mirror of
https://github.com/lordmathis/llamactl.git
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Add Docker support documentation and configuration for backends
This commit is contained in:
71
README.md
71
README.md
@@ -14,6 +14,7 @@
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### 🔗 Universal Compatibility
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- **OpenAI API Compatible**: Drop-in replacement - route requests by instance name
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- **Multi-Backend Support**: Native support for llama.cpp, MLX (Apple Silicon optimized), and vLLM
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- **Docker Support**: Run backends in containers
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### 🌐 User-Friendly Interface
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- **Web Dashboard**: Modern React UI for visual management (unlike CLI-only tools)
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@@ -32,6 +33,7 @@
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# For llama.cpp: https://github.com/ggml-org/llama.cpp#quick-start
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# For MLX on macOS: pip install mlx-lm
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# For vLLM: pip install vllm
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# Or use Docker - no local installation required
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# 2. Download and run llamactl
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LATEST_VERSION=$(curl -s https://api.github.com/repos/lordmathis/llamactl/releases/latest | grep '"tag_name":' | sed -E 's/.*"([^"]+)".*/\1/')
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@@ -112,6 +114,7 @@ You need `llama-server` from [llama.cpp](https://github.com/ggml-org/llama.cpp)
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brew install llama.cpp
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# Or build from source - see llama.cpp docs
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# Or use Docker - no local installation required
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```
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**For MLX backend (macOS only):**
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@@ -139,9 +142,51 @@ python -m venv vllm-env
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source vllm-env/bin/activate
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pip install vllm
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# For production deployments, consider container-based installation
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# Or use Docker - no local installation required
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```
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## Docker Support
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llamactl supports running backends in Docker containers with identical behavior to native execution. This is particularly useful for:
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- Production deployments without local backend installation
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- Isolating backend dependencies
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- GPU-accelerated inference using official Docker images
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### Docker Configuration
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Enable Docker support using the new structured backend configuration:
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```yaml
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backends:
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llama-cpp:
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command: "llama-server"
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docker:
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enabled: true
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image: "ghcr.io/ggml-org/llama.cpp:server"
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args: ["run", "--rm", "--network", "host", "--gpus", "all"]
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vllm:
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command: "vllm"
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args: ["serve"]
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docker:
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enabled: true
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image: "vllm/vllm-openai:latest"
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args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
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```
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### Key Features
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- **Host Networking**: Uses `--network host` for seamless port management
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- **GPU Support**: Includes `--gpus all` for GPU acceleration
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- **Environment Variables**: Configure container environment as needed
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- **Flexible Configuration**: Per-backend Docker settings with sensible defaults
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### Requirements
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- Docker installed and running
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- For GPU support: nvidia-docker2 (Linux) or Docker Desktop with GPU support
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- No local backend installation required when using Docker
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## Configuration
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llamactl works out of the box with sensible defaults.
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@@ -154,9 +199,27 @@ server:
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enable_swagger: false # Enable Swagger UI for API docs
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backends:
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llama_executable: llama-server # Path to llama-server executable
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mlx_lm_executable: mlx_lm.server # Path to mlx_lm.server executable
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vllm_executable: vllm # Path to vllm executable
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llama-cpp:
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command: "llama-server"
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args: []
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docker:
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enabled: false
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image: "ghcr.io/ggml-org/llama.cpp:server"
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args: ["run", "--rm", "--network", "host", "--gpus", "all"]
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environment: {}
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vllm:
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command: "vllm"
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args: ["serve"]
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docker:
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enabled: false
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image: "vllm/vllm-openai:latest"
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args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
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environment: {}
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mlx:
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command: "mlx_lm.server"
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args: []
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instances:
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port_range: [8000, 9000] # Port range for instances
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@@ -20,9 +20,27 @@ server:
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enable_swagger: false # Enable Swagger UI for API docs
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backends:
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llama_executable: llama-server # Path to llama-server executable
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mlx_lm_executable: mlx_lm.server # Path to mlx_lm.server executable
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vllm_executable: vllm # Path to vllm executable
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llama-cpp:
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command: "llama-server"
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args: []
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docker:
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enabled: false
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image: "ghcr.io/ggml-org/llama.cpp:server"
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args: ["run", "--rm", "--network", "host", "--gpus", "all"]
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environment: {}
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vllm:
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command: "vllm"
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args: ["serve"]
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docker:
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enabled: false
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image: "vllm/vllm-openai:latest"
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args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
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environment: {}
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mlx:
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command: "mlx_lm.server"
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args: []
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instances:
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port_range: [8000, 9000] # Port range for instances
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@@ -90,18 +108,40 @@ server:
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- `LLAMACTL_ENABLE_SWAGGER` - Enable Swagger UI (true/false)
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### Backend Configuration
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```yaml
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backends:
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llama_executable: "llama-server" # Path to llama-server executable (default: "llama-server")
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mlx_lm_executable: "mlx_lm.server" # Path to mlx_lm.server executable (default: "mlx_lm.server")
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vllm_executable: "vllm" # Path to vllm executable (default: "vllm")
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llama-cpp:
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command: "llama-server"
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args: []
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docker:
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enabled: false # Enable Docker runtime (default: false)
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image: "ghcr.io/ggml-org/llama.cpp:server"
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args: ["run", "--rm", "--network", "host", "--gpus", "all"]
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environment: {}
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vllm:
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command: "vllm"
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args: ["serve"]
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docker:
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enabled: false
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image: "vllm/vllm-openai:latest"
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args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
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environment: {}
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mlx:
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command: "mlx_lm.server"
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args: []
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# MLX does not support Docker
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```
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**Environment Variables:**
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- `LLAMACTL_LLAMA_EXECUTABLE` - Path to llama-server executable
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- `LLAMACTL_MLX_LM_EXECUTABLE` - Path to mlx_lm.server executable
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- `LLAMACTL_VLLM_EXECUTABLE` - Path to vllm executable
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**Backend Configuration Fields:**
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- `command`: Executable name/path for the backend
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- `args`: Default arguments prepended to all instances
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- `docker`: Docker-specific configuration (optional)
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- `enabled`: Boolean flag to enable Docker runtime
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- `image`: Docker image to use
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- `args`: Additional arguments passed to `docker run`
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- `environment`: Environment variables for the container (optional)
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### Instance Configuration
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@@ -88,6 +88,21 @@ Here are basic example configurations for each backend:
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}
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```
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## Docker Support
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Llamactl can run backends in Docker containers. To enable Docker for a backend, add a `docker` section to that backend in your YAML configuration file (e.g. `config.yaml`) as shown below:
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```yaml
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backends:
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vllm:
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command: "vllm"
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args: ["serve"]
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docker:
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enabled: true
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image: "vllm/vllm-openai:latest"
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args: ["run", "--rm", "--network", "host", "--gpus", "all", "--shm-size", "1g"]
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```
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## Using the API
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You can also manage instances via the REST API:
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