@@ -68,15 +68,95 @@ If you want to install **tile-lang** in development mode, you can run the follow
pip install-e .
```
We currently provide three methods to install **tile-lang**:
We currently provide four methods to install **tile-lang**:
1.[Install from Source (using your own TVM installation)](#install-method-1)
2.[Install from Source (using the bundled TVM submodule)](#install-method-2)
3.[Install Using the Provided Script](#install-method-3)
1.[Install Using Docker](#install-method-1)(Recommended)
2.[Install from Source (using your own TVM installation)](#install-method-2)
3.[Install from Source (using the bundled TVM submodule)](#install-method-3)
4.[Install Using the Provided Script](#install-method-4)
(install-method-1)=
### Method 1: Install from Source (Using Your Own TVM Installation)
### Method 1: Install Using Docker (Recommended)
For users who prefer a containerized environment with all dependencies pre-configured, **tile-lang** provides Docker images for different CUDA versions. This method is particularly useful for ensuring consistent environments across different systems and is the **recommended approach** for most users.
**Prerequisites:**
- Docker installed on your system
- NVIDIA Docker runtime (nvidia-docker2) for GPU support
You can now run TileLang examples and develop your applications within the containerized environment. The Docker image comes with all necessary dependencies pre-installed, including CUDA toolkit, TVM, and TileLang itself.
**Example Usage:**
After accessing the container, you can run TileLang examples:
```bash
cd /home/tilelang/examples
python elementwise/test_example_elementwise.py
```
This Docker-based installation method provides a complete, isolated environment that works seamlessly on systems with compatible NVIDIA GPUs like the B200, ensuring optimal performance for TileLang applications.
(install-method-2)=
### Method 2: Install from Source (Using Your Own TVM Installation)
If you already have a compatible TVM installation, follow these steps: