mirror of
https://github.com/vale981/ray
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47 lines
1.6 KiB
Python
47 lines
1.6 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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from ray.rllib.utils.typing import Dict, SampleBatchType
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from ray.util.iter_metrics import MetricsContext
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# Backward compatibility.
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from ray.rllib.utils.metrics import ( # noqa: F401
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LAST_TARGET_UPDATE_TS,
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NUM_TARGET_UPDATES,
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APPLY_GRADS_TIMER,
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COMPUTE_GRADS_TIMER,
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SYNCH_WORKER_WEIGHTS_TIMER as WORKER_UPDATE_TIMER,
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GRAD_WAIT_TIMER,
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SAMPLE_TIMER,
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LEARN_ON_BATCH_TIMER,
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LOAD_BATCH_TIMER,
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)
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STEPS_SAMPLED_COUNTER = "num_steps_sampled"
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AGENT_STEPS_SAMPLED_COUNTER = "num_agent_steps_sampled"
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STEPS_TRAINED_COUNTER = "num_steps_trained"
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STEPS_TRAINED_THIS_ITER_COUNTER = "num_steps_trained_this_iter"
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AGENT_STEPS_TRAINED_COUNTER = "num_agent_steps_trained"
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# End: Backward compatibility.
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# Asserts that an object is a type of SampleBatch.
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def _check_sample_batch_type(batch: SampleBatchType) -> None:
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if not isinstance(batch, (SampleBatch, MultiAgentBatch)):
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raise ValueError(
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"Expected either SampleBatch or MultiAgentBatch, "
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"got {}: {}".format(type(batch), batch)
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)
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# Returns pipeline global vars that should be periodically sent to each worker.
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def _get_global_vars() -> Dict:
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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() -> MetricsContext:
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"""Return shared metrics for the training workflow.
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This only applies if this algorithm has an execution plan."""
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return LocalIterator.get_metrics()
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