mirror of
https://github.com/vale981/ray
synced 2025-03-09 12:56:46 -04:00
130 lines
3.3 KiB
Python
130 lines
3.3 KiB
Python
# flake8: noqa
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# isort: skip_file
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# __checkpoint_quick_start__
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from ray.train.tensorflow import to_air_checkpoint
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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 = to_air_checkpoint(model)
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# __checkpoint_quick_end__
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# __use_trainer_checkpoint_start__
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import pandas as pd
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import ray
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from ray.air import train_test_split
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from ray.train.xgboost import XGBoostTrainer
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bc_df = pd.read_csv(
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"https://air-example-data.s3.us-east-2.amazonaws.com/breast_cancer.csv"
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)
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dataset = ray.data.from_pandas(bc_df)
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# Optionally, read directly from s3
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# dataset = ray.data.read_csv("s3://air-example-data/breast_cancer.csv")
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# Split data into train and validation.
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train_dataset, valid_dataset = train_test_split(dataset, test_size=0.3)
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trainer = XGBoostTrainer(
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scaling_config={"num_workers": 2},
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label_column="target",
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params={
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"objective": "binary:logistic",
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"eval_metric": ["logloss", "error"],
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},
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datasets={"train": train_dataset},
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num_boost_round=5,
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)
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result = trainer.fit()
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checkpoint = result.checkpoint
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# __use_trainer_checkpoint_end__
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# __batch_pred_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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# Create a test dataset by dropping the target column.
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test_dataset = valid_dataset.map_batches(
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lambda df: df.drop("target", axis=1), batch_format="pandas"
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)
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batch_predictor = BatchPredictor.from_checkpoint(checkpoint, XGBoostPredictor)
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# Bulk batch prediction.
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batch_predictor.predict(test_dataset)
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# __batch_pred_end__
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# __online_inference_start__
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import requests
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from fastapi import Request
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import pandas as pd
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from ray import serve
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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.start(detached=True)
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deployment = PredictorDeployment.options(name="XGBoostService")
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deployment.deploy(
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XGBoostPredictor, checkpoint, batching_params=False, http_adapter=adapter
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)
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print(deployment.url)
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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(deployment.url, json=[sample_input]).json()
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print(output)
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# __online_inference_end__
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# __basic_checkpoint_start__
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from ray.air.checkpoint import Checkpoint
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# Create checkpoint data dict
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checkpoint_data = {"data": 123}
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# Create checkpoint object from data
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checkpoint = Checkpoint.from_dict(checkpoint_data)
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# Save checkpoint to a directory on the file system.
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path = checkpoint.to_directory()
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# This path can then be passed around,
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# # e.g. to a different function or a different script.
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# You can also use `checkpoint.to_uri/from_uri` to
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# read from/write to cloud storage
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# In another function or script, recover Checkpoint object from path
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checkpoint = Checkpoint.from_directory(path)
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# Convert into dictionary again
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recovered_data = checkpoint.to_dict()
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# It is guaranteed that the original data has been recovered
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assert recovered_data == checkpoint_data
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# __basic_checkpoint_end__
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