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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 [ ] Docker or container runtime installed
[ ] NVIDIA Container Toolkit configured [ ] NVIDIA Container Toolkit configured
[ ] Verify GPU access: `nvidia-smi` [ ] 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 [ ] Port 8080 available for marimo notebook access
## Ancillary files ## 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/${MODEL}/assets/numpy-som.py) — reference implementation of self-organized map training algorithm in NumPy - [**NumPy SOM implementation**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${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/${MODEL}/assets/som-jax.py) — multiple iteratively refined implementations of SOM algorithm in JAX - [**JAX SOM implementations**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${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/${MODEL}/assets/Dockerfile) — package dependencies and container setup specifications - [**Environment configuration**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/Dockerfile) — package dependencies and container setup specifications
- [**Course guide notebook**]() — overall material navigation and learning path
## Time & risk ## 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 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 ```bash
2. add yourself to the `docker` group so you can use `docker` without `sudo`. sudo usermod -aG docker $USER
newgrp docker
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. sudo systemctl restart docker
```
## Step 3. Clone the playbook repository ## 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 ## 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 ```bash
cd jax/assets 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. Visually inspect the SOM training output on random color data to confirm algorithm correctness.
## Step 10. Validate installation ## Step 10. Troubleshooting
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
Common issues and their solutions: 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 | | 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 | | Package conflicts in Docker build | Outdated environment file | Update environment file for Blackwell |
## Step 12. Cleanup and rollback ## Step 11. Next steps
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
Apply JAX optimization techniques to your own NumPy-based machine learning code. Apply JAX optimization techniques to your own NumPy-based machine learning code.