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https://github.com/vale981/ray
synced 2025-03-10 05:16:49 -04:00
4b9a89ad90
The package "ml" should be renamed to "air". Main question: Keep a `ml.py` with `from ray.air import *` for some level of backwards compatibility? I'd go for no to force people to use the new structure.
89 lines
2.6 KiB
Python
89 lines
2.6 KiB
Python
# flake8: noqa
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# __shared_dataset_start__
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import ray
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from ray.air.utils.check_ingest import DummyTrainer
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from ray.tune.tuner import Tuner, TuneConfig
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ray.init(num_cpus=5)
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# Generate a synthetic 100MiB tensor dataset.
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dataset = ray.data.range_tensor(500, shape=(80, 80, 4), parallelism=10)
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# Create an example trainer that simply loops over the data a few times.
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trainer = DummyTrainer(datasets={"train": dataset}, runtime_seconds=1)
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# Run the Trainer 4x in parallel with Tune.
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tuner = Tuner(
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trainer,
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tune_config=TuneConfig(num_samples=4),
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)
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tuner.fit()
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# __shared_dataset_end__
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ray.shutdown()
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# __indep_dataset_start__
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import ray
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from ray import tune
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from ray.air.utils.check_ingest import DummyTrainer
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from ray.tune.tuner import Tuner, TuneConfig
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ray.init(num_cpus=5)
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def make_ds_1():
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"""Dataset creator function 1."""
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return ray.data.range_tensor(500, shape=(80, 80, 4), parallelism=10)
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def make_ds_2():
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"""Dataset creator function 2."""
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return ray.data.range_tensor(50, shape=(80, 80, 4), parallelism=10)
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# Create an example trainer that simply loops over the data a few times.
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trainer = DummyTrainer(datasets={}, runtime_seconds=1)
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# Run the Trainer 4x in parallel with Tune.
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# Two trials will use the dataset created by `make_ds_1`, and two trials will
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# use the dataset created by `make_ds_2`.
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tuner = Tuner(
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trainer,
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# Instead of passing Dataset references directly, we pass functions that
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# generate the dataset when called.
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param_space={"datasets": {"train": tune.grid_search([make_ds_1, make_ds_2])}},
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tune_config=TuneConfig(num_samples=2),
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)
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tuner.fit()
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# __indep_dataset_end__
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# __check_ingest_1__
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import ray
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from ray.air.preprocessors import Chain, BatchMapper
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from ray.air.utils.check_ingest import DummyTrainer
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# Generate a synthetic dataset of ~10GiB of float64 data. The dataset is sharded
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# into 100 blocks (parallelism=100).
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dataset = ray.data.range_tensor(50000, shape=(80, 80, 4), parallelism=100)
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# An example preprocessor chain that just scales all values by 4.0 in two stages.
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preprocessor = Chain(
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BatchMapper(lambda df: df * 2),
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BatchMapper(lambda df: df * 2),
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)
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# __check_ingest_1_end__
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# __check_ingest_2__
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# Setup the dummy trainer that prints ingest stats.
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# Run and print ingest stats.
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trainer = DummyTrainer(
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scaling_config={"num_workers": 1, "use_gpu": False},
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datasets={"train": dataset},
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preprocessor=preprocessor,
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runtime_seconds=1, # Stop after this amount or time or 1 epoch is read.
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prefetch_blocks=1, # Number of blocks to prefetch when reading data.
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batch_size=None, # Use whole blocks as batches.
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)
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trainer.fit()
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# __check_ingest_2_end__
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