------------------ + +.. autofunction:: ccc.coef.impl_gpu.ccc + +CPU implementation +------------------ + +.. autofunction:: ccc.coef.impl.ccc diff --git a/docs/source/benchmarking.rst b/docs/source/benchmarking.rst new file mode 100644 index 00000000..805f4702 --- /dev/null +++ b/docs/source/benchmarking.rst @@ -0,0 +1,109 @@ +Benchmarking +============ + +CCC-GPU ships a small benchmarking command, ``ccc-gpu-bench`` (also runnable as +``python -m ccc.bench``), for reproducible, structured performance measurement +decoupled from the pytest suite. It is installed with the package. + +Modes +----- + +``ccc-gpu-bench`` has three sub-commands: + +``coef`` + End-to-end GPU-vs-CPU coefficient benchmark, sweeping a grid over feature + counts, sample (object) counts, and CPU worker counts (``n_jobs``). Supports + ``--pvalue-n-perms`` and ``--return-parts`` variants and ``--gpu-only`` / + ``--cpu-only``. + +``ari`` + Kernel-level Adjusted Rand Index GPU-vs-CPU micro-benchmark. + +``scaling`` + CPU parallelism (``n_jobs``) scaling for the CPU implementation. + +Quick start +----------- + +.. code-block:: bash + + # Fast sanity sweep (finishes in seconds); prints JSON Lines to stdout + ccc-gpu-bench coef --preset smoke + + # Kernel-level ARI smoke benchmark + ccc-gpu-bench ari --preset smoke + + # CPU n_jobs scaling smoke benchmark + ccc-gpu-bench scaling --preset smoke + +Common options (all modes): + +- ``--preset {smoke,paper}`` -- named grid; explicit grid flags override individual axes. +- ``-o, --output PATH`` -- output file (default: stdout; ``-`` also means stdout). +- ``--format {jsonl,csv}`` -- output format (default: ``jsonl``). +- ``--seed INT`` -- random seed (default: 42). +- ``--repeats INT`` -- timed repeats per case (default: 3). +- ``--warmup INT`` -- untimed warmup calls (default: 1). +- ``--profile`` -- add a per-category CPU profile (and an ``nsys`` hint for GPU runs). + +Grid flags for ``coef`` / ``scaling``: ``--features``, ``--samples``, +``--n-jobs`` (each accepts multiple values). For ``ari``: ``--n-features``, +``--n-parts``, ``--n-objs``, ``--k``. + +Output schema +------------- + +Records are written incrementally (one per grid case), so an interrupted sweep +still leaves a parseable file. In ``jsonl`` format each line is one JSON object; +in ``csv`` format the header is taken from the first record and any nested value +is JSON-encoded. + +Every record embeds environment metadata from :func:`ccc.bench.env.capture_environment` +(``package_version``, ``python_version``, ``platform``, ``cpu_model``, +``cpu_count``, ``gpu_present``, ``gpu_name``, ``gpu_driver``, ``cuda_runtime``) +so results are self-describing and comparable across machines and commits. + +A ``coef`` record additionally contains: + +.. list-table:: + :header-rows: 1 + + * - Field + - Meaning + * - ``mode`` + - ``"coef"`` + * - ``timestamp`` + - ISO-8601 time the case finished + * - ``n_features`` / ``n_samples`` / ``n_jobs`` + - grid point for this case + * - ``pvalue_n_perms`` / ``return_parts`` + - variant flags (``null`` / ``false`` when unused) + * - ``seed`` / ``repeats`` / ``warmup`` + - run configuration + * - ``n_coefficients`` + - number of pairwise coefficients computed (``n*(n-1)/2``) + * - ``gpu_time_min_s`` / ``gpu_time_mean_s`` + - GPU timing (min and mean over repeats; ``null`` with ``--cpu-only``) + * - ``cpu_time_min_s`` / ``cpu_time_mean_s`` + - CPU timing (``null`` with ``--gpu-only``) + * - ``speedup`` + - ``cpu_time_min_s / gpu_time_min_s`` (``null`` if a side is missing) + +The ``ari`` and ``scaling`` records follow the same pattern with mode-specific +grid fields. + +Reproducing the README performance table +----------------------------------------- + +The speedup table in the README / :doc:`introduction` was produced with the +``paper`` preset of the ``coef`` mode (a feature sweep at 1000 fixed samples). +On the reference GPU box: + +.. code-block:: bash + + ccc-gpu-bench coef --preset paper --format csv -o coef_paper.csv + +The ``paper`` grid runs a large feature sweep and its CPU reference for the +biggest cases takes minutes per point, so run it on the dedicated GPU machine. +The ``speedup`` column of the resulting records corresponds to the +"CCC-GPU vs. CCC" column of the table. diff --git a/docs/source/conf.py b/docs/source/conf.py index 82bb89a6..d1186419 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -5,18 +5,27 @@ import os import sys +from importlib.metadata import PackageNotFoundError +from importlib.metadata import version as _pkg_version -# Add the project root to Python path for autodoc -sys.path.insert(0, os.path.abspath('../../')) +# Add the package source dir (libs/) to the path so autodoc can import `ccc`. +sys.path.insert(0, os.path.abspath('../../libs')) # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information project = 'CCC-GPU' -copyright = '2025, Milton Pividori, Haoyu Zhang, Kevin Fotso' -author = 'Milton Pividori, Haoyu Zhang, Kevin Fotso' -release = '0.2.0' -version = '0.2.0' +copyright = '2025-2026, Milton Pividori, Haoyu Zhang, Kevin Fotso, Marc Subirana-Granés' +author = 'Milton Pividori, Haoyu Zhang, Kevin Fotso, Marc Subirana-Granés' + +# Single-source the version from the installed package metadata (which comes +# from `[project].version` in pyproject.toml). Falls back to a literal when the +# package is not installed in the docs-build environment (e.g. Read the Docs). +try: + release = _pkg_version('cccgpu') +except PackageNotFoundError: + release = '0.2.4' +version = release # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration @@ -89,9 +98,9 @@ # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output -# The theme to use for HTML and HTML Help pages. -html_theme = 'haiku' -# Theme options for haiku theme (much simpler) +# The theme to use for HTML and HTML Help pages. sphinx_rtd_theme is the theme +# installed by docs/requirements.txt and used on Read the Docs. +html_theme = 'sphinx_rtd_theme' html_theme_options = {} # Add any paths that contain custom static files (such as style sheets) here, @@ -167,8 +176,11 @@ on_rtd = os.environ.get('READTHEDOCS', None) == 'True' if on_rtd: - # Don't try to import modules that require GPU/CUDA when building docs - autodoc_mock_imports = ['ccc_cuda_ext', 'cupy', 'numba', 'rmm'] + # Mock only the compiled CUDA extension and CUDA-only libraries, which cannot + # be built/installed on the Read the Docs runners. The pure-Python runtime + # deps (numpy, numba, scipy, ...) are installed via docs/requirements.txt so + # autodoc can import and render the real `ccc` docstrings. + autodoc_mock_imports = ['ccc_cuda_ext', 'cupy', 'rmm'] # MathJax configuration for mathematical expressions mathjax3_config = { diff --git a/docs/source/development/bindings.rst b/docs/source/development/bindings.rst index e614611b..ec749dfe 100644 --- a/docs/source/development/bindings.rst +++ b/docs/source/development/bindings.rst @@ -1,4 +1,37 @@ -Bindings -=============== +Python/CUDA Bindings +==================== -TBD +The GPU coefficient computation lives in a compiled extension module, +``ccc_cuda_ext``, built from the CUDA C++ sources in ``libs/ccc_cuda_ext/`` and +exposed to Python with `pybind11 `_. + +Layout +------ + +- ``binder.cu`` -- the pybind11 module definition (``PYBIND11_MODULE``). It + declares the Python-visible functions and converts between NumPy arrays and + the device buffers used by the kernels. +- ``coef.cu`` / ``coef.cuh`` -- ``compute_coef``, the primary entry point used by + :func:`ccc.coef.impl_gpu.ccc`. Given the precomputed partitions it runs the + ARI kernels on the GPU and returns the coefficients, the maximizing partition + indexes, and (optionally) permutation p-values. +- ``metrics.cu`` / ``metrics.cuh`` -- the Adjusted Rand Index kernels. +- ``math.cuh`` -- small device-side helpers. + +How it is built +--------------- + +The extension is compiled by CMake (see the root ``CMakeLists.txt``) and driven +by ``scikit-build-core`` during ``pip install``. ``pybind11_add_module`` builds +the shared object, and the CUDA architectures the wheel targets are set via +``-DCMAKE_CUDA_ARCHITECTURES`` in ``pyproject.toml`` (``[tool.scikit-build]``). +See :doc:`build_cuda_module` for a step-by-step local build. + +Calling convention +------------------ + +``ccc.coef.impl_gpu`` prepares the per-feature partitions on the host (NumPy) and +hands them to ``ccc_cuda_ext.compute_coef`` together with the feature/cluster/ +object counts and the ``return_parts`` / ``pvalue_n_perms`` flags. The extension +returns NumPy arrays, which the Python layer reshapes into the polymorphic return +value documented in :doc:`../api`. diff --git a/docs/source/development/package_publishing.rst b/docs/source/development/package_publishing.rst index 9a525512..f442bfc2 100644 --- a/docs/source/development/package_publishing.rst +++ b/docs/source/development/package_publishing.rst @@ -143,6 +143,38 @@ Before building a new release: git tag -a v0.2.1 -m "Release version 0.2.1" git push origin v0.2.1 +Release checklist +----------------- + +``pyproject.toml`` ``[project].version`` is the single source of truth for the +version: ``ccc.__version__`` and the Sphinx docs read it from the installed +package metadata, so they update automatically. A few files are **not** +auto-synced and must be updated by hand at release time: + +1. **Version** + + - Bump ``[project].version`` in ``pyproject.toml``. + - Update ``version:`` in ``CITATION.cff`` to match. + - Update the fallback literal in ``libs/ccc/__init__.py`` (used only when the + package is run from a source checkout without an install) and the fallback + in ``docs/source/conf.py``. + +2. **Citation / license** + + - Keep the citation consistent across ``CITATION.cff``, ``README.md``, and + ``docs/source/introduction.rst`` (authors, journal/year, DOI). + - Keep the license consistent across the ``LICENSE`` file (authoritative), + ``CITATION.cff``, ``README.md``, and the ``pyproject.toml`` ``license`` field. + +3. **Support claims** + + - If the CUDA architecture list (``CMAKE_CUDA_ARCHITECTURES``) or the + Python/CUDA support range changes, update the README badges/text, the docs + ``installation`` page, and the ``pyproject.toml`` classifiers together. + +4. Build, run ``twine check dist/*``, publish to Test PyPI, verify, then publish + to production PyPI. + Platform Tag Compatibility -------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index 8ab8b0ad..480916bb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,5 +17,7 @@ CCC is based on clustering data points using individual features, and then compu installation introduction usage + api + benchmarking algorithms development/index diff --git a/docs/source/installation.rst b/docs/source/installation.rst index 70b1065c..0f8c2b83 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -5,49 +5,41 @@ Prerequisites ----------------- Hardware requirements: -- GPU with CUDA Compute Capability 8.6 or higher + +- NVIDIA GPU with CUDA compute capability 7.5 or higher (the wheels ship native + code for 7.5, 8.0, 8.6, 8.9, and 9.0) + Software requirements: -- OS: Linux x86_64 + +- OS: Linux x86_64 (glibc 2.28 or later) +- Python 3.10 to 3.14 +- NVIDIA driver providing CUDA 12.0 or higher Quick Install with pip ---------------------- -The ``cccgpu`` package is now available for installation via pip from test PyPI. - -However, note that cccgpu depends on `libstdc++`. For a smooth installation, we recommend using a wrapper conda environment to install it: +The ``cccgpu`` package is available on PyPI: .. code-block:: bash - conda create -n ccc-gpu-toolchain-env -c conda-forge python=3.10 pip pytest libstdcxx-ng && conda activate ccc-gpu-toolchain-env - -Support for more Python versions and architectures requires extra effort, and will be added soon. + pip install cccgpu -Then, install the package in the toolchain environment: +``cccgpu`` depends on ``libstdc++``. If your system copy is too old, install it +into a conda environment first, for example: .. code-block:: bash - pip install --index-url https://test.pypi.org/simple/ \ - --extra-index-url https://pypi.org/simple/ \ - --only-binary=cccgpu cccgpu + conda create -n ccc-gpu -c conda-forge python=3.12 pip pytest libstdcxx-ng + conda activate ccc-gpu + pip install cccgpu -Then try running some tests to verify the installation: +Then verify the installation: .. code-block:: bash python -c "from ccc.coef.impl_gpu import ccc as ccc_gpu; import numpy as np; print(ccc_gpu(np.random.rand(100), np.random.rand(100)))" -**Command options explained:** - -- ``--index-url https://test.pypi.org/simple/``: Specifies test PyPI as the primary package index to search for ``cccgpu`` -- ``--extra-index-url https://pypi.org/simple/``: Adds the main PyPI repository as a fallback to install dependencies (numpy, scipy, numba, etc.) that may not be available on test PyPI -- ``--only-binary=cccgpu``: Ensures that only binary wheels are installed for ``cccgpu`` package, so you don't need to compile it from source -- ``cccgpu``: The package name to install - -.. note:: - This installs from test PyPI while the package is in testing phase. Once stable, it will be available from the main PyPI repository with a simple ``pip install cccgpu`` command. - - Install from Source ------------------- diff --git a/docs/source/introduction.rst b/docs/source/introduction.rst index edb7b1b5..744cf5b9 100644 --- a/docs/source/introduction.rst +++ b/docs/source/introduction.rst @@ -124,15 +124,13 @@ If you use CCC-GPU in your research, please cite: .. code-block:: bibtex - @article{zhang2025cccgpu, - title={CCC-GPU: A graphics processing unit (GPU)-optimized nonlinear correlation coefficient for large transcriptomic analyses}, - author={Zhang, Hang and Fotso, Kenneth and Pividori, Milton}, - journal={bioRxiv}, - year={2025}, - publisher={Cold Spring Harbor Laboratory}, - doi={10.1101/2025.06.03.657735}, - pmid={40502087}, - pmcid={PMC12157546} + @article{zhang2026cccgpu, + title={CCC-GPU: A graphics processing unit (GPU)-accelerated nonlinear correlation coefficient for large-scale transcriptomic analyses}, + author={Zhang, Haoyu and Fotso, Kevin and Subirana-Gran{\'e}s, Marc and Pividori, Milton}, + journal={Bioinformatics}, + year={2026}, + pages={btag068}, + doi={10.1093/bioinformatics/btag068}, } The original CCC implementation and methodology can be found at: https://github.com/greenelab/ccc diff --git a/docs/source/usage.rst b/docs/source/usage.rst index ab1ca1ce..cb16831c 100644 --- a/docs/source/usage.rst +++ b/docs/source/usage.rst @@ -101,9 +101,28 @@ Custom Clustering Parameters ccc_value = ccc(data1, data2, internal_n_clusters=[2, 3, 4, 5, 6]) print(f"CCC with cluster range [2-6]: {ccc_value:.3f}") -P-value Computation +Parallel Processing ~~~~~~~~~~~~~~~~~~~ +.. code-block:: python + + from ccc.coef.impl_gpu import ccc + import numpy as np + + # Generate larger dataset + np.random.seed(456) + data = np.random.randn(50, 1000) # 50 features, 1000 samples + + # Use multiple CPU cores for preprocessing + correlations = ccc(data, n_jobs=4) + print(f"Computed {len(correlations)} pairwise correlations") + +Computing p-values +------------------ + +Passing ``pvalue_n_perms`` (an integer greater than 0) asks ``ccc()`` to estimate +a p-value for each coefficient in addition to the coefficient itself. + .. code-block:: python from ccc.coef.impl_gpu import ccc @@ -114,25 +133,53 @@ P-value Computation x = np.random.randn(300) y = x + np.random.randn(300) * 0.5 - # Compute CCC with p-value using permutations + # Compute CCC with a permutation p-value ccc_value, p_value = ccc(x, y, pvalue_n_perms=1000) print(f"CCC: {ccc_value:.3f}, p-value: {p_value:.3f}") -Parallel Processing -~~~~~~~~~~~~~~~~~~~ +Method +~~~~~~ -.. code-block:: python +The p-value comes from a **one-sided permutation test**. One of the two +features' partitions is randomly shuffled ``pvalue_n_perms`` times and the CCC is +recomputed for each shuffle, building an empirical null distribution of +coefficients under the hypothesis of no association. The p-value is the fraction +of permuted coefficients at least as large as the observed one, with add-one +(Laplace) smoothing: - from ccc.coef.impl_gpu import ccc - import numpy as np +.. math:: - # Generate larger dataset - np.random.seed(456) - data = np.random.randn(50, 1000) # 50 features, 1000 samples + p = \frac{\#\{\text{permuted CCC} \ge \text{observed CCC}\} + 1}{\text{pvalue\_n\_perms} + 1} - # Use multiple CPU cores for preprocessing - correlations = ccc(data, n_jobs=4) - print(f"Computed {len(correlations)} pairwise correlations") +Interpretation +~~~~~~~~~~~~~~~ + +- The test is one-sided: a **smaller** p-value is stronger evidence that the + observed association is not due to chance. +- The ``+1`` terms mean the smallest p-value the test can report is + ``1 / (pvalue_n_perms + 1)`` (about ``1e-3`` for 999 permutations). Choose + ``pvalue_n_perms`` for the resolution you need -- if you want to distinguish + p-values below ``0.001``, you need more permutations. +- For a 2d input, the returned object becomes a ``(coefficients, p-values)`` + tuple of arrays aligned by pair; for a single 1d pair it is a + ``(coefficient, p-value)`` scalar tuple. + +.. note:: + + The GPU permutation p-values were corrected in the ``fix-cuda-correctness`` + change, so values may differ from pre-fix releases. The GPU and CPU + implementations use the same estimator. + +Cost +~~~~ + +Permutation p-values are expensive. For a 2d input the p-value is computed +independently for every one of the ``n * (n - 1) / 2`` feature pairs, and each +pair costs ``pvalue_n_perms`` extra CCC evaluations -- roughly +``n * (n - 1) / 2 * pvalue_n_perms`` additional coefficient computations in +total. This can be orders of magnitude more expensive than the point estimate +alone, so prefer small inputs or a modest permutation count, and parallelize the +permutation work with ``n_jobs``. Debug Logging ------------- @@ -161,6 +208,6 @@ Performance Tips 1. **Large Datasets**: CCC-GPU performs best on datasets with 1000+ features 2. **Memory Usage**: Monitor GPU memory usage for very large datasets 3. **Batch Processing**: For extremely large datasets, consider processing in batches -4. **CUDA Architecture**: Ensure your GPU supports the compiled CUDA architecture (75+) +4. **CUDA Architecture**: Ensure your GPU has CUDA compute capability 7.5 or newer (the wheels ship native code for 7.5, 8.0, 8.6, 8.9, and 9.0) For more examples, refer to the `original CCC repository `_. diff --git a/environment/README.md b/environment/README.md new file mode 100644 index 00000000..29d6286b --- /dev/null +++ b/environment/README.md @@ -0,0 +1,41 @@ +# Conda environments + +| File | Purpose | +|------|---------| +| `environment-gpu.yml` | **Source of truth** for the development / CI environment: Python 3.12, NumPy 2.x, numba ≥0.61, the CUDA 12.5 toolchain, pybind11 3.x, and the test + docs tooling needed to build the extension and run the suite. This is the single source file that `../conda-lock.yml` is generated from. | +| `environment-dev.yaml` | Tiny helper env (`sphinx`, `mamba`, `conda-lock`) for building the docs and regenerating the lock without polluting `base`. | + +The reproducible environment above deliberately excludes the research/analysis +dependencies (`matplotlib`, `seaborn`, `upsetplot`, `ipython`, `minepy`, +`requests`). Those back the in-repo `ccc.plots`/`methods`/`giant`/`corr` modules, +which are **not** part of the published wheel; install them separately in a +source checkout when running the `analysis/` notebooks. The former +`environment-benchmark.yaml` and `environment-toolchain.yaml` files were removed +to reduce drift. The only published package extra is `test` (`pip install ".[test]"`). + +## Regenerating the lock file + +`conda-lock.yml` is generated from `environment-gpu.yml` (its single declared +source). To re-solve the whole environment after changing a pin: + +```bash +# needs conda-lock (see environment-dev.yaml, or `pipx install conda-lock`) +conda-lock --file environment/environment-gpu.yml --conda mamba --lockfile conda-lock.yml +``` + +To bump a single package to the newest version allowed by the source pins: + +```bash +conda-lock lock --lockfile conda-lock.yml --update +``` + +Install the locked environment with: + +```bash +conda-lock install --name ccc-gpu conda-lock.yml # add `--conda mamba` for speed +``` + +> Note: solving a full CUDA-bundled environment can take several minutes. After +> changing channels or pins, regenerate from scratch (delete `conda-lock.yml` +> first) rather than using `--update`, which refuses to run across channel +> changes. diff --git a/environment/environment-benchmark.yaml b/environment/environment-benchmark.yaml deleted file mode 100644 index 86d8aa71..00000000 --- a/environment/environment-benchmark.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# Environment for benchmarking and profiling ccc-gpu -# -# Usage: -# conda env create -f environment/environment-benchmark.yaml -# conda activate ccc-gpu-benchmark -# -name: ccc-gpu-benchmark -channels: - - conda-forge -dependencies: - - python=3.10 - - numpy=1.26.* - - pandas=2.2.* - - matplotlib=3.* - - jupyter - - ipykernel - - numba=0.60.* - - scipy=1.15.* - - seaborn - - papermill - - pip - - pip: - - cccgpu -platforms: - - linux-64 diff --git a/environment/environment-gpu.yml b/environment/environment-gpu.yml index 33e46ba9..457b5d1f 100644 --- a/environment/environment-gpu.yml +++ b/environment/environment-gpu.yml @@ -1,29 +1,46 @@ -# Environment for publish ccc-gpu via conda, bundled with CUDA +# Source environment for the ccc-gpu development / build / test / docs stack, +# bundled with CUDA. This file is the single source of truth for `conda-lock.yml` +# (regenerate with the command in environment/README.md). Pins reflect the tested +# modern stack: Python 3.12, NumPy 2.x, numba >=0.61, CUDA 12.x, pybind11 3.x. +# +# Scope: this is the reproducible environment for building the extension and +# running the tests + docs. The research/analysis modules (ccc.plots/methods/ +# giant/corr) are NOT part of this env or the published wheel; install their +# dependencies (matplotlib/seaborn/upsetplot/ipython/minepy/requests) separately +# in a source checkout when running the `analysis/` notebooks. name: ccc-gpu channels: - conda-forge - nvidia dependencies: - - mamba - - conda-lock - - sphinx - - pip - - minepy - - cmake>=3.15 - - cuda=12.5 # We can refer to Cupy to see how they handle this CUDA dependency, that way we can make ccc-gpu also a pip package - - cupy=13.* - - python=3.10 - - ipython=8.* - - numba=0.60.* - - numpy=1.26.* - - pandas=2.2.* + - python=3.12.* + # --- build toolchain --- + - cmake>=3.26 + - ninja + - pybind11=3.* + - scikit-build-core>=0.10 + - gcc_linux-64=12.* + - gxx_linux-64=12.* + - spdlog - pre-commit=4.* - - pybind11=2.* + # --- CUDA toolkit (nvcc for building) + GPU runtime --- + - cuda-toolkit=12.5.* + - cuda-version=12.5 + - cupy + # --- core runtime deps --- + - numpy=2.* + - numba>=0.61 + - pandas>=2.2 + - scipy>=1.13 + - scikit-learn>=1.5 + - pyyaml + # --- test + docs --- - pytest=8.* - - scipy=1.15.* - - scikit-learn=1.6.* - - seaborn=0.13.* - - upsetplot=0.9.* + - sphinx>=7.1 + - sphinx-rtd-theme>=2.0 + # --- lock tooling --- + - conda-lock + - mamba + - pip platforms: - linux-64 - # - win-64 diff --git a/environment/environment-toolchain.yaml b/environment/environment-toolchain.yaml deleted file mode 100644 index 978fd022..00000000 --- a/environment/environment-toolchain.yaml +++ /dev/null @@ -1,11 +0,0 @@ -# Minimal environment containing python, pip to help verify the installation from pypi, without cuda bundled -name: ccc-gpu-toolchain-env -channels: - - conda-forge -dependencies: - - pip - - python=3.11 - - pytest - - libstdcxx-ng -platforms: - - linux-64 diff --git a/libs/ccc/__init__.py b/libs/ccc/__init__.py index f98e1d65..04668859 100644 --- a/libs/ccc/__init__.py +++ b/libs/ccc/__init__.py @@ -1,4 +1,11 @@ from __future__ import annotations -# Remember to change also setup.py with the version here -__version__ = "0.2.2" +from importlib.metadata import PackageNotFoundError, version + +try: + # Single source of truth: the installed distribution's version, which comes + # from `[project].version` in pyproject.toml. + __version__ = version("cccgpu") +except PackageNotFoundError: # pragma: no cover - source tree without an install + # Fallback for running from a source checkout that was never installed. + __version__ = "0.2.4" diff --git a/libs/ccc/coef/impl.py b/libs/ccc/coef/impl.py index f2d92fbd..6849d9fc 100644 --- a/libs/ccc/coef/impl.py +++ b/libs/ccc/coef/impl.py @@ -301,7 +301,7 @@ def get_coords_from_index(n_obj: int, idx: int) -> tuple[int]: (such as genes), a condensed 1d array can be created with pairwise comparisons between genes, as well as a squared symmetric matrix. This function receives the number of objects and the index of the condensed - array, and returns the coordiates of the squared symmetric matrix. + array, and returns the coordinates of the squared symmetric matrix. Args: n_obj: the number of objects. @@ -504,7 +504,7 @@ def cdist_func(x, y): continue # compare all partitions of one object to the all the partitions - # of the other object, and get the maximium ARI + # of the other object, and get the maximum ARI max_ari_list[idx], max_part_idx_list[idx] = compute_ccc( obji_parts, objj_parts, cdist_func ) @@ -592,7 +592,7 @@ def ccc( n_jobs: int = 1, pvalue_n_perms: int = None, partitioning_executor: str = "thread", -) -> tuple[NDArray[float], NDArray[float], NDArray[np.uint64], NDArray[np.int16]]: +) -> float | NDArray[np.float64] | tuple: """ This is the main function that computes the Clustermatch Correlation Coefficient (CCC) between two arrays. The implementation supports numerical @@ -616,8 +616,29 @@ def ccc( None will use all available cores (`os.cpu_count()`), and negative values will use `os.cpu_count() + n_jobs` (exception will be raised if this expression yields a result less than 1). Default is 1. - pvalue_n_perms: if given, it computes the p-value of the - coefficient using the given number of permutations. + pvalue_n_perms: if given (an integer > 0), also estimate a p-value for + each coefficient with a one-sided permutation test. One of the two + features' partitions is randomly shuffled ``pvalue_n_perms`` times + and the CCC is recomputed each time; the p-value is the fraction of + permuted coefficients greater than or equal to the observed one, with + add-one (Laplace) smoothing:: + + p = (#{permuted CCC >= observed CCC} + 1) / (pvalue_n_perms + 1) + + The test is one-sided: a *smaller* p-value is stronger evidence that + the association is not due to chance. Because of the ``+1`` in the + numerator and denominator, the smallest resolvable p-value is + ``1 / (pvalue_n_perms + 1)`` (e.g. ~1e-3 for 999 permutations), so + pick ``pvalue_n_perms`` for the resolution you need. If ``None`` or + ``0`` (the default), no p-value is computed. + + Cost warning: for a 2d input the p-value is computed independently + for every one of the ``n * (n - 1) / 2`` feature pairs, each costing + ``pvalue_n_perms`` extra CCC evaluations -- a total of about + ``n * (n - 1) / 2 * pvalue_n_perms`` additional coefficient + computations, which can be orders of magnitude more expensive than + the point estimate alone. Prefer small inputs or a modest permutation + count, and parallelize with ``n_jobs``. partitioning_executor: Executor type used for partitioning the data. It can be either "thread" (default) or "process". If "thread", it will use ThreadPoolExecutor for parallelization, which uses less memory. If @@ -626,22 +647,34 @@ def ccc( Returns: - If returns_parts is True, then it returns a tuple with three values: - 1) the coefficients, 2) the partitions indexes that maximized the coefficient - for each object pair, and 3) the partitions for all objects. - If return_parts is False, only CCC values are returned. - - cm_values: if x is 2d np.array with x.shape[0] > 2, then cm_values is a 1d - condensed array of pairwise coefficients. It has size (n * (n - 1)) / 2, - where n is the number of rows in x. If x and y are given, and they are 1d, - then cm_values is a scalar. The CCC is always between 0 and 1 (inclusive). If - any of the two variables being compared has no variation (all values are the - same), the coefficient is not defined (np.nan). If pvalue_n_permutations is - an integer greater than 0, then cm_vlaues is a tuple with two elements: - the first element are the CCC values, and the second element are the p-values - using pvalue_n_permutations permutations. - - max_parts: an array with n * (n - 1)) / 2 rows (one for each object + The return type is polymorphic; it depends on the input shape and on the + ``pvalue_n_perms`` / ``return_parts`` flags: + + - 1d ``x`` and ``y`` (a single feature pair): the coefficient is a scalar + ``float``. + - 2d ``x`` (``n`` features/rows): the coefficients are a 1d condensed + array ``cm_values`` of length ``n * (n - 1) / 2`` (the upper triangle of + the pairwise matrix, compatible with + ``scipy.spatial.distance.squareform``). + + When ``pvalue_n_perms`` is an integer greater than 0, the coefficient + result is replaced by a 2-tuple ``(cm_values, cm_pvalues)`` whose elements + have matching shapes (two scalars for a single pair; two 1d arrays for a + 2d input). + + When ``return_parts`` is True, a 3-tuple ``(coefficients, max_parts, + parts)`` is returned instead of the coefficients alone -- and + ``coefficients`` is itself the ``(cm_values, cm_pvalues)`` tuple described + above when ``pvalue_n_perms`` was given. + + cm_values: the CCC coefficient(s). Each value is between 0 and 1 + (inclusive), or ``np.nan`` when one of the two variables has no + variation (all values are the same) so the coefficient is undefined. + + cm_pvalues: present only when ``pvalue_n_perms`` > 0. The one-sided + permutation p-value(s), aligned with ``cm_values`` (same shape). + + max_parts: an array with ``n * (n - 1) / 2`` rows (one for each object pair) and two columns. It has the indexes pointing to each object's partition (parts, see below) that maximized the ARI. If cm_values[idx] is nan, then max_parts[idx] will be meaningless. @@ -741,7 +774,7 @@ def ccc( cm_pvalues = np.full(n_features_comp, np.nan) # for each object pair being compared, max_parts has the indexes of the - # partitions that maximimized the ARI + # partitions that maximized the ARI max_parts = np.zeros((n_features_comp, 2), dtype=np.uint64) with ( diff --git a/libs/ccc/coef/impl_gpu.py b/libs/ccc/coef/impl_gpu.py index 9d720984..918de1ce 100644 --- a/libs/ccc/coef/impl_gpu.py +++ b/libs/ccc/coef/impl_gpu.py @@ -346,12 +346,16 @@ def ccc( n_jobs: int = 1, pvalue_n_perms: int = None, partitioning_executor: str = "thread", -) -> tuple[NDArray[float], NDArray[float], NDArray[np.uint64], NDArray[np.int16]]: +) -> float | NDArray[np.float64] | tuple: """ This is the main function that computes the Clustermatch Correlation Coefficient (CCC) between two arrays. The implementation supports numerical and categorical data. + This is the GPU-accelerated implementation; the coefficient computation runs + on the GPU (values are computed in float32, so they may differ from the CPU + implementation in ``ccc.coef.impl`` by a small tolerance). + Args: x: 1d or 2d numerical array with the data. NaN are not supported. If it is 2d, then the coefficient is computed for each pair of rows @@ -370,8 +374,30 @@ def ccc( None will use all available cores (`os.cpu_count()`), and negative values will use `os.cpu_count() + n_jobs` (exception will be raised if this expression yields a result less than 1). Default is 1. - pvalue_n_perms: if given, it computes the p-value of the - coefficient using the given number of permutations. + pvalue_n_perms: if given (an integer > 0), also estimate a p-value for + each coefficient with a one-sided permutation test (computed on the + GPU). One of the two features' partitions is randomly shuffled + ``pvalue_n_perms`` times and the CCC is recomputed each time; the + p-value is the fraction of permuted coefficients greater than or equal + to the observed one, with add-one (Laplace) smoothing:: + + p = (#{permuted CCC >= observed CCC} + 1) / (pvalue_n_perms + 1) + + The test is one-sided: a *smaller* p-value is stronger evidence that + the association is not due to chance. Because of the ``+1`` in the + numerator and denominator, the smallest resolvable p-value is + ``1 / (pvalue_n_perms + 1)`` (e.g. ~1e-3 for 999 permutations), so + pick ``pvalue_n_perms`` for the resolution you need. If ``None`` or + ``0`` (the default), no p-value is computed. (The GPU permutation + p-values were corrected in the ``fix-cuda-correctness`` change, so + values may differ from pre-fix releases.) + + Cost warning: for a 2d input the p-value is computed independently + for every one of the ``n * (n - 1) / 2`` feature pairs, each costing + ``pvalue_n_perms`` extra CCC evaluations -- a total of about + ``n * (n - 1) / 2 * pvalue_n_perms`` additional coefficient + computations, which can be orders of magnitude more expensive than + the point estimate alone. partitioning_executor: Executor type used for partitioning the data. It can be either "thread" (default) or "process". If "thread", it will use ThreadPoolExecutor for parallelization, which uses less memory. If @@ -380,22 +406,34 @@ def ccc( Returns: - If returns_parts is True, then it returns a tuple with three values: - 1) the coefficients, 2) the partitions indexes that maximized the coefficient - for each object pair, and 3) the partitions for all objects. - If return_parts is False, only CCC values are returned. - - cm_values: if x is 2d np.array with x.shape[0] > 2, then cm_values is a 1d - condensed array of pairwise coefficients. It has size (n * (n - 1)) / 2, - where n is the number of rows in x. If x and y are given, and they are 1d, - then cm_values is a scalar. The CCC is always between 0 and 1 (inclusive). If - any of the two variables being compared has no variation (all values are the - same), the coefficient is not defined (np.nan). If pvalue_n_permutations is - an integer greater than 0, then cm_vlaues is a tuple with two elements: - the first element are the CCC values, and the second element are the p-values - using pvalue_n_permutations permutations. - - max_parts: an array with n * (n - 1)) / 2 rows (one for each object + The return type is polymorphic; it depends on the input shape and on the + ``pvalue_n_perms`` / ``return_parts`` flags: + + - 1d ``x`` and ``y`` (a single feature pair): the coefficient is a scalar + ``float``. + - 2d ``x`` (``n`` features/rows): the coefficients are a 1d condensed + array ``cm_values`` of length ``n * (n - 1) / 2`` (the upper triangle of + the pairwise matrix, compatible with + ``scipy.spatial.distance.squareform``). + + When ``pvalue_n_perms`` is an integer greater than 0, the coefficient + result is replaced by a 2-tuple ``(cm_values, cm_pvalues)`` whose elements + have matching shapes (two scalars for a single pair; two 1d arrays for a + 2d input). + + When ``return_parts`` is True, a 3-tuple ``(coefficients, max_parts, + parts)`` is returned instead of the coefficients alone -- and + ``coefficients`` is itself the ``(cm_values, cm_pvalues)`` tuple described + above when ``pvalue_n_perms`` was given. + + cm_values: the CCC coefficient(s). Each value is between 0 and 1 + (inclusive), or ``np.nan`` when one of the two variables has no + variation (all values are the same) so the coefficient is undefined. + + cm_pvalues: present only when ``pvalue_n_perms`` > 0. The one-sided + permutation p-value(s), aligned with ``cm_values`` (same shape). + + max_parts: an array with ``n * (n - 1) / 2`` rows (one for each object pair) and two columns. It has the indexes pointing to each object's partition (parts, see below) that maximized the ARI. If cm_values[idx] is nan, then max_parts[idx] will be meaningless. @@ -496,7 +534,7 @@ def ccc( cm_pvalues = np.full(n_features_comp, np.nan) # for each object pair being compared, max_parts has the indexes of the - # partitions that maximimized the ARI + # partitions that maximized the ARI max_parts = np.zeros((n_features_comp, 2), dtype=np.uint64) with ( diff --git a/libs/ccc/plots.py b/libs/ccc/plots.py index 73680512..669d309b 100644 --- a/libs/ccc/plots.py +++ b/libs/ccc/plots.py @@ -46,13 +46,37 @@ import seaborn as sns from IPython.display import display from scipy import stats -from seaborn.distributions import _freedman_diaconis_bins from upsetplot import UpSet from ccc.coef import ccc from ccc.utils import human_format +def _freedman_diaconis_bins(a: np.ndarray) -> int: + """Number of histogram bins from the Freedman-Diaconis rule. + + Public reimplementation of the (private) ``seaborn.distributions. + _freedman_diaconis_bins`` helper so we do not depend on a seaborn internal. + Bin width is ``2 * IQR / n**(1/3)``; falls back to ``sqrt(n)`` bins when the + IQR is zero. + + Args: + a: 1d array of values. + + Returns: + The suggested number of bins (at least 1). + """ + a = np.asarray(a) + if len(a) < 2: + return 1 + iqr = np.subtract(*np.nanpercentile(a, [75, 25])) + h = 2 * iqr / (len(a) ** (1 / 3)) + # fall back to sqrt(n) bins if the IQR (and thus bin width) is zero + if h == 0: + return int(np.sqrt(a.size)) + return int(np.ceil((a.max() - a.min()) / h)) + + def plot_histogram( data: pd.DataFrame, figsize: tuple = (10, 7), diff --git a/pyproject.toml b/pyproject.toml index 8a8902d8..b43c39d7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,11 +1,9 @@ [build-system] requires = [ - "scikit-build-core", - "pybind11>=2.11.0", - "cmake>=4.0", + "scikit-build-core>=0.10", + "pybind11>=2.13,<4", + "cmake>=3.26", "ninja", - "setuptools", - "wheel", ] build-backend = "scikit_build_core.build" @@ -15,7 +13,9 @@ version = "0.2.4" description = "The Clustermatch Correlation Coefficient (CCC) with GPU acceleration" readme = "README.md" requires-python = ">=3.10" -license = { text = "BSD-2-Clause Plus Patent" } +# SPDX license expression (PEP 639); matches the LICENSE file (BSD-2-Clause Plus +# Patent). Per PEP 639 the deprecated `License ::` trove classifier is omitted. +license = "BSD-2-Clause-Patent" authors = [ { name = "Milton Pividori", email = "milton.pividori@cuanschutz.edu" }, { name = "Haoyu Zhang", email = "haoyu_z@outlook.com" }, @@ -23,10 +23,15 @@ authors = [ dependencies = ["numpy", "scipy", "numba", "pandas", "scikit-learn", "pyyaml"] classifiers = [ "Programming Language :: Python :: 3", - "License :: OSI Approved :: BSD License", - "Operating System :: OS Independent", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", + "Operating System :: POSIX :: Linux", "Development Status :: 5 - Production/Stable", "Environment :: Console", + "Environment :: GPU :: NVIDIA CUDA :: 12", ] [project.urls] @@ -34,16 +39,26 @@ Homepage = "https://github.com/pivlab/ccc-gpu" Issues = "https://github.com/pivlab/ccc-gpu/issues" [project.optional-dependencies] -test = ["pytest", "pytest-cov"] +# Test-only dependencies. +test = ["pytest"] +# NOTE: the research/plotting modules (ccc.plots/methods/giant/corr) are +# intentionally NOT part of the published wheel (see wheel.exclude below); they +# live in the repo for the `analysis/` work. Their dependencies +# (matplotlib/seaborn/upsetplot/ipython/minepy/requests) are provided by the +# development environment (`environment/environment-gpu.yml`), not as PyPI +# extras, since an extra cannot ship the excluded modules. [project.scripts] ccc-gpu-bench = "ccc.bench.cli:main" [tool.scikit-build] -# Configure scikit-build-core -cmake.version = ">=4.0" +# Configure scikit-build-core. +# Keep this CMake floor in sync with `cmake_minimum_required` in CMakeLists.txt. +cmake.version = ">=3.26" cmake.args = [ - "-DCMAKE_CUDA_ARCHITECTURES=75", # Adjust for your target CUDA architecture + # Fat wheel: native SASS for Turing (7.5) -> Hopper (9.0); PTX from 9.0 + # forward-compats newer GPUs. The lowest arch (7.5) is the documented floor. + "-DCMAKE_CUDA_ARCHITECTURES=75;80;86;89;90", ] build.verbose = true wheel.packages = ["libs/ccc"] # Directory containing your Python packages @@ -60,7 +75,7 @@ wheel.exclude = [ "ccc/giant.py", "ccc/corr.py", ] -# Note: wheel.py-api removed to support multiple Python versions (3.10-3.15) +# Note: wheel.py-api removed to support multiple Python versions (3.10-3.14) wheel.platlib = true # Contains compiled extensions [tool.pytest.ini_options] @@ -178,8 +193,10 @@ before-all = [ "yum clean all", # Install GCC 13 toolset (provides GCC 13.2 under /opt/rh/gcc-toolset-13) "yum install -y gcc-toolset-13", - # Install CUDA 12.9 toolkit (supports GCC 6.x - 15.x, compatible with GCC 14 in manylinux_2_28) - "yum install -y cuda-toolkit-12-5", + # Install CUDA 12.8 toolkit (supports the GCC 13 toolset above; stays inside + # the 12.x driver-compatibility window). Keep this comment and the package + # version in sync. + "yum install -y cuda-toolkit-12-8", # Print nvcc version with green prefix "echo -e '\\033[32m[cccgpu-info]:\\033[0m'", "nvcc --version",