TensorFlow is a powerful open-source software library for dataflow and differentiable programming across a range of tasks. Distributed TensorFlow is an extension that allows TensorFlow to scale to multiple machines, which is essential for large-scale machine learning tasks. This tutorial will guide you through the basics of setting up and using distributed TensorFlow.

Quick Start

Before diving into distributed TensorFlow, ensure that you have a basic understanding of TensorFlow and its core concepts.

Setting Up

To set up distributed TensorFlow, you will need to have TensorFlow installed and configured to run across multiple machines. Here's a quick overview:

  1. Install TensorFlow: Make sure you have TensorFlow installed on each machine.
  2. Set Up a Cluster: Define a cluster of machines to run your TensorFlow tasks.
  3. Launch TensorFlow Jobs: Start TensorFlow jobs on each machine in the cluster.

Basic Concepts

Here are some of the key concepts you need to understand when working with distributed TensorFlow:

  • Cluster: A cluster is a collection of machines that work together to run TensorFlow tasks.
  • Master: The master machine coordinates the distributed TensorFlow jobs.
  • Worker: Worker machines execute the TensorFlow tasks.

Step-by-Step Guide

Step 1: Install TensorFlow

First, make sure you have TensorFlow installed on each machine. You can do this by running:

pip install tensorflow

Step 2: Set Up a Cluster

You can use tools like minikube or kubeadm to set up a cluster. Here's an example using minikube:

minikube start

Step 3: Launch TensorFlow Jobs

Once your cluster is set up, you can launch TensorFlow jobs on each machine. Here's an example using the tf.distribute.Strategy API:

import tensorflow as tf

strategy = tf.distribute.MirroredStrategy()

with strategy.scope():
    model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(10, activation='relu', input_shape=(32,)),
        tf.keras.layers.Dense(1)
    ])

model.compile(optimizer='adam',
              loss='mean_squared_error')

# Assume `train_data` and `test_data` are your training and testing datasets.
model.fit(train_data, epochs=10, validation_data=test_data)

Resources

For more detailed information and tutorials, check out the following resources:

TensorFlow Cluster