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https://github.com/matifali/dockerdl
Deep Learning Docker Image
https://github.com/matifali/dockerdl
cuda deep-learning docker jupyter numpy python pytorch tensorflow
Last synced: 5 days ago
JSON representation
Deep Learning Docker Image
- Host: GitHub
- URL: https://github.com/matifali/dockerdl
- Owner: matifali
- License: mit
- Created: 2022-09-13T21:16:27.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-19T12:40:32.000Z (3 months ago)
- Last Synced: 2024-12-08T20:47:39.492Z (13 days ago)
- Topics: cuda, deep-learning, docker, jupyter, numpy, python, pytorch, tensorflow
- Language: Dockerfile
- Homepage: https://hub.docker.com/r/matifali/dockerdl
- Size: 188 KB
- Stars: 78
- Watchers: 3
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Awesome Lists containing this project
- awesome-docker - DockerDL - Deep Learning Docker Images. Don't waste time setting up a deep learning env when you can get a deep learning environment with everything pre-installed. (Development with Docker / Development Environment)
README
# DockerDL [](https://github.com/matifali/dockerdl/actions/workflows/docker-publish.yml)  
## Deep Learning Docker Image
Don't waste time on setting up a deep learning environment while you can get a deep learning environment with everything pre-installed.
## List of Packages installed
- [conda](https://docs.conda.io/en/latest/miniconda.html)
- [Jupyter lab](https://jupyter.org/)
- [Matplotlib](https://matplotlib.org/)
- [NLTK](https://www.nltk.org/)
- [Numpy](https://numpy.org/)
- [Pandas](https://pandas.pydata.org/)
- [Plotly](https://plotly.com/)
- [PyTorch](https://pytorch.org/)
- [Scikit-Learn](https://scikit-learn.org/)
- [Seaborn](https://seaborn.pydata.org/)
- [TensorFlow](https://www.tensorflow.org/)
- [zellij](https://github.com/zellij-org/zellij)
## Image variants and tags
| Variant | Tag | Conda | PyTorch | TensorFlow | Image size |
| ---------------------------- | -------------------- | ------------------ | ------------------ | ------------------ | -------------------------------------------------------------------------------------------------------------------------------- |
| Conda | `conda` | :heavy_check_mark: | :x: | :x: |  |
| Tensorflow | `tf` | :x: | :x: | :heavy_check_mark: |  |
| PyTorch | `torch` | :x: | :heavy_check_mark: | :x: |  |
| PyTorch + Tensorflow | `tf-torch`, `latest` | :x: | :heavy_check_mark: | :heavy_check_mark: |  |
| PyTorch + Tensorflow + Conda | `tf-torch-conda` | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |  |
You can see the full list of tags [https://hub.docker.com/r/matifali/dockerdl/tags](https://hub.docker.com/r/matifali/dockerdl/tags?page=1&ordering=last_updated).
## Requirements
1. [Docker](https://docs.docker.com/engine/install/)
2. [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) [^1]
3. Linux, or Windows with [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install)
## Fast Start
```shell
docker run --gpus all --rm -it -h dockerdl matifali/dockerdl bash
```
### JupyterLab server without conda
```shell
docker run --gpus all --rm -it -h dockerdl -p 8888:8888 matifali/dockerdl jupyter lab --no-browser --port 8888 --ServerApp.token='' --ip='*'
```
Connect by opening in your browser.
## Customize the image
### Clone the repo
```shell
git clone https://github.com/matifali/dockerdl.git
```
### Add or delete packages
Modify the corresponding `[Dockerfile]` to add or delete packages.
> [!NOTE]
> You may have to rebuild the `dockerdl-base` if you are building a custom image and then use it as a base image. See [Build](#build) section.
### Build
The following `--build-arg` are available for the `dockerdl-base` image.
| Argument | Description | Default | Possible Values |
| ------------ | -------------- | --------- | ------------------------- |
| `USERNAME` | User name | `coder` | Any string or `$USER` |
| `USERID` | User ID | `1000` | `$(id -u $USER)` |
| `GROUPID` | Group ID | `1000` | `$(id -g $USER)` |
| `CUDA_VER` | CUDA version | `12.4.1` | |
| `UBUNTU_VER` | Ubuntu version | `22.04` | `22.04`, `20.04`, `18.04` |
> [!WARNING]
> **Not all combinations of `--build-arg` are tested.**
#### Step 1
Build the base image
```shell
docker build -t dockerdl-base:latest --build-arg USERNAME=coder --build-arg CUDA_VER=12.4.1 --build-arg UBUNTU_VER=22.04 -f base.Dockerfile .
```
#### Step 2
Build the image you want with the base image as the base image.
```shell
docker build -t dockerdl:tf --build-arg TF_VERSION=2.12.0 -f tf.Dockerfile .
```
or
```shell
docker build -t dockerdl:torch --build-arg -f torch.Dockerfile .
```
## How to connect
### VS Code
1. Install [vscode](https://code.visualstudio.com/Download).
2. Install the following extensions:
1. [Docker](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-docker).
2. [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python).
3. [Remote Development](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.vscode-remote-extensionpack).
4. Follow the instructions [here](https://code.visualstudio.com/docs/remote/containers#_quick-start-open-an-existing-folder-in-a-container).
### Coder
1. Install Coder. ().
2. Use deeplearning template which references these images ().
### JetBrains PyCharm Professional
Follow the instructions [here](https://www.jetbrains.com/help/pycharm/using-docker-as-a-remote-interpreter.html).
## Issues
If you find any issue please feel free to create an [issue](https://github.com/matifali/dockerdL/issues/new/choose) and submit a PR.
## Support
- Give a star (⭐) if using this has helped you.
- [](https://github.com/sponsors/matifali)
## References
[^1]: This image is based on [nvidia/cuda](https://hub.docker.com/r/nvidia/cuda) and uses [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) to access the GPU.