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Use Pytorch to fine-tune models locally
Follow the network setup instructions from the Connect two Sparks playbook to establish connectivity between your DGX Spark nodes.
This includes:
To easily manage containers without sudo, you must be in the docker group. If you choose to skip this step, you will need to run Docker commands with sudo.
Open a new terminal and test Docker access. In the terminal, run:
docker ps
If you see a permission denied error (something like permission denied while trying to connect to the Docker daemon socket), add your user to the docker group so that you don't need to run the command with sudo .
sudo usermod -aG docker $USER
newgrp docker
Ensure the NVIDIA drivers and the NVIDIA Container Toolkit are installed on each node (both manager and workers) that will provide GPU resources. This package enables Docker containers to access the host's GPU hardware. Ensure you complete the installation steps, including the Docker configuration for NVIDIA Container Toolkit.
First, find your GPU UUID by running:
nvidia-smi -a | grep UUID
Next, modify the Docker daemon configuration to advertise the GPU to Swarm. Edit /etc/docker/daemon.json:
sudo nano /etc/docker/daemon.json
Add or modify the file to include the nvidia runtime and GPU UUID (replace GPU-45cbf7b3-f919-7228-7a26-b06628ebefa1 with your actual GPU UUID):
{
"runtimes": {
"nvidia": {
"path": "nvidia-container-runtime",
"runtimeArgs": []
}
},
"default-runtime": "nvidia",
"node-generic-resources": [
"NVIDIA_GPU=GPU-45cbf7b3-f919-7228-7a26-b06628ebefa1"
]
}
Modify the NVIDIA Container Runtime to advertise the GPUs to the Swarm by uncommenting the swarm-resource line in the config.toml file. You can do this either with your preferred text editor (e.g., vim, nano...) or with the following command:
sudo sed -i 's/^#\s*\(swarm-resource\s*=\s*".*"\)/\1/' /etc/nvidia-container-runtime/config.toml
Finally, restart the Docker daemon to apply all changes:
sudo systemctl restart docker
Repeat these steps on all nodes.
On whichever node you want to use as primary, run the following swarm initialization command
docker swarm init --advertise-addr $(ip -o -4 addr show enp1s0f0np0 | awk '{print $4}' | cut -d/ -f1) $(ip -o -4 addr show enp1s0f1np1 | awk '{print $4}' | cut -d/ -f1)
The typical output of the above would be similar to the following:
Swarm initialized: current node (node-id) is now a manager.
To add a worker to this swarm, run the following command:
docker swarm join --token <worker-token> <advertise-addr>:<port>
To add a manager to this swarm, run 'docker swarm join-token manager' and follow the instructions.
Now we can proceed with setting up the worker nodes of your cluster. Repeat these steps on all worker nodes.
Run the command suggested by the docker swarm init on each worker node to join the Docker swarm
docker swarm join --token <worker-token> <advertise-addr>:<port>
On both nodes, download the pytorch-ft-entrypoint.sh script into the directory containing your finetuning scripts and configuration files and run the following command to make it executable:
chmod +x $PWD/pytorch-ft-entrypoint.sh
On your primary node, deploy the Finetuning multi-node stack by downloading the docker-compose.yml file into the same directory as in the previous step and running the following command:
docker stack deploy -c $PWD/docker-compose.yml finetuning-multinode
NOTE
Ensure you download both files into the same directory from which you are running the command.
You can verify the status of your worker nodes using the following
docker stack ps finetuning-multinode
If everything is healthy, you should see a similar output to the following:
nvidia@spark-1b3b:~$ docker stack ps finetuning-multinode
ID NAME IMAGE NODE DESIRED STATE CURRENT STATE ERROR PORTS
vlun7z9cacf9 finetuning-multinode_finetunine.1 nvcr.io/nvidia/pytorch:25.10-py3 spark-1d84 Running Starting 2 seconds ago
tjl49zicvxoi finetuning-multinode_finetunine.2 nvcr.io/nvidia/pytorch:25.10-py3 spark-1b3b Running Starting 2 seconds ago
NOTE
If your "Current state" is not "Running", see troubleshooting section for more information.
You can use docker ps to find your Docker container ID. You can save the container ID in a variable as shown below. Run this command on both nodes.
export FINETUNING_CONTAINER=$(docker ps -q -f name=finetuning-multinode)
For multi-node runs, we provide 2 configuration files:
These configuration files need to be adapted:
machine_rank on each of your nodes according to its rank. Your master node should have a rank 0. The second node has a rank 1.main_process_ip using the IP address of your master node. Ensure that both configuration files have the same value. Use ifconfig on your main node to find the correct value for the CX-7 IP address.The fields that need to be filled in your YAML files:
machine_rank: 0
main_process_ip: < TODO: specify IP >
main_process_port: < TODO: specify port >
All the scripts and configuration files are available in this repository.
Once you successfully run the previous steps, you can use one of the run-multi-llama_* scripts for finetuning available in this repository. Here is an example for Llama3 70B using LoRa for finetuning and FSDP2.
# Need to specify huggingface token for model download.
export HF_TOKEN=<your-huggingface-token>
docker exec \
-e HF_TOKEN=$HF_TOKEN \
-it $FINETUNING_CONTAINER bash -c '
bash /workspace/install-requirements;
accelerate launch --config_file=/workspace/configs/config_fsdp_lora.yaml /workspace/Llama3_70B_LoRA_finetuning.py'
During the run, the progress bar of the finetuning will appear on your main node's stdout only. This is an expected behavior as accelerate uses a wrapper around tqdm to display the progress on the main process only as explained here. Using nvidia-smi on the worker node should show that the GPU is used.
Stop and remove containers by using the following command on the leader node:
docker stack rm finetuning-multinode
Remove downloaded models to free disk space:
rm -rf $HOME/.cache/huggingface/hub/models--meta-llama* $HOME/.cache/huggingface/hub/datasets*