mirror of
https://github.com/vale981/ray
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42 lines
1.4 KiB
Python
42 lines
1.4 KiB
Python
from ray.util.iter import LocalIterator
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from ray.rllib.policy.sample_batch import SampleBatch, MultiAgentBatch
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# Counters for training progress (keys for metrics.counters).
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STEPS_SAMPLED_COUNTER = "num_steps_sampled"
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STEPS_TRAINED_COUNTER = "num_steps_trained"
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# Counters to track target network updates.
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LAST_TARGET_UPDATE_TS = "last_target_update_ts"
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NUM_TARGET_UPDATES = "num_target_updates"
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# Performance timers (keys for metrics.timers).
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APPLY_GRADS_TIMER = "apply_grad"
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COMPUTE_GRADS_TIMER = "compute_grads"
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WORKER_UPDATE_TIMER = "update"
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GRAD_WAIT_TIMER = "grad_wait"
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SAMPLE_TIMER = "sample"
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LEARN_ON_BATCH_TIMER = "learn"
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LOAD_BATCH_TIMER = "load"
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# Instant metrics (keys for metrics.info).
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LEARNER_INFO = "learner"
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# Asserts that an object is a type of SampleBatch.
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def _check_sample_batch_type(batch):
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if not isinstance(batch, (SampleBatch, MultiAgentBatch)):
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raise ValueError("Expected either SampleBatch or MultiAgentBatch, "
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"got {}: {}".format(type(batch), batch))
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# Returns pipeline global vars that should be periodically sent to each worker.
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def _get_global_vars():
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metrics = LocalIterator.get_metrics()
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return {"timestep": metrics.counters[STEPS_SAMPLED_COUNTER]}
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def _get_shared_metrics():
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"""Return shared metrics for the training workflow.
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This only applies if this trainer has an execution plan."""
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return LocalIterator.get_metrics()
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