mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
b948405532
* patch up pbt * Sat Jan 27 01:00:03 PST 2018 * Sat Jan 27 01:04:14 PST 2018 * Sat Jan 27 01:04:21 PST 2018 * Sat Jan 27 01:15:15 PST 2018 * Sat Jan 27 01:15:42 PST 2018 * Sat Jan 27 01:16:14 PST 2018 * Sat Jan 27 01:38:42 PST 2018 * Sat Jan 27 01:39:21 PST 2018 * add pbt * Sat Jan 27 01:41:19 PST 2018 * Sat Jan 27 01:44:21 PST 2018 * Sat Jan 27 01:45:46 PST 2018 * Sat Jan 27 16:54:42 PST 2018 * Sat Jan 27 16:57:53 PST 2018 * clean up test * Sat Jan 27 18:01:15 PST 2018 * Sat Jan 27 18:02:54 PST 2018 * Sat Jan 27 18:11:18 PST 2018 * Sat Jan 27 18:11:55 PST 2018 * Sat Jan 27 18:14:09 PST 2018 * review * try out a ppo example * some tweaks to ppo example * add postprocess hook * Sun Jan 28 15:00:40 PST 2018 * clean up custom explore fn * Sun Jan 28 15:10:21 PST 2018 * Sun Jan 28 15:14:53 PST 2018 * Sun Jan 28 15:17:04 PST 2018 * Sun Jan 28 15:33:13 PST 2018 * Sun Jan 28 15:56:40 PST 2018 * Sun Jan 28 15:57:36 PST 2018 * Sun Jan 28 16:00:35 PST 2018 * Sun Jan 28 16:02:58 PST 2018 * Sun Jan 28 16:29:50 PST 2018 * Sun Jan 28 16:30:36 PST 2018 * Sun Jan 28 16:31:44 PST 2018 * improve tune doc * concepts * update humanoid * Fri Feb 2 18:03:33 PST 2018 * fix example * show error file
181 lines
7 KiB
Bash
Executable file
181 lines
7 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 CartPole-v0 \
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--run DQN \
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--stop '{"training_iteration": 2}' \
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--config '{"multi_gpu": true, "optimizer": {"sgd_batch_size": 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 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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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_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_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/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/rllib/examples/multiagent_mountaincar.py
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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_pendulum.py
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