chore: Regenerate all playbooks

README.md

@@ -46,7 +46,7 @@ Each playbook includes prerequisites, step-by-step instructions, troubleshooting
 - [Text to Knowledge Graph](nvidia/txt2kg/)
 - [Unsloth on DGX Spark](nvidia/unsloth/)
 - [Vibe Coding in VS Code](nvidia/vibe-coding/)
-- [Install and Use vLLM for Inference](nvidia/vllm/)
+- [vLLM for Inference](nvidia/vllm/)
 - [VS Code](nvidia/vscode/)
 - [Build a Video Search and Summarization (VSS) Agent](nvidia/vss/)
 

nvidia/comfy-ui/README.md

@@ -93,13 +93,13 @@ Verify the virtual environment is active by checking the command prompt shows `(
 
 ## Step 3. Install PyTorch with CUDA support
 
-Install PyTorch with CUDA 12.9 support.
+Install PyTorch with CUDA 13.0 support.
 
 ```bash
 pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu130
 ```
 
-This installation targets CUDA 12.9 compatibility with Blackwell architecture GPUs.
+This installation targets CUDA 13.0 compatibility with Blackwell architecture GPUs.
 
 ## Step 4. Clone ComfyUI repository
 

nvidia/connect-to-your-spark/README.md

@@ -67,6 +67,8 @@ applications, and manage your DGX Spark remotely from your laptop.
 - **Time estimate:** 5-10 minutes
 - **Risk level:** Low - SSH setup involves credential configuration but no system-level changes to the DGX Spark device
 - **Rollback:** SSH key removal can be done by editing `~/.ssh/authorized_keys` on your DGX Spark.
+- **Last Updated:** 10/28/2025
+  * Minor copyedits
 
 ## Connect with NVIDIA Sync
 

nvidia/connect-two-sparks/README.md

@@ -52,6 +52,9 @@ All required files for this playbook can be found [here on GitHub](https://githu
 
 - **Rollback:** Network changes can be reversed by removing netplan configs or IP assignments
 
+- **Last Updated:** 11/24/2025
+  * Minor copyedits
+
 ## Run on Two Sparks
 
 ## Step 1. Ensure Same Username on Both Systems

nvidia/cuda-x-data-science/README.md

@@ -34,6 +34,8 @@ You will accelerate popular machine learning algorithms and data analytics opera
   * Data download slowness or failure due to network issues
   * Kaggle API generation failure requiring retries
 * **Rollback:** No permanent system changes made during normal usage.
+* **Last Updated:** 11/07/2025
+  * Minor copyedits
 
 ## Instructions
 

nvidia/flux-finetuning/README.md

@@ -47,6 +47,8 @@ The setup includes:
   * Docker permission issues may require user group changes and session restart
   * The recipe would require hyperparameter tuning and a high-quality dataset for the best results
 * **Rollback**: Stop and remove Docker containers, delete downloaded models if needed.
+* **Last Updated:** 11/07/2025
+  * Minor copyedits
 
 ## Instructions
 

nvidia/jax/README.md

@@ -65,6 +65,8 @@ All required assets can be found [here on GitHub](https://github.com/NVIDIA/dgx-
   * Package dependency conflicts in Python environment
   * Performance validation may require architecture-specific optimizations
 * **Rollback:** Container environments provide isolation; remove containers and restart to reset state.
+* **Last Updated:** 11/07/2025
+  * Minor copyedits
 
 ## Instructions
 

nvidia/llama-factory/README.md

@@ -67,6 +67,8 @@ model adaptation for specialized domains while leveraging hardware-specific opti
 * **Duration:** 30-60 minutes for initial setup, 1-7 hours for training depending on model size and dataset.
 * **Risks:** Model downloads require significant bandwidth and storage. Training may consume substantial GPU memory and require parameter tuning for hardware constraints.
 * **Rollback:** Remove Docker containers and cloned repositories. Training checkpoints are saved locally and can be deleted to reclaim storage space.
+* **Last Updated:** 10/12/2025
+  * First publication
 
 ## Instructions
 

nvidia/multi-modal-inference/README.md

@@ -65,6 +65,9 @@ All necessary files can be found in the TensorRT repository [here on GitHub](htt
   - Remove downloaded models from HuggingFace cache
   - Then exit the container environment
 
+* **Last Updated:** 10/12/2025
+  * First publication
+
 ## Instructions
 
 ## Step 1. Launch the TensorRT container environment

nvidia/nccl/README.md

@@ -41,9 +41,11 @@ and proper GPU topology detection.
 
 ## Time & risk
 
-- **Duration**: 30 minutes for setup and validation
+* **Duration**: 30 minutes for setup and validation
-- **Risk level**: Medium - involves network configuration changes
+* **Risk level**: Medium - involves network configuration changes
-- **Rollback**: The NCCL & NCCL Tests repositories can be deleted from DGX Spark
+* **Rollback**: The NCCL & NCCL Tests repositories can be deleted from DGX Spark
+* **Last Updated:** 10/12/2025
+  * First publication
 
 ## Run on two Sparks
 

nvidia/nemo-fine-tune/README.md

@@ -47,6 +47,8 @@ All necessary files for the playbook can be found [here on GitHub](https://githu
 * **Duration:** 45-90 minutes for complete setup and initial model fine-tuning
 * **Risks:** Model downloads can be large (several GB), ARM64 package compatibility issues may require troubleshooting, distributed training setup complexity increases with multi-node configurations
 * **Rollback:** Virtual environments can be completely removed; no system-level changes are made to the host system beyond package installations.
+* **Last Updated:** 10/22/2025
+  * Minor copyedits
 
 ## Instructions
 

nvidia/ollama/README.md

@@ -44,13 +44,16 @@ the powerful GPU capabilities of your Spark device without complex network confi
 
 ## Time & risk
 
-**Duration**: 10-15 minutes for initial setup, 2-3 minutes for model download (varies by model size)
+* **Duration**: 10-15 minutes for initial setup, 2-3 minutes for model download (varies by model size)
 
-**Risk level**: Low - No system-level changes, easily reversible by stopping the custom app
+* **Risk level**: Low - No system-level changes, easily reversible by stopping the custom app
 
-**Rollback**: Stop the custom app in NVIDIA Sync and uninstall Ollama with standard package
+* **Rollback**: Stop the custom app in NVIDIA Sync and uninstall Ollama with standard package
 removal if needed
 
+* **Last Updated:** 10/12/2025
+  * First publication
+
 ## Instructions
 
 ## Step 1. Verify Ollama installation status

nvidia/open-webui/README.md

@@ -38,6 +38,8 @@ You will have a fully functional Open WebUI installation running on your DGX Spa
 * **Risks**:
   * Docker permission issues may require user group changes and session restart
   * Large model downloads may take significant time depending on network speed
+* **Last Updated:** 10/28/2025
+  * Minor copyedits
 
 ## Set up Open WebUI on Remote Spark with NVIDIA Sync
 

nvidia/pytorch-fine-tune/README.md

@@ -51,6 +51,8 @@ ALl files required for fine-tuning are included in the folder in [the GitHub rep
 
 * **Time estimate:** 30-45 mins for setup and runing fine-tuning. Fine-tuning run time varies depending on model size 
 * **Risks:** Model downloads can be large (several GB), ARM64 package compatibility issues may require troubleshooting.
+* **Last Updated:** 11/07/2025
+  * Fix broken commands to access files from GitHub
 
 ## Instructions
 

nvidia/rag-ai-workbench/README.md

@@ -58,7 +58,7 @@ architectures.
 * **Estimated time:** 30-45 minutes (including AI Workbench installation if needed)
 * **Risk level:** Low - Uses pre-built containers and established APIs
 * **Rollback:** Simply delete the cloned project from AI Workbench to remove all components. No system changes are made outside the AI Workbench environment.
-* **Last Updated:** 11/21/2025
+* **Last Updated:** 10/28/2025
   * Minor copyedits
 
 ## Instructions

nvidia/speculative-decoding/README.md

@@ -55,6 +55,8 @@ These examples demonstrate how to accelerate large language model inference whil
 * **Duration:** 10-20 minutes for setup, additional time for model downloads (varies by network speed)
 * **Risks:** GPU memory exhaustion with large models, container registry access issues, network timeouts during downloads
 * **Rollback:** Stop Docker containers and optionally clean up downloaded model cache.
+* **Last Updated:** 10/12/2025
+  * First publication
 
 ## Instructions
 

nvidia/tailscale/README.md

@@ -73,7 +73,7 @@ all traffic automatically encrypted and NAT traversal handled transparently.
   * Network connectivity issues during initial setup
   * Authentication provider service dependencies
 * **Rollback**: Tailscale can be completely removed with `sudo apt remove tailscale` and all network routing automatically reverts to default settings.
-* **Last Updated:** 11/21/2025
+* **Last Updated:** 11/07/2025
   * Minor copyedits
 
 ## Instructions

nvidia/trt-llm/README.md

@@ -1,6 +1,6 @@
 # TRT LLM for Inference
 
-> Install and configure TRT LLM to run on a single Spark or on two Sparks
+> Install and use TensorRT-LLM on DGX Spark Sparks
 
 ## Table of Contents
 
@@ -117,6 +117,8 @@ Reminder: not all model architectures are supported for NVFP4 quantization.
 * **Duration**: 45-60 minutes for setup and API server deployment
 * **Risk level**: Medium - container pulls and model downloads may fail due to network issues
 * **Rollback**: Stop inference servers and remove downloaded models to free resources.
+* **Last Updated:** 10/18/2025
+  * Fix broken links
 
 ## Single Spark
 

nvidia/unsloth/README.md

@@ -55,6 +55,9 @@ The Python test script can be found [here on GitHub](https://github.com/NVIDIA/d
   * CUDA toolkit configuration issues may prevent kernel compilation
   * Memory constraints on smaller models require batch size adjustments
 * **Rollback**: Uninstall packages with `pip uninstall unsloth torch torchvision`.
+* **Last Updated:** 11/07/2025
+  * Add required python dependencies
+  * Fix broken commands to access files on GitHub
 
 ## Instructions
 

nvidia/vibe-coding/README.md

@@ -43,6 +43,8 @@ You'll have a fully configured DGX Spark system capable of:
 * **Duration:** About 30 minutes 
 * **Risks:** Data download slowness or failure due to network issues
 * **Rollback:** No permanent system changes made during normal usage.
+* **Last Updated:** 10/21/2025
+  * First publication
 
 ## Instructions
 

nvidia/vllm/README.md

@@ -1,6 +1,6 @@
-# Install and Use vLLM for Inference
+# vLLM for Inference
 
-> Use a container or build vLLM from source for Spark
+> Install and use vLLM on DGX Spark
 
 ## Table of Contents
 
@@ -52,6 +52,8 @@ support for ARM64.
 * **Duration:** 30 minutes for Docker approach
 * **Risks:** Container registry access requires internal credentials
 * **Rollback:** Container approach is non-destructive.
+* **Last Updated:** 10/18/2025
+  * Minor copyedits
 
 ## Instructions
 

nvidia/vss/README.md

@@ -52,6 +52,9 @@ You will deploy NVIDIA's VSS AI Blueprint on NVIDIA Spark hardware with Blackwel
   * Network configuration conflicts if shared network already exists
   * Remote API endpoints may have rate limits or connectivity issues (hybrid deployment)
 * **Rollback:** Stop all containers with `docker compose down`, remove shared network with `docker network rm vss-shared-network`, and clean up temporary media directories.
+* **Last Updated:** 10/18/2025
+  * Update required OS and Driver versions
+  * Add instructions to fully local VSS deployment
 
 ## Instructions