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Remove smoke tests for e2e multi node trainings (ray-project#37324)
Signed-off-by: Artur Niederfahrenhorst <attaismyname@googlemail.com> Signed-off-by: e428265 <arvind.chandramouli@lmco.com>
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release/rllib_tests/smoke_tests/smoke_test_basic_multi_node_training_learner.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,119 +0,0 @@ | ||
| import ray | ||
| from ray import air, tune | ||
| from ray.rllib.algorithms.ppo import PPOConfig | ||
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| def run_with_tuner_n_rollout_worker_2_gpu(config): | ||
| """Run training with n rollout workers and 2 learner workers with gpu.""" | ||
| config = config.rollouts(num_rollout_workers=5) | ||
| tuner = tune.Tuner( | ||
| "PPO", | ||
| param_space=config, | ||
| run_config=air.RunConfig( | ||
| storage_path="/mnt/cluster_storage", | ||
| stop={"timesteps_total": 128}, | ||
| failure_config=air.FailureConfig(fail_fast=True), | ||
| ), | ||
| ) | ||
| tuner.fit() | ||
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| def run_with_tuner_0_rollout_worker_2_gpu(config): | ||
| """Run training with 0 rollout workers with 2 learner workers with gpu.""" | ||
| config = config.rollouts(num_rollout_workers=0) | ||
| tuner = tune.Tuner( | ||
| "PPO", | ||
| param_space=config, | ||
| run_config=air.RunConfig( | ||
| storage_path="/mnt/cluster_storage", | ||
| stop={"timesteps_total": 128}, | ||
| failure_config=air.FailureConfig(fail_fast=True), | ||
| ), | ||
| ) | ||
| tuner.fit() | ||
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| def run_tuner_n_rollout_workers_0_gpu(config): | ||
| """Run training with n rollout workers, multiple learner workers, and no gpu.""" | ||
| config = config.rollouts(num_rollout_workers=5) | ||
| config = config.resources( | ||
| num_cpus_per_learner_worker=1, | ||
| num_learner_workers=2, | ||
| ) | ||
|
|
||
| tuner = tune.Tuner( | ||
| "PPO", | ||
| param_space=config, | ||
| run_config=air.RunConfig( | ||
| storage_path="/mnt/cluster_storage", | ||
| stop={"timesteps_total": 128}, | ||
| failure_config=air.FailureConfig(fail_fast=True), | ||
| ), | ||
| ) | ||
| tuner.fit() | ||
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| def run_tuner_n_rollout_workers_1_gpu_local(config): | ||
| """Run training with n rollout workers, local learner, and 1 gpu.""" | ||
| config = config.rollouts(num_rollout_workers=5) | ||
| config = config.resources( | ||
| num_gpus_per_learner_worker=1, | ||
| num_learner_workers=0, | ||
| ) | ||
|
|
||
| tuner = tune.Tuner( | ||
| "PPO", | ||
| param_space=config, | ||
| run_config=air.RunConfig( | ||
| storage_path="/mnt/cluster_storage", | ||
| stop={"timesteps_total": 128}, | ||
| failure_config=air.FailureConfig(fail_fast=True), | ||
| ), | ||
| ) | ||
| tuner.fit() | ||
|
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| def test_multi_node_training_smoke(): | ||
| """A smoke test to see if we can run multi node training without pg problems. | ||
| This test is run on a 3 node cluster. The head node is a m5.xlarge (4 cpu), | ||
| the worker nodes are 2 g4dn.xlarge (1 gpu, 4 cpu) machines. | ||
| """ | ||
|
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| ray.init() | ||
|
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| config = ( | ||
| PPOConfig() | ||
| .training( | ||
| _enable_learner_api=True, | ||
| model={ | ||
| "fcnet_hiddens": [256, 256, 256], | ||
| "fcnet_activation": "relu", | ||
| "vf_share_layers": True, | ||
| }, | ||
| train_batch_size=128, | ||
| ) | ||
| .rl_module(_enable_rl_module_api=True) | ||
| .environment("CartPole-v1") | ||
| .resources( | ||
| num_gpus_per_learner_worker=1, | ||
| num_learner_workers=2, | ||
| ) | ||
| .rollouts(num_rollout_workers=2) | ||
| .reporting(min_time_s_per_iteration=0, min_sample_timesteps_per_iteration=10) | ||
| ) | ||
| for fw in ["tf2", "torch"]: | ||
| config = config.framework(fw, eager_tracing=True) | ||
|
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| run_with_tuner_0_rollout_worker_2_gpu(config) | ||
| run_with_tuner_n_rollout_worker_2_gpu(config) | ||
| run_tuner_n_rollout_workers_0_gpu(config) | ||
| run_tuner_n_rollout_workers_1_gpu_local(config) | ||
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| if __name__ == "__main__": | ||
| import sys | ||
| import pytest | ||
|
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| sys.exit(pytest.main(["-v", __file__])) | ||