mirror of
https://github.com/vale981/ray
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18dcf1ac25
* use nightly * switch ml cpu to ray cpu * fix * add pytest * add more pytest * add constraint * add tensorflow * fix merge conflict * add tblib * fix * add back uninstall |
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| .. | ||
| learning_tests | ||
| multi_gpu_learning_tests | ||
| multi_gpu_with_attention_learning_tests | ||
| multi_gpu_with_lstm_learning_tests | ||
| performance_tests | ||
| stress_tests | ||
| unit_gpu_tests | ||
| 1gpu_4cpus.yaml | ||
| 2gpus_32cpus.yaml | ||
| 4gpus_64cpus.yaml | ||
| 4gpus_544_cpus.yaml | ||
| 8gpus_64cpus.yaml | ||
| 8gpus_96cpus.yaml | ||
| 12gpus_192cpus.yaml | ||
| app_config.yaml | ||
| README.rst | ||
| rllib_tests.yaml | ||
| wait_cluster.py | ||
RLlib Tests =========== This directory contains various RLlib release tests. You should run these tests with the `releaser <https://github.com/ray-project/releaser>`_ tool. Overview -------- Currently, there are 3 RLlib tests: 1. ``learning_tests`` - Tests, whether major algos (tf+torch) can learn in Atari or PyBullet envs in ~30-60min. 1. ``stress_tests`` - Runs 4 IMPALA Atari jobs, each one using 1GPU and 128CPUs (needs autoscaling to succeed). 1. ``unit_gpu_tests`` - Tests, whether all of RLlib's example scripts can be run on a GPU. Generally the releaser tool will run all tests in parallel. Acceptance criteria ------------------- These tests are considered passing when they throw no error at the end of the output log.