How to Use Multiple GPUs with TensorFlow

How to Use Multiple GPUs with TensorFlow

Learn how to use multiple GPUs with the open source machine learning platform, TensorFlow. This guide will show you how to configure your system for optimal performance.

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Introduction

If you have a computer with several GPUs, you can harness them all for training your TensorFlow models by using the tf.distribute.Strategy API. This API provides multiple mechanisms for distributing your training across multiple GPUs and devices. In this guide, we will show you how to use multiple GPUs with TensorFlow and explore the benefits of doing so.

Why Use Multiple GPUs?

GPUs are very good at parallel processing, meaning they can perform multiple computations at the same time. This makes them ideal for training machine learning models, which can require a lot of computation power.

Using multiple GPUs can speed up training by distributing the work across multiple devices. This can be especially helpful if you have a large dataset or complex model.

There are a few different ways to use multiple GPUs with TensorFlow. The simplest way is to use the `tf.contrib.estimator.multi_gpu_estimator` class, which is designed to work with any estimator (including pre-existing estimators).

You can also use the `tf.DistributeStrategy` class to more finely control how your data and computation are distributed across devices. This is useful if you need to do things like train different parts of a model on different types of GPUs (e.g. using a GPU for inference and a TPU for training).

Finally, if you’re using Keras with TensorFlow, you can use the `multi_gpu_model` utility to more easily train your models on multiple GPUs.

How to Use Multiple GPUs with TensorFlow

TensorFlow is a powerful tool for machine learning, but working with large datasets can be challenging. One way to overcome this limitation is to use multiple GPUs.

In this tutorial, we’ll show you how to set up and use multiple GPUs with TensorFlow. We’ll also discuss some of the benefits and drawbacks of using multiple GPUs.

Setting up TensorFlow to use multiple GPUs is relatively simple. The first step is to make sure that each GPU has its own copy of the TensorFlow graph. To do this, we need to use the “with tf.device” context manager.

Next, we need to specify which operations should be run on which GPU. In most cases, it will be obvious which operations can be run in parallel on different GPUs. For example, if we have a convolutional layer, we can compute the forward pass on each GPU in parallel.

However, there are some operations that can’t be run in parallel on different GPUs. For example, if we have two fully connected layers, we need to compute the forward pass for the first layer on all of the data before we can start computing the forward pass for the second layer. In these cases, we need to use a special “all-reduce” operation that combines the results from all of the GPUs before continuing.

Once we’ve specified which operations should be run on which GPUs, we can launch the TensorFlow session as usual. The only difference is that we need to specify which devices (i.e., GPUs) are available for TensorFlow to use:

With these changes in place, TensorFlow will automatically distribute the computations across all of the available GPUs. In most cases, this will result in a significant speedup over using a single GPU.
Right now I’m using GTX 1070s , GTX 1060s’s and GTX 1050 Ti’s

Tips for Using Multiple GPUs

If you have more than one GPU in your system, you can take advantage of TensorFlow’s multi-GPU support. Here are some tips for using multiple GPUs:

-One way to use multiple GPUs is to use the ‘with tf.device(‘/gpu:X’)’ context manager, where ‘X’ is the GPU number. This will allow you to run operations on a specific GPU.
-Another way to use multiple GPUs is to use TensorFlow’s ‘tf.contrib.keras’ high-level APIs. Keras will automatically use available GPUs if you have more than one in your system.
-If you’re training a model with a large dataset, you can also take advantage of TensorFlow’s ‘Dataset’ API to distribute training data across multiple gpus.

Conclusion

With the release of TensorFlow 1.4, now you can easily train your models on multiple GPUs! This is especially beneficial if you have a large dataset or complex model. Simply specify the number of GPUs you want to use, and TensorFlow will take care of the rest!

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