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chore: Regenerate all playbooks
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@ -25,7 +25,7 @@ Each playbook includes prerequisites, step-by-step instructions, troubleshooting
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- [Connect to your Spark](nvidia/connect-to-your-spark/)
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- [DGX Dashboard](nvidia/dgx-dashboard/)
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- [FLUX.1 Dreambooth LoRA Fine-tuning](nvidia/flux-finetuning/)
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- [Optimized Jax](nvidia/jax/)
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- [Optimized JAX](nvidia/jax/)
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- [Llama Factory](nvidia/llama-factory/)
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- [MONAI-Reasoning-CXR-3B Model](nvidia/monai-reasoning/)
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- [Build and Deploy a Multi-Agent Chatbot](nvidia/multi-agent-chatbot/)
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@ -1,6 +1,6 @@
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# Optimized Jax
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# Optimized JAX
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> Develop with Optimized Jax
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> Develop with Optimized JAX
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## Table of Contents
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@ -17,10 +17,10 @@ JAX lets you write **NumPy-style Python code** and run it fast on GPUs without w
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- **NumPy on accelerators**: Use `jax.numpy` just like NumPy, but arrays live on the GPU.
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- **Function transformations**:
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- `jit` → Compiles your function into fast GPU code.
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- `grad` → Gives you automatic differentiation.
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- `vmap` → Vectorizes your function across batches.
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- `pmap` → Runs across multiple GPUs in parallel.
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- `jit` → Compiles your function into fast GPU code
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- `grad` → Gives you automatic differentiation
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- `vmap` → Vectorizes your function across batches
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- `pmap` → Runs across multiple GPUs in parallel
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- **XLA backend**: JAX hands your code to XLA (Accelerated Linear Algebra compiler), which fuses operations and generates optimized GPU kernels.
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## What you'll accomplish
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@ -40,12 +40,12 @@ GPU acceleration and performance optimization capabilities.
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## Prerequisites
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[ ] NVIDIA Spark device with Blackwell architecture
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[ ] ARM64 (AArch64) processor architecture
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[ ] Docker or container runtime installed
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[ ] NVIDIA Container Toolkit configured
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[ ] Verify GPU access: `nvidia-smi`
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[ ] Port 8080 available for marimo notebook access
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- NVIDIA Spark device with Blackwell architecture
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- ARM64 (AArch64) processor architecture
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- Docker or container runtime installed
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- NVIDIA Container Toolkit configured
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- Verify GPU access: `nvidia-smi`
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- Port 8080 available for marimo notebook access
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## Ancillary files
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@ -119,7 +119,7 @@ docker run --gpus all --rm -it \
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jax-on-spark
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```
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## Step 5. Access marimo interface
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## Step 5. Access the marimo interface
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Connect to the marimo notebook server to begin the JAX tutorial.
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@ -130,7 +130,7 @@ Connect to the marimo notebook server to begin the JAX tutorial.
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The interface will load a table-of-contents display and brief introduction to marimo.
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## Step 6. Complete JAX introduction tutorial
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## Step 6. Complete the JAX introduction tutorial
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Work through the introductory material to understand JAX programming model differences from NumPy.
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@ -172,7 +172,7 @@ Common issues and their solutions:
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| Symptom | Cause | Fix |
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|---------|--------|-----|
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| `nvidia-smi` not found | Missing NVIDIA drivers | Install NVIDIA drivers for ARM64 |
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| Container fails to access GPU | Missing NVIDIA Container Toolkit | Install nvidia-container-toolkit |
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| Container fails to access GPU | Missing NVIDIA Container Toolkit | Install `nvidia-container-toolkit` |
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| JAX only uses CPU | CUDA/JAX version mismatch | Reinstall JAX with CUDA support |
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| Port 8080 unavailable | Port already in use | Use `-p 8081:8080` or kill process on 8080 |
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| Package conflicts in Docker build | Outdated environment file | Update environment file for Blackwell |
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@ -13,7 +13,7 @@
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## Overview
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## Basic Idea
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## Basic idea
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NCCL (NVIDIA Collective Communication Library) enables high-performance GPU-to-GPU communication
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across multiple nodes. This walkthrough sets up NCCL for multi-node distributed training on
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@ -14,7 +14,7 @@
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## Overview
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## Basic Idea
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## Basic idea
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This walkthrough demonstrates how to set up and run an agentic retrieval-augmented generation (RAG)
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project using NVIDIA AI Workbench. You'll use AI Workbench to clone and run a pre-built agentic RAG
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@ -39,16 +39,16 @@ architectures.
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## Prerequisites
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- [ ] DGX Spark system with NVIDIA AI Workbench installed or ready to install
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- [ ] Free NVIDIA API key: Generate at [NGC API Keys](https://org.ngc.nvidia.com/setup/api-keys)
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- [ ] Free Tavily API key: Generate at [Tavily](https://tavily.com/)
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- [ ] Internet connection for cloning repositories and accessing APIs
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- [ ] Web browser for accessing the Gradio interface
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- DGX Spark system with NVIDIA AI Workbench installed or ready to install
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- Free NVIDIA API key: Generate at [NGC API Keys](https://org.ngc.nvidia.com/setup/api-keys)
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- Free Tavily API key: Generate at [Tavily](https://tavily.com/)
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- Internet connection for cloning repositories and accessing APIs
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- Web browser for accessing the Gradio interface
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**Verification commands:**
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## Verification commands
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* Verify the NVIDIA AI Workbench application exists on your DGX Spark system
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* Verify your API keys are valid and up-to-date
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- Verify the NVIDIA AI Workbench application exists on your DGX Spark system
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- Verify your API keys are valid and up-to-date
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## Time & risk
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@ -74,7 +74,7 @@ On your DGX Spark system, open the **NVIDIA AI Workbench** application and click
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### Troubleshooting installation issues
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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.
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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.
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## Step 2. Verify API key requirements
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@ -94,7 +94,7 @@ This step clones the pre-built agentic RAG project from GitHub into your AI Work
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From the AI Workbench landing page, select the **Local** location if not done so already, then click **Clone Project** from the top right corner.
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Paste this Git repository URL in the clone dialog: ``https://github.com/NVIDIA/workbench-example-agentic-rag``.
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Paste this Git repository URL in the clone dialog: https://github.com/NVIDIA/workbench-example-agentic-rag
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Click **Clone** to begin the clone and build process.
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@ -12,7 +12,7 @@
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## Overview
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## Basic Idea
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## Basic idea
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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.
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## What you'll accomplish
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@ -175,23 +175,28 @@ rm -rf ~/.vscode
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## Access with NVIDIA Sync
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## Step 1. Install and Open NVIDIA Sync
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## Step 1. Install and configure NVIDIA Sync
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## Step 2. Add your Spark to NVIDIA Sync
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Follow the [NVIDIA Sync setup guide](/spark/connect-to-your-spark/sync) to:
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- Install NVIDIA Sync for your operating system
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- Configure which development tools you want to use (VS Code, Cursor, Terminal, etc.)
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- Add your DGX Spark device by providing its hostname/IP and credentials
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## Step 3. Install VS Code locally
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NVIDIA Sync will automatically configure SSH key-based authentication for secure, password-free access.
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## Step 4. Open Sync and launch VS Code
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## Step 2. Launch VS Code through NVIDIA Sync
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- Click the NVIDIA Sync icon in your system tray/taskbar
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- Ensure your device is connected (click "Connect" if needed)
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- Click on "VS Code" to launch it with an automatic SSH connection to your Spark
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- Wait for the remote connection to be established (may ask your local machine for a password or to authorize the connection)
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- 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.
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- 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
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## Step 3. Validation and follow-ups
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## Step 5. Validation and Follow-ups
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- 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.
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- Specify a file path or directory and start editing/writing files
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- Install extensions
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- Clone repos
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- Locally host LLM code assistant
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- Verify that you can access your Spark's filesystem with VS Code as a text editor
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- Open the integrated terminal in VS Code and run test commands like `hostnamectl` and `whoami` to ensure you are remotely accessing your Spark
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- Navigate to a specific file path or directory and start editing/writing files
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- Install VS Code extensions for your development workflow (Python, Docker, GitLens, etc.)
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- Clone repositories from GitHub or other version control systems
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- Configure and locally host an LLM code assistant if desired
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