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DockerDL

github.com/matifali/dockerdl DevOps & Containers Open-source projectOpen source

DockerDL provides a deep learning Docker image intended to simplify setup of a deep learning environment.

Visit DockerDL 24 alternatives
github.com/matifali/dockerdl
DockerDL homepage screenshot

About DockerDL

DockerDL is an open source repository that provides Docker images with a deep learning environment already set up. The images include JupyterLab, PyTorch, TensorFlow, and common data science libraries such as NumPy, Pandas, Matplotlib, and scikit-learn. Three variants are available: PyTorch, TensorFlow, and a combined PyTorch plus TensorFlow image.

The Dockerfiles are in the repository, so users can add or remove packages and build a custom base image. Running the images requires Docker, the nvidia-container-toolkit, an NVIDIA driver that supports CUDA 12 (version 525 or later), and either Linux or Windows with WSL2. The conda-based image tags are no longer built, although the last conda-based images remain on Docker Hub.

Key features

  • Pre-installed JupyterLab, PyTorch, TensorFlow, NumPy, Pandas, and scikit-learn
  • Three image variants: PyTorch, TensorFlow, and combined PyTorch plus TensorFlow
  • Python 3.13 installed by uv in /opt/venv on Ubuntu 26.04
  • Dockerfiles for adding or deleting packages in a cloned repository
  • Custom base image built by editing the FROM line in base.Dockerfile
  • Connection guidance for VS Code, Coder, and JetBrains PyCharm Professional
Best for: Developers and data practitioners who want a ready-made deep learning environment in Docker and have an NVIDIA GPU on Linux or Windows with WSL2.
Good for
  • Common deep learning and data science packages are pre-installed, so users do not need to set them up manually
  • The Dockerfiles are open for editing, so the image contents can be changed
  • Released under the MIT license
Watch out for
  • Requires an NVIDIA driver supporting CUDA 12 (version 525 or later) and nvidia-container-toolkit
  • Supported only on Linux or on Windows with WSL2
  • The conda and tf-torch-conda tags are no longer built

DockerDL at a glance

Type
Open-source project
Pricing
Not confirmed. Check the site
Platforms
Linux, Windows
Open source
Yes · Source code
Language
English
Last checked
Oct 6, 2026 · Online when checked
Listed
Oct 5, 2026
open sourcelinuxdockergit

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DockerDL FAQ

What does the host system need to run DockerDL images?
Docker and nvidia-container-toolkit are required, along with Linux or Windows with WSL2. The host NVIDIA driver must support CUDA 12, which means version 525 or later. CUDA and cuDNN come from the pip wheels inside the image.
Which image variants are available?
There are three variants: a TensorFlow image (tf), a PyTorch image (torch), and a combined PyTorch plus TensorFlow image (tf-torch, also tagged latest). The full list of tags is on Docker Hub.
Are conda-based images still built?
No. The conda and tf-torch-conda tags are no longer built. The last conda-based images remain available on Docker Hub.
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Listing maintained by Chris McClure and the WebIndexly editors · Updated Oct 12, 2026 · How we rank