Update documentation: remove Web UI guide and adjust navigation links

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# Managing Instances
Learn how to effectively manage your Llama.cpp instances with Llamactl.
Learn how to effectively manage your Llama.cpp instances with Llamactl through both the Web UI and API.
## Instance Lifecycle
## Overview
### Creating Instances
Llamactl provides two ways to manage instances:
Instances can be created through the Web UI or API:
- **Web UI**: Accessible at `http://localhost:8080` with an intuitive dashboard
- **REST API**: Programmatic access for automation and integration
#### Via Web UI
1. Click "Add Instance" button
2. Fill in the configuration form
3. Click "Create"
### Authentication
If authentication is enabled:
1. Navigate to the web UI
2. Enter your credentials
3. Bearer token is stored for the session
### Theme Support
- Switch between light and dark themes
- Setting is remembered across sessions
## Instance Cards
Each instance is displayed as a card showing:
- **Instance name**
- **Health status badge** (unknown, ready, error, failed)
- **Action buttons** (start, stop, edit, logs, delete)
## Create Instance
### Via Web UI
1. Click the **"Add Instance"** button on the dashboard
2. Enter a unique **Name** for your instance (only required field)
3. Configure model source (choose one):
- **Model Path**: Full path to your downloaded GGUF model file
- **HuggingFace Repo**: Repository name (e.g., `microsoft/Phi-3-mini-4k-instruct-gguf`)
- **HuggingFace File**: Specific file within the repo (optional, uses default if not specified)
4. Configure optional instance management settings:
- **Auto Restart**: Automatically restart instance on failure
- **Max Restarts**: Maximum number of restart attempts
- **Restart Delay**: Delay in seconds between restart attempts
- **On Demand Start**: Start instance when receiving a request to the OpenAI compatible endpoint
- **Idle Timeout**: Minutes before stopping idle instance (set to 0 to disable)
5. Configure optional llama-server backend options:
- **Threads**: Number of CPU threads to use
- **Context Size**: Context window size (ctx_size)
- **GPU Layers**: Number of layers to offload to GPU
- **Port**: Network port (auto-assigned by llamactl if not specified)
- **Additional Parameters**: Any other llama-server command line options (see [llama-server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md))
6. Click **"Create"** to save the instance
### Via API
#### Via API
```bash
curl -X POST http://localhost:8080/api/instances \
# Create instance with local model file
curl -X POST http://localhost:8080/api/instances/my-instance \
-H "Content-Type: application/json" \
-d '{
"name": "my-instance",
"model_path": "/path/to/model.gguf",
"port": 8081
"backend_type": "llama_cpp",
"backend_options": {
"model": "/path/to/model.gguf",
"threads": 8,
"ctx_size": 4096
}
}'
# Create instance with HuggingFace model
curl -X POST http://localhost:8080/api/instances/phi3-mini \
-H "Content-Type: application/json" \
-d '{
"backend_type": "llama_cpp",
"backend_options": {
"hf_repo": "microsoft/Phi-3-mini-4k-instruct-gguf",
"hf_file": "Phi-3-mini-4k-instruct-q4.gguf",
"gpu_layers": 32
},
"auto_restart": true,
"max_restarts": 3
}'
```
### Starting and Stopping
## Start Instance
#### Start an Instance
### Via Web UI
1. Click the **"Start"** button on an instance card
2. Watch the status change to "Unknown"
3. Monitor progress in the logs
4. Instance status changes to "Ready" when ready
### Via API
```bash
# Via API
curl -X POST http://localhost:8080/api/instances/{name}/start
# The instance will begin loading the model
```
#### Stop an Instance
## Stop Instance
### Via Web UI
1. Click the **"Stop"** button on an instance card
2. Instance gracefully shuts down
### Via API
```bash
# Via API
curl -X POST http://localhost:8080/api/instances/{name}/stop
# Graceful shutdown with configurable timeout
```
### Monitoring Status
## Edit Instance
Check instance status in real-time:
```bash
# Get instance details
curl http://localhost:8080/api/instances/{name}
# Get health status
curl http://localhost:8080/api/instances/{name}/health
```
## Instance States
Instances can be in one of several states:
- **Stopped**: Instance is not running
- **Starting**: Instance is initializing and loading the model
- **Running**: Instance is active and ready to serve requests
- **Stopping**: Instance is shutting down gracefully
- **Error**: Instance encountered an error
## Configuration Management
### Updating Instance Configuration
### Via Web UI
1. Click the **"Edit"** button on an instance card
2. Modify settings in the configuration dialog
3. Changes require instance restart to take effect
4. Click **"Update & Restart"** to apply changes
### Via API
Modify instance settings:
```bash
@@ -84,82 +132,55 @@ curl -X PUT http://localhost:8080/api/instances/{name} \
!!! note
Configuration changes require restarting the instance to take effect.
### Viewing Configuration
## View Logs
### Via Web UI
1. Click the **"Logs"** button on any instance card
2. Real-time log viewer opens
### Via API
Check instance status in real-time:
```bash
# Get current configuration
curl http://localhost:8080/api/instances/{name}/config
# Get instance details
curl http://localhost:8080/api/instances/{name}/logs
```
## Resource Management
## Delete Instance
### Memory Usage
### Via Web UI
1. Click the **"Delete"** button on an instance card
2. Only stopped instances can be deleted
3. Confirm deletion in the dialog
Monitor memory consumption:
### Via API
```bash
curl -X DELETE http://localhost:8080/api/instances/{name}
```
## Instance Proxy
Llamactl proxies all requests to the underlying llama-server instances.
```bash
# Get resource usage
curl http://localhost:8080/api/instances/{name}/stats
# Get instance details
curl http://localhost:8080/api/instances/{name}/proxy/
```
### CPU and GPU Usage
Check llama-server [docs](https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md) for more information.
Track performance metrics:
### Instance Health
- CPU thread utilization
- GPU memory usage (if applicable)
- Request processing times
#### Via Web UI
## Troubleshooting Common Issues
1. The health status badge is displayed on each instance card
### Instance Won't Start
#### Via API
1. **Check model path**: Ensure the model file exists and is readable
2. **Port conflicts**: Verify the port isn't already in use
3. **Resource limits**: Check available memory and CPU
4. **Permissions**: Ensure proper file system permissions
### Performance Issues
1. **Adjust thread count**: Match to your CPU cores
2. **Optimize context size**: Balance memory usage and capability
3. **GPU offloading**: Use `gpu_layers` for GPU acceleration
4. **Batch size tuning**: Optimize for your workload
### Memory Problems
1. **Reduce context size**: Lower memory requirements
2. **Disable memory mapping**: Use `no_mmap` option
3. **Enable memory locking**: Use `memory_lock` for performance
4. **Monitor system resources**: Check available RAM
## Best Practices
### Production Deployments
1. **Resource allocation**: Plan memory and CPU requirements
2. **Health monitoring**: Set up regular health checks
3. **Graceful shutdowns**: Use proper stop procedures
4. **Backup configurations**: Save instance configurations
5. **Log management**: Configure appropriate logging levels
### Development Environments
1. **Resource sharing**: Use smaller models for development
2. **Quick iterations**: Optimize for fast startup times
3. **Debug logging**: Enable detailed logging for troubleshooting
## Batch Operations
### Managing Multiple Instances
Check the health status of your instances:
```bash
# Start all instances
curl -X POST http://localhost:8080/api/instances/start-all
# Stop all instances
curl -X POST http://localhost:8080/api/instances/stop-all
# Get status of all instances
curl http://localhost:8080/api/instances
curl http://localhost:8080/api/instances/{name}/proxy/health
```