---
title: ASL Recognition
emoji: "\U0001F44B"
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: "5.29.0"
app_file: app.py
---

# ASL application development environment

A ready-to-use ASL application development environment for VS Code. Includes **LangChain**, **LlamaIndex**, **Hugging Face Transformers**, **vLLM** (GPU only), and API clients for OpenAI and Anthropic. Available in both GPU and CPU-only configurations.

## What's included

### GPU Environment

| Category | Details |
|----------|---------|
| **Base Image** | NVIDIA PyTorch 24.08 |
| **GPU** | CUDA 12.6, PyTorch 2.4 (GPU-enabled) |
| **Python** | 3.10 |
| **LLM Frameworks** | LangChain, LlamaIndex, Transformers, smolagents, vLLM |
| **API Clients** | OpenAI, Anthropic, Ollama |
| **Vector Store** | ChromaDB, sentence-transformers |
| **Tools** | Gradio, accelerate, datasets, tiktoken |

### CPU Environment

| Category | Details |
|----------|---------|
| **Base Image** | Python 3.11-slim |
| **Python** | 3.11, PyTorch (CPU) |
| **LLM Frameworks** | LangChain, LlamaIndex, Transformers, smolagents |
| **API Clients** | OpenAI, Anthropic, Ollama |
| **Vector Store** | ChromaDB, sentence-transformers |
| **Tools** | Gradio, accelerate, datasets, tiktoken |


## Project structure

```
llms-devcontainer/
├── .devcontainer/
│   ├── gpu/
│   │   └── devcontainer.json   # GPU dev container configuration
│   └── cpu/
│       └── devcontainer.json   # CPU dev container configuration
├── data/                       # Store datasets here
├── logs/                       # Training/experiment logs
├── models/                     # Saved model files
├── src/
│   └── environment_test.py     # Verify your setup
├── .gitignore
├── LICENSE
└── README.md
```

## Requirements

### GPU Environment
- **NVIDIA GPU** (Pascal or newer) with driver ≥545
- **Docker** with GPU support ([Windows](https://docs.docker.com/desktop/setup/install/windows-install) | [Linux](https://docs.docker.com/desktop/setup/install/linux))
- **VS Code** with the [Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)

> **Linux users:** Also install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)

### CPU Environment
- **Docker** ([Windows](https://docs.docker.com/desktop/setup/install/windows-install) | [Linux](https://docs.docker.com/desktop/setup/install/linux))
- **VS Code** with the [Dev Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)

### GPU compatibility

The GPU environment requires an NVIDIA GPU with **compute capability 6.0+** (Pascal architecture 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 |

Check your GPU's compute capability: [NVIDIA CUDA GPUs](https://developer.nvidia.com/cuda-gpus)

## Quick start

1. **Fork** this repository (click "Fork" button above)

2. **Clone** your fork:
   ```bash
   git clone https://github.com/<your-username>/asl-recognition.git
   ```

3. **Open VS Code**

4. **Open Folder in Container** from the VS Code command palette (Ctrl+Shift+P), start typing `Open Folder in`...
   - Select the **GPU** or **CPU** configuration when prompted

5. **Verify** by running `python src/environment_test.py`

## Using as a template for new projects

You can use your fork as a template to quickly create new LLM application projects:

### One-time setup: Make your fork a template

1. Go to your fork on GitHub
2. Click **Settings** → scroll to **Template repository**
3. Check the box to enable it

### Creating a new project from your template

1. Go to your fork on GitHub
2. Click the green **Use this template** button → **Create a new repository**
3. Enter your new repository name and settings
4. Click **Create repository**
5. **Clone** your new repository:
   ```bash
   git clone https://github.com/<your-username>/my-new-project.git
   ```

Now you have a fresh LLM application project with the dev container configuration ready to go!

## Adding Python packages

### Using pip directly

Install packages in the container terminal:

```bash
pip install <package-name>
```

> **Note:** Packages installed this way will be lost when the container is rebuilt.

### Using requirements.txt (Recommended)

For persistent packages that survive container rebuilds:

1. **Create** a `requirements.txt` file in the repository root:
   ```
   langchain-community
   faiss-cpu
   ```

2. **Update** the appropriate `.devcontainer/*/devcontainer.json` to install packages on container creation by adding a `postCreateCommand`:
   ```json
   "postCreateCommand": "pip install -r requirements.txt"
   ```

3. **Rebuild** the container (`F1` → "Dev Containers: Rebuild Container")

Now your packages will be automatically installed whenever the container is created.

## Gradio Web UI

Both environments include Gradio for building interactive demos. To run a Gradio app:

```python
import gradio as gr

def greet(name):
    return f"Hello, {name}!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch(server_name="0.0.0.0")
```

The default Gradio port (7860) is accessible from your host machine.

## Keeping your fork updated

```bash
# Add upstream (once)
git remote add upstream https://github.com/gperdrizet/llms-devcontainer.git

# Sync
git fetch upstream
git merge upstream/main
```

## Troubleshooting

| Problem | Solution |
|---------|----------|
| Docker won't start | Enable virtualization in BIOS |
| Permission denied (Linux) | Add user to docker group, then log out/in |
| GPU not detected | Update NVIDIA drivers (≥545), install NVIDIA Container Toolkit |
| Container build fails | Check internet connection |
| vLLM not available | vLLM requires GPU; use the GPU devcontainer |
| Module not found | Rebuild container after adding to requirements.txt |

# Dataset Acknowledgements
@misc{https://www.kaggle.com/grassknoted/aslalphabet_akash nagaraj_2018,
title={ASL Alphabet},
url={https://www.kaggle.com/dsv/29550},
DOI={10.34740/KAGGLE/DSV/29550},

Garg, Bindu; Kasar, Manisha; Kashyap, Achyut; Vats, 
Amber; Sharma, Gunjan; Hange, Aditya (2025), “SignAlphaSet”, 
Mendeley Data, V2, doi: 10.17632/8fmvr9m98w.2


WLASL (World Level American Sign Language) Video
All the WLASL data is intended for academic and computational use only. No commercial usage is allowed.

Made by Dongxu Li and Hongdong Li. Please read the WLASL paper and visit the official website and repository.

Licensed under the Computational Use of Data Agreement (C-UDA). Please refer to the C-UDA-1.0 page for more information.

Subjects:	Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
Cite as:	arXiv:2304.05934 [cs.CV]
 	(or arXiv:2304.05934v2 [cs.CV] for this version) 
https://doi.org/10.48550/arXiv.2304.05934


Inspiration
How to classify word-level action recognition to text?
What is the most accurate model to do word-level sign language recognition?