86cff17e7ef1b355e1ab4862ccd664ed4c856227,python/ray/util/sgd/torch/examples/cifar_pytorch_example.py,,tune_example,#,103

Before Change

        "num_workers": num_workers,
        "initialization_hook": initialization_hook,
        "use_gpu": use_gpu,
        "config": {
            "lr": tune.choice([1e-4, 1e-3]),
            BATCH_SIZE: 128,
            "test_mode": test_mode
        },
        "backend": "nccl" if use_gpu else "gloo"
    }

After Change

        scheduler_creator=scheduler_creator,
        initialization_hook=initialization_hook,
        num_workers=num_workers,
        config={
            "test_mode": test_mode,  // user-defined param to subset the data
            BATCH_SIZE: 128 * num_workers,
        },
        use_gpu=use_gpu,
        scheduler_step_freq="epoch",
        use_fp16=use_fp16)

    pbt_scheduler = PopulationBasedTraining(
        time_attr="training_iteration",
        metric="val_loss",
        mode="min",
        perturbation_interval=1,
        hyperparam_mutations={
            // distribution for resampling
            "lr": lambda: np.random.uniform(0.001, 1),
            // allow perturbations within this set of categorical values
            "momentum": [0.8, 0.9, 0.99],
        })

    reporter = CLIReporter()
    reporter.add_metric_column("val_loss", "loss")
    reporter.add_metric_column("val_accuracy", "acc")

    analysis = tune.run(
        TorchTrainable,
        num_samples=4,
        config={
            "lr": tune.choice([0.001, 0.01, 0.1]),
            "momentum": 0.8
        },
        stop={"training_iteration": 2 if test_mode else 100},
        max_failures=3,  // used for fault tolerance
        checkpoint_freq=3,  // used for fault tolerance

Instances