Step #3: Pulling Docker Images from NGC

Now that your machine is properly configured for Docker and authenticated with NGC, you can now pull images from NGC. You may browse the available images by navigating to in a web browser. In the menu on the left of the screen, you will see a “Containers” button under the “Catalog” drop down. Clicking the “Containers” button will show the list of hosted Docker images.


The list can be filtered or searched for a specific image that fits the needs of your application. For our purposes, we will use the latest PyTorch image which includes a tuned version of upstream PyTorch which supports NVIDIA GPUs including some examples that can be run interactively.

To find the PyTorch image, type “PyTorch” in the search bar and click the first box listed. This will open a new page with further details about the image including a README, tags, security information, and image layers.


To get the latest tag published for the PyTorch image, click the “Tags” tab. Tags are used to identify versions of Docker images as they change over time. Base images of the same image name with different tags might use the same code at a specific version but could have additional updates such as a different Operating System, different runtime libraries, or other changes.

The most recent image will be listed at the top of the table. Most images hosted on NGC use a YY.MM-py3 versioning scheme where “YY” is the last two digits of the year, “MM” are the zero-padded month, and “-py3” indicates the default version of Python installed in the image is Python3 (as opposed to “-py2” which indicates Python2). NVIDIA is constantly testing, tuning, and upgrading the base images hosted on NGC and it is generally recommended to pull the latest image whenever possible to take advantage of all updates.

By clicking the ellipses to the right of the latest tag, a “Pull Tag” button will appear. Click this button to copy a command to your clipboard which can be run on your workstation.


After copying the command, return to the terminal on your workstation and paste it. The command should look similar to the following (we are using the “22.03-py3” tag in this example, but a different container or tag would also work):


docker pull

Executing this command will begin downloading and extracting the selected image. The output will look similar to the following:


22.03-py3: Pulling from nvidia/pytorch 4d32b49e2995: Pull complete 45893188359a: Pull complete 5ad1f2004580: Pull complete 6ddc1d0f9183: Pull complete 4cc43a803109: Downloading [==========================================> ] 1.877GB/2.222GB e94a4481e933: Download complete 3e7e4c9bc2b1: Download complete 9463aa3f5627: Download complete a4a0c690bc7d: Download complete 59d451175f69: Download complete eaf45e9f32d1: Download complete d8d16d6af76d: Download complete 9e04bda98b05: Download complete 4f4fb700ef54: Download complete 600169a9eeb9: Download complete 5ab4a4c79587: Download complete cc9c383bbe7e: Download complete 28f0a4457214: Download complete c9fa92ce1ad6: Download complete 6de187d8986c: Download complete 776dd6381718: Download complete fbcec89863dc: Download complete cc9bd67c83a5: Download complete b14fad4790ba: Download complete 2e662c1f02bf: Download complete 43fd87c93a0e: Download complete c25c11c90147: Download complete 21f8a0e7678c: Download complete 38498f1bac59: Downloading [================================================> ] 671.6MB/698.5MB be1dcb77f928: Downloading [==============================================> ] 662MB/717.3MB cbb8b770c5f1: Waiting 570f80eab64d: Pulling fs layer 3a68c3ed38b1: Waiting 44048c6af4c5: Waiting aa81cbfe793b: Waiting 7be5ff0e99ab: Pulling fs layer fcaebb8600f0: Waiting 5fcfa55efbe2: Waiting 30e2d0565dc1: Waiting 571612189481: Waiting 41b606023507: Waiting b46fc2bc9527: Waiting 218cc4dc57a5: Pulling fs layer 312f4a0d232a: Waiting e4b105bef9a0: Pulling fs layer 775c8d6f4c51: Waiting

Once the image has been pulled, you will see this message:


Digest: sha256:aba37c9ec089ce56e30686eafb535685ad31c53996b0e44626893e292157bf17 Status: Downloaded newer image for

To verify the image has been properly installed, run “docker images | grep”. This will list details of the image similar to the following:

     22.03-py3 4730bc516b92 7 days ago 14.6GB

If you have previously downloaded PyTorch images from NGC, there may be additional lines listed above for other tags.

© Copyright 2022-2023, NVIDIA. Last updated on Jan 10, 2023.