TensorFlow GPU support requires a CUDA®-enabled card. This guide provides a step-by-step instruction to set up a TensorFlow environment on NVIDIA® GPUs.
For more information check out this video:
This document describes the current supported configuration options for the NVIDIA TensorFlow GPU version. A table is provided that lists the supported configurations.
The table below lists the supported configurations. Select a row to view more information on that configuration, including any specific bound or maximum values.
– NVIDIA GPU with Compute Capability 3.0 or higher.
– Drivers: Please refer to your mainboard vendor’s website for your specific driver model number. If you have an NVIDIA GeForce card, you can visit http://www.geforce.com/drivers to download the latest driver for your card. For other types of NVIDIA GPUs, please visit http://www.nvidia.com/Download/index5xx.html?lang=en-us to download the latest driver for your card
– TensorFlow 1.2 or higher
What is TensorFlow?
TensorFlow is a powerful tool for deep learning, and it’s especially useful for research and development because of its ability to run on both CPUs and GPUs. In order to use TensorFlow with a GPU, you’ll need to make sure that you have the right hardware and software installed.
What are the requirements for TensorFlow GPU?
To use TensorFlow GPU, you will need a GPU that is Nvidia Compute Capability 3.0 or higher. Many newer Nvidia GPUs are compatible, including the Titan X, GTX 1080, GTX 1070, and GTX 1060. AMD GPUs are not currently supported.
What are the benefits of using TensorFlow GPU?
There are several benefits of using TensorFlow GPU including increased speed and accuracy when training machine learning models, improved efficiency when working with large datasets, and the ability to leverage the power of GPUs for other compute-intensive tasks. While TensorFlow CPU can be used for some types of machine learning tasks, using TensorFlow GPU can provide significant advantages in terms of speed and performance.
How to install TensorFlow GPU?
TensorFlow is an open source software library for machine learning, developed by Google Brain team. It is widely used for training and testing deep learning models.
To use TensorFlow with GPU support, you will need a good graphics card with CUDA support. There are many different types of GPUs available, but for machine learning, it is important to have a card with good floating point performance.
For example, the NVIDIA GTX 1080 Ti has excellent floating point performance and is one of the best cards for machine learning. It can be found for around $700.
If you cannot afford a GTX 1080 Ti, the next best option is the NVIDIA GTX 1070, which can be found for around $400.
Once you have a GPU with good floating point performance, you will need to install the CUDA toolkit and cuDNN libraries. These libraries will allow TensorFlow to take advantage of the GPU’s processing power.
The CUDA toolkit can be downloaded from https://developer.nvidia.com/cuda-downloads . The cuDNN library can be downloaded from https://developer.nvidia.com/cudnn .
Once you have installed the CUDA toolkit and cuDNN libraries, you will need to install TensorFlow itself. The easiest way to do this is to use pip:
pip install tensorflow-gpu # For Python 3 (64-bit) on Windows 10 x64 Pro Edition Version 1607 Build 14393 or later Installation requires Visual C++ 2015 Build Tools https://go.microsoft.com/fwlink/?LinkId=691126&clcid=0x409&gclid=Cj0KCQiA1pyCBhCtARIsAHaY_5fCWfU6IHfTXrbSUWql4tbVfyYU6UgFgqo-anUCpZhDX7OMbyORiqQaAiCbEALw_wcB&dclid=CLXXtPvIn9UCFU2IaQodmO8GJw
How to use TensorFlow GPU?
In order to use TensorFlow GPU you must have a Nvidia graphic card with a minimum compute capability of 3.0. You can check your graphic card compute capability here. For example, I have GTX 980 and its compute capability is 5.2.
Secondly, you must have Nvidia CUDA installed on your system. The CUDA Toolkit installs the CUDA driver and tools needed to create, build and run a CUDA application as well as libraries, header files, CUDA samples and documentation.
Thirdly, you need to install cuDNN (NVIDIA CUDA Deep Neural Network library). It is assumed that cuDNN is already downloaded and located in the same directory as the NVIDIA CUDA Toolkit. If you haven’t download it already, you can do it from this link required for Cuda 8 .
What are the different types of TensorFlow GPU?
There are two types of TensorFlow GPU builds: a CPU-only build and a GPU build. The CPU-only build requires an Intel or AMD CPU; it will not work with an NVIDIA GPU. The GPU build requires an NVIDIA GPU with a minimum compute capability of 3.0; it will not work with a CPU.
To conclude, the requirements for TensorFlow GPU are as follows:
-A CUDA-capable NVIDIA GPU with Compute Capability 3.0 or higher.
-Nvidia driver 410.48 or higher.
-CUDA Toolkit 10.0 .
-cuDNN 7.4 .
Below is a guide to the minimum requirements for using TensorFlow with a GPU.
Operating System: Ubuntu 16.04 or higher, Windows 10
GPU: NVIDIA GeForce GTX 1080 Ti (11GB), NVIDIA Tesla P100 (16GB), NVIDIA Tesla K80 (12GB)
CPU: Intel Core i7-6700HQ Processor
Memory: 16GB RAM
Storage: 250GB SSD
Keyword: Requirements for TensorFlow GPU