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ray/test/jenkins_tests/run_multi_node_tests.sh

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#!/usr/bin/env bash
# Cause the script to exit if a single command fails.
set -e
# Show explicitly which commands are currently running.
set -x
ROOT_DIR=$(cd "$(dirname "${BASH_SOURCE:-$0}")"; pwd)
DOCKER_SHA=$($ROOT_DIR/../../build-docker.sh --output-sha --no-cache)
echo "Using Docker image" $DOCKER_SHA
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pong-ram-v4 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v0 \
--run A2C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--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}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--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"}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pendulum-v0 \
--run ES \
--stop '{"training_iteration": 2}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pong-v0 \
--run ES \
--stop '{"training_iteration": 2}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--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"}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run APEX \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "timesteps_per_iteration": 1000, "gpu": false, "min_iter_time_s": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env FrozenLake-v0 \
--run DQN \
--stop '{"training_iteration": 2}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env FrozenLake-v0 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v4 \
--run DQN \
--stop '{"training_iteration": 2}' \
--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}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env MontezumaRevenge-v0 \
--run PPO \
--stop '{"training_iteration": 2}' \
--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]]}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "model": {"use_lstm": true}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pong-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env FrozenLake-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pendulum-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_parallel_data_loaders": 2, "replay_proportion": 1.0}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--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}}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
rllib train \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
sh /ray/test/jenkins_tests/multi_node_tests/test_rllib_eval.sh
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_local.py
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_checkpoint_restore.py
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_policy_evaluator.py
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_serving_env.py
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_lstm.py
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_multi_agent_env.py
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_supported_spaces.py
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/tune_mnist_ray.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/pbt_example.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/hyperband_example.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/async_hyperband_example.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/tune_mnist_ray_hyperband.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/tune_mnist_async_hyperband.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/hyperopt_example.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/tune_mnist_keras.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/mnist_pytorch.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/mnist_pytorch_trainable.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/genetic_example.py \
--smoke-test
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/examples/multiagent_cartpole.py --num-iters=2
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=IMPALA --stop=100
docker run -e "RAY_USE_XRAY=1" --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/experimental/sgd/test_sgd.py --num-iters=2
# No Xray for PyTorch
docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v4 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "use_pytorch": true, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84, "channel_major": true}, "preprocessor_pref": "rllib"}'
# No Xray for PyTorch
docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "use_pytorch": true}'
python3 $ROOT_DIR/multi_node_docker_test.py \
--docker-image=$DOCKER_SHA \
--num-nodes=5 \
--num-redis-shards=10 \
--use-raylet \
--test-script=/ray/test/jenkins_tests/multi_node_tests/test_0.py
python3 $ROOT_DIR/multi_node_docker_test.py \
--docker-image=$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 \
--use-raylet \
--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 \
--use-raylet \
--test-script=/ray/test/jenkins_tests/multi_node_tests/large_memory_test.py
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