mirror of
https://github.com/wassname/ray.git
synced 2026-08-18 12:20:14 +08:00
Eric Liang
2d543b6e19
This introduces rllib.Evaluator and rllib.Optimizer classes. Optimizers encapsulate a particular distributed optimization strategy for RL. Evaluators encapsulate the model graph, and once implemented, any Optimizer may be "plugged in" to any algorithm that implements the Evaluator interface.
155 lines
6.0 KiB
Bash
Executable File
155 lines
6.0 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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python $ROOT_DIR/multi_node_docker_test.py \
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--docker-image=$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
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python $ROOT_DIR/multi_node_docker_test.py \
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--docker-image=$DOCKER_SHA \
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--num-nodes=5 \
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--num-redis-shards=5 \
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--num-gpus=0,1,2,3,4 \
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--num-drivers=7 \
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--driver-locations=0,1,0,1,2,3,4 \
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--test-script=/ray/test/jenkins_tests/multi_node_tests/remove_driver_test.py
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python $ROOT_DIR/multi_node_docker_test.py \
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--docker-image=$DOCKER_SHA \
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--num-nodes=5 \
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--num-redis-shards=2 \
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--num-gpus=0,0,5,6,50 \
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--num-drivers=100 \
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--test-script=/ray/test/jenkins_tests/multi_node_tests/many_drivers_test.py
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python $ROOT_DIR/multi_node_docker_test.py \
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--docker-image=$DOCKER_SHA \
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--num-nodes=1 \
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--mem-size=60G \
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--shm-size=60G \
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--test-script=/ray/test/jenkins_tests/multi_node_tests/large_memory_test.py
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# Test that the example applications run.
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# docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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# python /ray/examples/lbfgs/driver.py
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# docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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# python /ray/examples/rl_pong/driver.py \
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# --iterations=3
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# docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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# python /ray/examples/hyperopt/hyperopt_simple.py
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# docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
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# python /ray/examples/hyperopt/hyperopt_adaptive.py
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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": 16}'
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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, "sgd_stepsize": 1e-4, "sgd_batchsize": 64, "timesteps_per_batch": 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 '{"kl_coeff": 1.0, "num_sgd_iter": 10, "sgd_stepsize": 1e-4, "sgd_batchsize": 64, "timesteps_per_batch": 2000, "num_workers": 1, "use_gae": false}'
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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, "timesteps_per_batch": 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 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, "timesteps_per_batch": 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 A3C \
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--stop '{"training_iteration": 2}' \
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--config '{"use_lstm": false}'
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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 '{"async_updates": true, "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 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_batchsize": 64, "timesteps_per_batch": 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, "sgd_stepsize": 1e-4, "sgd_batchsize": 64, "timesteps_per_batch": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}, "extra_frameskip": 4}'
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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_lstm": false, "use_pytorch": true, "model": {"grayscale": true, "zero_mean": false, "dim": 80, "channel_major": true}}'
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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/tune/examples/tune_mnist_ray.py \
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--fast
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