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[rllib] Silent tests (#4151)

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This commit is contained in:
Eric Liang
2019-02-28 16:32:22 -08:00
committed by GitHub
parent 88e14feb53
commit b809ef0107
3 changed files with 420 additions and 390 deletions
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ci/jenkins_tests/run_multi_node_tests.sh
+5 -390
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@@ -14,376 +14,12 @@ ROOT_DIR=$(cd "$(dirname "${BASH_SOURCE:-$0}")"; pwd)
DOCKER_SHA=$($ROOT_DIR/../../build-docker.sh --output-sha --no-cache)
echo "Using Docker image" $DOCKER_SHA
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
######################## RLLIB TESTS #################################
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pong-ram-v4 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
source $ROOT_DIR/run_rllib_tests.sh
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v0 \
--run A2C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0.1}' \
--ray-num-gpus 1
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"remote_worker_envs": true, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pendulum-v0 \
--run ES \
--stop '{"training_iteration": 2}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pong-v0 \
--run ES \
--stop '{"training_iteration": 2}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run APEX \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env FrozenLake-v0 \
--run DQN \
--stop '{"training_iteration": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env FrozenLake-v0 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v4 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env MontezumaRevenge-v0 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "use_pytorch": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pong-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env FrozenLake-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pendulum-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
rllib train \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1, "batch_mode": "complete_episodes", "parameter_noise": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run MARWIL \
--stop '{"training_iteration": 2}' \
--config '{"input": "/ray/python/ray/rllib/test/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "shuffle_buffer_size": 10}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"input": "/ray/python/ray/rllib/test/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "soft_q": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_local.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_io.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_checkpoint_restore.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_policy_evaluator.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_nested_spaces.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_external_env.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=PG --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=PPO --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/parametric_action_cartpole.py --run=DQN --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/custom_loss.py --iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_lstm.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PPO
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DQN
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DDPG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_multi_agent_env.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_supported_spaces.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_env_with_subprocess.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/test_rollout.sh
# Run all single-agent regression tests (3x retry each)
for yaml in $(ls $ROOT_DIR/../../python/ray/rllib/tuned_examples/regression_tests); do
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/run_regression_tests.py /ray/python/ray/rllib/tuned_examples/regression_tests/$yaml
done
# Try a couple times since it's stochastic
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/multiagent_pendulum.py || \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/multiagent_pendulum.py || \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/multiagent_pendulum.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/multiagent_cartpole.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/test/test_avail_actions_qmix.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/cartpole_lstm.py --run=IMPALA --stop=100
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/contrib/random_agent/random_agent.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=PG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=QMIX
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/examples/twostep_game.py --stop=2000 --run=APEX_QMIX
######################## SGD TESTS #################################
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/experimental/sgd/test_sgd.py --num-iters=2 \
@@ -409,27 +45,7 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/experimental/sgd/mnist_example.py --num-iters=1 \
--num-workers=1 --devices-per-worker=1 --strategy=ps --tune
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v4 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84}, "preprocessor_pref": "rllib"}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/rllib/train.py \
--env PongDeterministic-v4 \
--run IMPALA \
--stop='{"timesteps_total": 40000}' \
--ray-object-store-memory=500000000 \
--config '{"num_workers": 1, "num_gpus": 0, "num_envs_per_worker": 64, "sample_batch_size": 50, "train_batch_size": 50, "learner_queue_size": 1}'
######################## RAY BACKEND TESTS #################################
python3 $ROOT_DIR/multi_node_docker_test.py \
--docker-image=$DOCKER_SHA \
@@ -461,6 +77,7 @@ python3 $ROOT_DIR/multi_node_docker_test.py \
--shm-size=60G \
--test-script=/ray/ci/jenkins_tests/multi_node_tests/large_memory_test.py
######################## TUNE TESTS #################################
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
pytest /ray/python/ray/tune/test/cluster_tests.py
@@ -528,5 +145,3 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/python/ray/tune/examples/skopt_example.py \
--smoke-test
ci/jenkins_tests/run_rllib_tests.sh
+394
View File
@@ -0,0 +1,394 @@
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env PongDeterministic-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env Pong-ram-v4 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env PongDeterministic-v0 \
--run A2C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0.1}' \
--ray-num-gpus 1
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"remote_worker_envs": true, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env Pendulum-v0 \
--run ES \
--stop '{"training_iteration": 2}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env Pong-v0 \
--run ES \
--stop '{"training_iteration": 2}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run A3C \
--stop '{"training_iteration": 2}' \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run APEX \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env FrozenLake-v0 \
--run DQN \
--stop '{"training_iteration": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env FrozenLake-v0 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env PongDeterministic-v4 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env MontezumaRevenge-v0 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "use_pytorch": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env Pong-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env FrozenLake-v0 \
--run PG \
--stop '{"training_iteration": 2}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env Pendulum-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 2}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1, "batch_mode": "complete_episodes", "parameter_noise": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run MARWIL \
--stop '{"training_iteration": 2}' \
--config '{"input": "/ray/python/ray/rllib/test/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "shuffle_buffer_size": 10}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 2}' \
--config '{"input": "/ray/python/ray/rllib/test/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "soft_q": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_local.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_io.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_checkpoint_restore.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_policy_evaluator.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_nested_spaces.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_external_env.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/parametric_action_cartpole.py --run=PG --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/parametric_action_cartpole.py --run=PPO --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/parametric_action_cartpole.py --run=DQN --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_lstm.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/batch_norm_model.py --num-iters=1 --run=PPO
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/batch_norm_model.py --num-iters=1 --run=PG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/batch_norm_model.py --num-iters=1 --run=DQN
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/batch_norm_model.py --num-iters=1 --run=DDPG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_multi_agent_env.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_supported_spaces.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
pytest /ray/python/ray/tune/test/cluster_tests.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_env_with_subprocess.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_rollout.sh
# Run all single-agent regression tests (3x retry each)
for yaml in $(ls $ROOT_DIR/../../python/ray/rllib/tuned_examples/regression_tests); do
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/run_regression_tests.py \
/ray/python/ray/rllib/tuned_examples/regression_tests/$yaml
done
# Try a couple times since it's stochastic
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/multiagent_pendulum.py || \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/multiagent_pendulum.py || \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/multiagent_pendulum.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/multiagent_cartpole.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/multiagent_two_trainers.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh test/test_avail_actions_qmix.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/cartpole_lstm.py --run=PPO --stop=200
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/cartpole_lstm.py --run=IMPALA --stop=100
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/custom_metrics_and_callbacks.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh contrib/random_agent/random_agent.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/twostep_game.py --stop=2000 --run=PG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/twostep_game.py --stop=2000 --run=QMIX
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh examples/twostep_game.py --stop=2000 --run=APEX_QMIX
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env PongDeterministic-v4 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84}, "preprocessor_pref": "rllib"}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 2}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/python/ray/rllib/test/run_silent.sh train.py \
--env PongDeterministic-v4 \
--run IMPALA \
--stop='{"timesteps_total": 40000}' \
--ray-object-store-memory=500000000 \
--config '{"num_workers": 1, "num_gpus": 0, "num_envs_per_worker": 64, "sample_batch_size": 50, "train_batch_size": 50, "learner_queue_size": 1}'
python/ray/rllib/test/run_silent.sh
Executable
+21
View File
@@ -0,0 +1,21 @@
#!/bin/bash
TMPFILE=`mktemp`
DIRECTORY=`dirname $0`
SCRIPT=$1
shift
if [ -x $DIRECTORY/../$SCRIPT ]; then
$DIRECTORY/../$SCRIPT "$@" >$TMPFILE 2>&1
else
python $DIRECTORY/../$SCRIPT "$@" >$TMPFILE 2>&1
fi
CODE=$?
if [ $CODE != 0 ]; then
cat $TMPFILE
echo "FAILED $CODE"
exit $CODE
fi
exit 0