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RunDeepProfiler

How to use RunDeepProfiler

Windows

  1. Follow the instructions to install CellProfiler from source: Installing plugins with dependencies, using CellProfiler from source;

  2. Clone DeepProfiler repository:

git clone https://github.com/broadinstitute/DeepProfiler.git
  1. Install DeepProfiler
cd DeepProfiler /
pip install -e .
  1. Install dependencies by running:
cd CellProfiler-plugins
pip install -e .[deepprofiler]

Run Example

  1. Use the folder with images and files available on CellProfiler-plugins > test > test_deepprofiler link here as an example to run the test pipeline test_deepprofiler.cppipe

  2. Don't forget to select the config, model, and DeepProfiler directories to your local paths.

Using GPU

If you want to use a GPU to run the model (this is recommended for speed), you'll need a compatible version of Tensorflow and a supported GPU. General instructions are available at this link.

  1. Your GPU should be visible in Device Manager under Display Adaptors. If your GPU isn't there, you likely need to install drivers. Here is where you can find NVIDIA GPU drivers if you need to install them.

  2. To test whether the GPU is configured correctly:

  • Run python on the command line (i.e., in Command Prompt or Terminal) to start an interactive session
  • Then run the following
import tensorflow as tf
tf.test.is_gpu_available(
  cuda_only=False, min_cuda_compute_capability=None
)
  • If this returns True, you're all set
  • If this returns False, you likely need to install/reinstall torch. See here for your exact command.
  • Exit the session with exit() then install tensorflow if necessary.

NOTE: You might get a warning like this:

W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2022-05-26 20:24:21.906286: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.

If you don't have a GPU, this is not a problem. If you do, your configuration is incorrect and you need to try reinstalling drivers and the correct version of CUDA for your system.