pylcm: publish benchmarks for 7ed04e311210

Author: hmgaudecker

pylcm-benchmarks/index.json

@@ -1,4 +1,4 @@
 {
     "asv-version": "0.6.5",
-    "timestamp": 1773952448221
+    "timestamp": 1773955599875
 }

pylcm-benchmarks/info.json

@@ -1,2 +1,2 @@
 <?xml version='1.0' encoding='utf-8'?>
-<feed xmlns="http://www.w3.org/2005/Atom"><id>tag:pylcm.asv,1970-01-01:/1e4c9ef7af8c5eca1de02b894ceccf70144c4cdf35ffe9abb6ea650fb7a16281</id><author><name>Airspeed Velocity</name></author><title xml:lang="en">pylcm performance regressions</title><updated>2026-03-19T20:34:08Z</updated></feed>
+<feed xmlns="http://www.w3.org/2005/Atom"><id>tag:pylcm.asv,1970-01-01:/1e4c9ef7af8c5eca1de02b894ceccf70144c4cdf35ffe9abb6ea650fb7a16281</id><author><name>Airspeed Velocity</name></author><title xml:lang="en">pylcm performance regressions</title><updated>2026-03-19T21:26:39Z</updated></feed>

pylcm-benchmarks/regressions.xml

@@ -1,4 +1,30 @@
 {
+    "bench_mahler_yum.TimeMahlerYum.time_mahler_yum": {
+        "code": "class TimeMahlerYum:\n    def time_mahler_yum(self):\n        self.model.simulate(\n            params=self.model_params,\n            initial_conditions=self.initial_conditions,\n            log_level=\"off\",\n        )\n\n    def setup(self):\n        import jax.numpy as jnp\n    \n        from lcm_examples.mahler_yum_2024 import (\n            MAHLER_YUM_MODEL,\n            START_PARAMS,\n            create_inputs,\n        )\n    \n        start_params_without_beta = {\n            k: v for k, v in START_PARAMS.items() if k != \"beta\"\n        }\n    \n        self.model = MAHLER_YUM_MODEL\n        common_params, initial_states, _discount_factor_type = create_inputs(\n            seed=0,\n            n_simulation_subjects=_N_SUBJECTS,\n            **start_params_without_beta,\n        )\n        self.model_params = {\n            \"alive\": {\n                \"discount_factor\": START_PARAMS[\"beta\"][\"mean\"],\n                **common_params,\n            },\n        }\n        self.initial_conditions = {\n            **initial_states,\n            \"regime\": jnp.full(\n                _N_SUBJECTS,\n                self.model.regime_names_to_ids[\"alive\"],\n                dtype=jnp.int32,\n            ),\n        }\n        start = time.perf_counter()\n        self.model.simulate(\n            params=self.model_params,\n            initial_conditions=self.initial_conditions,\n            log_level=\"off\",\n        )\n        self._warmup_time = time.perf_counter() - start",
+        "min_run_count": 2,
+        "name": "bench_mahler_yum.TimeMahlerYum.time_mahler_yum",
+        "number": 0,
+        "param_names": [],
+        "params": [],
+        "repeat": 0,
+        "rounds": 2,
+        "sample_time": 0.01,
+        "timeout": 1200,
+        "type": "time",
+        "unit": "seconds",
+        "version": "4bc10dcb1ccd31d6a2a3ecc9d49000490fd77b914c74a4882c3e88cd5f0a9f6d",
+        "warmup_time": -1
+    },
+    "bench_mahler_yum.TimeMahlerYum.track_warmup": {
+        "code": "class TimeMahlerYum:\n    def track_warmup(self):\n        return self._warmup_time\n\n    def setup(self):\n        import jax.numpy as jnp\n    \n        from lcm_examples.mahler_yum_2024 import (\n            MAHLER_YUM_MODEL,\n            START_PARAMS,\n            create_inputs,\n        )\n    \n        start_params_without_beta = {\n            k: v for k, v in START_PARAMS.items() if k != \"beta\"\n        }\n    \n        self.model = MAHLER_YUM_MODEL\n        common_params, initial_states, _discount_factor_type = create_inputs(\n            seed=0,\n            n_simulation_subjects=_N_SUBJECTS,\n            **start_params_without_beta,\n        )\n        self.model_params = {\n            \"alive\": {\n                \"discount_factor\": START_PARAMS[\"beta\"][\"mean\"],\n                **common_params,\n            },\n        }\n        self.initial_conditions = {\n            **initial_states,\n            \"regime\": jnp.full(\n                _N_SUBJECTS,\n                self.model.regime_names_to_ids[\"alive\"],\n                dtype=jnp.int32,\n            ),\n        }\n        start = time.perf_counter()\n        self.model.simulate(\n            params=self.model_params,\n            initial_conditions=self.initial_conditions,\n            log_level=\"off\",\n        )\n        self._warmup_time = time.perf_counter() - start",
+        "name": "bench_mahler_yum.TimeMahlerYum.track_warmup",
+        "param_names": [],
+        "params": [],
+        "timeout": 1200,
+        "type": "track",
+        "unit": "seconds",
+        "version": "0181098296c6c87437bbddd63c05b32bb69a829f0508b8605b0e874c83a83208"
+    },
     "bench_mortality.TimeMortality.time_mortality": {
         "code": "class TimeMortality:\n    def time_mortality(self):\n        self.model.simulate(\n            params=self.model_params,\n            initial_conditions=self.initial_conditions,\n            log_level=\"off\",\n        )\n\n    def setup(self):\n        import jax.numpy as jnp\n    \n        from lcm_examples import mortality\n    \n        self.model = mortality.get_model(n_periods=4)\n        self.model_params = mortality.get_params(n_periods=4)\n        self.initial_conditions = {\n            \"age\": jnp.full(_N_SUBJECTS, 40.0),\n            \"wealth\": jnp.full(_N_SUBJECTS, 100.0),\n            \"regime\": jnp.zeros(_N_SUBJECTS, dtype=jnp.int32),\n        }\n        start = time.perf_counter()\n        self.model.simulate(\n            params=self.model_params,\n            initial_conditions=self.initial_conditions,\n            log_level=\"off\",\n        )\n        self._warmup_time = time.perf_counter() - start",
         "min_run_count": 2,

pylcm-benchmarks/results/benchmarks.json

@@ -0,0 +1 @@
+{"commit_hash": "7ed04e3112102196aaf0f6ad64140188de7caef8", "env_name": "existing-py_home_hmg_econ_dev-pylcm_pylcm_.pixi_envs_tests-cuda13_bin_python", "date": 1773954304000, "params": {"arch": "x86_64", "cpu": "AMD Ryzen 7 3800X 8-Core Processor", "machine": "hmg-home", "num_cpu": "16", "os": "Linux 6.17.0-19-generic", "ram": "33589665792", "python": "/home/hmg/econ/dev-pylcm/pylcm/.pixi/envs/tests-cuda13/bin/python"}, "python": "/home/hmg/econ/dev-pylcm/pylcm/.pixi/envs/tests-cuda13/bin/python", "requirements": {}, "env_vars": {}, "result_columns": ["result", "params", "version", "started_at", "duration", "stats_ci_99_a", "stats_ci_99_b", "stats_q_25", "stats_q_75", "stats_number", "stats_repeat", "samples", "profile"], "results": {"bench_mahler_yum.TimeMahlerYum.time_mahler_yum": [[3.9573462405242026], [], "4bc10dcb1ccd31d6a2a3ecc9d49000490fd77b914c74a4882c3e88cd5f0a9f6d", 1773954933503, 369.93, [1.9728], [5.9419], [3.9375], [3.9772], [1], [2]], "bench_mortality.TimeMortality.time_mortality": [[0.6129938369849697], [], "36e8142817bc0bfcc800b869fecc199513db179ea95c1139537be92ef34e11b9", 1773955211938, 55.766, [0.25957], [0.949], [0.49653], [0.72075], [1], [4]], "bench_precautionary_savings.TimeGridLookup.time_grid_lookup": [[0.12554695902508684, 0.13234167703194544, 0.13927986900671385, 0.24361987749580294], [["500", "2000"], ["'lin'", "'irreg'"]], "247a48d47fa4cbfa70c051589dd55474c0a7f8661f227ac4db04f15d2902440b", 1773955250935, 218.0, [0.11593, 0.088132, 0.090226, 0.19095], [0.13534, 0.19267, 0.19285, 0.28222], [0.12299, 0.12836, 0.12502, 0.23386], [0.1282, 0.14438, 0.1558, 0.24634], [1, 1, 1, 1], [4, 4, 4, 4]], "bench_precautionary_savings.TimeSimulate.time_simulate": [[0.48614223147160374, 4.060837497992907], [["1000", "10000"]], "dc1da16d03f2a065aeaee1246f1c46a7ca9230deb8b4304b2514987afec67fb0", 1773955404276, 117.35, [0.41903, 2.8616], [0.58024, 5.2601], [0.47679, 4.0488], [0.50899, 4.0728], [1, 1], [4, 2]], "bench_precautionary_savings.TimeSolve.time_solve": [[0.021682971506379545, 0.0211195659940131, 0.021762300981208682], [["50", "200", "500"]], "8abe06f1cc01621d46050aed732f05dba0585e2b39e7eca4f9e7720d325fbebf", 1773955486049, 96.636, [0.019483, 0.018871, 0.019975], [0.024332, 0.027352, 0.036303], [0.020845, 0.020491, 0.021639], [0.022902, 0.021696, 0.022808], [1, 1, 1], [10, 9, 8]], "bench_precautionary_savings.TimeSolveAndSimulate.time_solve_and_simulate": [[0.5996074819995556], [], "c5c46823ca742568bddab2a455b51c820a2fcf7ee22d3a146abc67b7ef5819d5", 1773955548139, 55.662, [0.38072], [0.82462], [0.53225], [0.67002], [1], [4]], "bench_mahler_yum.TimeMahlerYum.track_warmup": [[83.41894659399986], [], "0181098296c6c87437bbddd63c05b32bb69a829f0508b8605b0e874c83a83208", 1773955119513, 92.425], "bench_mortality.TimeMortality.track_warmup": [[8.831608437001705], [], "1d9eaabbe599fec179c437751b931352f571a80dc5ebce93a17d6cfd6cbcf33c", 1773955240090, 10.845], "bench_precautionary_savings.TimeGridLookup.track_warmup": [[8.650894236983731, 9.05656610801816, 8.504911112016998, 9.496457194967661], [["500", "2000"], ["'lin'", "'irreg'"]], "978600f2cf15b6b06b819d6679210c511beda141a4dcb39ae061467a6b881d4d", 1773955360906, 43.37], "bench_precautionary_savings.TimeSimulate.track_warmup": [[5.321203041996341, 9.827855095034465], [["1000", "10000"]], "b497e029eb6931b5be7141dc53d0abc85ba5aa78397b31b5c820bf57c68f8253", 1773955462423, 23.626], "bench_precautionary_savings.TimeSolve.track_warmup": [[2.343733052024618, 2.8818408960360102, 3.4140173299820162], [["50", "200", "500"]], "fa5b0af0f0d9f07d9e194a1c3ef76a209b8268d5b274a29412b229ae5c52bb11", 1773955534216, 13.922], "bench_precautionary_savings.TimeSolveAndSimulate.track_warmup": [[8.800000775954686], [], "0a452f38395cf04b58130d4e66feddd7e40748df98b7ddcf80b5b8b571a6cfd7", 1773955576192, 10.729]}, "durations": {"<build>": 3.075599670410156e-05}, "version": 2}