mirror of
https://github.com/vale981/ray
synced 2025-03-09 12:56:46 -04:00
be5dd8eb5e
* wip * add * timeout fix * const ref * comments * fix * fix * Move actor state into actor handle * comments 2 * enable by default * temp reorder * some fixes * add debug code * tmp * fix * wip * remove dbg * fix compile * fix * fix check * remove non direct tests * Increment ref count before resolving value * rename * fix another bug * tmp * tmp * Fix object pinning * build change * lint * ActorManager * tmp * ActorManager * fix test component failures * Remove old code * Remove unused * fix * fix * fix resources * fix advanced * eric's diff * blacklist * blacklist * cleanup * annotate * disable tests for now * remove * fix * fix * clean up verbosity * fix test * fix concurrency test * Update .travis.yml * Update .travis.yml * Update .travis.yml * split up analysis suite * split up trial runner suite * fix detached direct actors * fix * split up advanced tesT * lint * fix core worker test hang * fix bad check fail which breaks test_cluster.py in tune * fix some minor diffs in test_cluster * less workers * make less stressful * split up test * retry flaky tests * remove old test flags * fixes * lint * Update worker_pool.cc * fix race * fix * fix bugs in node failure handling * fix race condition * fix bugs in node failure handling * fix race condition * nits * fix test * disable heartbeatS * disable heartbeatS * fix * fix * use worker id * fix max fail * debug exit * fix merge, and apply [PATCH] fix concurrency test * [patch] fix core worker test hang * remove NotifyActorCreation, and return worker on completion of actor creation task * remove actor diied callback * Update core_worker.cc * lint * use task manager * fix merge * fix deadlock * wip * merge conflits * fix * better sysexit handling * better sysexit handling * better sysexit handling * check id * better debug * task failed msg * task failed msg * retry failed tasks with delay * retry failed tasks with delay * clip deps * fix * fix core worker tests * fix task manager test * fix all tests * cleanup * set to 0 for direct tests * dont check worker id for ownership rpc * dont check worker id for ownership rpc * debug messages * add comment * remove debug statements * nit * check worker id * fix test * owner * fix tests
492 lines
24 KiB
Bash
Executable file
492 lines
24 KiB
Bash
Executable file
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_catalog.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_optimizers.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_filters.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_evaluators.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_eager_support.py
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env PongDeterministic-v0 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pong-ram-v4 \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env PongDeterministic-v0 \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"remote_worker_envs": true, "remote_env_batch_wait_ms": 99999999, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run PPO \
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--stop '{"training_iteration": 2}' \
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--config '{"remote_worker_envs": true, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "num_gpus": 0}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run ES \
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--stop '{"training_iteration": 1}' \
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--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pong-v0 \
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--run ES \
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--stop '{"training_iteration": 1}' \
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--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 1}' \
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--config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run APEX \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run DQN \
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--stop '{"training_iteration": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env PongDeterministic-v4 \
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--run DQN \
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--stop '{"training_iteration": 1}' \
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--config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env MontezumaRevenge-v0 \
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--run PPO \
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--stop '{"training_iteration": 1}' \
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--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v1 \
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--run A3C \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "use_pytorch": true}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pong-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run PG \
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--stop '{"training_iteration": 1}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run DDPG \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "num_aggregation_workers": 2, "min_iter_time_s": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 1}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env MountainCarContinuous-v0 \
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--run DDPG \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--run DDPG \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run APEX_DDPG \
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--ray-num-cpus 8 \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env Pendulum-v0 \
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--run APEX_DDPG \
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--ray-num-cpus 8 \
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--stop '{"training_iteration": 1}' \
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--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1, "batch_mode": "complete_episodes", "parameter_noise": false}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--env CartPole-v0 \
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--run MARWIL \
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--stop '{"training_iteration": 1}' \
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--config '{"input": "/ray/rllib/tests/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "shuffle_buffer_size": 10}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output /ray/rllib/train.py \
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--stop '{"training_iteration": 1}' \
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--config '{"input": "/ray/rllib/tests/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "soft_q": true}'
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docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
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/ray/ci/suppress_output python /ray/rllib/tests/test_local.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_reproducibility.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_dependency.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_legacy.py
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/ray/ci/suppress_output python /ray/rllib/tests/test_io.py
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/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=PG --stop=50
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/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=PPO --stop=50
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/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=DQN --stop=50
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/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PPO
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/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PG
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/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DQN
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# Run all single-agent regression tests (3x retry each)
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for yaml in $(ls $ROOT_DIR/../../rllib/tuned_examples/regression_tests); do
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--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84}, "preprocessor_pref": "rllib"}'
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