ray.rllib.execution.train_ops.multi_gpu_train_one_step#

ray.rllib.execution.train_ops.multi_gpu_train_one_step(algorithm, train_batch) → Dict[source]#

Multi-GPU version of train_one_step.

Uses the policies’ load_batch_into_buffer and learn_on_loaded_batch methods to be more efficient wrt CPU/GPU data transfers. For example, when doing multiple passes through a train batch (e.g. for PPO) using config.num_sgd_iter, the actual train batch is only split once and loaded once into the GPU(s).

from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
algo = [...]
train_batch = synchronous_parallel_sample(algo.workers)
# This trains the policy on one batch.
print(multi_gpu_train_one_step(algo, train_batch)))

{"default_policy": ...}

Updates the NUM_ENV_STEPS_TRAINED and NUM_AGENT_STEPS_TRAINED counters as well as the LOAD_BATCH_TIMER and LEARN_ON_BATCH_TIMER timers of the Algorithm instance.