mirror of
https://github.com/vale981/ray
synced 2025-03-09 21:06:39 -04:00
5cbc597494
* Suppress duplicate pre-emptive object pushes. * Add test. * Fix linting * Remove timer and inline recent_pushes_ into local_objects_. * Improve test. * Fix * Fix linting * Enable retrying pull from same object manager. Randomize object manager. * Speed up test * Linting * Add test. * Minor * Lengthen pull timeout and reissue pull every time a new object becomes available. * Increase pull timeout in test. * Wait for nodes to start in object manager test. * Wait longer for nodes to start up in test. * Small fixes. * _submit -> _remote * Change assert to warning.
392 lines
15 KiB
Bash
Executable file
392 lines
15 KiB
Bash
Executable file
#!/usr/bin/env bash
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# Cause the script to exit if a single command fails.
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set -e
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# Show explicitly which commands are currently running.
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set -x
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ROOT_DIR=$(cd "$(dirname "${BASH_SOURCE:-$0}")"; pwd)
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DOCKER_SHA=$($ROOT_DIR/../../build-docker.sh --output-sha --no-cache)
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echo "Using Docker image" $DOCKER_SHA
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env PongDeterministic-v0 \
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--run A3C \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env Pong-ram-v4 \
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--run A3C \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env PongDeterministic-v0 \
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--run A2C \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/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 '{"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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/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 '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/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 '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/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 '{"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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env Pendulum-v0 \
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--run ES \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env Pong-v0 \
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--run ES \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run A3C \
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--stop '{"training_iteration": 2}' \
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run APEX \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run DQN \
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--stop '{"training_iteration": 2}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run PPO \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env PongDeterministic-v4 \
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--run DQN \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env MontezumaRevenge-v0 \
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--run PPO \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v1 \
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--run A3C \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 2, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 2}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 2}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run PG \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env Pong-v0 \
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--run PG \
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--stop '{"training_iteration": 2}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env FrozenLake-v0 \
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--run PG \
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--stop '{"training_iteration": 2}' \
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--config '{"sample_batch_size": 500, "num_workers": 1}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env Pendulum-v0 \
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--run DDPG \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 2}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_parallel_data_loaders": 2, "replay_proportion": 1.0}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v0 \
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--run IMPALA \
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--stop '{"training_iteration": 2}' \
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--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_parallel_data_loaders": 2, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env MountainCarContinuous-v0 \
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--run DDPG \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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rllib train \
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--env MountainCarContinuous-v0 \
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--run DDPG \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 1}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/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": 2}' \
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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=10G --memory=10G $DOCKER_SHA \
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sh /ray/test/jenkins_tests/multi_node_tests/test_rllib_eval.sh
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_local.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_checkpoint_restore.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_policy_evaluator.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_nested_spaces.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_external_env.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_lstm.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_multi_agent_env.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_supported_spaces.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/test/test_env_with_subprocess.py
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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/ray/python/ray/rllib/test/test_rollout.sh
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/tune_mnist_ray.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/pbt_example.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/hyperband_example.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/async_hyperband_example.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/tune_mnist_ray_hyperband.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/tune_mnist_async_hyperband.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/hyperopt_example.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/tune_mnist_keras.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/mnist_pytorch.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/mnist_pytorch_trainable.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/tune/examples/genetic_example.py \
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--smoke-test
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/examples/multiagent_cartpole.py --num-iters=2
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=IMPALA --stop=100
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/experimental/sgd/test_sgd.py --num-iters=2 \
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--batch-size=1 --strategy=simple
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/experimental/sgd/test_sgd.py --num-iters=2 \
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--batch-size=1 --strategy=ps
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/experimental/sgd/mnist_example.py --num-iters=1 \
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--num-workers=1 --devices-per-worker=1 --strategy=ps
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/experimental/sgd/mnist_example.py --num-iters=1 \
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--num-workers=1 --devices-per-worker=1 --strategy=ps --tune
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env PongDeterministic-v4 \
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--run A3C \
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--stop '{"training_iteration": 2}' \
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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, "channel_major": true}, "preprocessor_pref": "rllib"}'
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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python /ray/python/ray/rllib/train.py \
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--env CartPole-v1 \
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--run A3C \
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--stop '{"training_iteration": 2}' \
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--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
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|
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docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA python -m pytest /ray/test/object_manager_test.py
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|
|
|
python3 $ROOT_DIR/multi_node_docker_test.py \
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|
--docker-image=$DOCKER_SHA \
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|
--num-nodes=5 \
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|
--num-redis-shards=10 \
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|
--test-script=/ray/test/jenkins_tests/multi_node_tests/test_0.py
|
|
|
|
python3 $ROOT_DIR/multi_node_docker_test.py \
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|
--docker-image=$DOCKER_SHA \
|
|
--num-nodes=5 \
|
|
--num-redis-shards=5 \
|
|
--num-gpus=0,1,2,3,4 \
|
|
--num-drivers=7 \
|
|
--driver-locations=0,1,0,1,2,3,4 \
|
|
--test-script=/ray/test/jenkins_tests/multi_node_tests/remove_driver_test.py
|
|
|
|
python3 $ROOT_DIR/multi_node_docker_test.py \
|
|
--docker-image=$DOCKER_SHA \
|
|
--num-nodes=5 \
|
|
--num-redis-shards=2 \
|
|
--num-gpus=0,0,5,6,50 \
|
|
--num-drivers=100 \
|
|
--test-script=/ray/test/jenkins_tests/multi_node_tests/many_drivers_test.py
|
|
|
|
python3 $ROOT_DIR/multi_node_docker_test.py \
|
|
--docker-image=$DOCKER_SHA \
|
|
--num-nodes=1 \
|
|
--mem-size=60G \
|
|
--shm-size=60G \
|
|
--test-script=/ray/test/jenkins_tests/multi_node_tests/large_memory_test.py
|