How to Use TensorFlow with a GPU on Windows

How to Use TensorFlow with a GPU on Windows

This guide shows you how to use TensorFlow with a GPU on Windows 10.

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Introduction

GPU support for TensorFlow is available on Windows starting with TensorFlow 1.5. To use a GPU with TensorFlow, you must have a GPU that meets the minimum requirements for CUDA c7.0 and cuDNN v5.1. These requirements can be found in the NVIDIA documentation.

Installing TensorFlow with GPU support on Windows requires two steps: installing the right version of TensorFlow and then installing the right CUDA and cuDNN software for your GPU. This guide walks you through both steps.

What is TensorFlow?

TensorFlow is a powerful open-source software library for data analysis and machine learning. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it is widely used by both academia and industry for a variety of tasks such as training neural networks, performing statistical modeling, and more.

TensorFlow with a GPU on Windows

TensorFlow is a powerful tool for machine learning, but it can be challenging to get it up and running on your own machine. If you’re using Windows, you’ll need to use a virtual environment to ensure that you have the right version of TensorFlow installed.

GPU support is available for Windows using TensorFlow 1.4 or higher. To use a GPU with TensorFlow on Windows, you must have a NVIDIA GPU with the correct drivers installed. You can find out if your GPU is supported by TensorFlow [here](https://www.tensorflow.org/install/gpu#hardware_requirements).

Once you have a supported GPU, you’ll need to install the right version of TensorFlow for your system. The latest version of TensorFlow can be found [here](https://www.tensorflow.org/install). Be sure to select the `PIP INSTALL` option and choose the appropriate `whl` file for your system.

Once TensorFlow is installed, you can activate your virtual environment and start using TensorFlow with aGPU.

Installing TensorFlow

TensorFlow is a powerful tool for machine learning, but it can be difficult to get started. In this guide, we’ll show you how to install and use TensorFlow with a GPU on Windows.

We’ll be using the Anaconda distribution of Python, which comes with many useful packages for data science. If you don’t already have Anaconda installed, you can download it here.

Once you have Anaconda installed, open the Anaconda Prompt and create a new environment for TensorFlow:

conda create -n tensorflow-gpu python=3.5

This will create a new environment called “tensorflow-gpu” with Python 3.5 installed. Next, activate the environment:

activate tensorflow-gpu

Now that the environment is activated, we can install TensorFlow:

pip install tensorflow-gpu==1.4.0

This will install TensorFlow 1.4.0 with support for GPU acceleration. Be sure to include the “==1.4.0” at the end of the pip install command so that you don’t accidentally install a newer (and possibly incompatible) version of TensorFlow.

Configuring TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

In order to use TensorFlow with a GPU on Windows, you must have a NVIDIA GPU with a compute capability > 3.0. For more information on compute capabilities and which GPUs are supported, see the NVIDIA documentation. You will also need to install the NVIDIA CUDA Toolkit and cuDNN libraries.

Once you have installed the NVIDIA CUDA Toolkit and cuDNN libraries, you can configure TensorFlow to use them by editing your .bashrc file. Add the following lines to your .bashrc file:

export PATH=/usr/local/cuda-8.0/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}
export CUDA_HOME=/usr/local/cuda-8.0

You will need to replace /usr/local/cuda-8.0 with the path to your NVIDIA CUDA installation. Once you have edited your .bashrc file, run source ~/.bashrc to apply the changes.

You can now verify that TensorFlow is configured to use your GPU by running the following code:
from tensorflow import gfile
with gfile.FastGFile(‘test.jpg’, ‘rb’) as f:
image = f.read()

Running TensorFlow

TensorFlow is a powerful open-source software library for numerical computation that allows users to create sophisticated machine learning models. While originally developed for use with the Linux operating system, TensorFlow can also be used on Windows with the help of a few third-party tools. In this tutorial, we will show you how to install TensorFlow on a Windows machine and run your first TensorFlow program using a GPU.

First, you will need to install Python 3.5 or higher. You can find pre-compiled binaries for Python 3.5 here: https://www.python.org/ftp/python/3.5.4/python-3.5.4-amd64.exe

Once Python is installed, you will need to install the following dependencies:

* NumPy: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
* SciPy: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
* six: https://pypi.org/project/six/#files
* protobuf: https://github.com/google/protobuf/releases
* h5py: https://pypi.org/project/h5py/#files
* Pillow: https://pypi.org/project/Pillow/#files
* matplotlib: https://pypi.org/project/matplotlib/#files
* graphviz: https://graphviz.gitlab_.io/_pages_en/_download/_windows/[email protected]_option=Download&version=stable&windows=8+64-bit&msvc=14+64&arch=x86-64&interface=win32&dl=graphviz+2..38..msi__linkcode_windows__linkcode_name=”1B633C40DD7ECA3BC9F7974260DB02D28ECFFC6C”%40Windows+7%2F8%40graphviz+installer+GPGPUV100#files

Monitoring TensorFlow

TensorFlow is a powerful tool for machine learning, but it can be challenging to get it working on a GPU. In this guide, we’ll show you how to monitor your TensorFlow installation and make sure it’s running properly on your GPU.

We’ll start by showing you how to check whether TensorFlow is using your GPU. Then, we’ll show you how to monitor your GPU usage with TensorBoard. Finally, we’ll give you some tips for troubleshooting common problems.

With this guide, you’ll be able to make sure that TensorFlow is using your GPU and troubleshoot any issues you may have.

Tips and Tricks

If you’re just getting started with TensorFlow and want to get up and running as quickly as possible on a Windows machine, these are a few tips and tricks that can help you get the most out of your setup.

Firstly, make sure you have a supported version of Windows and a compatible graphics card. You can find a list of supported cards here. If your card isn’t on the list, don’t worry – it may still work, but you may need to use a different version of TensorFlow or install some additional drivers.

Once you’ve checked that your system meets the requirements, the next thing to do is ensure that you have the latest version of TensorFlow installed. You can do this by running `pip install – upgrade tensorflow` from a command prompt.

Once TensorFlow is installed, you should be able to run `python -c “import tensorflow”` from a command prompt without getting any errors. If you do get an error, make sure you have set your `PYTHONPATH` environment variable correctly – it should point to the directory where TensorFlow is installed (typically something like `C:Python35Libsite-packagestensorflow`).

Once everything is set up, you’re ready to start using TensorFlow with a GPU! The first thing to do is identify which devices are available – run `from tensorflow.python.client import device_lib; print(device_lib.list_local_devices())`. This will print out all the devices available, including any GPUs that are present.

If there are no GPUs listed or if you get an error saying “No module named ‘tensorflow.python.client’”, then TensorFlow isn’t finding your GPU – make sure that your drivers are installed correctly and that your graphics card is enabled in Device Manager (it should appear under “Display adapters”).

Once you’ve verified that TensorFlow can see your GPU, the next thing to do is tell it whichGPU to use by default – this can be done with the following code:

import os
os.environ[“CUDA_VISIBLE_DEVICES”]=”0″ # substitute 0 for 1 if using GPU 1, etc.

If you have more than one GPU in your system, you can specify which ones to use by providing a comma-separated list of GPU IDs (e.g. “0,1” for both GPUs). By default, TensorFlow will try to use all available GPUs so it’s important to specify which ones you want to use or else performance may suffer.

Troubleshooting

If you’re running into problems using TensorFlow with a GPU on Windows, here are a few things to try:

-First, make sure that your GPU is compatible with TensorFlow. Check the list of supported GPUs to see if your card is included.

-If your GPU is supported, make sure that you have the latest drivers installed. You can usually find these on your manufacturer’s website.

-If you’re still having trouble, try using TensorFlow with CPU only. You can do this by specifying the “device” argument when you create a session, e.g.:

with tf.Session(config=tf.ConfigProto(device_count={‘GPU’: 0})) as sess:

Conclusion

Congratulations, you have now successfully set up TensorFlow with a GPU on Windows! If you’re interested in learning more about how to use TensorFlow, make sure to check out our other tutorials.

Keyword: How to Use TensorFlow with a GPU on Windows

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