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Set RAY_FORCE_DIRECT=1 for run_rllib_tests, test_basic (#6171)

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This commit is contained in:
Eric Liang
2019-11-25 14:12:11 -08:00
committed by GitHub
parent c9314098b9
commit 64a3a7239e
16 changed files with 229 additions and 123 deletions
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.gitignore
+1
View File
@@ -5,6 +5,7 @@
/python/ray/pickle5_files/
/python/build
/python/dist
/python/python-driver-*
/thirdparty/pkg/
/build/java
ci/jenkins_tests/run_multi_node_tests.sh
+4 -4
View File
@@ -15,6 +15,10 @@ DOCKER_SHA=$($ROOT_DIR/../../build-docker.sh --output-sha --no-cache)
SUPPRESS_OUTPUT=$ROOT_DIR/../suppress_output
echo "Using Docker image" $DOCKER_SHA
######################## RLLIB TESTS #################################
source $ROOT_DIR/run_rllib_tests.sh
######################## EXAMPLE TESTS #################################
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
@@ -32,10 +36,6 @@ $SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE}
$SUPPRESS_OUTPUT docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
python /ray/doc/examples/doc_code/tf_example.py
######################## RLLIB TESTS #################################
source $ROOT_DIR/run_rllib_tests.sh
######################## TUNE TESTS #################################
bash $ROOT_DIR/run_tune_tests.sh ${MEMORY_SIZE} ${SHM_SIZE} $DOCKER_SHA
ci/jenkins_tests/run_rllib_tests.sh
+104 -104
View File
@@ -1,62 +1,62 @@
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_catalog.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_catalog.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_optimizers.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_optimizers.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_filters.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_filters.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_evaluators.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_evaluators.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_eager_support.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_eager_support.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env PongDeterministic-v0 \
--run A3C \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pong-ram-v4 \
--run A3C \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env PongDeterministic-v0 \
--run A2C \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 1}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"free_log_std": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 1}' \
--config '{"simple_optimizer": false, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 1}' \
--config '{"simple_optimizer": true, "num_sgd_iter": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 1}' \
@@ -64,215 +64,215 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
--ray-num-gpus 1
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 1}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "use_gae": false, "batch_mode": "complete_episodes"}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 1}' \
--config '{"remote_worker_envs": true, "remote_env_batch_wait_ms": 99999999, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run PPO \
--stop '{"training_iteration": 2}' \
--config '{"remote_worker_envs": true, "num_envs_per_worker": 2, "num_workers": 1, "train_batch_size": 100, "sgd_minibatch_size": 50}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pendulum-v0 \
--run APPO \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "num_gpus": 0}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pendulum-v0 \
--run ES \
--stop '{"training_iteration": 1}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pong-v0 \
--run ES \
--stop '{"training_iteration": 1}' \
--config '{"stepsize": 0.01, "episodes_per_batch": 20, "train_batch_size": 100, "num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run A3C \
--stop '{"training_iteration": 1}' \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 1}' \
--config '{"lr": 1e-3, "schedule_max_timesteps": 100000, "exploration_fraction": 0.1, "exploration_final_eps": 0.02, "dueling": false, "hiddens": [], "model": {"fcnet_hiddens": [64], "fcnet_activation": "relu"}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run APEX \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "timesteps_per_iteration": 1000, "num_gpus": 0, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env FrozenLake-v0 \
--run DQN \
--stop '{"training_iteration": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env FrozenLake-v0 \
--run PPO \
--stop '{"training_iteration": 1}' \
--config '{"num_sgd_iter": 10, "sgd_minibatch_size": 64, "train_batch_size": 1000, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env PongDeterministic-v4 \
--run DQN \
--stop '{"training_iteration": 1}' \
--config '{"lr": 1e-4, "schedule_max_timesteps": 2000000, "buffer_size": 10000, "exploration_fraction": 0.1, "exploration_final_eps": 0.01, "sample_batch_size": 4, "learning_starts": 10000, "target_network_update_freq": 1000, "gamma": 0.99, "prioritized_replay": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env MontezumaRevenge-v0 \
--run PPO \
--stop '{"training_iteration": 1}' \
--config '{"kl_coeff": 1.0, "num_sgd_iter": 10, "lr": 1e-4, "sgd_minibatch_size": 64, "train_batch_size": 2000, "num_workers": 1, "model": {"dim": 40, "conv_filters": [[16, [8, 8], 4], [32, [4, 4], 2], [512, [5, 5], 1]]}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "use_pytorch": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "model": {"use_lstm": true, "max_seq_len": 100}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run PG \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1, "num_envs_per_worker": 10}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pong-v0 \
--run PG \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env FrozenLake-v0 \
--run PG \
--stop '{"training_iteration": 1}' \
--config '{"sample_batch_size": 500, "num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pendulum-v0 \
--run DDPG \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "num_aggregation_workers": 2, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run IMPALA \
--stop '{"training_iteration": 1}' \
--config '{"num_gpus": 0, "num_workers": 2, "min_iter_time_s": 1, "num_data_loader_buffers": 2, "replay_buffer_num_slots": 100, "replay_proportion": 1.0, "model": {"use_lstm": true}}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env MountainCarContinuous-v0 \
--run DDPG \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
@@ -280,7 +280,7 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pendulum-v0 \
--run APEX_DDPG \
--ray-num-cpus 8 \
@@ -288,196 +288,196 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
--config '{"num_workers": 2, "optimizer": {"num_replay_buffer_shards": 1}, "learning_starts": 100, "min_iter_time_s": 1, "batch_mode": "complete_episodes", "parameter_noise": false}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run MARWIL \
--stop '{"training_iteration": 1}' \
--config '{"input": "/ray/rllib/tests/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "shuffle_buffer_size": 10}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v0 \
--run DQN \
--stop '{"training_iteration": 1}' \
--config '{"input": "/ray/rllib/tests/data/cartpole_small", "learning_starts": 0, "input_evaluation": ["wis", "is"], "soft_q": true}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_local.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_local.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_reproducibility.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_reproducibility.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_dependency.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_dependency.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_legacy.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_legacy.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_io.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_io.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_checkpoint_restore.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_checkpoint_restore.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_rollout_worker.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_rollout_worker.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_nested_spaces.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_nested_spaces.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_external_env.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_external_env.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_external_multi_agent_env.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_external_multi_agent_env.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_model.py --run=A2C --stop=50
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_keras_model.py --run=A2C --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_model.py --run=PPO --stop=50
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_keras_model.py --run=PPO --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_model.py --run=DQN --stop=50
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_keras_model.py --run=DQN --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=PG --stop=50
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/parametric_action_cartpole.py --run=PG --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=PPO --stop=50
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/parametric_action_cartpole.py --run=PPO --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/parametric_action_cartpole.py --run=DQN --stop=50
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/parametric_action_cartpole.py --run=DQN --stop=50
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_lstm.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_lstm.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PPO
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PPO
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PG
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=PG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DQN
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DQN
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DDPG
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/batch_norm_model.py --num-iters=1 --run=DDPG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_multi_agent_env.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_multi_agent_env.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_supported_spaces.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_supported_spaces.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_env_with_subprocess.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_env_with_subprocess.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/tests/test_rollout.sh
/ray/ci/suppress_output --force-direct /ray/rllib/tests/test_rollout.sh
# Run all single-agent regression tests (3x retry each)
for yaml in $(ls $ROOT_DIR/../../rllib/tuned_examples/regression_tests); do
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/run_regression_tests.py \
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/run_regression_tests.py \
/ray/rllib/tuned_examples/regression_tests/$yaml
done
# Try a couple times since it's stochastic
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/multiagent_pendulum.py || \
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/multiagent_pendulum.py || \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/multiagent_pendulum.py || \
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/multiagent_pendulum.py || \
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/multiagent_pendulum.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/multiagent_pendulum.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/multiagent_cartpole.py --num-iters=2
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/multiagent_cartpole.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/multiagent_two_trainers.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_avail_actions_qmix.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_avail_actions_qmix.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/cartpole_lstm.py --run=PPO --stop=200
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/cartpole_lstm.py --run=IMPALA --stop=100
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/cartpole_lstm.py --run=IMPALA --stop=100
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/cartpole_lstm.py --stop=200 --use-prev-action-reward
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_loss.py --iters=2
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_loss.py --iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/rollout_worker_custom_workflow.py
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/rollout_worker_custom_workflow.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/eager_execution.py --iters=2
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/eager_execution.py --iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_tf_policy.py --iters=2
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_tf_policy.py --iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_torch_policy.py --iters=2
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_torch_policy.py --iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/rollout_worker_custom_workflow.py
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/rollout_worker_custom_workflow.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_metrics_and_callbacks.py --num-iters=2
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/contrib/random_agent/random_agent.py
/ray/ci/suppress_output --force-direct python /ray/rllib/contrib/random_agent/random_agent.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/centralized_critic.py --stop=2000
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/centralized_critic.py --stop=2000
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/centralized_critic_2.py --stop=2000
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/centralized_critic_2.py --stop=2000
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/twostep_game.py --stop=2000 --run=contrib/MADDPG
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/twostep_game.py --stop=2000 --run=contrib/MADDPG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/twostep_game.py --stop=2000 --run=PG
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/twostep_game.py --stop=2000 --run=PG
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/twostep_game.py --stop=2000 --run=QMIX
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/twostep_game.py --stop=2000 --run=QMIX
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/twostep_game.py --stop=2000 --run=APEX_QMIX
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/twostep_game.py --stop=2000 --run=APEX_QMIX
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/autoregressive_action_dist.py --stop=150
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/autoregressive_action_dist.py --stop=150
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env PongDeterministic-v4 \
--run A3C \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false, "model": {"use_lstm": false, "grayscale": true, "zero_mean": false, "dim": 84}, "preprocessor_pref": "rllib"}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env CartPole-v1 \
--run A3C \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env Pendulum-v0 \
--run A3C \
--stop '{"training_iteration": 1}' \
--config '{"num_workers": 2, "use_pytorch": true, "sample_async": false}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output /ray/rllib/train.py \
/ray/ci/suppress_output --force-direct /ray/rllib/train.py \
--env PongDeterministic-v4 \
--run IMPALA \
--stop='{"timesteps_total": 40000}' \
@@ -485,13 +485,13 @@ docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
--config '{"num_workers": 1, "num_gpus": 0, "num_envs_per_worker": 32, "sample_batch_size": 50, "train_batch_size": 50, "learner_queue_size": 1}'
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/agents/impala/vtrace_test.py
/ray/ci/suppress_output --force-direct python /ray/rllib/agents/impala/vtrace_test.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/tests/test_ignore_worker_failure.py
/ray/ci/suppress_output --force-direct python /ray/rllib/tests/test_ignore_worker_failure.py
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatAfterMeEnv
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatAfterMeEnv
docker run --rm --shm-size=${SHM_SIZE} --memory=${MEMORY_SIZE} $DOCKER_SHA \
/ray/ci/suppress_output python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatInitialEnv
/ray/ci/suppress_output --force-direct python /ray/rllib/examples/custom_keras_rnn_model.py --run=PPO --stop=50 --env=RepeatInitialEnv
ci/suppress_output
+6
View File
@@ -19,6 +19,12 @@ watchdog() {
watchdog & 2>/dev/null
WATCHDOG_PID=$!
if [ "$1" == "--force-direct" ]; then
echo "Setting RAY_FORCE_DIRECT=1"
export RAY_FORCE_DIRECT=1
shift
fi
time "$@" >$TMPFILE 2>&1
CODE=$?
python/ray/tests/BUILD
+18 -2
View File
@@ -1,11 +1,19 @@
py_test(
name = "test_actor",
size = "large",
size = "medium",
srcs = ["test_actor.py"],
tags = ["exclusive"],
deps = ["//:ray_lib"],
)
py_test(
name = "test_actor_direct",
size = "medium",
srcs = ["test_actor_direct.py", "test_actor.py"],
tags = ["exclusive", "manual"],
deps = ["//:ray_lib"],
)
py_test(
name = "test_actor_resources",
size = "large",
@@ -24,12 +32,20 @@ py_test(
py_test(
name = "test_basic",
size = "large",
size = "medium",
srcs = ["test_basic.py"],
tags = ["exclusive"],
deps = ["//:ray_lib"],
)
py_test(
name = "test_basic_direct",
size = "medium",
srcs = ["test_basic_direct.py", "test_basic.py"],
tags = ["exclusive"],
deps = ["//:ray_lib"],
)
py_test(
name = "test_advanced",
size = "large",
python/ray/tests/test_actor.py
+8
View File
@@ -18,6 +18,8 @@ import ray.test_utils
import ray.cluster_utils
from ray.test_utils import run_string_as_driver
RAY_FORCE_DIRECT = bool(os.environ.get("RAY_FORCE_DIRECT"))
def test_actor_init_error_propagated(ray_start_regular):
@ray.remote
@@ -807,6 +809,7 @@ def test_exception_raised_when_actor_node_dies(ray_start_cluster_head):
@pytest.mark.skipif(
os.environ.get("RAY_USE_NEW_GCS") == "on",
reason="Hanging with new GCS API.")
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="no ft yet")
def test_actor_init_fails(ray_start_cluster_head):
cluster = ray_start_cluster_head
remote_node = cluster.add_node()
@@ -832,6 +835,7 @@ def test_actor_init_fails(ray_start_cluster_head):
assert results == [1 for actor in actors]
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="no ft yet")
def test_reconstruction_suppression(ray_start_cluster_head):
cluster = ray_start_cluster_head
num_nodes = 5
@@ -1148,6 +1152,7 @@ def setup_queue_actor():
ray.shutdown()
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="TODO support block/unblock")
def test_fork(setup_queue_actor):
queue = setup_queue_actor
@@ -1166,6 +1171,7 @@ def test_fork(setup_queue_actor):
assert filtered_items == list(range(1))
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="TODO support block/unblock")
def test_fork_consistency(setup_queue_actor):
queue = setup_queue_actor
@@ -1197,6 +1203,7 @@ def test_fork_consistency(setup_queue_actor):
assert filtered_items == list(range(num_items_per_fork))
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="TODO support block/unblock")
def test_pickled_handle_consistency(setup_queue_actor):
queue = setup_queue_actor
@@ -1230,6 +1237,7 @@ def test_pickled_handle_consistency(setup_queue_actor):
assert filtered_items == list(range(num_items_per_fork))
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="TODO support block/unblock")
def test_nested_fork(setup_queue_actor):
queue = setup_queue_actor
python/ray/tests/test_actor_direct.py
+16
View File
@@ -0,0 +1,16 @@
"""Wrapper script that sets RAY_FORCE_DIRECT."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pytest
import sys
import os
if __name__ == "__main__":
os.environ["RAY_FORCE_DIRECT"] = "1"
sys.exit(
pytest.main(
["-v",
os.path.join(os.path.dirname(__file__), "test_actor.py")]))
python/ray/tests/test_basic.py
+27
View File
@@ -5,6 +5,7 @@ from __future__ import print_function
import collections
import io
import os
import json
import logging
import re
@@ -23,6 +24,8 @@ import ray.test_utils
logger = logging.getLogger(__name__)
RAY_FORCE_DIRECT = bool(os.environ.get("RAY_FORCE_DIRECT"))
def test_simple_serialization(ray_start_regular):
primitive_objects = [
@@ -91,6 +94,7 @@ def test_simple_serialization(ray_start_regular):
assert type(obj) == type(new_obj_2)
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="resource shape not implemented")
def test_fair_queueing(shutdown_only):
ray.init(
num_cpus=1, _internal_config=json.dumps({
@@ -1025,6 +1029,7 @@ def test_defining_remote_functions(shutdown_only):
assert ray.get(m.remote(1)) == 2
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="reconstruction not implemented")
def test_submit_api(shutdown_only):
ray.init(num_cpus=2, num_gpus=1, resources={"Custom": 1})
@@ -1083,6 +1088,7 @@ def test_submit_api(shutdown_only):
assert ray.get([id1, id2, id3, id4]) == [0, 1, "test", 2]
@pytest.mark.skipif(RAY_FORCE_DIRECT, reason="reconstruction not implemented")
def test_many_fractional_resources(shutdown_only):
ray.init(num_cpus=2, num_gpus=2, resources={"Custom": 2})
@@ -1318,6 +1324,27 @@ def test_direct_call_chain(ray_start_cluster):
assert ray.get(x) == 100
def test_direct_inline_arg_memory_corruption(ray_start_regular):
@ray.remote
def f():
return np.zeros(1000, dtype=np.uint8)
@ray.remote
class Actor(object):
def __init__(self):
self.z = []
def add(self, x):
self.z.append(x)
for prev in self.z:
assert np.sum(prev) == 0, ("memory corruption detected", prev)
a = Actor.options(is_direct_call=True).remote()
f_direct = f.options(is_direct_call=True)
for i in range(100):
ray.get(a.add.remote(f_direct.remote()))
def test_direct_actor_enabled(ray_start_regular):
@ray.remote
class Actor(object):
python/ray/tests/test_basic_direct.py
+16
View File
@@ -0,0 +1,16 @@
"""Wrapper script that sets RAY_FORCE_DIRECT."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import pytest
import sys
import os
if __name__ == "__main__":
os.environ["RAY_FORCE_DIRECT"] = "1"
sys.exit(
pytest.main(
["-v",
os.path.join(os.path.dirname(__file__), "test_basic.py")]))
src/ray/core_worker/core_worker.cc
+8 -7
View File
@@ -28,11 +28,6 @@ void BuildCommonTaskSpec(
// Set task arguments.
for (const auto &arg : args) {
if (arg.IsPassedByReference()) {
// TODO(ekl) remove this check once we deprecate TaskTransportType::RAYLET
if (transport_type == ray::TaskTransportType::RAYLET) {
RAY_CHECK(!arg.GetReference().IsDirectCallType())
<< "Passing direct call objects to non-direct tasks is not allowed.";
}
builder.AddByRefArg(arg.GetReference());
} else {
builder.AddByValueArg(arg.GetValue());
@@ -169,6 +164,7 @@ CoreWorker::CoreWorker(const WorkerType worker_type, const Language language,
RAY_CHECK_OK(plasma_store_provider_->Put(obj, obj_id));
},
ref_counting_enabled ? reference_counter_ : nullptr, raylet_client_));
resolver_.reset(new LocalDependencyResolver(memory_store_));
// Create an entry for the driver task in the task table. This task is
// added immediately with status RUNNING. This allows us to push errors
@@ -621,7 +617,12 @@ Status CoreWorker::CreateActor(const RayFunction &function,
*return_actor_id = actor_id;
TaskSpecification task_spec = builder.Build();
PinObjectReferences(task_spec, TaskTransportType::RAYLET);
return raylet_client_->SubmitTask(task_spec);
// TODO(ekl) if we moved actor creation to use direct call tasks, then we won't
// need to manually resolve direct call args here.
resolver_->ResolveDependencies(task_spec, [this, task_spec]() {
RAY_CHECK_OK(raylet_client_->SubmitTask(task_spec));
});
return Status::OK();
}
Status CoreWorker::SubmitActorTask(const ActorID &actor_id, const RayFunction &function,
@@ -874,7 +875,7 @@ Status CoreWorker::BuildArgsForExecutor(const TaskSpecification &task,
metadata = std::make_shared<LocalMemoryBuffer>(
const_cast<uint8_t *>(task.ArgMetadata(i)), task.ArgMetadataSize(i));
}
args->at(i) = std::make_shared<RayObject>(data, metadata);
args->at(i) = std::make_shared<RayObject>(data, metadata, /*copy_data*/ true);
arg_reference_ids->at(i) = ObjectID::Nil();
}
}
src/ray/core_worker/core_worker.h
+3
View File
@@ -522,6 +522,9 @@ class CoreWorker {
/// Map from actor ID to a handle to that actor.
absl::flat_hash_map<ActorID, std::unique_ptr<ActorHandle>> actor_handles_;
/// Resolve local and remote dependencies for actor creation.
std::unique_ptr<LocalDependencyResolver> resolver_;
///
/// Fields related to task execution.
///
src/ray/core_worker/transport/dependency_resolver.cc
+1 -1
View File
@@ -42,7 +42,7 @@ void DoInlineObjectValue(const ObjectID &obj_id, std::shared_ptr<RayObject> valu
RAY_CHECK(found) << "obj id " << obj_id << " not found";
}
void LocalDependencyResolver::ResolveDependencies(const TaskSpecification &task,
void LocalDependencyResolver::ResolveDependencies(TaskSpecification &task,
std::function<void()> on_complete) {
absl::flat_hash_set<ObjectID> local_dependencies;
for (size_t i = 0; i < task.NumArgs(); i++) {
src/ray/core_worker/transport/dependency_resolver.h
+1 -2
View File
@@ -22,8 +22,7 @@ class LocalDependencyResolver {
/// Note: This method **will mutate** the given TaskSpecification.
///
/// Postcondition: all direct call ids in arguments are converted to values.
void ResolveDependencies(const TaskSpecification &task,
std::function<void()> on_complete);
void ResolveDependencies(TaskSpecification &task, std::function<void()> on_complete);
/// Return the number of tasks pending dependency resolution.
/// TODO(ekl) this should be exposed in worker stats.
src/ray/core_worker/transport/raylet_transport.cc
+2 -1
View File
@@ -17,7 +17,8 @@ void CoreWorkerRayletTaskReceiver::HandleAssignTask(
rpc::SendReplyCallback send_reply_callback) {
const Task task(request.task());
const auto &task_spec = task.GetTaskSpecification();
RAY_LOG(DEBUG) << "Received task " << task_spec.TaskId();
RAY_LOG(DEBUG) << "Received task " << task_spec.TaskId() << " is create "
<< task_spec.IsActorCreationTask();
// Set the resource IDs for this task.
// TODO: convert the resource map to protobuf and change this.
src/ray/raylet/raylet_client.cc
+8
View File
@@ -230,6 +230,14 @@ RayletClient::RayletClient(std::shared_ptr<ray::rpc::NodeManagerWorkerClient> gr
ray::Status RayletClient::SubmitTask(const ray::TaskSpecification &task_spec) {
ray::rpc::SubmitTaskRequest request;
for (size_t i = 0; i < task_spec.NumArgs(); i++) {
if (task_spec.ArgByRef(i)) {
for (size_t j = 0; j < task_spec.ArgIdCount(i); j++) {
RAY_CHECK(!task_spec.ArgId(i, j).IsDirectCallType())
<< "Passing direct call objects to non-direct tasks is not allowed.";
}
}
}
request.mutable_task_spec()->CopyFrom(task_spec.GetMessage());
return grpc_client_->SubmitTask(request, /*callback=*/nullptr);
}
src/ray/rpc/client_call.h
+6 -2
View File
@@ -198,8 +198,12 @@ class ClientCallManager {
auto status = cqs_[index].AsyncNext(&got_tag, &ok, deadline);
if (status == grpc::CompletionQueue::SHUTDOWN) {
break;
}
if (status != grpc::CompletionQueue::TIMEOUT) {
} else if (status == grpc::CompletionQueue::TIMEOUT && shutdown_) {
// If we timed out and shutdown, then exit immediately. This should not
// be needed, but gRPC seems to not return SHUTDOWN correctly in these
// cases (e.g., test_wait will hang on shutdown without this check).
break;
} else if (status != grpc::CompletionQueue::TIMEOUT) {
auto tag = reinterpret_cast<ClientCallTag *>(got_tag);
if (ok && !main_service_.stopped() && !shutdown_) {
// Post the callback to the main event loop.