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README.md
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@ -25,7 +25,7 @@ Each playbook includes prerequisites, step-by-step instructions, troubleshooting
- [Connect to your Spark](nvidia/connect-to-your-spark/) - [Connect to your Spark](nvidia/connect-to-your-spark/)
- [DGX Dashboard](nvidia/dgx-dashboard/) - [DGX Dashboard](nvidia/dgx-dashboard/)
- [FLUX.1 Dreambooth LoRA Fine-tuning](nvidia/flux-finetuning/) - [FLUX.1 Dreambooth LoRA Fine-tuning](nvidia/flux-finetuning/)
- [Optimized Jax](nvidia/jax/) - [Optimized JAX](nvidia/jax/)
- [Llama Factory](nvidia/llama-factory/) - [Llama Factory](nvidia/llama-factory/)
- [MONAI-Reasoning-CXR-3B Model](nvidia/monai-reasoning/) - [MONAI-Reasoning-CXR-3B Model](nvidia/monai-reasoning/)
- [Build and Deploy a Multi-Agent Chatbot](nvidia/multi-agent-chatbot/) - [Build and Deploy a Multi-Agent Chatbot](nvidia/multi-agent-chatbot/)

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nvidia/jax/README.md
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@ -1,6 +1,6 @@
# Optimized Jax # Optimized JAX
> Develop with Optimized Jax > Develop with Optimized JAX
## Table of Contents ## Table of Contents
@ -17,10 +17,10 @@ JAX lets you write **NumPy-style Python code** and run it fast on GPUs without w
- **NumPy on accelerators**: Use `jax.numpy` just like NumPy, but arrays live on the GPU. - **NumPy on accelerators**: Use `jax.numpy` just like NumPy, but arrays live on the GPU.
- **Function transformations**: - **Function transformations**:
- `jit` → Compiles your function into fast GPU code. - `jit` → Compiles your function into fast GPU code
- `grad` → Gives you automatic differentiation. - `grad` → Gives you automatic differentiation
- `vmap` → Vectorizes your function across batches. - `vmap` → Vectorizes your function across batches
- `pmap` → Runs across multiple GPUs in parallel. - `pmap` → Runs across multiple GPUs in parallel
- **XLA backend**: JAX hands your code to XLA (Accelerated Linear Algebra compiler), which fuses operations and generates optimized GPU kernels. - **XLA backend**: JAX hands your code to XLA (Accelerated Linear Algebra compiler), which fuses operations and generates optimized GPU kernels.
## What you'll accomplish ## What you'll accomplish
@ -40,12 +40,12 @@ GPU acceleration and performance optimization capabilities.
## Prerequisites ## Prerequisites
[ ] NVIDIA Spark device with Blackwell architecture - NVIDIA Spark device with Blackwell architecture
[ ] ARM64 (AArch64) processor architecture - ARM64 (AArch64) processor architecture
[ ] 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`
[ ] Port 8080 available for marimo notebook access - Port 8080 available for marimo notebook access
## Ancillary files ## Ancillary files
@ -119,7 +119,7 @@ docker run --gpus all --rm -it \
jax-on-spark jax-on-spark
``` ```
## Step 5. Access marimo interface ## Step 5. Access the marimo interface
Connect to the marimo notebook server to begin the JAX tutorial. Connect to the marimo notebook server to begin the JAX tutorial.
@ -130,7 +130,7 @@ Connect to the marimo notebook server to begin the JAX tutorial.
The interface will load a table-of-contents display and brief introduction to marimo. The interface will load a table-of-contents display and brief introduction to marimo.
## Step 6. Complete JAX introduction tutorial ## Step 6. Complete the JAX introduction tutorial
Work through the introductory material to understand JAX programming model differences from NumPy. Work through the introductory material to understand JAX programming model differences from NumPy.
@ -172,7 +172,7 @@ Common issues and their solutions:
| Symptom | Cause | Fix | | Symptom | Cause | Fix |
|---------|--------|-----| |---------|--------|-----|
| `nvidia-smi` not found | Missing NVIDIA drivers | Install NVIDIA drivers for ARM64 | | `nvidia-smi` not found | Missing NVIDIA drivers | Install NVIDIA drivers for ARM64 |
| Container fails to access GPU | Missing NVIDIA Container Toolkit | Install nvidia-container-toolkit | | Container fails to access GPU | Missing NVIDIA Container Toolkit | Install `nvidia-container-toolkit` |
| JAX only uses CPU | CUDA/JAX version mismatch | Reinstall JAX with CUDA support | | JAX only uses CPU | CUDA/JAX version mismatch | Reinstall JAX with CUDA support |
| 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 |

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nvidia/nccl/README.md
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@ -13,7 +13,7 @@
## Overview ## Overview
## Basic Idea ## Basic idea
NCCL (NVIDIA Collective Communication Library) enables high-performance GPU-to-GPU communication NCCL (NVIDIA Collective Communication Library) enables high-performance GPU-to-GPU communication
across multiple nodes. This walkthrough sets up NCCL for multi-node distributed training on across multiple nodes. This walkthrough sets up NCCL for multi-node distributed training on

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nvidia/rag-ai-workbench/README.md
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@ -14,7 +14,7 @@
## Overview ## Overview
## Basic Idea ## Basic idea
This walkthrough demonstrates how to set up and run an agentic retrieval-augmented generation (RAG) This walkthrough demonstrates how to set up and run an agentic retrieval-augmented generation (RAG)
project using NVIDIA AI Workbench. You'll use AI Workbench to clone and run a pre-built agentic RAG project using NVIDIA AI Workbench. You'll use AI Workbench to clone and run a pre-built agentic RAG
@ -39,16 +39,16 @@ architectures.
## Prerequisites ## Prerequisites
- [ ] DGX Spark system with NVIDIA AI Workbench installed or ready to install - DGX Spark system with NVIDIA AI Workbench installed or ready to install
- [ ] Free NVIDIA API key: Generate at [NGC API Keys](https://org.ngc.nvidia.com/setup/api-keys) - Free NVIDIA API key: Generate at [NGC API Keys](https://org.ngc.nvidia.com/setup/api-keys)
- [ ] Free Tavily API key: Generate at [Tavily](https://tavily.com/) - Free Tavily API key: Generate at [Tavily](https://tavily.com/)
- [ ] Internet connection for cloning repositories and accessing APIs - Internet connection for cloning repositories and accessing APIs
- [ ] Web browser for accessing the Gradio interface - Web browser for accessing the Gradio interface
**Verification commands:** ## Verification commands
* Verify the NVIDIA AI Workbench application exists on your DGX Spark system - Verify the NVIDIA AI Workbench application exists on your DGX Spark system
* Verify your API keys are valid and up-to-date - Verify your API keys are valid and up-to-date
## Time & risk ## Time & risk
@ -74,7 +74,7 @@ On your DGX Spark system, open the **NVIDIA AI Workbench** application and click
### Troubleshooting installation issues ### Troubleshooting installation issues
If you encounter the error message: `An error occurred ... container tool failed to reach ready state. try again: docker is not running`, reboot your DGX Spark system to restart the docker service, then reopen NVIDIA AI Workbench. If you encounter the error message: `An error occurred ... container tool failed to reach ready state. try again: docker is not running` reboot your DGX Spark system to restart the docker service, then reopen NVIDIA AI Workbench.
## Step 2. Verify API key requirements ## Step 2. Verify API key requirements
@ -94,7 +94,7 @@ This step clones the pre-built agentic RAG project from GitHub into your AI Work
From the AI Workbench landing page, select the **Local** location if not done so already, then click **Clone Project** from the top right corner. From the AI Workbench landing page, select the **Local** location if not done so already, then click **Clone Project** from the top right corner.
Paste this Git repository URL in the clone dialog: ``https://github.com/NVIDIA/workbench-example-agentic-rag``. Paste this Git repository URL in the clone dialog: https://github.com/NVIDIA/workbench-example-agentic-rag
Click **Clone** to begin the clone and build process. Click **Clone** to begin the clone and build process.

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nvidia/vscode/README.md
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@ -12,7 +12,7 @@
## Overview ## Overview
## Basic Idea ## Basic idea
This walkthrough establishes a local Visual Studio Code development environment directly on DGX Spark devices. By installing VS Code natively on the ARM64-based Spark system, you gain access to a full-featured IDE with extensions, integrated terminal, and Git integration while leveraging the specialized hardware for development and testing. This walkthrough establishes a local Visual Studio Code development environment directly on DGX Spark devices. By installing VS Code natively on the ARM64-based Spark system, you gain access to a full-featured IDE with extensions, integrated terminal, and Git integration while leveraging the specialized hardware for development and testing.
## What you'll accomplish ## What you'll accomplish
@ -175,23 +175,28 @@ rm -rf ~/.vscode
## Access with NVIDIA Sync ## Access with NVIDIA Sync
## Step 1. Install and Open NVIDIA Sync ## Step 1. Install and configure NVIDIA Sync
## Step 2. Add your Spark to NVIDIA Sync Follow the [NVIDIA Sync setup guide](/spark/connect-to-your-spark/sync) to:
- Install NVIDIA Sync for your operating system
- Configure which development tools you want to use (VS Code, Cursor, Terminal, etc.)
- Add your DGX Spark device by providing its hostname/IP and credentials
## Step 3. Install VS Code locally NVIDIA Sync will automatically configure SSH key-based authentication for secure, password-free access.
## Step 4. Open Sync and launch VS Code ## Step 2. Launch VS Code through NVIDIA Sync
- Click the NVIDIA Sync icon in your system tray/taskbar
- Ensure your device is connected (click "Connect" if needed)
- Click on "VS Code" to launch it with an automatic SSH connection to your Spark
- Wait for the remote connection to be established (may ask your local machine for a password or to authorize the connection) - Wait for the remote connection to be established (may ask your local machine for a password or to authorize the connection)
- It may prompt you to "trust the authors of the files in this folder" when you first land in the home directory after a successful ssh connection. - It may prompt you to "trust the authors of the files in this folder" when you first land in the home directory after a successful SSH connection
## Step 3. Validation and follow-ups
- Verify that you can access your Spark's filesystem with VS Code as a text editor
## Step 5. Validation and Follow-ups - Open the integrated terminal in VS Code and run test commands like `hostnamectl` and `whoami` to ensure you are remotely accessing your Spark
- Navigate to a specific file path or directory and start editing/writing files
- Verify that you can access your Spark's filesystem with VSCode as a text editor. Run test commands in the terminal like `hostnamectl` and `whoami` to ensure you are remotely accessing your spark. - Install VS Code extensions for your development workflow (Python, Docker, GitLens, etc.)
- Specify a file path or directory and start editing/writing files - Clone repositories from GitHub or other version control systems
- Install extensions - Configure and locally host an LLM code assistant if desired
- Clone repos
- Locally host LLM code assistant