在哪里使用射线演员导入张量流？

Question

使用ray actor并行运行几个tensorflow模型，我问自己在哪里导入tensorflow：

# [1] maybe import tensorflow here?

@ray.remote(num_cpus=1)
class Remote_Runner:
    # [2] maybe import tensorflow here?
    def __init__(self, weights):
        # [3] maybe import tensorflow here?
        self.model=My_model()
        self.model.set_weights(wegihts)

    def do_something_with_model:
        self.model.do_something()

由于'side-effects of importing TensorFlow and setting global state'，文档中给出的示例提到在actor中导入tensorflow，但仅给出了一个射线远程函数的示例。那么我应该运行 'import tensorflow as tf' 在 [1]、[2] 或 [3]，还是其他地方？此处是否有可遵循的最佳实践，[1]、[2] 和 [3] 之间的区别是什么，即在每种情况下我如何访问 tensorflow 以及它们何时执行？

Answer 1

现在可能已修复此问题，但最安全的选择是在 [3] 导入 tensorflow。

Answer 2

根据 https://docs.ray.io/en/latest/using-ray-with-tensorflow.html 中提到的最佳实践，最好在 My_model()

中导入 tensorflow

你可以考虑下面的例子

def create_keras_model():
    from tensorflow import keras
    from tensorflow.keras import layers
    model = keras.Sequential()
    # Adds a densely-connected layer with 64 units to the model:
    model.add(layers.Dense(64, activation="relu", input_shape=(32, )))
    # Add another:
    model.add(layers.Dense(64, activation="relu"))
    # Add a softmax layer with 10 output units:
    model.add(layers.Dense(10, activation="softmax"))

    model.compile(
        optimizer=keras.optimizers.RMSprop(0.01),
        loss=keras.losses.categorical_crossentropy,
        metrics=[keras.metrics.categorical_accuracy])
    return model

import ray
import numpy as np

ray.init()

def random_one_hot_labels(shape):
    n, n_class = shape
    classes = np.random.randint(0, n_class, n)
    labels = np.zeros((n, n_class))
    labels[np.arange(n), classes] = 1
    return labels


# Use GPU wth
# @ray.remote(num_gpus=1)
@ray.remote
class Network(object):
    def __init__(self):
        self.model = create_keras_model()
        self.dataset = np.random.random((1000, 32))
        self.labels = random_one_hot_labels((1000, 10))

    def train(self):
        history = self.model.fit(self.dataset, self.labels, verbose=False)
        return history.history

    def get_weights(self):
        return self.model.get_weights()

    def set_weights(self, weights):
        # Note that for simplicity this does not handle the optimizer state.
        self.model.set_weights(weights)

在哪里使用射线演员导入张量流？

Where to import tensorflow using ray actors?

python

import

python-import

ray

tensorflow