hiro/ray
1
0
Fork 0
mirror of https://github.com/vale981/ray synced 2025-03-06 10:31:39 -05:00
Code Issues Projects Releases Packages Wiki Activity Actions

testing with --rm and docker run (#1240)

Browse source 
Add --rm to docker run for Jenkins tests.
This commit is contained in:
shane 2017-11-22 10:20:04 -08:00 • committed by Robert Nishihara
parent ad044cbe8f
commit 9af8dc568a
1 changed files with 18 additions and 18 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

36
test/jenkins_tests/run_multi_node_tests.sh
View file

@ -43,105 +43,105 @@ python $ROOT_DIR/multi_node_docker_test.py \
# Test that the example applications run.
# docker run --shm-size=10G --memory=10G $DOCKER_SHA \
# docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
# python /ray/examples/lbfgs/driver.py
# docker run --shm-size=10G --memory=10G $DOCKER_SHA \
# docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
# python /ray/examples/rl_pong/driver.py \
# --iterations=3
# docker run --shm-size=10G --memory=10G $DOCKER_SHA \
# docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
# python /ray/examples/hyperopt/hyperopt_simple.py
# docker run --shm-size=10G --memory=10G $DOCKER_SHA \
# docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
# python /ray/examples/hyperopt/hyperopt_adaptive.py
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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": 16}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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, "sgd_stepsize": 1e-4, "sgd_batchsize": 64, "timesteps_per_batch": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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, "sgd_stepsize": 1e-4, "sgd_batchsize": 64, "timesteps_per_batch": 2000, "num_workers": 1, "use_gae": false}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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, "timesteps_per_batch": 100}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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, "timesteps_per_batch": 100}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"use_lstm": false}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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 --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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 --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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_batchsize": 64, "timesteps_per_batch": 1000, "num_workers": 1}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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 --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --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, "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}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
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_lstm": false, "use_pytorch": true, "model": {"grayscale": true, "zero_mean": false, "dim": 80, "channel_major": true}}'
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_checkpoint_restore.py
docker run --shm-size=10G --memory=10G $DOCKER_SHA \
docker run --rm --shm-size=10G --memory=10G $DOCKER_SHA \
python /ray/python/ray/tune/examples/tune_mnist_ray.py \
--fast