Object Service is a lightweight data server. It makes your datasets, tables and project metadata available to the 3LC Hub in your browser. It reads from your local disk (or cloud storage) and serves it over a local port. Runs anywhere — even a laptop. No GPU needed.
Compute Service powers all AI workloads: model training (YOLO, timm), auto-labeling (SAM3), data analysis, import/export, and more. It runs on a machine with an NVIDIA GPU. You can spin it up on-demand and shut it down when not in use.
Both services stay on your infrastructure. Nothing is uploaded to the cloud. The 3LC Hub in your browser connects to them over local ports (or an SSH tunnel).
2
Where will you run the services?
Pick where your GPU is — that's where the Compute Service needs to run. Object Service can run on the same machine or separately.
3
Platform & package manager
Select the OS of the machine where services will run, and your preferred Python installer. All commands below update automatically.
Operating system
Package manager
Don't have uv? Install: curl -LsSf https://astral.sh/uv/install.sh | sh
4
Connect to your server
Open a terminal on your laptop and SSH into your remote machine. You'll typically need the .pem key file you downloaded when creating the instance.
Where's my key? On AWS, you download the .pem file when creating the instance. On Azure, when creating the VM. Move it to ~/.ssh/ and set permissions: chmod 400 ~/.ssh/your-key.pem
4
Install 3LC
Create a Python environment and install all packages. Run these on the machine where the services will run.
Important: The PyTorch CUDA step must run after the package installs above, otherwise Python may fall back to CPU-only.
Replace <api_key> with the key you copied from your 3LC account.
This will create a file in the 3LC settings directory that contains your API key. Alternatively, you can set the API key as an environment variable TLC_API_KEY.
6
Start the services
Open two terminal windows on the machine and start each service. Leave them running.
The Object Service shows a TUI interface. The Compute Service will output the URL where it may be reached.
7
Forward ports to your browser
The services run on your remote machine, but your browser runs on your laptop. An SSH tunnel bridges the two — no public ports needed.
This makes localhost:5016 and
localhost:5020
on your laptop reach the remote services.
Keep this running while using the Hub. Add -o ServerAliveInterval=60 to prevent idle disconnects.
✓
Verify connection
Let's check if your browser can reach both services now.
Keeping up to date
We iterate fast. Run this regularly, then restart the Compute Service.
Troubleshooting
Make sure the PyTorch CUDA step ran after the package installs. Verify:
python -c "import torch; print(torch.cuda.is_available())"
Check that the Compute Service is running and reachable at http://localhost:5020/health. If remote, check your SSH tunnel.
Add -o ServerAliveInterval=60 or use autossh for auto-reconnect.