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Merge pull request #70 from d-kleine/main

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Updated Docker readme
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Sebastian Raschka 2024-03-14 06:50:26 -05:00 committed by GitHub
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49
appendix-A/04_optional-docker-environment/README.md
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@ -27,15 +27,49 @@ git clone https://github.com/rasbt/LLMs-from-scratch.git
cd LLMs-from-scratch
```
2. Type `code .` in the terminal to open the project in VS Code. Alternatively, you can launch VS Code and select the project to open from the UI.
2. In Docker Desktop, make sure that ***desktop-linux* builder** is running and will be used to build the Docker container (see *Docker Desktop* -> *Change settings* -> *Builders* -> *desktop-linux* -> *...* -> *Use*)
3. Install the **Remote Development** extension from the VS Code *Extensions* menu on the left-hand side.
3. If you have a [CUDA-supported GPU](https://developer.nvidia.com/cuda-gpus), you can speed up the training and inference:
3.1 Install **NVIDIA Container Toolkit** as described [here](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-with-apt). NVIDIA Container Toolkit is supported as written [here](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#nvidia-compute-software-support-on-wsl-2).
4. Open the DevContainer.
3.2 Add *nvidia* as runtime in Docker Engine daemon config (see *Docker Desktop* -> *Change settings* -> *Docker Engine*). Add these lines to your config:
```json
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
```
For example, the full Docker Engine daemon config json code should look like that:
```json
{
"builder": {
"gc": {
"defaultKeepStorage": "20GB",
"enabled": true
}
},
"experimental": false,
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
}
}
```
and restart Docker Desktop.
4. Type `code .` in the terminal to open the project in VS Code. Alternatively, you can launch VS Code and select the project to open from the UI.
5. Install the **Remote Development** extension from the VS Code *Extensions* menu on the left-hand side.
6. Open the DevContainer.
Since the `.devcontainer` folder is present in the main `LLMs-from-scratch` directory (folders starting with `.` may be invisible in your OS depending on your settings), VS Code should automatically detect it and ask whether you would like to open the project in a devcontainer. If it doesn't, simply press `Ctrl + Shift + P` to open the command palette and start typing `dev containers` to see a list of all DevContainer-specific options.
5. Select **Reopen in Container**.
7. Select **Reopen in Container**.
Docker will now begin the process of building the Docker image specified in the `.devcontainer` configuration if it hasn't been built before, or pull the image if it's available from a registry.
@ -46,12 +80,7 @@ Once completed, VS Code will automatically connect to the container and reopen t
> [!WARNING]
> If you are encountering an error during the build process, this is likely because your machine does not support NVIDIA container toolkit because your machine doesn't have a compatible GPU. In this case, edit the `devcontainer.json` file to remove the `"runArgs": ["--runtime=nvidia", "--gpus=all"],` line and run the "Reopen Dev Container" procedure again.
6. Finished.
8. Finished.
Once the image has been pulled and built, you should have your project mounted inside the container with all the packages installed, ready for development.