https://github.com/gperdrizet/deeplearning-devcontainer
Containerized development environment for deeplearning projects
https://github.com/gperdrizet/deeplearning-devcontainer
deep-learning devops mlops neural-networks python pytorch tensorflow
Last synced: about 5 hours ago
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Containerized development environment for deeplearning projects
- Host: GitHub
- URL: https://github.com/gperdrizet/deeplearning-devcontainer
- Owner: gperdrizet
- License: mit
- Created: 2026-03-19T04:21:11.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-06-18T19:47:13.000Z (18 days ago)
- Last Synced: 2026-06-18T21:25:16.739Z (18 days ago)
- Topics: deep-learning, devops, mlops, neural-networks, python, pytorch, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 94.7 KB
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Deep learning development environment
[](https://github.com/gperdrizet/deeplearning-devcontainer/actions/workflows/sync-release.yml)
[](https://www.python.org/)
[](https://pytorch.org/)
[](https://www.tensorflow.org/)
[](https://developer.nvidia.com/cuda-toolkit)
[](https://hub.docker.com/r/gperdrizet/deeplearning-nvidia)
[](https://hub.docker.com/r/gperdrizet/deeplearning-cpu)
[](https://hub.docker.com/r/gperdrizet/deeplearning-mac)
A ready-to-use deep learning environment for VS Code, designed for AI/ML bootcamp students. Includes both **PyTorch** and **TensorFlow** with three configurations for wide hardware compatibility.
## Requirements
**All users**
- [Docker Desktop](https://docs.docker.com/desktop/) (Windows / Mac) or [Docker Engine](https://docs.docker.com/engine/install/) (Linux)
- [VS Code](https://code.visualstudio.com/) with the [Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
**NVIDIA GPU users** (also required)
- NVIDIA driver ≥570 ([download](https://www.nvidia.com/Download/index.aspx))
- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) *(Linux only - not needed on Windows)*
> **Mac users:** GPU acceleration (Metal/MPS) does not pass through to Docker containers. The Mac configuration uses native ARM64 CPU, no extra setup needed beyond Docker Desktop.
## Quick start
1. **Fork** this repository (click **Fork** at the top of this page)
2. **Clone** your fork:
```bash
git clone https://github.com//deeplearning-devcontainer.git
```
3. **Open the folder in VS Code**, then open the Command Palette (`Ctrl+Shift+P` / `Cmd+Shift+P`) and run **Dev Containers: Open Folder in Container**
> VS Code will ask which configuration to use, pick the one that matches your machine (see table below).
4. **Verify** your setup by running `notebooks/environment_test.ipynb`
## Which configuration should I use?
| If you have... | Choose this |
|----------------|-------------|
| NVIDIA GPU (GTX 10xx / RTX / Quadro / Tesla) | **DeepLearning NVIDIA** |
| Windows or Linux machine, no NVIDIA GPU | **DeepLearning CPU** |
| Apple Silicon Mac (M1 / M2 / M3 / M4) | **DeepLearning Mac** |
Not sure if your GPU is compatible? Check: [NVIDIA CUDA GPUs](https://developer.nvidia.com/cuda-gpus) (need compute capability ≥6.0).
## Using as a template for new projects
Fork this repo once, then use it as a GitHub template to spin up new projects instantly.
### One-time setup
1. Go to your fork on GitHub
2. Click **Settings** → scroll to **Template repository** → enable it
### Creating a new project
1. Go to your fork and click **Use this template** → **Create a new repository**
2. Name your new repo and click **Create repository**
3. Clone it and start working:
```bash
git clone https://github.com//my-new-project.git
```
4. **Clean it up** - remove anything that doesn't belong to your project:
- Update `README.md` to describe your project
- Delete unused devcontainer configs (e.g. if you only use NVIDIA, remove `cpu/` and `mac/`)
- Remove or replace `notebooks/environment_test.ipynb` with your own notebooks
- Clear `logs/`, `models/`, and `data/`
```bash
git add -A && git commit -m "Initial project setup" && git push
```
## Adding Python packages
### Temporary (lost on container rebuild)
```bash
pip install
```
### Permanent (recommended)
1. Create a `requirements.txt` in the repository root:
```
scikit-image
plotly
```
2. Add a `postCreateCommand` to the relevant `.devcontainer/*/devcontainer.json`:
```json
"postCreateCommand": "pip install -r requirements.txt"
```
3. Rebuild the container (`Ctrl+Shift+P` → **Dev Containers: Rebuild Container**)
## TensorBoard
To launch TensorBoard:
1. Open the Command Palette (`Ctrl+Shift+P` / `Cmd+Shift+P`)
2. Run **Python: Launch TensorBoard**
3. Select the `logs/` directory when prompted
TensorBoard will open in a new tab inside VS Code. Save your training logs to the `logs/` directory.
## Optuna dashboard
Right-click your Optuna database file and select **Open in Optuna Dashboard**. The dashboard is also accessible via its built-in web server on port 8080.
## Keeping your fork updated
```bash
# Add upstream once
git remote add upstream https://github.com/gperdrizet/deeplearning-devcontainer.git
# Pull in updates
git fetch upstream && git merge upstream/main
```
## What's included
### NVIDIA environment
| Category | Details |
|----------|---------|
| **Base image** | `nvcr.io/nvidia/tensorflow:25.02-tf2-py3` |
| **GPU** | CUDA 12.8, cuDNN 9.7, CuPy 13.6 |
| **ML frameworks** | PyTorch 2.11, TensorFlow 2.17, Keras 3.3 |
| **Python** | 3.12, NumPy 1.26, Pandas 2.2, Scikit-learn 1.5 |
| **Tools** | JupyterLab, TensorBoard, Optuna, Plotly, python-dotenv |
### CPU environment
| Category | Details |
|----------|---------|
| **Base image** | `python:3.12-slim` |
| **ML frameworks** | PyTorch 2.11 (CPU), TensorFlow 2.17 |
| **Python** | 3.12, NumPy 1.26, Pandas 2.2, Scikit-learn 1.5 |
| **Tools** | JupyterLab, TensorBoard, Optuna, Plotly, python-dotenv |
### Mac environment
| Category | Details |
|----------|---------|
| **Base image** | `python:3.12-slim` (linux/arm64) |
| **ML frameworks** | PyTorch 2.11 (ARM64, from PyPI), TensorFlow 2.17 |
| **Python** | 3.12, NumPy 1.26, Pandas 2.2, Scikit-learn 1.5 |
| **Tools** | JupyterLab, TensorBoard, Optuna, Plotly, python-dotenv |
## GPU compatibility (NVIDIA)
Requires compute capability ≥6.0 (Pascal / GTX 10xx or newer):
| Architecture | Example GPUs | Compute Capability |
|--------------|--------------|-------------------|
| Pascal | GTX 1050-1080, Tesla P100 | 6.0-6.1 |
| Volta | Tesla V100, Titan V | 7.0 |
| Turing | RTX 2060-2080, GTX 1660 | 7.5 |
| Ampere | RTX 3060-3090, A100 | 8.0-8.6 |
| Ada Lovelace | RTX 4060-4090 | 8.9 |
| Hopper | H100, H200 | 9.0 |
| Blackwell | RTX 5070-5090, B100, B200 | 10.0 |
## Project structure
```
deeplearning-devcontainer/
├── .devcontainer/
│ ├── nvidia/
│ │ └── devcontainer.json # NVIDIA GPU configuration
│ ├── cpu/
│ │ └── devcontainer.json # CPU configuration
│ └── mac/
│ └── devcontainer.json # Mac (ARM64) configuration
├── data/ # Store datasets here
├── logs/ # TensorBoard logs
├── models/ # Saved model files
├── notebooks/
│ ├── environment_test.ipynb # Verify your setup
│ └── functions/ # Helper modules for notebooks
├── .gitignore
├── LICENSE
└── README.md
```
## Troubleshooting
| Problem | Solution |
|---------|----------|
| Docker won't start | Enable virtualization in BIOS / enable WSL2 on Windows |
| Permission denied (Linux) | Add your user to the docker group, then log out and back in |
| GPU not detected | Update NVIDIA drivers (≥570); Linux: install [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) |
| Container build fails | Check your internet connection |
| Module not found | Add the package to `requirements.txt` and rebuild the container |