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chore: Regenerate all playbooks

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6
nvidia/connect-two-sparks/README.md
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@ -40,9 +40,9 @@ a functional distributed computing environment.
## Ancillary files ## Ancillary files
All required files for this playbook can be found [here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/connect-two-sparks/) All required files for this playbook can be found [here on GitHub](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/connect-two-sparks/)
- [**discover-sparks.sh**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/connect-two-sparks/assets/discover-sparks) script for automatic node discovery and SSH key distribution - [**discover-sparks.sh**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/connect-two-sparks/assets/discover-sparks) script for automatic node discovery and SSH key distribution
## Time & risk ## Time & risk
@ -171,7 +171,7 @@ ip addr show enp1s0f1np1
#### Option 1: Automatically configure SSH #### Option 1: Automatically configure SSH
Run the DGX Spark [**discover-sparks.sh**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/connect-two-sparks/assets/discover-sparks) script from one of the nodes to automatically discover and configure SSH: Run the DGX Spark [**discover-sparks.sh**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/connect-two-sparks/assets/discover-sparks) script from one of the nodes to automatically discover and configure SSH:
```bash ```bash
bash ./discover-sparks bash ./discover-sparks

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nvidia/cuda-x-data-science/README.md
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@ -55,7 +55,7 @@ You will accelerate popular machine learning algorithms and data analytics opera
## Step 4. Cloning the playbook repository ## Step 4. Cloning the playbook repository
- Clone the github repository and go the assets folder place in cuda-x-data-science folder - Clone the github repository and go the assets folder place in cuda-x-data-science folder
```bash ```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets git clone https://github.com/NVIDIA/dgx-spark-playbooks
``` ```
- Place the **kaggle.json** created in Step 1 in the assets folder - Place the **kaggle.json** created in Step 1 in the assets folder

2
nvidia/dgx-dashboard/README.md
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@ -32,7 +32,7 @@ You will learn how to access and use the DGX Dashboard on your DGX Spark device.
## Ancillary files ## Ancillary files
- Python code snippet for SDXL found [here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/dgx-dashboard/assets/jupyter-cell.py) - Python code snippet for SDXL found [here on GitHub](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/dgx-dashboard/assets/jupyter-cell.py)
## Time & risk ## Time & risk

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nvidia/flux-finetuning/README.md
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@ -72,7 +72,7 @@ newgrp docker
In a terminal, clone the repository and navigate to the flux-finetuning directory. In a terminal, clone the repository and navigate to the flux-finetuning directory.
```bash ```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets git clone https://github.com/NVIDIA/dgx-spark-playbooks
``` ```
## Step 3. Model download ## Step 3. Model download

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nvidia/jax/README.md
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@ -50,12 +50,12 @@ GPU acceleration and performance optimization capabilities.
## Ancillary files ## Ancillary files
All required assets can be found [here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main) All required assets can be found [here on GitHub](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main)
- [**JAX introduction notebook**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/jax/assets/jax-intro.py) — covers JAX programming model differences from NumPy and performance evaluation - [**JAX introduction notebook**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/jax/assets/jax-intro.py) — covers JAX programming model differences from NumPy and performance evaluation
- [**NumPy SOM implementation**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/jax/assets/numpy-som.py) — reference implementation of self-organized map training algorithm in NumPy - [**NumPy SOM implementation**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/jax/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/nvidia/jax/assets/som-jax.py) — multiple iteratively refined implementations of SOM algorithm in JAX - [**JAX SOM implementations**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/jax/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/nvidia/jax/assets/Dockerfile) — package dependencies and container setup specifications - [**Environment configuration**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/jax/assets/Dockerfile) — package dependencies and container setup specifications
## Time & risk ## Time & risk
@ -93,7 +93,7 @@ newgrp docker
## Step 2. Clone the playbook repository ## Step 2. Clone the playbook repository
```bash ```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets git clone https://github.com/NVIDIA/dgx-spark-playbooks
``` ```
## Step 3. Build the Docker image ## Step 3. Build the Docker image

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nvidia/multi-agent-chatbot/README.md
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@ -72,7 +72,7 @@ newgrp docker
## Step 2. Clone the repository ## Step 2. Clone the repository
```bash ```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets git clone https://github.com/NVIDIA/dgx-spark-playbooks
cd multi-agent-chatbot/assets cd multi-agent-chatbot/assets
``` ```

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nvidia/protein-folding/README.md
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@ -121,7 +121,7 @@ space and network bandwidth.
```bash ```bash
## Clone OpenFold repository ## Clone OpenFold repository
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets git clone https://github.com/NVIDIA/dgx-spark-playbooks
cd nvidia/protein-folding/assets cd nvidia/protein-folding/assets
pip install -e . pip install -e .
``` ```

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nvidia/pytorch-fine-tune/README.md
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@ -34,7 +34,7 @@ Recipes are specifically for DIGITS SPARK. Please make sure that OS and drivers
## Ancillary files ## Ancillary files
ALl files required for fine-tuning are included in the folder in [the GitHub repository here](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/pytorch-fine-tune). ALl files required for fine-tuning are included in the folder in [the GitHub repository here](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/pytorch-fine-tune).
## Time & risk ## Time & risk
@ -92,7 +92,7 @@ huggingface-cli login
## Step 6: Clone the git repo with fine-tuning recipes ## Step 6: Clone the git repo with fine-tuning recipes
```bash ```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets git clone https://github.com/NVIDIA/dgx-spark-playbooks
cd nvidia/pytorch-fine-tune/assets cd nvidia/pytorch-fine-tune/assets
``` ```

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nvidia/sglang/README.md
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@ -44,7 +44,7 @@ vision-language tasks using models like DeepSeek-V2-Lite.
## Ancillary files ## Ancillary files
- An offline inference python script [found here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/sglang/assets/offline-inference.py) - An offline inference python script [found here on GitHub](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/sglang/assets/offline-inference.py)
### Time & risk ### Time & risk
@ -168,7 +168,7 @@ print(f"Response: {response.json()['text']}")
Launch a new container instance for offline inference to demonstrate local model usage without Launch a new container instance for offline inference to demonstrate local model usage without
HTTP server. This runs entirely within the container for batch processing scenarios. HTTP server. This runs entirely within the container for batch processing scenarios.
TO DO: NEEDS TO HAVE SCRIPT FROM ASSETS PROPERLY INCORPORATED. [See here](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/sglang/assets) TO DO: NEEDS TO HAVE SCRIPT FROM ASSETS PROPERLY INCORPORATED. [See here](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/sglang/assets)
## Step 8. Validate installation ## Step 8. Validate installation

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nvidia/trt-llm/README.md
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@ -76,11 +76,11 @@ inference through kernel-level optimizations, efficient memory layouts, and adva
## Ancillary files ## Ancillary files
All required assets can be found [here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main) All required assets can be found [here on GitHub](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main)
- [**discover-sparks.sh**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/trt-llm/assets/discover-sparks.sh) — script to automatically discover and configure SSH between Spark nodes - [**discover-sparks.sh**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/trt-llm/assets/discover-sparks.sh) — script to automatically discover and configure SSH between Spark nodes
- [**trtllm-mn-entrypoint.sh**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/trt-llm/assets/trtllm-mn-entrypoint.sh) — container entrypoint script for multi-node setup - [**trtllm-mn-entrypoint.sh**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/trt-llm/assets/trtllm-mn-entrypoint.sh) — container entrypoint script for multi-node setup
- [**docker-compose.yml**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/trt-llm/assets/docker-compose.yml) — Docker Compose configuration for multi-node deployment - [**docker-compose.yml**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/trt-llm/assets/docker-compose.yml) — Docker Compose configuration for multi-node deployment
## Model Support Matrix ## Model Support Matrix
@ -511,13 +511,13 @@ Run the command suggested by the docker swarm init on each worker node to join t
docker swarm join --token <worker-token> <advertise-addr>:<port> docker swarm join --token <worker-token> <advertise-addr>:<port>
``` ```
On both nodes, download the [**trtllm-mn-entrypoint.sh**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/trt-llm/assets/trtllm-mn-entrypoint.sh) script into your home directory and run the following command to make it executable: On both nodes, download the [**trtllm-mn-entrypoint.sh**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/trt-llm/assets/trtllm-mn-entrypoint.sh) script into your home directory and run the following command to make it executable:
```bash ```bash
chmod +x $HOME/trtllm-mn-entrypoint.sh chmod +x $HOME/trtllm-mn-entrypoint.sh
``` ```
On your primary node, deploy the TRT-LLM multi-node stack by downloading the [**docker-compose.yml**](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/trt-llm/assets/docker-compose.yml) file into your home directory and running the following command: On your primary node, deploy the TRT-LLM multi-node stack by downloading the [**docker-compose.yml**](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/trt-llm/assets/docker-compose.yml) file into your home directory and running the following command:
```bash ```bash
docker stack deploy -c $HOME/docker-compose.yml trtllm-multinode docker stack deploy -c $HOME/docker-compose.yml trtllm-multinode
``` ```

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nvidia/txt2kg/README.md
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@ -61,7 +61,7 @@ The setup includes:
In a terminal, clone the txt2kg repository and navigate to the project directory. In a terminal, clone the txt2kg repository and navigate to the project directory.
```bash ```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets git clone https://github.com/NVIDIA/dgx-spark-playbooks
cd nvidia/txt2kg/assets cd nvidia/txt2kg/assets
``` ```

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nvidia/unsloth/README.md
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@ -44,7 +44,7 @@ parameter-efficient fine-tuning methods like LoRA and QLoRA.
## Ancillary files ## Ancillary files
The Python test script can be found [here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/unsloth/assets/test_unsloth.py) The Python test script can be found [here on GitHub](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/unsloth/assets/test_unsloth.py)
## Time & risk ## Time & risk
@ -96,10 +96,10 @@ pip install --no-deps bitsandbytes
## Step 6. Create Python test script ## Step 6. Create Python test script
Curl the test script [here](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/unsloth/assets/test_unsloth.py) into the container. Curl the test script [here](https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/unsloth/assets/test_unsloth.py) into the container.
```bash ```bash
curl -O https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/nvidia/unsloth/assets/test_unsloth.py curl -O https://github.com/NVIDIA/dgx-spark-playbooks/blob/main/nvidia/unsloth/assets/test_unsloth.py
``` ```
We will use this test script to validate the installation with a simple fine-tuning task. We will use this test script to validate the installation with a simple fine-tuning task.

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nvidia/vlm-finetuning/README.md
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@ -79,7 +79,7 @@ newgrp docker
In a terminal, clone the repository and navigate to the VLM fine-tuning directory. In a terminal, clone the repository and navigate to the VLM fine-tuning directory.
```bash ```bash
git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets git clone https://github.com/NVIDIA/dgx-spark-playbooks
``` ```
## Step 3. Build the Docker container ## Step 3. Build the Docker container