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README.md
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@ -48,7 +48,7 @@ Each playbook includes prerequisites, step-by-step instructions, troubleshooting
- [Install and Use vLLM for Inference](nvidia/vllm/)
- [Vision-Language Model Fine-tuning](nvidia/vlm-finetuning/)
- [VS Code](nvidia/vscode/)
- [Build a Video Search and Summarization (VSS) Agent](nvidia/vss/)
- [Video Search and Summarization](nvidia/vss/)
## Resources

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nvidia/nemo-fine-tune/README.md
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@ -52,7 +52,7 @@ All necessary files for the playbook can be found [here on GitHub](https://githu
## Step 1. Verify system requirements
Check your NVIDIA Spark device meets the prerequisites for NeMo AutoModel installation. This step runs on the host system to confirm CUDA toolkit availability and Python version compatibility.
Check your NVIDIA Spark device meets the prerequisites for [NeMo AutoModel](https://github.com/NVIDIA-NeMo/Automodel) installation. This step runs on the host system to confirm CUDA toolkit availability and Python version compatibility.
```bash
## Verify CUDA installation
@ -169,6 +169,19 @@ uv run --frozen --no-sync python -c "import nemo_automodel; print('✅ NeMo Auto
## Check available examples
ls -la examples/
## Example output:
$ ls -la examples/
total 36
drwxr-xr-x 9 akoumparouli domain-users 4096 Oct 16 14:52 .
drwxr-xr-x 16 akoumparouli domain-users 4096 Oct 16 14:52 ..
drwxr-xr-x 3 akoumparouli domain-users 4096 Oct 16 14:52 benchmark
drwxr-xr-x 3 akoumparouli domain-users 4096 Oct 16 14:52 diffusion
drwxr-xr-x 20 akoumparouli domain-users 4096 Oct 16 14:52 llm_finetune
drwxr-xr-x 3 akoumparouli domain-users 4096 Oct 14 09:27 llm_kd
drwxr-xr-x 2 akoumparouli domain-users 4096 Oct 16 14:52 llm_pretrain
drwxr-xr-x 6 akoumparouli domain-users 4096 Oct 14 09:27 vlm_finetune
drwxr-xr-x 2 akoumparouli domain-users 4096 Oct 14 09:27 vlm_generate
```
## Step 8. Explore available examples
@ -193,36 +206,37 @@ First, export your HF_TOKEN so that gated models can be downloaded.
export HF_TOKEN=<your_huggingface_token>
```
> [!NOTE]
> Please Replace `<your_huggingface_token>` with your Hugging Face access token to access gated models (e.g., Llama).
**Full Fine-tuning example:**
Once inside the `Automodel` directory you cloned from github, run:
```bash
uv run --frozen --no-sync \
examples/llm_finetune/finetune.py \
-c examples/llm_finetune/llama3_2/llama3_2_1b_squad.yaml \
--step_scheduler.local_batch_size 1 \
--loss_fn._target_ nemo_automodel.components.loss.te_parallel_ce.TEParallelCrossEntropy \
--model.pretrained_model_name_or_path Qwen/Qwen3-8B
```
These overrides ensure the Qwen3-8B SFT run behaves as expected:
- `--model.pretrained_model_name_or_path`: selects the Qwen/Qwen3-8B model to fine-tune (weights fetched via your Hugging Face token).
- `--loss_fn._target_`: uses the TransformerEngine-parallel cross-entropy loss variant compatible with tensor-parallel training for large LLMs.
- `--step_scheduler.local_batch_size`: sets the per-GPU micro-batch size to 1 to fit in memory; overall effective batch size is still driven by gradient accumulation and data/tensor parallel settings from the recipe.
> Replace `<your_huggingface_token>` with your personal Hugging Face access token. A valid token is required to download any gated model.
>
> - Generate a token: [Hugging Face tokens](https://huggingface.co/settings/tokens), guide available [here](https://huggingface.co/docs/hub/en/security-tokens).
> - Request and receive access on each model's page (and accept license/terms) before attempting downloads.
> - Llama-3.1-8B: [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B)
> - Qwen3-8B: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
> - Mixtral-8x7B: [mistralai/Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B)
>
> The same steps apply for any other gated model you use: visit its model card on Hugging Face, request access, accept the license, and wait for approval.
**LoRA fine-tuning example:**
Execute a basic fine-tuning example to validate the complete setup. This demonstrates parameter-efficient fine-tuning using a small model suitable for testing.
For the examples below, we are using YAML for configuration, and parameter overrides are passed as command line arguments.
```bash
## Run basic LLM fine-tuning example
uv run --frozen --no-sync \
examples/llm_finetune/finetune.py \
-c examples/llm_finetune/llama3_2/llama3_2_1b_squad_peft.yaml \
--model.pretrained_model_name_or_path meta-llama/Llama-3.1-8B
--model.pretrained_model_name_or_path meta-llama/Llama-3.1-8B \
--packed_sequence.packed_sequence_size 1024 \
--step_scheduler.max_steps 100
```
These overrides ensure the Llama-3.1-8B LoRA run behaves as expected:
- `--model.pretrained_model_name_or_path`: selects the Llama-3.1-8B model to fine-tune from the Hugging Face model hub (weights fetched via your Hugging Face token).
- `--packed_sequence.packed_sequence_size`: sets the packed sequence size to 1024 to enable packed sequence training.
- `--step_scheduler.max_steps`: sets the maximum number of training steps. We set it to 100 for demonstation purposes, please adjust this based on your needs.
**QLoRA fine-tuning example:**
We can use QLoRA to fine-tune large models in a memory-efficient manner.
@ -233,50 +247,61 @@ examples/llm_finetune/finetune.py \
-c examples/llm_finetune/llama3_1/llama3_1_8b_squad_qlora.yaml \
--model.pretrained_model_name_or_path meta-llama/Meta-Llama-3-70B \
--loss_fn._target_ nemo_automodel.components.loss.te_parallel_ce.TEParallelCrossEntropy \
--step_scheduler.local_batch_size 1
--step_scheduler.local_batch_size 1 \
--packed_sequence.packed_sequence_size 1024 \
--step_scheduler.max_steps 100
```
These overrides ensure the 70B QLoRA run behaves as expected:
- `--model.pretrained_model_name_or_path`: selects the 70B base model to fine-tune (weights fetched via your Hugging Face token).
- `--loss_fn._target_`: uses the TransformerEngine-parallel cross-entropy loss variant compatible with tensor-parallel training for large LLMs.
- `--step_scheduler.local_batch_size`: sets the per-GPU micro-batch size to 1 to fit 70B in memory; overall effective batch size is still driven by gradient accumulation and data/tensor parallel settings from the recipe.
- `--step_scheduler.max_steps`: sets the maximum number of training steps. We set it to 100 for demonstation purposes, please adjust this based on your needs.
- `--packed_sequence.packed_sequence_size`: sets the packed sequence size to 1024 to enable packed sequence training.
## Step 10. Validate training output
**Full Fine-tuning example:**
Check that fine-tuning completed successfully and inspect the generated model artifacts. This confirms the training pipeline works correctly on your Spark device.
Once inside the `Automodel` directory you cloned from GitHub, run:
```bash
## Check training logs
ls -la logs/
## Verify model checkpoint creation
ls -la checkpoints/
## Test model inference (if applicable)
uv run python -c "
import torch
print('GPU available:', torch.cuda.is_available())
print('GPU count:', torch.cuda.device_count())
"
uv run --frozen --no-sync \
examples/llm_finetune/finetune.py \
-c examples/llm_finetune/qwen/qwen3_8b_squad_spark.yaml \
--model.pretrained_model_name_or_path Qwen/Qwen3-8B \
--step_scheduler.local_batch_size 1 \
--step_scheduler.max_steps 100 \
--packed_sequence.packed_sequence_size 1024
```
These overrides ensure the Qwen3-8B SFT run behaves as expected:
- `--model.pretrained_model_name_or_path`: selects the Qwen/Qwen3-8B model to fine-tune from the Hugging Face model hub (weights fetched via your Hugging Face token). Adjust this if you want to fine-tune a different model.
- `--step_scheduler.max_steps`: sets the maximum number of training steps. We set it to 100 for demonstation purposes, please adjust this based on your needs.
- `--step_scheduler.local_batch_size`: sets the per-GPU micro-batch size to 1 to fit in memory; overall effective batch size is still driven by gradient accumulation and data/tensor parallel settings from the recipe.
## Step 11. Validate complete setup
Perform final validation to ensure all components are working correctly. This comprehensive check confirms the environment is ready for production fine-tuning workflows.
## Step 10. Validate successful training completion
Validate the fine-tuned model by inspecting artifacts contained in the checkpoint directory.
```bash
## Test complete pipeline
uv run python -c "
import nemo_automodel
import torch
print('✅ NeMo AutoModel version:', nemo_automodel.__version__)
print('✅ CUDA available:', torch.cuda.is_available())
print('✅ GPU count:', torch.cuda.device_count())
print('✅ Setup complete')
"
## Inspect logs and checkpoint output.
## The LATEST is a symlink pointing to the latest checkpoint.
## The checkpoint is the one that was saved during training.
## below is an example of the expected output (username and domain-users are placeholders).
ls -lah checkpoints/LATEST/
## $ ls -lah checkpoints/LATEST/
## total 32K
## drwxr-xr-x 6 akoumparouli domain-users 4.0K Oct 16 22:33 .
## drwxr-xr-x 4 akoumparouli domain-users 4.0K Oct 16 22:33 ..
## -rw-r--r-- 1 akoumparouli domain-users 1.6K Oct 16 22:33 config.yaml
## drwxr-xr-x 2 akoumparouli domain-users 4.0K Oct 16 22:33 dataloader
## drwxr-xr-x 2 akoumparouli domain-users 4.0K Oct 16 22:33 model
## drwxr-xr-x 2 akoumparouli domain-users 4.0K Oct 16 22:33 optim
## drwxr-xr-x 2 akoumparouli domain-users 4.0K Oct 16 22:33 rng
## -rw-r--r-- 1 akoumparouli domain-users 1.3K Oct 16 22:33 step_scheduler.pt
```
## Step 13. Cleanup and rollback
## Step 11. Cleanup and rollback (Optional)
Remove the installation and restore the original environment if needed. These commands safely remove all installed components.
@ -297,8 +322,42 @@ pip3 uninstall uv
## Clear Python cache
rm -rf ~/.cache/pip
```
## Step 12. Optional: Publish your fine-tuned model checkpoint on Hugging Face Hub
## Step 14. Next steps
Publish your fine-tuned model checkpoint on Hugging Face Hub.
> [!NOTE]
> This is an optional step and is not required for using the fine-tuned model.
> It is useful if you want to share your fine-tuned model with others or use it in other projects.
> You can also use the fine-tuned model in other projects by cloning the repository and using the checkpoint.
> To use the fine-tuned model in other projects, you need to have the Hugging Face CLI installed.
> You can install the Hugging Face CLI by running `pip install huggingface-cli`.
> For more information, please refer to the [Hugging Face CLI documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli).
> [!TIP]
> You can use the `hf` command to upload the fine-tuned model checkpoint to Hugging Face Hub.
> For more information, please refer to the [Hugging Face CLI documentation](https://huggingface.co/docs/huggingface_hub/en/guides/cli).
```bash
## Publish the fine-tuned model checkpoint to Hugging Face Hub
## will be published under the namespace <your_huggingface_username>/my-cool-model, adjust name as needed.
hf upload my-cool-model checkpoints/LATEST/model
```
> [!TIP]
> The above command can fail if you don't have write permissions to the Hugging Face Hub, with the HF_TOKEN you used.
> Sample error message:
> ```bash
> akoumparouli@1604ab7-lcedt:/mnt/4tb/auto/Automodel8$ hf upload my-cool-model checkpoints/LATEST/model
> Traceback (most recent call last):
> File "/home/akoumparouli/.local/lib/python3.10/site-packages/huggingface_hub/utils/_http.py", line 409, in hf_raise_for_status
> response.raise_for_status()
> File "/home/akoumparouli/.local/lib/python3.10/site-packages/requests/models.py", line 1024, in raise_for_status
> raise HTTPError(http_error_msg, response=self)
> requests.exceptions.HTTPError: 403 Client Error: Forbidden for url: https://huggingface.co/api/repos/create
> ```
> To fix this, you need to create an access token with *write* permissions, please see the Hugging Face guide [here](https://huggingface.co/docs/hub/en/security-tokens) for instructions.
## Step 12. Next steps
Begin using NeMo AutoModel for your specific fine-tuning tasks. Start with provided recipes and customize based on your model requirements and dataset.
@ -310,7 +369,7 @@ cp recipes/llm_finetune/finetune.py my_custom_training.py
## Then run: uv run my_custom_training.py
```
Explore the [NeMo AutoModel GitHub repository](https://github.com/NVIDIA-NeMo/Automodel) for advanced recipes, documentation, and community examples. Consider setting up custom datasets, experimenting with different model architectures, and scaling to multi-node distributed training for larger models.
Explore the [NeMo AutoModel GitHub repository](https://github.com/NVIDIA-NeMo/Automodel) for more recipes, documentation, and community examples. Consider setting up custom datasets, experimenting with different model architectures, and scaling to multi-node distributed training for larger models.
## Troubleshooting
@ -324,8 +383,8 @@ Explore the [NeMo AutoModel GitHub repository](https://github.com/NVIDIA-NeMo/Au
| Cannot access gated repo for URL | Certain HuggingFace models have restricted access | Regenerate your [HuggingFace token](https://huggingface.co/docs/hub/en/security-tokens); and request access to the [gated model](https://huggingface.co/docs/hub/en/models-gated#customize-requested-information) on your web browser |
> [!NOTE]
> DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU.
> With many applications still updating to take advantage of UMA, you may encounter memory issues even when within
> DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU.
> With many applications still updating to take advantage of UMA, you may encounter memory issues even when within
> the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:
```bash
sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'

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nvidia/vibe-coding/README.md
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@ -167,10 +167,7 @@ Add additional model entries for any other Ollama models you wish to host remote
## Common Issues
**1. Ollama not starting**
- Verify GPU drivers are installed correctly.
Run `nvidia-smi` in the terminal. If the command fails check DGX Dashboard for updates to your DGX Spark.
If there are no updates or updates do not correct the issue, create a thread on the DGX Spark/GB10 User forum here :
https://forums.developer.nvidia.com/c/accelerated-computing/dgx-spark-gb10/dgx-spark-gb10/
- Verify Docker and GPU drivers are installed correctly.
- Run `ollama serve` on the DGX Spark to view Ollama logs.
**2. Continue can't connect over the network**

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nvidia/vllm/README.md
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@ -340,7 +340,7 @@ http://192.168.100.10:8265
| 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 |
## Common Issues for running on two Sparks
## Common Issues for running on two Starks
| Symptom | Cause | Fix |
|---------|--------|-----|
| Node 2 not visible in Ray cluster | Network connectivity issue | Verify QSFP cable connection, check IP configuration |

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nvidia/vss/README.md
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@ -1,4 +1,4 @@
# Build a Video Search and Summarization (VSS) Agent
# Video Search and Summarization
> Run the VSS Blueprint on your Spark
@ -30,8 +30,8 @@ You will deploy NVIDIA's VSS AI Blueprint on NVIDIA Spark hardware with Blackwel
## Prerequisites
- NVIDIA Spark device with ARM64 architecture and Blackwell GPU
- NVIDIA DGX OS 7.2.3 or higher
- Driver version 580.95.05 or higher installed: `nvidia-smi | grep "Driver Version"`
- FastOS 1.81.38 or compatible ARM64 system
- Driver version 580.82.09 or higher 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](https://org.ngc.nvidia.com/setup/api-keys)
@ -278,10 +278,6 @@ Open these URLs in your browser:
In this hybrid deployment, we would use NIMs from [build.nvidia.com](https://build.nvidia.com/). Alternatively, you can configure your own hosted endpoints by following the instructions in the [VSS remote deployment guide](https://docs.nvidia.com/vss/latest/content/installation-remote-docker-compose.html).
> [!NOTE]
> Fully local deployment using smaller LLM (Llama 3.1 8B) is also possible.
> To set up a fully local VSS deployment, follow the [instructions in the VSS documentation](https://docs.nvidia.com/vss/latest/content/vss_dep_docker_compose_arm.html#local-deployment-single-gpu-dgx-spark).
**9.1 Get NVIDIA API Key**
- Log in to https://build.nvidia.com/explore/discover.