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* Add an RLlib Tune experiment to UserTest suite. * Add ray.init() * Move example script to example/tune/, so it can be imported as module. * add __init__.py so our new module will get included in python wheel. * Add block device to RLlib test instances. * Reduce disk size a little bit. * Add metrics reporting * Allow max of 5 workers to accomodate all the worker tasks. * revert disk size change. * Minor updates * Trigger build * set max num workers * Add a compute cfg for autoscaled cpu and gpu nodes. * use 1gpu instance. * install tblib for debugging worker crashes. * Manually upgrade to pytorch 1.9.0 * -y * torch=1.9.0 * install torch on driver * Add an RLlib Tune experiment to UserTest suite. * Add ray.init() * Move example script to example/tune/, so it can be imported as module. * add __init__.py so our new module will get included in python wheel. * Add block device to RLlib test instances. * Reduce disk size a little bit. * Add metrics reporting * Allow max of 5 workers to accomodate all the worker tasks. * revert disk size change. * Minor updates * Trigger build * set max num workers * Add a compute cfg for autoscaled cpu and gpu nodes. * use 1gpu instance. * install tblib for debugging worker crashes. * Manually upgrade to pytorch 1.9.0 * -y * torch=1.9.0 * install torch on driver * bump timeout * Write a more informational result dict. * Revert changes to compute config files that are not used. * add smoke test * update * reduce timeout * Reduce the # of env per worker to 1. * Small fix for getting trial_states * Trigger build * simply result dict * lint * more lint * fix smoke test Co-authored-by: Amog Kamsetty <amogkamsetty@yahoo.com> |
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| .. | ||
| connect_tests | ||
| learning_tests | ||
| multi_gpu_learning_tests | ||
| multi_gpu_with_attention_learning_tests | ||
| multi_gpu_with_lstm_learning_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 | ||
| app_config.yaml | ||
| auto_scale.yaml | ||
| connect_driver_requirements.txt | ||
| 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.