May 2024
Intermediate
252 pages
5h 31m
Chinese
Content preview from Ray 分布式机器学习：利用Ray 进行大模型的数据处理、训练、推理和部署
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每当运行
AIR Trainer
或
Tuner
时，它们会生成特定于框架的检查点。你可以使
用这些检查点在多个
AIR
库（如
Tune
、
Train
或
Serve
）中加载模型以供使用。
你可以通过访问
Trainer
或
Tuner
的
.fit()
方法的结果获取检查点。在示例中，
这意味着你可以简单地访问
best_result
对象上的检查点，或者通过
result_
grid
中的任何其他条目访问检查点，如下所示：
checkpoint = best_result.checkpoint
print(checkpoint)
使用检查点的另一种主要方式是从特定于框架的已有模型中创建检查点。
AIR
支持的每个
ML
框架都可以执行此操作，但由于使用它定义简单模型最容易，
因此下面展示如何为顺序的
TensorFlow Keras
模型创建检查点：
from ray.train.tensorflow import TensorflowCheckpoint
import tensorflow as tf
model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(1,)),
tf.keras.layers.Dense(1)
])
keras_checkpoint = TensorflowCheckpoint.from_model(model)
拥有检查点的好处很多，因为它们是
AIR
的原生模型交换格式。你还可以使用
检查点在以后的阶段使用已训练的模型
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.