From 68184819022ea93c757bf501db5df90a1d501444 Mon Sep 17 00:00:00 2001 From: GitLab CI Date: Mon, 6 Oct 2025 02:38:01 +0000 Subject: [PATCH] chore: Regenerate all playbooks --- nvidia/llama-factory/README.md | 52 +++----- nvidia/multi-modal-inference/README.md | 16 +-- nvidia/nemo-fine-tune/README.md | 14 +-- nvidia/nim-llm/README.md | 8 +- nvidia/nvfp4-quantization/README.md | 10 +- nvidia/ollama/README.md | 9 +- nvidia/protein-folding/README.md | 10 +- nvidia/sglang/README.md | 12 +- nvidia/speculative-decoding/README.md | 10 +- nvidia/tailscale/README.md | 14 +-- nvidia/trt-llm/README.md | 14 +-- nvidia/unsloth/README.md | 8 +- nvidia/vllm/README.md | 160 ++++++++++++------------- nvidia/vss/README.md | 16 +-- 14 files changed, 167 insertions(+), 186 deletions(-) diff --git a/nvidia/llama-factory/README.md b/nvidia/llama-factory/README.md index 37867a6..5113b7b 100644 --- a/nvidia/llama-factory/README.md +++ b/nvidia/llama-factory/README.md @@ -5,36 +5,20 @@ ## Table of Contents - [Overview](#overview) - - [What you'll accomplish](#what-youll-accomplish) - - [What to know before starting](#what-to-know-before-starting) - - [Prerequisites](#prerequisites) - - [Ancillary files](#ancillary-files) - - [Time & risk](#time-risk) - [Instructions](#instructions) - - [Step 1. Verify system prerequisites](#step-1-verify-system-prerequisites) - - [Step 2. Launch PyTorch container with GPU support](#step-2-launch-pytorch-container-with-gpu-support) - - [Step 3. Clone LLaMA Factory repository](#step-3-clone-llama-factory-repository) - [Step 4. Install LLaMA Factory with dependencies](#step-4-install-llama-factory-with-dependencies) - - [Step 5. Configure PyTorch for CUDA 12.9 (if needed)](#step-5-configure-pytorch-for-cuda-129-if-needed) - - [Step 6. Prepare training configuration](#step-6-prepare-training-configuration) - - [Step 7. Launch fine-tuning training](#step-7-launch-fine-tuning-training) - - [Step 8. Validate training completion](#step-8-validate-training-completion) - - [Step 9. Test inference with fine-tuned model](#step-9-test-inference-with-fine-tuned-model) - - [Step 10. Troubleshooting](#step-10-troubleshooting) - - [Step 11. Cleanup and rollback](#step-11-cleanup-and-rollback) - - [Step 12. Next steps](#step-12-next-steps) --- ## Overview -### What you'll accomplish +## What you'll accomplish You'll set up LLaMA Factory on NVIDIA Spark with Blackwell architecture to fine-tune large language models using LoRA, QLoRA, and full fine-tuning methods. This enables efficient model adaptation for specialized domains while leveraging hardware-specific optimizations. -### What to know before starting +## What to know before starting - Basic Python knowledge for editing config files and troubleshooting - Command line usage for running shell commands and managing environments @@ -44,7 +28,7 @@ model adaptation for specialized domains while leveraging hardware-specific opti - Dataset preparation: formatting text data into JSON structure for instruction tuning - Resource management: adjusting batch size and memory settings for GPU constraints -### Prerequisites +## Prerequisites - NVIDIA Spark device with Blackwell architecture @@ -60,7 +44,7 @@ model adaptation for specialized domains while leveraging hardware-specific opti - Internet connection for downloading models from Hugging Face Hub -### Ancillary files +## Ancillary files - Official LLaMA Factory repository: https://github.com/hiyouga/LLaMA-Factory @@ -70,7 +54,7 @@ model adaptation for specialized domains while leveraging hardware-specific opti - Documentation: https://llamafactory.readthedocs.io/en/latest/getting_started/data_preparation.html -### Time & risk +## Time & risk **Duration:** 30-60 minutes for initial setup, 1-7 hours for training depending on model size and dataset. @@ -83,7 +67,7 @@ saved locally and can be deleted to reclaim storage space. ## Instructions -### Step 1. Verify system prerequisites +## Step 1. Verify system prerequisites Check that your NVIDIA Spark system has the required components installed and accessible. @@ -95,7 +79,7 @@ python --version git --version ``` -### Step 2. Launch PyTorch container with GPU support +## Step 2. Launch PyTorch container with GPU support Start the NVIDIA PyTorch container with GPU access and mount your workspace directory. > **Note:** This NVIDIA PyTorch container supports CUDA 13 @@ -104,7 +88,7 @@ Start the NVIDIA PyTorch container with GPU access and mount your workspace dire docker run --gpus all --ipc=host --ulimit memlock=-1 -it --ulimit stack=67108864 --rm -v "$PWD":/workspace nvcr.io/nvidia/pytorch:25.08-py3 bash ``` -### Step 3. Clone LLaMA Factory repository +## Step 3. Clone LLaMA Factory repository Download the LLaMA Factory source code from the official repository. @@ -121,9 +105,9 @@ Install the package in editable mode with metrics support for training evaluatio pip install -e ".[metrics]" ``` -### Step 5. Configure PyTorch for CUDA 12.9 (if needed) +## Step 5. Configure PyTorch for CUDA 12.9 (if needed) -#### If using standalone Python (skip if using Docker container) +*If using standalone Python (skip if using Docker container)* In a python virtual environment, uninstall existing PyTorch and reinstall with CUDA 12.9 support for ARM64 architecture. @@ -132,7 +116,7 @@ pip uninstall torch torchvision torchaudio pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu129 ``` -#### If using Docker container +*If using Docker container* PyTorch is pre-installed with CUDA support. Verify installation: @@ -140,7 +124,7 @@ PyTorch is pre-installed with CUDA support. Verify installation: python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')" ``` -### Step 6. Prepare training configuration +## Step 6. Prepare training configuration Examine the provided LoRA fine-tuning configuration for Llama-3. @@ -148,7 +132,7 @@ Examine the provided LoRA fine-tuning configuration for Llama-3. cat examples/train_lora/llama3_lora_sft.yaml ``` -### Step 7. Launch fine-tuning training +## Step 7. Launch fine-tuning training > **Note:** Login to your hugging face hub to download the model if the model is gated Execute the training process using the pre-configured LoRA setup. @@ -170,7 +154,7 @@ Example output: Figure saved at: saves/llama3-8b/lora/sft/training_loss.png ``` -### Step 8. Validate training completion +## Step 8. Validate training completion Verify that training completed successfully and checkpoints were saved. @@ -186,7 +170,7 @@ Expected output should show: - Training metrics showing decreasing loss values - Training loss plot saved as PNG file -### Step 9. Test inference with fine-tuned model +## Step 9. Test inference with fine-tuned model Run a simple inference test to verify the fine-tuned model loads correctly. @@ -194,7 +178,7 @@ Run a simple inference test to verify the fine-tuned model loads correctly. llamafactory-cli chat examples/inference/llama3_lora_sft.yaml ``` -### Step 10. Troubleshooting +## Step 10. Troubleshooting | Symptom | Cause | Fix | |---------|--------|-----| @@ -202,7 +186,7 @@ llamafactory-cli chat examples/inference/llama3_lora_sft.yaml | Model download fails or is slow | Network connectivity or Hugging Face Hub issues | Check internet connection, try using `HF_HUB_OFFLINE=1` for cached models | | Training loss not decreasing | Learning rate too high/low or insufficient data | Adjust `learning_rate` parameter or check dataset quality | -### Step 11. Cleanup and rollback +## Step 11. Cleanup and rollback > **Warning:** This will delete all training progress and checkpoints. @@ -220,7 +204,7 @@ exit # Exit container docker container prune -f ``` -### Step 12. Next steps +## Step 12. Next steps Test your fine-tuned model with custom prompts: diff --git a/nvidia/multi-modal-inference/README.md b/nvidia/multi-modal-inference/README.md index 8b33be9..eba93aa 100644 --- a/nvidia/multi-modal-inference/README.md +++ b/nvidia/multi-modal-inference/README.md @@ -35,14 +35,14 @@ FP8, FP4). ## Prerequisites -- [ ] NVIDIA Spark device with Blackwell GPU architecture -- [ ] Docker installed and accessible to current user -- [ ] NVIDIA Container Runtime configured -- [ ] Hugging Face account with valid token -- [ ] At least 48GB VRAM available for FP16 Flux.1 Schnell operations -- [ ] Verify GPU access: `nvidia-smi` -- [ ] Check Docker GPU integration: `docker run --rm --gpus all nvidia/cuda:12.0-base-ubuntu20.04 nvidia-smi` -- [ ] Confirm HF token access with permissions to FLUX repos: `echo $HF_TOKEN`, Sign in to your huggingface account You can create the token from create your token here (make sure you provide permissions to the token): https://huggingface.co/settings/tokens , Note the permissions to be checked and the repos: black-forest-labs/FLUX.1-dev and black-forest-labs/FLUX.1-dev-onnx (search for these repos when creating the user token) to be added. +- NVIDIA Spark device with Blackwell GPU architecture +- Docker installed and accessible to current user +- NVIDIA Container Runtime configured +- Hugging Face account with valid token +- At least 48GB VRAM available for FP16 Flux.1 Schnell operations +- Verify GPU access: `nvidia-smi` +- Check Docker GPU integration: `docker run --rm --gpus all nvidia/cuda:12.0-base-ubuntu20.04 nvidia-smi` +- Confirm HF token access with permissions to FLUX repos: `echo $HF_TOKEN`, Sign in to your huggingface account You can create the token from create your token here (make sure you provide permissions to the token): https://huggingface.co/settings/tokens , Note the permissions to be checked and the repos: black-forest-labs/FLUX.1-dev and black-forest-labs/FLUX.1-dev-onnx (search for these repos when creating the user token) to be added. ## Ancillary files diff --git a/nvidia/nemo-fine-tune/README.md b/nvidia/nemo-fine-tune/README.md index a6a8b2c..641c571 100644 --- a/nvidia/nemo-fine-tune/README.md +++ b/nvidia/nemo-fine-tune/README.md @@ -35,22 +35,22 @@ You'll establish a complete fine-tuning environment for large language models (1 ## Prerequisites -- [ ] NVIDIA Spark device with Blackwell architecture GPU access -- [ ] CUDA toolkit 12.0+ installed and configured +- NVIDIA Spark device with Blackwell architecture GPU access +- CUDA toolkit 12.0+ installed and configured ```bash nvcc --version ``` -- [ ] Python 3.10+ environment available +- Python 3.10+ environment available ```bash python3 --version ``` -- [ ] Minimum 32GB system RAM for efficient model loading and training -- [ ] Active internet connection for downloading models and packages -- [ ] Git installed for repository cloning +- Minimum 32GB system RAM for efficient model loading and training +- Active internet connection for downloading models and packages +- Git installed for repository cloning ```bash git --version ``` -- [ ] SSH access to your NVIDIA Spark device configured +- SSH access to your NVIDIA Spark device configured ## Ancillary files diff --git a/nvidia/nim-llm/README.md b/nvidia/nim-llm/README.md index 9093709..abbdadc 100644 --- a/nvidia/nim-llm/README.md +++ b/nvidia/nim-llm/README.md @@ -40,19 +40,19 @@ completions. ### Prerequisites -- [ ] DGX Spark device with NVIDIA drivers installed +- DGX Spark device with NVIDIA drivers installed ```bash nvidia-smi ``` -- [ ] Docker with NVIDIA Container Toolkit configured, instructions here: https://******.nvidia.com/dgx-docs/review/621/dgx-spark/latest/nvidia-container-runtime-for-docker.html +- Docker with NVIDIA Container Toolkit configured, instructions here: https://******.nvidia.com/dgx-docs/review/621/dgx-spark/latest/nvidia-container-runtime-for-docker.html ```bash docker run -it --gpus=all nvcr.io/nvidia/cuda:13.0.1-devel-ubuntu24.04 nvidia-smi ``` -- [ ] NGC account with API key from https://ngc.nvidia.com/setup/api-key +- NGC account with API key from https://ngc.nvidia.com/setup/api-key ```bash echo $NGC_API_KEY | grep -E '^[a-zA-Z0-9]{86}==' ``` -- [ ] Sufficient disk space for model caching (varies by model, typically 10-50GB) +- Sufficient disk space for model caching (varies by model, typically 10-50GB) ```bash df -h ~ ``` diff --git a/nvidia/nvfp4-quantization/README.md b/nvidia/nvfp4-quantization/README.md index f9ee9fd..046460b 100644 --- a/nvidia/nvfp4-quantization/README.md +++ b/nvidia/nvfp4-quantization/README.md @@ -40,11 +40,11 @@ inside a TensorRT-LLM container, producing an NVFP4 quantized model for deployme ## Prerequisites -- [ ] NVIDIA Spark device with Blackwell architecture GPU -- [ ] Docker installed with GPU support -- [ ] NVIDIA Container Toolkit configured -- [ ] At least 32GB of available storage for model files and outputs -- [ ] Hugging Face account with access to the target model +- NVIDIA Spark device with Blackwell architecture GPU +- Docker installed with GPU support +- NVIDIA Container Toolkit configured +- At least 32GB of available storage for model files and outputs +- Hugging Face account with access to the target model Verify your setup: ```bash diff --git a/nvidia/ollama/README.md b/nvidia/ollama/README.md index ad0ffe8..a241fcd 100644 --- a/nvidia/ollama/README.md +++ b/nvidia/ollama/README.md @@ -36,12 +36,9 @@ the powerful GPU capabilities of your Spark device without complex network confi ## Prerequisites -- [ ] DGX Spark device set up and connected to your network - - Verify with: `nvidia-smi` (should show Blackwell GPU information) -- [ ] NVIDIA Sync installed and connected to your Spark - - Verify connection status in NVIDIA Sync system tray application -- [ ] Terminal access to your local machine for testing API calls - - Verify with: `curl --version` +- DGX Spark device set up and connected to your network +- NVIDIA Sync installed and connected to your Spark +- Terminal access to your local machine for testing API calls diff --git a/nvidia/protein-folding/README.md b/nvidia/protein-folding/README.md index e777cec..60f970b 100644 --- a/nvidia/protein-folding/README.md +++ b/nvidia/protein-folding/README.md @@ -30,23 +30,23 @@ RTX Pro 6000 or DGX Spark workstation. ## Prerequisites -- [ ] NVIDIA GPU (RTX Pro 6000 or DGX Spark recommended) +- NVIDIA GPU (RTX Pro 6000 or DGX Spark recommended) ```bash nvidia-smi # Should show GPU with CUDA ≥12.9 ``` -- [ ] NVIDIA drivers and CUDA toolkit installed +- NVIDIA drivers and CUDA toolkit installed ```bash nvcc --version # Should show CUDA 12.9 or higher ``` -- [ ] Docker with NVIDIA Container Toolkit +- Docker with NVIDIA Container Toolkit ```bash docker run --rm --gpus all nvidia/cuda:12.9.0-base-ubuntu22.04 nvidia-smi ``` -- [ ] Python 3.8+ environment +- Python 3.8+ environment ```bash python3 --version # Should show 3.8 or higher ``` -- [ ] Sufficient disk space for databases (>3TB recommended) +- Sufficient disk space for databases (>3TB recommended) ```bash df -h # Check available space ``` diff --git a/nvidia/sglang/README.md b/nvidia/sglang/README.md index 230b35d..895e927 100644 --- a/nvidia/sglang/README.md +++ b/nvidia/sglang/README.md @@ -35,12 +35,12 @@ vision-language tasks using models like DeepSeek-V2-Lite. ## Prerequisites -- [ ] NVIDIA Spark device with Blackwell architecture -- [ ] Docker Engine installed and running: `docker --version` -- [ ] NVIDIA GPU drivers installed: `nvidia-smi` -- [ ] NVIDIA Container Toolkit configured: `docker run --rm --gpus all nvidia/cuda:12.9-base nvidia-smi` -- [ ] Sufficient disk space (>20GB available): `df -h` -- [ ] Network connectivity for pulling NGC containers: `ping nvcr.io` +- NVIDIA Spark device with Blackwell architecture +- Docker Engine installed and running: `docker --version` +- NVIDIA GPU drivers installed: `nvidia-smi` +- NVIDIA Container Toolkit configured: `docker run --rm --gpus all nvidia/cuda:12.9-base nvidia-smi` +- Sufficient disk space (>20GB available): `df -h` +- Network connectivity for pulling NGC containers: `ping nvcr.io` ## Ancillary files diff --git a/nvidia/speculative-decoding/README.md b/nvidia/speculative-decoding/README.md index d23ec15..028f072 100644 --- a/nvidia/speculative-decoding/README.md +++ b/nvidia/speculative-decoding/README.md @@ -40,17 +40,17 @@ These examples demonstrate how to accelerate large language model inference whil ## Prerequisites -- [ ] NVIDIA Spark device with sufficient GPU memory available (80GB+ recommended for GPT-OSS 120B) -- [ ] Docker with GPU support enabled +- NVIDIA Spark device with sufficient GPU memory available (80GB+ recommended for GPT-OSS 120B) +- Docker with GPU support enabled ```bash docker run --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi ``` -- [ ] Access to NVIDIA's internal container registry (for Eagle3 example) -- [ ] HuggingFace authentication configured (if needed for model downloads) +- Access to NVIDIA's internal container registry (for Eagle3 example) +- HuggingFace authentication configured (if needed for model downloads) ```bash huggingface-cli login ``` -- [ ] Network connectivity for model downloads +- Network connectivity for model downloads ## Time & risk diff --git a/nvidia/tailscale/README.md b/nvidia/tailscale/README.md index 574c10c..73311aa 100644 --- a/nvidia/tailscale/README.md +++ b/nvidia/tailscale/README.md @@ -51,13 +51,13 @@ all traffic automatically encrypted and NAT traversal handled transparently. ## Prerequisites -- [ ] NVIDIA Spark device running Ubuntu (ARM64/AArch64) -- [ ] Client device (Mac, Windows, or Linux) for remote access -- [ ] Internet connectivity on both devices -- [ ] Valid email account for Tailscale authentication (Google, GitHub, Microsoft) -- [ ] SSH server availability check: `systemctl status ssh` -- [ ] Package manager working: `sudo apt update` -- [ ] User account with sudo privileges on Spark device +- NVIDIA Spark device running Ubuntu (ARM64/AArch64) +- Client device (Mac, Windows, or Linux) for remote access +- Internet connectivity on both devices +- Valid email account for Tailscale authentication (Google, GitHub, Microsoft) +- SSH server availability check: `systemctl status ssh` +- Package manager working: `sudo apt update` +- User account with sudo privileges on Spark device ## Time & risk diff --git a/nvidia/trt-llm/README.md b/nvidia/trt-llm/README.md index 359955f..47c3465 100644 --- a/nvidia/trt-llm/README.md +++ b/nvidia/trt-llm/README.md @@ -54,13 +54,13 @@ inference through kernel-level optimizations, efficient memory layouts, and adva ## Prerequisites -- [ ] NVIDIA Spark device with Blackwell architecture GPUs -- [ ] NVIDIA drivers compatible with CUDA 12.x: `nvidia-smi` -- [ ] Docker installed and GPU support configured: `docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi` -- [ ] Hugging Face account with token for model access: `echo $HF_TOKEN` -- [ ] Sufficient GPU VRAM (16GB+ recommended for 70B models) -- [ ] Internet connectivity for downloading models and container images -- [ ] Network: open TCP ports 8355 (LLM) and 8356 (VLM) on host for OpenAI-compatible serving +- NVIDIA Spark device with Blackwell architecture GPUs +- NVIDIA drivers compatible with CUDA 12.x: `nvidia-smi` +- Docker installed and GPU support configured: `docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi` +- Hugging Face account with token for model access: `echo $HF_TOKEN` +- Sufficient GPU VRAM (16GB+ recommended for 70B models) +- Internet connectivity for downloading models and container images +- Network: open TCP ports 8355 (LLM) and 8356 (VLM) on host for OpenAI-compatible serving ## Model Support Matrix diff --git a/nvidia/unsloth/README.md b/nvidia/unsloth/README.md index a96c201..49f69de 100644 --- a/nvidia/unsloth/README.md +++ b/nvidia/unsloth/README.md @@ -36,10 +36,10 @@ parameter-efficient fine-tuning methods like LoRA and QLoRA. ## Prerequisites -- [ ] NVIDIA Spark device with Blackwell GPU architecture -- [ ] `nvidia-smi` shows a summary of GPU information -- [ ] CUDA 13.0 installed: `nvcc --version` -- [ ] Internet access for downloading models and datasets +- NVIDIA Spark device with Blackwell GPU architecture +- `nvidia-smi` shows a summary of GPU information +- CUDA 13.0 installed: `nvcc --version` +- Internet access for downloading models and datasets ##Ancillary files diff --git a/nvidia/vllm/README.md b/nvidia/vllm/README.md index 24bd32f..ec8c6a3 100644 --- a/nvidia/vllm/README.md +++ b/nvidia/vllm/README.md @@ -5,9 +5,9 @@ ## Table of Contents - [Overview](#overview) +- [Instructions](#instructions) - [Run on two Sparks](#run-on-two-sparks) - [Step 14. (Optional) Launch 405B inference server](#step-14-optional-launch-405b-inference-server) -- [Access through terminal](#access-through-terminal) --- @@ -29,14 +29,14 @@ support for ARM64. ## Prerequisites -- [ ] DGX Spark device with ARM64 processor and Blackwell GPU architecture -- [ ] CUDA 12.9 or CUDA 13.0 toolkit installed: `nvcc --version` shows CUDA toolkit version. -- [ ] Docker installed and configured: `docker --version` succeeds -- [ ] NVIDIA Container Toolkit installed -- [ ] Python 3.12 available: `python3.12 --version` succeeds -- [ ] Git installed: `git --version` succeeds -- [ ] Network access to download packages and container images -- [ ] > TODO: Verify memory and storage requirements for builds +- DGX Spark device with ARM64 processor and Blackwell GPU architecture +- CUDA 12.9 or CUDA 13.0 toolkit installed: `nvcc --version` shows CUDA toolkit version. +- Docker installed and configured: `docker --version` succeeds +- NVIDIA Container Toolkit installed +- Python 3.12 available: `python3.12 --version` succeeds +- Git installed: `git --version` succeeds +- Network access to download packages and container images + ## Time & risk @@ -46,6 +46,77 @@ support for ARM64. **Rollback:** Container approach is non-destructive. +## Instructions + +## Step 1. Pull vLLM container image + +Find the latest container build from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?version=25.09-py3 +``` +docker pull nvcr.io/nvidia/vllm:25.09-py3 +``` + +## Step 2. Test vLLM in container + +Launch the container and start vLLM server with a test model to verify basic functionality. + +```bash +docker run -it --gpus all -p 8000:8000 \ +nvcr.io/nvidia/vllm:25.09-py3 \ +vllm serve "Qwen/Qwen2.5-Math-1.5B-Instruct" +``` + +Expected output should include: +- Model loading confirmation +- Server startup on port 8000 +- GPU memory allocation details + +In another terminal, test the server: + +```bash +curl http://localhost:8000/v1/chat/completions \ +-H "Content-Type: application/json" \ +-d '{ + "model": "Qwen/Qwen2.5-Math-1.5B-Instruct", + "messages": [{"role": "user", "content": "12*17"}], + "max_tokens": 500 +}' +``` + +Expected response should contain `"content": "204"` or similar mathematical calculation. + +## Step 3. Troubleshooting + +| Symptom | Cause | Fix | +|---------|--------|-----| +| CUDA version mismatch errors | Wrong CUDA toolkit version | Reinstall CUDA 12.9 using exact installer | +| Container registry authentication fails | Invalid or expired GitLab token | Generate new auth token | +| SM_121a architecture not recognized | Missing LLVM patches | Verify SM_121a patches applied to LLVM source | +| Reduce MAX_JOBS to 1-2, add swap space | +| Environment variables not set | + +## Step 4. Cleanup and rollback + +For container approach (non-destructive): + +```bash +docker rm $(docker ps -aq --filter ancestor=******:5005/dl/dgx/vllm*) +docker rmi ******:5005/dl/dgx/vllm:main-py3.31165712-devel +``` + + +To remove CUDA 12.9: + +```bash +sudo /usr/local/cuda-12.9/bin/cuda-uninstaller +``` + +## Step 5. Next steps + +- **Production deployment:** Configure vLLM with your specific model requirements +- **Performance tuning:** Adjust batch sizes and memory settings for your workload +- **Monitoring:** Set up logging and metrics collection for production use +- **Model management:** Explore additional model formats and quantization options + ## Run on two Sparks ## Step 1. Verify hardware connectivity @@ -310,74 +381,3 @@ http://192.168.100.10:8265 ## - Persistent model caching across restarts ## - Alternative quantization methods (FP8, INT4) ``` - -## Access through terminal - -## Step 1. Pull vLLM container image - -Find the latest container build from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?version=25.09-py3 -``` -docker pull nvcr.io/nvidia/vllm:25.09-py3 -``` - -## Step 2. Test vLLM in container - -Launch the container and start vLLM server with a test model to verify basic functionality. - -```bash -docker run -it --gpus all -p 8000:8000 \ -nvcr.io/nvidia/vllm:25.09-py3 \ -vllm serve "Qwen/Qwen2.5-Math-1.5B-Instruct" -``` - -Expected output should include: -- Model loading confirmation -- Server startup on port 8000 -- GPU memory allocation details - -In another terminal, test the server: - -```bash -curl http://localhost:8000/v1/chat/completions \ --H "Content-Type: application/json" \ --d '{ - "model": "Qwen/Qwen2.5-Math-1.5B-Instruct", - "messages": [{"role": "user", "content": "12*17"}], - "max_tokens": 500 -}' -``` - -Expected response should contain `"content": "204"` or similar mathematical calculation. - -## Step 3. Troubleshooting - -| Symptom | Cause | Fix | -|---------|--------|-----| -| CUDA version mismatch errors | Wrong CUDA toolkit version | Reinstall CUDA 12.9 using exact installer | -| Container registry authentication fails | Invalid or expired GitLab token | Generate new auth token | -| SM_121a architecture not recognized | Missing LLVM patches | Verify SM_121a patches applied to LLVM source | -| Reduce MAX_JOBS to 1-2, add swap space | -| Environment variables not set | - -## Step 4. Cleanup and rollback - -For container approach (non-destructive): - -```bash -docker rm $(docker ps -aq --filter ancestor=******:5005/dl/dgx/vllm*) -docker rmi ******:5005/dl/dgx/vllm:main-py3.31165712-devel -``` - - -To remove CUDA 12.9: - -```bash -sudo /usr/local/cuda-12.9/bin/cuda-uninstaller -``` - -## Step 5. Next steps - -- **Production deployment:** Configure vLLM with your specific model requirements -- **Performance tuning:** Adjust batch sizes and memory settings for your workload -- **Monitoring:** Set up logging and metrics collection for production use -- **Model management:** Explore additional model formats and quantization options diff --git a/nvidia/vss/README.md b/nvidia/vss/README.md index d457e12..78b41d1 100644 --- a/nvidia/vss/README.md +++ b/nvidia/vss/README.md @@ -43,14 +43,14 @@ You will deploy NVIDIA's VSS AI Blueprint on NVIDIA Spark hardware with Blackwel ## Prerequisites -- [ ] NVIDIA Spark device with ARM64 architecture and Blackwell GPU -- [ ] FastOS 1.81.38 or compatible ARM64 system -- [ ] Driver version 580.82.09 installed: `nvidia-smi | grep "Driver Version"` -- [ ] CUDA version 13.0 installed: `nvcc --version` -- [ ] Docker installed and running: `docker --version && docker compose version` -- [ ] Access to NVIDIA Container Registry with NGC API Key -- [ ] [Optional] NVIDIA API Key for remote model endpoints (hybrid deployment only) -- [ ] Sufficient storage space for video processing (>10GB recommended in `/tmp/`) +- NVIDIA Spark device with ARM64 architecture and Blackwell GPU +- FastOS 1.81.38 or compatible ARM64 system +- Driver version 580.82.09 installed: `nvidia-smi | grep "Driver Version"` +- CUDA version 13.0 installed: `nvcc --version` +- Docker installed and running: `docker --version && docker compose version` +- Access to NVIDIA Container Registry with NGC API Key +- [Optional] NVIDIA API Key for remote model endpoints (hybrid deployment only) +- Sufficient storage space for video processing (>10GB recommended in `/tmp/`) ## Ancillary files