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chore: Regenerate all playbooks
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@ -125,12 +125,23 @@ Write a short README checklist for a Python project.
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Expected output should show the model responding in the terminal. When you are done, type `/bye` or press `Ctrl+D` to exit the interactive session before continuing.
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## Step 5. Launch Claude Code with Ollama
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## Step 5. Install and launch Claude Code with Ollama
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**Description**: Use Ollama's built-in [launch method](https://ollama.com/blog/launch) to start [Claude Code](https://docs.claude.com/en/docs/claude-code) against your local model. No environment variables or config files are required.
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**Description**: Install [Claude Code](https://docs.claude.com/en/docs/claude-code), then use Ollama's built-in [launch method](https://ollama.com/blog/launch) to start Claude Code against your local model. No environment variables or config files are required.
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```bash
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ollama launch claude
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curl -fsSL https://claude.ai/install.sh | bash
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claude --version
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```
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If Claude Code is already installed, just verify the version:
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```bash
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claude --version
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```
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```bash
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ollama launch claude --model qwen3.6
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```
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Expected output should show Claude Code starting and using the local Qwen3.6 model. Qwen3.6 ships with a 256K context window by default; adjust context length through Ollama's settings if you need to tune it further.
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@ -150,6 +161,8 @@ printf 'import math_utils\n\n\ndef test_add():\n assert math_utils.add(1, 2)
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If you do not already have pytest installed:
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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python3 -m pip install -U pytest
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```
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@ -165,7 +178,7 @@ Run the test:
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python3 -m pytest -q
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```
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Expected output should show the test passing.
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Expected output should show the test passing. When you are done, run `deactivate` to exit the virtual environment.
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## Step 7. Cleanup and rollback
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@ -259,7 +272,7 @@ Expected output should show the model responding. When you are done, type `/bye`
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**Description**: Use Ollama's built-in [launch method](https://ollama.com/blog/launch) to start [OpenCode](https://opencode.ai) against your local model. No [`opencode.json`](https://opencode.ai/docs/config/) provider configuration is required.
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```bash
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ollama launch opencode
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ollama launch opencode --model qwen3.6
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```
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If you want to pre-configure OpenCode without launching immediately:
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@ -285,6 +298,8 @@ printf 'import math_utils\n\n\ndef test_add():\n assert math_utils.add(1, 2)
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If you do not already have pytest installed:
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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python3 -m pip install -U pytest
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```
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@ -300,7 +315,7 @@ Run the test:
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python3 -m pytest -q
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```
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Expected output should show the test passing.
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Expected output should show the test passing. When you are done, run `deactivate` to exit the virtual environment.
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## Step 7. Cleanup and rollback
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@ -394,7 +409,7 @@ Expected output should show the model responding. When you are done, type `/bye`
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**Description**: Use Ollama's built-in [launch method](https://ollama.com/blog/launch) to start [Codex CLI](https://github.com/openai/codex) against your local model. No `~/.codex/config.toml` and no manual `npm install -g @openai/codex` are required — Ollama handles the Codex integration.
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```bash
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ollama launch codex
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ollama launch codex --model qwen3.6
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```
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Expected output should show Codex CLI starting with Ollama as the provider and Qwen3.6 as the model. Qwen3.6 ships with a 256K context window by default, which is well suited to Codex's agentic workflows.
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@ -414,6 +429,8 @@ printf 'import math_utils\n\n\ndef test_add():\n assert math_utils.add(1, 2)
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If you do not already have pytest installed:
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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python3 -m pip install -U pytest
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```
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@ -429,7 +446,7 @@ Run the test:
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python3 -m pytest -q
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```
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Expected output should show the test passing.
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Expected output should show the test passing. When you are done, run `deactivate` to exit the virtual environment.
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## Step 7. Cleanup and rollback
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@ -465,6 +482,10 @@ ollama rm qwen3.6
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| `connection refused` to localhost:11434 | Ollama service not running | Start with `ollama serve` or `sudo systemctl start ollama` |
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| `ollama launch <agent>` exits immediately | Agent integration failed to initialize | Re-run `ollama launch <agent>`; if it persists, check `journalctl -u ollama` |
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| Slow responses or OOM errors | Model variant too large for GPU memory | Switch to `qwen3.6:35b-a3b-nvfp4` or close other GPU workloads |
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| `python3 -m pip install -U pytest` reports `externally-managed-environment` | Ubuntu 24.04 protects the system Python environment | Create and activate a virtual environment first: `python3 -m venv .venv && source .venv/bin/activate` |
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| `ollama pull` reports that a model tag is a sharded GGUF | The selected model tag is not supported by Ollama | Use the Qwen3.6 commands in Step 3 instead of sharded GGUF tags |
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| `ollama run` fails with `CUDA error: context is destroyed` on a multi-GPU system | Ollama is initializing across a mixed-GPU topology | Pin Ollama to one GPU. For a foreground test, run `CUDA_VISIBLE_DEVICES=0 ollama serve`; for a system service, add `Environment="CUDA_VISIBLE_DEVICES=0"` to an Ollama systemd drop-in and restart Ollama |
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| A direct Claude Code setup using an Anthropic-compatible Ollama endpoint produces prose but does not edit files | Some model/server combinations do not emit tool calls reliably | Use `ollama launch claude` with Qwen3.6 as shown in this playbook |
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> [!NOTE]
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> DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing
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@ -51,10 +51,10 @@ You will deploy NVIDIA's VSS AI Blueprint on NVIDIA Spark hardware with Blackwel
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* Container startup can be resource-intensive and time-consuming with large model downloads
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* Network configuration conflicts if shared network already exists
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* Remote API endpoints may have rate limits or connectivity issues (hybrid deployment)
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* **Rollback:** Stop all containers with `scripts/dev-profile.sh down`
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* **Last Updated:** 3/16/2026
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* **Rollback:** Stop all containers with `deploy/docker/scripts/dev-profile.sh down`
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* **Last Updated:** 06/17/2026
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* Update required OS and Driver versions
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* Support for VSS 3.1.0 with Cosmos Reason 2 VLM
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* Support for VSS 3.2.0 with Cosmos Reason 2 VLM
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## Instructions
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@ -65,7 +65,7 @@ Check that your system meets the hardware and software [prerequisites](https://d
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```bash
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## Verify driver version
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nvidia-smi | grep "Driver Version"
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## Expected output: Driver Version: 580.126.09 or higher
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## Expected output: Driver Version: 580.95.05 or higher
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## Verify CUDA version
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nvcc --version
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@ -106,10 +106,19 @@ sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
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Clone the Video Search and Summarization repository from NVIDIA's public GitHub.
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**Note** Install Git LFS if not already present on the system
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```bash
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sudo apt-get install -y git-lfs && git lfs install
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```
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```bash
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## Clone the VSS AI Blueprint repository
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git clone https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization.git
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cd video-search-and-summarization
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git checkout tags/v3.2.0
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git lfs install
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git lfs pull
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```
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## Step 4. Run the cache cleaner script
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@ -148,12 +157,11 @@ sudo -b /usr/local/bin/sys-cache-cleaner.sh
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```
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> [!NOTE]
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+> The above runs the cache cleaner in the current session only; it does not persist across reboots. To have the cache cleaner run across reboots, create a systemd service instead.
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+>
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+> To stop the background cache cleaner:
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+> ```bash
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+> sudo pkill -f sys-cache-cleaner.sh
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+> ```
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The above runs the cache cleaner in the current session only; it does not persist across reboots. To have the cache cleaner run across reboots, create a systemd service instead.
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To stop the background cache cleaner:
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```bash
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sudo pkill -f sys-cache-cleaner.sh
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```
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## Step 5. Authenticate with NVIDIA Container Registry
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@ -172,13 +180,13 @@ docker login nvcr.io
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## Step 6. Choose deployment scenario
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Choose between two deployment options based on your requirements:
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Choose the deployment options based on your requirements:
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| Deployment Scenario | VLM (Cosmos-Reason2-8B)| LLM |
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|-------------------------------------------|------------------------|-------------------------------|
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| Standard VSS (Base) | Local | Remote |
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| Standard VSS (Alert Verification) | Local | Remote |
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| Standard VSS deployment (Real-Time Alerts)| Local | Remote |
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| Standard VSS (Base) | Local | Remote |
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| Standard VSS (Alert Verification) | Local | Remote |
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| Standard VSS deployment (Real-Time Alerts)| Local | Remote |
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## Step 7. Standard VSS
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@ -202,19 +210,21 @@ In this hybrid deployment, we would use NIMs from [build.nvidia.com](https://bui
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```bash
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## Start Standard VSS (Base)
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## Set NGC CLI API key and Hugging Face token (required for VA-MCP)
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export NGC_CLI_API_KEY='your_ngc_api_key'
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export HF_TOKEN='hf_your_token_here'
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export LLM_ENDPOINT_URL=https://your-llm-endpoint.com
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scripts/dev-profile.sh up -p base -H DGX-SPARK --use-remote-llm --llm <REMOTE LLM MODEL NAME>
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deploy/docker/scripts/dev-profile.sh up -p base -H DGX-SPARK --use-remote-llm --llm <REMOTE LLM MODEL NAME>
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## Start Standard VSS (Alert Verification)
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export NGC_CLI_API_KEY='your_ngc_api_key'
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export LLM_ENDPOINT_URL=https://your-llm-endpoint.com
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scripts/dev-profile.sh up -p alerts -m verification -H DGX-SPARK --use-remote-llm --llm <REMOTE LLM MODEL NAME>
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deploy/docker/scripts/dev-profile.sh up -p alerts -m verification -H DGX-SPARK --use-remote-llm --llm <REMOTE LLM MODEL NAME>
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## Start Standard VSS (Real-Time Alerts)
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export NGC_CLI_API_KEY='your_ngc_api_key'
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export LLM_ENDPOINT_URL=https://your-llm-endpoint.com
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scripts/dev-profile.sh up -p alerts -m real-time -H DGX-SPARK --use-remote-llm --llm <REMOTE LLM MODEL NAME>
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deploy/docker/scripts/dev-profile.sh up -p alerts -m real-time -H DGX-SPARK --use-remote-llm --llm <REMOTE LLM MODEL NAME>
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```
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> [!NOTE]
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@ -226,11 +236,11 @@ scripts/dev-profile.sh up -p alerts -m real-time -H DGX-SPARK --use-remote-llm -
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> • **OPENAI_API_KEY** — (optional) For remote LLM/VLM endpoints that require it
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> • **VLM_CUSTOM_WEIGHTS** — (optional) Absolute path to a custom weights directory
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>
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> Pass these additional flags to **`scripts/dev-profile.sh`** for remote LLM mode:
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> Pass these additional flags to **`deploy/docker/scripts/dev-profile.sh`** for remote LLM mode:
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> • **`--use-remote-llm`** — (required) Use a remote LLM, the base URL is read from **`LLM_ENDPOINT_URL`** in the environment
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> • **`--llm`** — (required) Remote LLM model name (for example: `nvidia/nvidia-nemotron-nano-9b-v2`). **Strongly recommended** for alert workflows (verification and real-time): use `nvidia/nvidia-nemotron-nano-9b-v2`. Omitting `--llm` may cause the script to use whatever model is returned by the remote endpoint.
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>
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> Run **`scripts/dev-profile.sh -h`** for a full list of supported arguments.
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> Run **`deploy/docker/scripts/dev-profile.sh --help`** for a full list of supported arguments.
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**7.3 Validate Standard VSS deployment**
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@ -241,7 +251,7 @@ Access the VSS UI to confirm successful deployment.
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```bash
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## Test Agent UI accessibility
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## If running locally on your Spark device, use localhost:
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curl -I http://localhost:3000
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curl -I http://localhost:7777
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## Expected: HTTP 200 response
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## If your Spark is running in Remote/Accessory mode, replace 'localhost' with the IP address or hostname of your Spark device.
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@ -250,20 +260,23 @@ hostname -I
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## Or to get the hostname:
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hostname
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## Then test accessibility (replace <SPARK_IP_OR_HOSTNAME> with the actual value):
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curl -I http://<SPARK_IP_OR_HOSTNAME>:3000
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curl -I http://<SPARK_IP_OR_HOSTNAME>:7777
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```
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Open `http://localhost:3000` or `http://<SPARK_IP_OR_HOSTNAME>:3000` in your browser to access the Agent interface.
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Open `http://localhost:7777` or `http://<SPARK_IP_OR_HOSTNAME>:7777` in your browser to access the Agent interface.
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## Step 8. Test video processing workflow
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Run a basic test to verify the video analysis pipeline is functioning based on your deployment. The UI comes with a few example videos pre-populated for uploading and testing
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Run a basic test to verify the video analysis pipeline is functioning based on your deployment.
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**For Standard VSS deployment**
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Follow the steps [here](https://docs.nvidia.com/vss/latest/quickstart.html#deploy) to navigate VSS Agent UI.
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- Access VSS Agent interface at `http://localhost:3000`
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- Download the sample data from NGC [here](https://docs.nvidia.com/vss/latest/quickstart.html#download-sample-data-from-ngc) and upload videos and test features [here](https://docs.nvidia.com/vss/latest/quickstart.html#download-sample-data-from-ngc)
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- Access VSS Agent interface at `http://localhost:7777`
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- Download the sample data from NGC [here](https://docs.nvidia.com/vss/latest/quickstart.html#download-sample-data-from-ngc) and upload videos and test features
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- Test Standard VSS deployment (Base) [here](https://docs.nvidia.com/vss/latest/quickstart.html#step-2-upload-a-video)
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- Test Standard VSS deployment (Alert Verification) [here](https://docs.nvidia.com/vss/latest/agent-workflow-alert-verification.html#step-2-add-a-video-stream)
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- Test Standard VSS deployment (Real-Time Alerts) [here](https://docs.nvidia.com/vss/latest/agent-workflow-rt-alert.html#step-2-add-a-video-stream)
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## Step 9. Cleanup and rollback
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@ -275,7 +288,7 @@ To completely remove the VSS deployment and free up system resources [Follow](ht
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```bash
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## For Standard VSS deployment
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scripts/dev-profile.sh down
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deploy/docker/scripts/dev-profile.sh down
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```
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## Step 10. Next steps
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@ -283,8 +296,8 @@ scripts/dev-profile.sh down
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With VSS deployed, you can now:
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**Standard VSS deployment:**
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- Access full VSS capabilities at port 3000
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- Test video summarization and Q&A features
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- Access full VSS capabilities at port 7777
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- Test video and Q&A features
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- Configure knowledge graphs and graph databases
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- Integrate with existing video processing workflows
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