How to Build a PyTorch Container Image
Introduction
PyTorch is a flexible machine learning library for building deep learning models that can perform a wide range of tasks such as image recognition and language processing.
This article explains how to build a PyTorch container image using the Vultr Container Registry.
Prerequisites
Deploy an instance using Vultr's GPU Marketplace App
Access the server using SSH.
Start the Docker service.
CONSOLEAdd the non-root user to the Docker group. For example,
linuxuser.CONSOLESwitch to the user:
CONSOLE
Set Up the Server
Create a new directory to store your PyTorch project files.
CONSOLESwitch to the directory.
CONSOLEClone the Pytorch project repository using Git.
CONSOLEList all files and verify that a new
pytorchdirectory is available.CONSOLESwitch to the
pytorchproject directory.CONSOLEList all directory files and verify that the available Dockerfile resources.
CONSOLEThe PyTorch project directory includes the following Dockerfile resources:
Dockerfile: Contains the PyTorch build context for both CPU systems.docker.makefile: Contains advanced GPU build context and image automation directives with support for custom dependency packages.
Use the above resources in the next sections to build a CPU or GPU system container image.
Build a PyTorch Container Image for CPU Systems
Follow the steps below to build a new PyTorch container image for CPU systems using the Dockerfile resource on your server.
Build a new PyTorch container image using the
Dockerfileresource. Replacepytorch-imagewith your desired image name.CONSOLEView all Docker images and verify that your new Pytoch image is available.
CONSOLEOutput:
Build a PyTorch Container Image for GPU Systems
Follow the steps below to build a new PyTorch container image for GPU systems using docker.Makefile that contains the CUDA dependency build context compatible with NVIDIA GPU servers.
Build a new PyTorch container image for GPU using the
docker.Makefile.CONSOLEWait at least
2 hoursfor the build process to complete. When successful, your output should look like the one below:View all Docker images on the server and verify that a new
root/pytorchimage is available.CONSOLEOutput:
Verify the PyTorch GPU image target CUDA version and verify that your system uses the driver version or higher.
CONSOLEOutput:
Based on the above output, NVIDIA GPU Servers with CUDA version
12.1or higher can run your PyTorch GPU container image.
Upload the PyTorch Container Image to the Vultr Container Registry
Open the Vultr Console.
Click Products and navigate to Container Registry on the main navigation bar.
Click your target Vultr Container Registry to open the management panel and view the registry access credentials.
Copy the Registry URL value, Username, and API Key to use when accessing the registry.
Switch to your server terminal session and log in to your Vultr Container Registry. Replace
exampleregistry,exampleuser,registry-passwordwith your actual registry details.CONSOLETag your PyTorch container image with your desired Vultr Container Registry tag. For example, tag your PyTorch GPU image ID with your registry repository tag such as
sjc.vultrcr.com/exampleregistry/pytorch-gpu-image.CONSOLEThe above command tags the Docker image with container ID
d3674f1db4d5with your Vultr Container Registry tag.View all Docker images on the server and verify that the new tagged image is available.
CONSOLEOutput:
Push the tagged image to your Vultr Container Registry repository.
CONSOLEOpen your Vultr Container Registry management panel and click Repositories on the top navigation bar to verify that the new repository is available.
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