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HenryAI
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KerasBERTv1-Data

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},
'task': {'type': 'worker', 'index': 0}
})
# Open a strategy scope and create/restore the model.
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
with strategy.scope():
model = make_or_restore_model()
callbacks = [
# This callback saves a SavedModel every 100 batches
keras.callbacks.ModelCheckpoint(filepath='path/to/cloud/location/ckpt',
save_freq=100),
keras.callbacks.TensorBoard('path/to/cloud/location/tb/')
]
model.fit(train_dataset,
callbacks=callbacks,
...)
On other workers:
# Set TF_CONFIG
worker_index = 1 # For instance
os.environ['TF_CONFIG'] = json.dumps({
'cluster': {
'worker': ["localhost:12345", "localhost:23456"]
},
'task': {'type': 'worker', 'index': worker_index}
})
# Open a strategy scope and create/restore the model.
# You can restore from the checkpoint saved by the chief.
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
with strategy.scope():
model = make_or_restore_model()
callbacks = [
keras.callbacks.ModelCheckpoint(filepath='local/path/ckpt', save_freq=100),
keras.callbacks.TensorBoard('local/path/tb/')
]
model.fit(train_dataset,
callbacks=callbacks,
...)
On the evaluator:
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model = make_or_restore_model() # Restore from the checkpoint saved by the chief.
results = model.evaluate(val_dataset)
# Then, log the results on a shared location, write TensorBoard logs, etc
Further reading
TensorFlow distributed training guide
Tutorial on multi-worker training with Keras
MirroredStrategy docs
MultiWorkerMirroredStrategy docs
Distributed training in tf.keras with Weights & BiasesTraining Keras models with TensorFlow Cloud
Author: Jonah Kohn
Date created: 2020/08/11
Last modified: 2020/08/11
Description: In-depth usage guide for TensorFlow Cloud.
View in Colab • GitHub source
Introduction
TensorFlow Cloud is a Python package that provides APIs for a seamless transition from local debugging to distributed training in Google Cloud. It simplifies the process of training TensorFlow models on the cloud into a single, simple function call, requiring minimal setup and no changes to your model. TensorFlow Cloud handles cloud-specific tasks such as creating VM instances and distribution strategies for your models automatically. This guide will demonstrate how to interface with Google Cloud through TensorFlow Cloud, and the wide range of functionality provided within TensorFlow Cloud. We'll start with the simplest use-case.
Setup
We'll get started by installing TensorFlow Cloud, and importing the packages we will need in this guide.
!pip install -q tensorflow_cloud
import tensorflow as tf
import tensorflow_cloud as tfc
from tensorflow import keras
from tensorflow.keras import layers
API overview: a first end-to-end example
Let's begin with a Keras model training script, such as the following CNN:
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
model = keras.Sequential(
[
keras.Input(shape=(28, 28)),
# Use a Rescaling layer to make sure input values are in the [0, 1] range.
layers.experimental.preprocessing.Rescaling(1.0 / 255),
# The original images have shape (28, 28), so we reshape them to (28, 28, 1)
layers.Reshape(target_shape=(28, 28, 1)),
# Follow-up with a classic small convnet
layers.Conv2D(32, 3, activation="relu"),
layers.MaxPooling2D(2),
layers.Conv2D(32, 3, activation="relu"),
layers.MaxPooling2D(2),
layers.Conv2D(32, 3, activation="relu"),
layers.Flatten(),
layers.Dense(128, activation="relu"),
layers.Dense(10),
]
)