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Deployment

The splatter node is available on Docker Hub. Both deployment options are Docker-based and still run the same splatter logic under the hood.

Initial Setup

  1. First check that your NVIDIA driver and CUDA toolkit meet the requirements in the Minimum Requirements and update as needed:

    nvidia-smi

    This should output information about your GPU. If not, please double check that your installed CUDA and driver versions are correct, and restart your computer.

  2. Ensure outbound HTTPS access to DDS/DMS endpoints from this host.

  3. Disable power-saving settings like automatic sleep or standby mode, to keep your computer on and able to receive jobs.

Prepare credentials + env file

Before you run the container, create a .env file based on the example file server/rust/.env.example with the required credentials:

REG_SECRET=your-registration-secret
SECP256K1_PRIVHEX=your-evm-private-key

Optional overrides (defaults shown):

DMS_BASE_URL=https://dms.auki.network/v1
DDS_BASE_URL=https://dds.auki.network
REQUEST_TIMEOUT_SECS=60
REGISTER_INTERVAL_SECS=120
REGISTER_MAX_RETRY=-1
LOG_FORMAT=text

Notes:

  • REG_SECRET comes from registering a compute node in the Posemesh Console.
  • SECP256K1_PRIVHEX is the hex-encoded private key of the staked EVM wallet for that node.

How to get the registration secret + wallet key:

⚠️ IMPORTANT: If you run both the reconstruction and splatter nodes, you need to stake them both, on different wallets.

  1. Log in to the Posemesh Console at https://console.auki.network/.
  2. Open the Manage Nodes page and create a compute node (choose the appropriate operation mode).
  3. Copy the registration credentials shown — you will use these as REG_SECRET.
  4. Go to Staking, connect your wallet, and stake the required amount of $AUKI for that node.
  5. Export the wallet private key (hex) and set it as SECP256K1_PRIVHEX.

Option 1 — Use the prebuilt image (recommended)

Pull the latest stable docker image. If you are upgrading from a previous version you must run this again to pull the updated image.

docker pull aukilabs/splatter-server:stable

Start Docker using the below command, ❗including all flags❗

docker run \
  --gpus all \
  --env-file .env \
  --name splatter-server \
  -d \
  aukilabs/splatter-server:stable

You can also pin a specific release tag, for example aukilabs/splatter-server:vX.Y.Z.

Verification ✅

  1. After deploying, please ensure the server started correctly by running

    docker ps

    This should show your newly started docker container, with the STATUS showing Up 45 seconds or similar. Copy the container ID of your server, then run:

    docker logs <container_id>

    You should see logs indicating the node is registering with DDS and polling DMS.

  2. Ensure your GPU and CUDA works correctly (using the container ID from above):

    docker exec <container_id> nvidia-smi

    This should show your GPU and driver information.

    Verify that torch detects your GPU:

    docker exec <container_id> python3 -c "import torch; print(torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CUDA not found')"

    If not, please double-check your setup, or see Troubleshooting.

Option 2 — Build Docker image from source

Building Docker

docker build -t splatter-server .

Run the image as described in Option 1, and follow the same verification steps.

Troubleshooting ⚠️

Here are some common issues you may encounter, with suggested fixes:

GPU not detected

  • Symptom: Server starts but jobs fail, GPU not utilised, CUDA not found, or similar.
  • Fixes:
    • Ensure you started the Docker container with --gpus all.
    • Check your driver and CUDA toolkit versions against Minimum Requirements.
    • Run nvidia-smi on host to confirm your GPU is visible.
    • Run nvcc --version to confirm your CUDA toolkit is installed, and with the correct version.
    • Restart your computer and try again.

Wrong GPU used

  • Symptom: The Docker container is using the wrong GPU, for example the integrated GPU instead of the discrete GPU.
  • Fixes:
    • Check your system activity to see which GPU is being used.
    • Run nvidia-smi -L to list all GPUs on your system.
    • Run the container with the correct GPU index specified in the --gpus flag, for example docker run --gpus '"device=0"' ....

Container killed or crashes under load

  • Symptom: Server stops or computer restarts during job processing
  • Fix:
    • Monitor system RAM and temperatures, and check for overheating or insufficient resources.

Docker crashes on Windows

  • Symptom: Container stops after a few minutes on Windows.
  • Fix:
    • Restart Docker Desktop.
    • If the issue persists, also restart your computer.

💡 Still stuck?
If your issue remains, please:

  1. Check docker logs <container_id> for error messages.
  2. Share logs and system specs with the Auki team for support.