ray/rllib/tests/test_exec_api.py

import unittest

import ray
from ray.rllib.agents.a3c import A2CTrainer
from ray.rllib.execution.common import STEPS_SAMPLED_COUNTER, STEPS_TRAINED_COUNTER
from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
from ray.rllib.utils.test_utils import framework_iterator


class TestDistributedExecution(unittest.TestCase):
    """General tests for the distributed execution API."""

    @classmethod
    def setUpClass(cls):
        ray.init(num_cpus=4)

    @classmethod
    def tearDownClass(cls):
        ray.shutdown()

    def test_exec_plan_stats(ray_start_regular):
        for fw in framework_iterator(frameworks=("torch", "tf")):
            trainer = A2CTrainer(
                env="CartPole-v0",
                config={
                    "min_time_s_per_reporting": 0,
                    "framework": fw,
                    "_disable_execution_plan_api": False,
                },
            )
            result = trainer.train()
            assert isinstance(result, dict)
            assert "info" in result
            assert LEARNER_INFO in result["info"]
            assert STEPS_SAMPLED_COUNTER in result["info"]
            assert STEPS_TRAINED_COUNTER in result["info"]
            assert "timers" in result
            assert "learn_time_ms" in result["timers"]
            assert "learn_throughput" in result["timers"]
            assert "sample_time_ms" in result["timers"]
            assert "sample_throughput" in result["timers"]
            assert "update_time_ms" in result["timers"]

    def test_exec_plan_save_restore(ray_start_regular):
        for fw in framework_iterator(frameworks=("torch", "tf")):
            trainer = A2CTrainer(
                env="CartPole-v0",
                config={
                    "min_time_s_per_reporting": 0,
                    "framework": fw,
                    "_disable_execution_plan_api": False,
                },
            )
            res1 = trainer.train()
            checkpoint = trainer.save()
            for _ in range(2):
                res2 = trainer.train()
            assert res2["timesteps_total"] > res1["timesteps_total"], (res1, res2)
            trainer.restore(checkpoint)

            # Should restore the timesteps counter to the same as res2.
            res3 = trainer.train()
            assert res3["timesteps_total"] < res2["timesteps_total"], (res2, res3)


if __name__ == "__main__":
    import pytest
    import sys

    sys.exit(pytest.main(["-v", __file__]))