mirror of
https://github.com/vale981/ray
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73 lines
2 KiB
Python
73 lines
2 KiB
Python
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# flake8: noqa
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# isort: skip_file
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# __config_scaling_1__
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from ray.air import session
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from ray.train.torch import TorchTrainer
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from ray.air.config import ScalingConfig
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import ray.data
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def train_loop_per_worker():
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# By default, bulk loading is used and returns a Dataset object.
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data_shard = session.get_dataset_shard("train")
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# Manually iterate over the data 10 times (10 epochs).
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for _ in range(10):
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for batch in data_shard.iter_batches():
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print("Do some training on batch", batch)
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trainer = TorchTrainer(
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train_loop_per_worker,
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scaling_config=ScalingConfig(num_workers=1),
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datasets={"train": ray.data.range_tensor(1000)},
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)
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trainer.fit()
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# __config_scaling_1_end__
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# __config_scaling_2__
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from ray.air import session
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from ray.train.torch import TorchTrainer
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import ray.data
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from ray.air.config import ScalingConfig
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from ray import tune
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from ray.tune.tuner import Tuner
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from ray.tune.tune_config import TuneConfig
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def train_loop_per_worker():
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# By default, bulk loading is used and returns a Dataset object.
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data_shard = session.get_dataset_shard("train")
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# Manually iterate over the data 10 times (10 epochs).
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for _ in range(10):
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for batch in data_shard.iter_batches():
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print("Do some training on batch", batch)
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trainer = TorchTrainer(
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train_loop_per_worker,
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scaling_config=ScalingConfig(num_workers=1),
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datasets={"train": ray.data.range_tensor(1000)},
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)
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param_space = {
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"scaling_config": ScalingConfig(num_workers=tune.grid_search([1, 2])),
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"params": {
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"objective": "binary:logistic",
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"tree_method": "approx",
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"eval_metric": ["logloss", "error"],
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"eta": tune.loguniform(1e-4, 1e-1),
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"subsample": tune.uniform(0.5, 1.0),
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"max_depth": tune.randint(1, 9),
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},
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}
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tuner = Tuner(
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trainable=trainer,
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param_space=param_space,
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tune_config=TuneConfig(mode="min", metric="train-error"),
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)
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results = tuner.fit()
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# __config_scaling_2_end__
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