mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
144 lines
3.7 KiB
Python
144 lines
3.7 KiB
Python
# flake8: noqa
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# isort: skip_file
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# __air_preprocessors_start__
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import ray
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import pandas as pd
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from sklearn.datasets import load_breast_cancer
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from ray.data.preprocessors import *
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# Split data into train and validation.
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dataset = ray.data.read_csv("s3://anonymous@air-example-data/breast_cancer.csv")
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train_dataset, valid_dataset = dataset.train_test_split(test_size=0.3)
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test_dataset = valid_dataset.drop_columns(["target"])
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columns_to_scale = ["mean radius", "mean texture"]
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preprocessor = StandardScaler(columns=columns_to_scale)
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# __air_preprocessors_end__
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# __air_trainer_start__
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from ray.train.xgboost import XGBoostTrainer
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from ray.air.config import ScalingConfig
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num_workers = 2
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use_gpu = False
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# XGBoost specific params
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params = {
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"tree_method": "approx",
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"objective": "binary:logistic",
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"eval_metric": ["logloss", "error"],
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"max_depth": 2,
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}
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trainer = XGBoostTrainer(
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scaling_config=ScalingConfig(
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num_workers=num_workers,
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use_gpu=use_gpu,
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),
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label_column="target",
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params=params,
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datasets={"train": train_dataset, "valid": valid_dataset},
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preprocessor=preprocessor,
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num_boost_round=5,
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)
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result = trainer.fit()
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# __air_trainer_end__
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# __air_trainer_output_start__
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print(result.metrics)
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print(result.checkpoint)
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# __air_trainer_output_end__
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# __air_tuner_start__
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from ray import tune
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from ray.tune.tuner import Tuner, TuneConfig
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tuner = Tuner(
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trainer,
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param_space={"params": {"max_depth": tune.randint(1, 9)}},
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tune_config=TuneConfig(num_samples=5, metric="train-logloss", mode="min"),
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)
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result_grid = tuner.fit()
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best_result = result_grid.get_best_result()
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print(best_result)
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# __air_tuner_end__
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# __air_checkpoints_start__
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checkpoint = result.checkpoint
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print(checkpoint)
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# Checkpoint(local_path=..../checkpoint_000005)
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tuned_checkpoint = result_grid.get_best_result().checkpoint
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print(tuned_checkpoint)
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# Checkpoint(local_path=..../checkpoint_000005)
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# __air_checkpoints_end__
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# __checkpoint_adhoc_start__
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from ray.train.tensorflow import TensorflowCheckpoint
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import tensorflow as tf
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# This can be a trained model.
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def build_model() -> tf.keras.Model:
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model = tf.keras.Sequential(
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[
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tf.keras.layers.InputLayer(input_shape=(1,)),
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tf.keras.layers.Dense(1),
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]
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)
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return model
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model = build_model()
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checkpoint = TensorflowCheckpoint.from_model(model)
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# __checkpoint_adhoc_end__
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# __air_batch_predictor_start__
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from ray.train.batch_predictor import BatchPredictor
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from ray.train.xgboost import XGBoostPredictor
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batch_predictor = BatchPredictor.from_checkpoint(result.checkpoint, XGBoostPredictor)
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# Bulk batch prediction.
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predicted_probabilities = batch_predictor.predict(test_dataset)
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# Pipelined batch prediction: instead of processing the data in bulk, process it
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# incrementally in windows of the given size.
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pipeline = batch_predictor.predict_pipelined(test_dataset, bytes_per_window=1048576)
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for batch in pipeline.iter_batches():
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print("Pipeline result", batch)
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# __air_batch_predictor_end__
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# __air_deploy_start__
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from ray import serve
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from fastapi import Request
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from ray.serve import PredictorDeployment
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from ray.serve.http_adapters import json_request
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async def adapter(request: Request):
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content = await request.json()
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print(content)
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return pd.DataFrame.from_dict(content)
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serve.run(
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PredictorDeployment.options(name="XGBoostService").bind(
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XGBoostPredictor, result.checkpoint, batching_params=False, http_adapter=adapter
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)
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)
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# __air_deploy_end__
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# __air_inference_start__
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import requests
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sample_input = test_dataset.take(1)
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sample_input = dict(sample_input[0])
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output = requests.post("http://localhost:8000/", json=[sample_input]).json()
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print(output)
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# __air_inference_end__
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