wehub-resource-sync
129 lines
3.7 KiB
Python
129 lines
3.7 KiB
Python
# flake8: noqa
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# isort: skip_file
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# __lightgbm_start__
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import pandas as pd
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import lightgbm as lgb
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# 1. Load your data as a `lightgbm.Dataset`.
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train_df = pd.read_csv("s3://ray-example-data/iris/train/1.csv")
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eval_df = pd.read_csv("s3://ray-example-data/iris/val/1.csv")
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train_X = train_df.drop("target", axis=1)
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train_y = train_df["target"]
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eval_X = eval_df.drop("target", axis=1)
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eval_y = eval_df["target"]
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train_set = lgb.Dataset(train_X, label=train_y)
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eval_set = lgb.Dataset(eval_X, label=eval_y)
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# 2. Define your LightGBM model training parameters.
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params = {
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"objective": "multiclass",
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"num_class": 3,
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"metric": ["multi_logloss", "multi_error"],
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"verbosity": -1,
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"boosting_type": "gbdt",
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"num_leaves": 31,
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"learning_rate": 0.05,
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"feature_fraction": 0.9,
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"bagging_fraction": 0.8,
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"bagging_freq": 5,
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}
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# 3. Do non-distributed training.
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model = lgb.train(
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params,
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train_set,
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valid_sets=[eval_set],
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valid_names=["eval"],
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num_boost_round=100,
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)
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# __lightgbm_end__
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# __lightgbm_ray_start__
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import lightgbm as lgb
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import ray.train
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from ray.train.lightgbm import LightGBMTrainer, RayTrainReportCallback
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# 1. Load your data as a Ray Data Dataset.
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train_dataset = ray.data.read_csv("s3://anonymous@ray-example-data/iris/train")
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eval_dataset = ray.data.read_csv("s3://anonymous@ray-example-data/iris/val")
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def train_func():
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# 2. Load your data shard as a `lightgbm.Dataset`.
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# Get dataset shards for this worker
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train_shard = ray.train.get_dataset_shard("train")
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eval_shard = ray.train.get_dataset_shard("eval")
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# Convert shards to PyArrow tables. LightGBM (>=4.2.0) supports PyArrow
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# natively, which avoids a round-trip through pandas.
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import pyarrow as pa
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train_table = pa.concat_tables(
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train_shard.iter_batches(batch_format="pyarrow", batch_size=None)
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)
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eval_table = pa.concat_tables(
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eval_shard.iter_batches(batch_format="pyarrow", batch_size=None)
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)
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train_X = train_table.drop(["target"])
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train_y = train_table.column("target")
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eval_X = eval_table.drop(["target"])
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eval_y = eval_table.column("target")
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train_set = lgb.Dataset(train_X, label=train_y)
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eval_set = lgb.Dataset(eval_X, label=eval_y)
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# 3. Define your LightGBM model training parameters.
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params = {
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"objective": "multiclass",
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"num_class": 3,
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"metric": ["multi_logloss", "multi_error"],
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"verbosity": -1,
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"boosting_type": "gbdt",
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"num_leaves": 31,
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"learning_rate": 0.05,
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"feature_fraction": 0.9,
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"bagging_fraction": 0.8,
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"bagging_freq": 5,
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# Adding the lines below are the only changes needed
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# for your `lgb.train` call!
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"tree_learner": "data_parallel",
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"pre_partition": True,
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**ray.train.lightgbm.get_network_params(),
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}
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# 4. Do distributed data-parallel training.
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# Ray Train sets up the necessary coordinator processes and
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# environment variables for your workers to communicate with each other.
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model = lgb.train(
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params,
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train_set,
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valid_sets=[eval_set],
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valid_names=["eval"],
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num_boost_round=100,
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# Optional: Use the `RayTrainReportCallback` to save and report checkpoints.
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callbacks=[RayTrainReportCallback()],
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)
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# 5. Configure scaling and resource requirements.
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scaling_config = ray.train.ScalingConfig(num_workers=2, resources_per_worker={"CPU": 2})
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# 6. Launch distributed training job.
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trainer = LightGBMTrainer(
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train_func,
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scaling_config=scaling_config,
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datasets={"train": train_dataset, "eval": eval_dataset},
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)
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result = trainer.fit()
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# 7. Load the trained model.
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model = RayTrainReportCallback.get_model(result.checkpoint)
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# __lightgbm_ray_end__
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