talemate/docs/getting-started/installation/docker.md

Quick install instructions

1. git clone https://github.com/vegu-ai/talemate.git
2. cd talemate
3. copy config file
   1. linux: cp config.example.yaml config.yaml
   2. windows: copy config.example.yaml config.yaml (or just copy the file and rename it via the file explorer)
4. Start Talemate:
   1. NVIDIA GPU host with the NVIDIA Container Toolkit installed: docker compose up
   2. Host without an NVIDIA GPU: docker compose -f docker-compose.cpu.yml up
5. Navigate your browser to http://localhost:8082

The default Compose configuration requires an NVIDIA GPU and the NVIDIA Container Toolkit. It reserves all NVIDIA GPUs and does not fall back to CPU execution.

On a host without an NVIDIA GPU, you must use:

docker compose -f docker-compose.cpu.yml up

!!! info "Pre-built Images" The default setup uses a pre-built image from GitHub Container Registry. To build it locally with CUDA enabled, use docker compose -f docker-compose.yml -f docker-compose.manual.yml up --build. For a local CPU-only build, use the standalone command docker compose -f docker-compose.manual.yml up --build; combining docker-compose.cpu.yml and docker-compose.manual.yml is also supported.

Verify CUDA access

With the container running, verify that Docker exposed the GPU and that PyTorch can use it:

docker compose exec talemate nvidia-smi
docker compose exec talemate /app/.venv/bin/python -B -c "import torch; print(torch.__version__, torch.version.cuda, torch.cuda.is_available())"

The final value from the Python command should be True. See Common issues if either command fails.

!!! note When connecting local APIs running on the hostmachine (e.g. text-generation-webui), you need to use host.docker.internal as the hostname.

!!! info "Pi Bridge" The image ships with the pi coding agent preinstalled for the Pi Bridge client. pi's configuration (models.json, auth.json) lives in the ./pi directory next to the compose file.