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chore: Regenerate all playbooks

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@ -45,7 +45,6 @@ GPU acceleration and performance optimization capabilities.
[ ] Docker or container runtime installed
[ ] NVIDIA Container Toolkit configured
[ ] Verify GPU access: `nvidia-smi`
[ ] Verify Docker GPU support: `docker run --gpus all --rm nvcr.io/nvidia/cuda:13.0.1-runtime-ubuntu24.04 nvidia-smi`
[ ] Port 8080 available for marimo notebook access
## Ancillary files
@ -56,7 +55,7 @@ All required assets can be found [here on GitHub](https://gitlab.com/nvidia/dgx-
- [**NumPy SOM implementation**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/numpy-som.py) — reference implementation of self-organized map training algorithm in NumPy
- [**JAX SOM implementations**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/som-jax.py) — multiple iteratively refined implementations of SOM algorithm in JAX
- [**Environment configuration**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/Dockerfile) — package dependencies and container setup specifications
- [**Course guide notebook**]() — overall material navigation and learning path
## Time & risk
@ -85,12 +84,13 @@ uname -m
docker run --gpus all --rm nvcr.io/nvidia/cuda:13.0.1-runtime-ubuntu24.04 nvidia-smi
```
If the `docker` command fails with a permission error, you can either
If the `docker` command fails with a permission error, you can either run the command with `sudo`, or add yourself to the `docker` group to use `docker` without `sudo`.
1. run it with `sudo`, e.g., `sudo docker run --gpus all --rm nvcr.io/nvidia/cuda:13.0.1-runtime-ubuntu24.04 nvidia-smi`, or
2. add yourself to the `docker` group so you can use `docker` without `sudo`.
To add yourself to the `docker` group, first run `sudo usermod -aG docker $USER`. Then, as your user account, either run `newgrp docker` or log out and log back in.
```bash
sudo usermod -aG docker $USER
newgrp docker
sudo systemctl restart docker
```
## Step 3. Clone the playbook repository
@ -101,7 +101,7 @@ git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-
## Step 3. Build the Docker image
> **Warning:** This command will download a base image and build a container locally to support this environment
> **Warning:** This command will download a base image and build a container locally to support this environment.
```bash
cd jax/assets
@ -165,21 +165,7 @@ The notebooks will show you how to check the performance of each SOM training im
Visually inspect the SOM training output on random color data to confirm algorithm correctness.
## Step 10. Validate installation
Confirm all components are working correctly and notebooks execute successfully.
```bash
## Test GPU JAX functionality
python -c "import jax; print(jax.devices()); print(jax.device_count())"
## Verify JAX can access GPU
python -c "import jax.numpy as jnp; x = jnp.array([1, 2, 3]); print(x.device())"
```
Expected output should show GPU devices detected and JAX arrays placed on GPU.
## Step 11. Troubleshooting
## Step 10. Troubleshooting
Common issues and their solutions:
@ -191,24 +177,7 @@ Common issues and their solutions:
| Port 8080 unavailable | Port already in use | Use `-p 8081:8080` or kill process on 8080 |
| Package conflicts in Docker build | Outdated environment file | Update environment file for Blackwell |
## Step 12. Cleanup and rollback
Remove containers and reset environment if needed.
> **Warning:** This will remove all container data and downloaded images.
```bash
## Stop and remove containers
docker stop $(docker ps -q)
docker system prune -f
## Reset pipenv environment
pipenv --rm
```
To rollback: Re-run installation steps from Step 2.
## Step 13. Next steps
## Step 11. Next steps
Apply JAX optimization techniques to your own NumPy-based machine learning code.