# Building a PyTorch Container Image > Source: > Published: 2026-10-29 17:05:13+00:00 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 guide walks through cloning the PyTorch source repository, building a container image for either CPU or GPU systems, and pushing the finished image to a container registry. By the end, you'll have a custom PyTorch container image built from source and published to a registry, ready to pull into any Docker-compatible environment. Start the Docker service. ``` bash $ sudo systemctl start docker ``` Add the non-root user to the Docker group. For example, `linuxuser`. ``` bash $ sudo usermod -aG docker linuxuser ``` Switch to the user: ``` bash $ su - linuxuser ``` **1. Create a new directory to store your PyTorch project files:** ``` bash $ mkdir llama-project ``` **2. Switch to the directory:** ``` bash $ cd llama-project ``` **3. Clone the PyTorch project repository using Git:** ``` bash $ git clone https://github.com/pytorch/pytorch ``` **4. List all files and verify that a new `pytorch` directory is available:** ``` bash $ ls ``` **5. Switch to the `pytorch` project directory:** ``` bash $ cd pytorch ``` **6. List all directory files and verify the available Dockerfile resources:** ``` bash $ ls -a ``` The PyTorch project directory includes the following Dockerfile resources: `Dockerfile`: Contains the PyTorch build context for CPU systems.` docker.makefile`: Contains advanced GPU build context and image automation directives with support for custom dependency packages. Use these resources in the next sections to build a CPU or GPU system container image. Follow the steps below to build a new PyTorch container image for CPU systems using the `Dockerfile` resource on your server. **1. Build a new PyTorch container image using the `Dockerfile` resource. Replace `pytorch-image` with your desired image name:** ``` bash $ docker build -f Dockerfile -t pytorch-image . ``` **2. View all Docker images and verify that your new PyTorch image is available:** ``` bash $ docker images ``` Output: ``` REPOSITORY TAG IMAGE ID CREATED SIZE pytorch-image latest d3674f1db4d5 2 hours ago 11.9GB ``` Follow the steps below to build a new PyTorch container image for GPU systems using `docker.Makefile`, which contains the CUDA dependency build context compatible with NVIDIA GPU servers. **1. Build a new PyTorch container image for GPU using the `docker.Makefile`:** ``` bash $ docker build -f Dockerfile -t pytorch-image . ``` Wait at least `2 hours` for the build process to complete. When successful, your output should look like the one below: ``` php #29 29224.1 copying torch/include/ATen/ops/_sparse_coo_tensor_with_dims_ops.h -> build/lib.linux-x86_64-cpython-310/torch/include/ATen/ops #29 29224.1 copying torch/include/ATen/ops/absolute_compositeimplicitautograd_dispatch.h -> build/lib.linux-x86_64-cpython-310/torch/include/ATen/ops #29 29224.1 copying torch/include/ATen/ops/gradient_ops.h -> build/lib.linux-x86_64-cpython-310/torch/include/ATen/ops #29 29224.1 copying torch/include/ATen/ops/crow_indices_compositeexplicitautograd_dispatch.h -> build/lib.linux-x86_64-cpython-310/torch/include/ATen/ops #29 29224.1 copying torch/include/ATen/ops/isinf.h -> build/lib.linux-x86_64-cpython-310/torch/include/ATen/ops #29 29224.1 copying torch/include/ATen/ops/_fore #29 29224.1 [output clipped, log limit 2MiB reached] #29 29235.3 warning: no previously-included files matching '*.o' found anywhere in distribution #29 29235.4 warning: no previously-included files matching '*.dylib' found anywhere in distribution #29 29235.7 warning: no previously-included files matching '*.swp' found anywhere in distribution #29 DONE 29239.6s #30 [dev 1/1] COPY --from=build /opt/conda /opt/conda #30 DONE 10.8s #31 exporting to image #31 exporting layers #31 exporting layers 41.0s done #31 writing image sha256:d3674f1db4d5e9585a5e5b64a0cdfcb0c7d312358e267000e23b6fa9d8e7e917 done #31 naming to docker.io/root/pytorch:v1.8.0-rc1-38416-g4dc160864b3-cuda12.1-cudnn8-devel done #31 DONE 41.0s ``` **2. View all Docker images on the server and verify that a new `root/pytorch` image is available:** ``` bash $ docker images REPOSITORY TAG IMAGE ID CREATED SIZE root/pytorch v1.8.0-rc1-38416-g4dc160864b3-cuda12.1-cudnn8-devel d3674f1db4d5 3 hours ago 20.9GB ``` **3. Verify the PyTorch GPU image's target CUDA version and confirm your system uses that driver version or higher:** ``` bash $ cat docker.Makefile | grep CUDA_VERSION CUDA_VERSION_SHORT ?= 12.1 CUDA_VERSION ?= 12.1.1 BASE_DEVEL = nvidia/cuda:$(CUDA_VERSION)-cudnn$(CUDNN_VERSION)-devel-ubuntu22.04 ``` Based on the above output, NVIDIA GPU servers with CUDA version `12.1` or higher can run your PyTorch GPU container image. **1. Open your container registry's management panel and copy the registry URL, username, and API key (or password) to use when accessing the registry.** **2. Switch to your server terminal session and log in to your container registry. Replace `your-registry-url`, `exampleregistry`, `exampleuser`, and `registry-password` with your actual registry details:** ``` bash $ docker login https://your-registry-url/exampleregistry -u exampleuser -p registry-password ``` **3. Tag your PyTorch container image with your desired registry tag. For example, tag your PyTorch GPU image ID with a registry repository tag such as `your-registry-url/exampleregistry/pytorch-gpu-image`:** ``` bash $ docker tag d3674f1db4d5 your-registry-url/exampleregistry/pytorch-gpu-image ``` The above command tags the Docker image with container ID `d3674f1db4d5` with your registry tag. **4. View all Docker images on the server and verify that the new tagged image is available:** ``` bash $ docker images REPOSITORY TAG IMAGE ID CREATED SIZE root/pytorch v1.8.0-rc1-38416-g4dc160864b3-cuda12.1-cudnn8-devel d3674f1db4d5 4 hours ago 20.9GB your-registry-url/exampleregistry/pytorch-gpu-image latest d3674f1db4d5 4 hours ago 20.9GB ``` **5. Push the tagged image to your container registry repository:** ``` bash $ docker push your-registry-url/exampleregistry/pytorch-gpu-image ``` **6. Open your container registry's management panel and check the repositories list to verify that the new repository is available.** For the full guide with additional tips, visit the original article on **[Vultr Docs](https://docs.vultr.com/how-to-build-a-pytorch-container-image)**.