This guide walks you through the process of installing TensorFlow on a GPU-enabled system. If you are new to TensorFlow, we recommend starting with the CPUs only version first.
Check out our video for more information:
TensorFlow and GPUs
TensorFlow is a powerful open-source software library for data analysis and machine learning, and GPUs are specialized digital processors that can dramatically accelerate certain types of computations. By using TensorFlow on a GPU, you can take advantage of the massive computational power of these devices to train sophisticated machine learning models in less time.
Getting started with TensorFlow on a GPU can be a complex task, but there are a few resources that can help you get up and running quickly. This guide will provide an overview of the steps you need to take to get started with TensorFlow on a GPU, including how to install the software, set up your hardware, and get your first model training.
Why Use a GPU with TensorFlow?
GPUs are well suited for computationally intensive tasks such as image and video processing, deep learning, and scientific computing. TensorFlow is a good choice for these types of tasks because it is designed to be fast and efficient.
A GPU can offer a significant performance boost over a CPU for certain types of tasks. In order to take advantage of this, you will need to install TensorFlow with GPU support. This guide will show you how to do this on a system with an NVIDIA GPU.
How to Install TensorFlow with GPU Support
If you’re just getting started with TensorFlow, then it’s recommended that you start with a CPU version. However, if you plan on doing any serious work with neural networks or machine learning, then you’ll want to install the TensorFlow GPU version.
The good news is that installing the GPU version of TensorFlow is actually quite easy. All you need is a compatible NVIDIA GPU and the right drivers.
Here’s a quick guide on how to install TensorFlow with GPU support:
1. First, make sure that your NVIDIA GPU is compatible with TensorFlow. The current list of supported GPUs can be found here.
2. Next, you’ll need to install the right drivers for your NVIDIA GPU. You can find the latest drivers here.
3. Once you have the drivers installed, you can download and install the TensorFlowGPU Python package using pip:
pip install tensorflow-gpu==1.8
TensorFlow GPU Support on Amazon Web Services
TensorFlow is a popular open source library for numerical computation that allows users to easily create and train neural networks. TensorFlow supports both CPU and GPU-based computation, making it a great choice for training large, deep neural networks.
GPUs are well-suited for the parallelizable nature of deep learning and training neural networks, so using a GPU with TensorFlow can provide significant speedups.
If you’re using Amazon Web Services (AWS) to train your TensorFlow models, you can take advantage of GPU-based instances. AWS offers several instance types that come with one or more NVIDIA GPUs.
In this article, we’ll show you how to get started with TensorFlow on an AWS GPU instance. We’ll also provide some tips on troubleshooting and optimization.
Using TensorFlow with a GPU on Google Cloud Platform
Today, I’m going to show you how to use TensorFlow with a GPU on Google Cloud Platform. This is a great way to get started with TensorFlow because it can be difficult to install on your own machine.
First, you’ll need to create a Google Cloud Platform account and set up a project. Then, you’ll need to enable billing for your project. Next, you’ll need to create a virtual machine instance. I recommend using the n1-standard-2 machine type, which has 2 CPUs and 7.5 GB of memory.
Once your instance is created, you’ll need to SSH into it and install the GPU driver. You can do this by running the following command:
sudo apt-get install nvidia-cuda-toolkit
After the driver is installed, you’ll need to install TensorFlow. You can do this by running the following command:
pip install tensorflow-gpu==1.8.0
Now that TensorFlow is installed, you’re ready to start using it!
TensorFlow GPU Support on Microsoft Azure
TensorFlow is a popular open-source platform for machine learning that enables developers to easily create sophisticated, large-scale neural network models. While TensorFlow can run on a CPU or GPU, using a GPU can provide significant speedups when training large models.
If you’re new to TensorFlow, we recommend checking out the excellent Getting Started with TensorFlow guide. Once you’ve got a handle on the basics, you can come back here to learn how to use TensorFlow on a GPU on Microsoft Azure.
GPUs are available in select Azure Databricks runtime versions. To check if your runtime includes GPU support, go to the cluster’s page in the Azure portal, and look for “GPU support” under the “Features” section.
When creating a cluster, you can specify whether to enable GPU support:
TensorFlow GPU Support on Paperspace
TensorFlow is a powerful tool for machine learning, but it can be challenging to get started. If you’re looking to use TensorFlow on a GPU, Paperspace can help.
Paperspace provides an easy way to get started with TensorFlow on a GPU. All you need is a Paperspace account and a supported GPU instance type. You can then install TensorFlow and start using it for your machine learning projects.
To get started, sign up for a Paperspace account and create a new GPU instances. Then, follow the instructions below to install TensorFlow on your instance.
1. Connect to your GPU instance via SSH.
2. Update your system packages and install the dependencies required for TensorFlow:
sudo apt-get update && sudo apt-get upgrade
sudo apt-get install build-essential libatlas-base-dev gfortran python3 python3-pip libhdf5-serial-dev htop libopencv-dev
3. Install TensorFlow:
sudo pip3 install tensorflow==1.12.*
Using TensorFlow with a GPU on FloydHub
TensorFlow is a powerful tool for machine learning, but training large models can be prohibitively expensive on CPUs. Using a graphics processing unit (GPU) can significantly speed up training time.
FloydHub’s GPU drivers are up-to-date and work out-of-the-box with most deep learning libraries, including TensorFlow. In this guide, we’ll show you how to train your TensorFlow models on FloydHub using a GPU.
First, you’ll need to set up a FloydHub account and install the floyd-cli tool. You can find instructions for doing so in the Getting Started guide.
Once you have floyd-cli installed, you can create and enter a project by running the following commands:
$ floyd init myproject
$ cd myproject
Next, you’ll need to upload your TensorFlow model to your project’s directory. For this example, we’ll assume that your model is stored in a file called model.py .
Once your model is uploaded, you can create a GPU job by running the following command:
$ floyd run – gpu – env tensorflow1.0 – data diSgciLH4WA7jvU3EqqLDa:Glove6B50D840B30C0647 – mode jupyter – tensorboard – memory 6GB “python model.py”
This will launch a TensorFlow GPU job on FloydHub. The job will run for as long as you keep the Jupyter notebook open (or until it reaches the maximum runtime of 6 hours). You can view the progress of your job in the Jupyter notebook or by clicking on the “Jobs” tab in the FloydHub web UI.
When your job is complete, you can view your training results in TensorBoard by clicking on the “TensorBoard” tab in the Jupyter notebook or by clicking on the “TensorBoard” tab in the FloydHub web UI.
TensorFlow GPU Support on Preemtible VMs
TensorFlow GPU support on Google Compute Engine is now available on Preemptible VMs. Preemptible VMs are suitable for many workloads, including short-lived or fault-tolerant workloads, as well as batch jobs where the workflow can be interrupted. For example, deep learning training typically involves large compute workloads that can take hours or days to complete. With Preemptible VMs and TensorFlow GPU support, you can now train your models faster and cheaper.
To get started, simply create a new Preemptible VM with a GPU. You can then install TensorFlow and run your training scripts as usual. TensorFlow will automatically detect and use your GPU for faster training.
If you’re new to TensorFlow, check out the official tutorials to get started with deep learning on GPUs.
In this final section, we’ll briefly touch on two important topics: how to get started with TensorFlow on a GPU, and where to go from here.
If you’re new to TensorFlow or GPUs in general, don’t worry – we’ll walk you through everything you need to know. Just follow these simple steps:
1. Install the dependencies for TensorFlow on a GPU.
2. Configure your system for accelerated computation with a GPU.
3. Verify that TensorFlow is using your GPU.
With that out of the way, let’s get started!
Keyword: How to Get Started with TensorFlow on a GPU