Show connected cluster nodes in analyze page right panel, displaying hostname, IP, GPU status, weight, and last seen time

Add documentations dropdown menu in navbar, moving API documentation and admin API documentation links

Add admin dropdown menu in navbar for admin users, moving train, cluster tokens, cluster nodes, and configurations links

- Store connected_at as UTC ISO string with Z suffix
- Handle datetime objects from database as UTC timestamps
- Ensure uptime calculation uses correct UTC times regardless of server timezone

- Store connected_at as proper UTC timestamp in database using FROM_UNIXTIME/datetime
- Update web interface to handle datetime objects and timestamps correctly
- Ensure uptime starts from actual connection time, not offset by timezone

- Fix cluster master to properly detect GPU backends from client capabilities
- Extract available_backends from capabilities list instead of gpu_info
- Ensure clients with GPU workers are correctly identified as GPU-enabled

- Fix cluster client uptime calculation to start from 0 by using explicit UTC timestamps
- Fix cluster client workers not showing by populating cluster_master.clients dictionary
- Ensure connected_at uses current UTC time instead of database CURRENT_TIMESTAMP

- Get real worker process information from cluster master instead of placeholder data
- Display correct number of workers and their actual backends
- Improve accuracy of cluster node statistics

- Update connected_at timestamp on each successful connection
- Uptime now resets to 00:00:00 on reconnections as expected

- Extract real client IP address from websocket connection
- Preserve connected_at timestamp for accurate uptime calculation
- Send full GPU device info from client to master for proper VRAM reporting

- Handle timestamp strings in sorting function
- Parse ISO timestamp strings to numeric values for proper sorting
- Prevent TypeError when sorting by last_seen timestamp

- Add ALTER TABLE to create connected_at column in existing databases
- Handle migration gracefully for databases created before schema update

- Move subprocess import to function scope to avoid UnboundLocalError
- Ensure subprocess is available for fallback GPU detection

- Add connected_at timestamp to track when client first connected
- Calculate uptime from connection time instead of last seen time
- Update database schema and API to use proper uptime tracking

- Update detect_gpu_backends to collect actual VRAM for each GPU device
- Store device info including VRAM in gpu_info sent to master
- Use real VRAM data in cluster nodes API instead of hardcoded values
- Ensure consistent VRAM reporting between master and clients

- Use green background color to indicate connected nodes
- Remove status text column for cleaner interface
- Update colspan values for table messages

- Store cluster client info in database for persistence
- Update API to read connected clients from database
- Maintain compatibility with existing web interface

- Use _get_client_by_websocket instead of non-existent _get_client_by_socket
- Fixes client connection error during process registration

- Client connects to wss://host:port instead of wss://host:port/cluster
- Fixes connection loop issue

- Client now attempts to reconnect if connection is lost
- Prevents processes from being restarted on reconnection
- Maintains persistent cluster node operation

- Correct the dict comprehension for registering processes with master
- Fix duplicate entries and incorrect model assignment
- Apply same fix to restart workers function

- Workers need a local backend to connect to even in client mode
- Add backend startup and readiness check for cluster clients
- Ensure proper cleanup on exit

- Ignore cluster.crt and cluster.key generated certificates
- Remove committed certificates from repository

- Remove 'path' parameter from _handle_client method
- Compatible with websockets 12+ which removed the path argument

- Modify ClusterMaster to accept host parameter
- Start cluster master in vidai.py when running as master
- Use --cluster-host and --cluster-port for websocket server binding
- Default to 0.0.0.0:5003 for cluster master

- Rename 'flash' variable to 'flash_enabled' to avoid shadowing the flash() function
- Resolve TypeError when saving admin configuration

- Remove analysis_backend and training_backend fields from /admin/config
- These are now configured per worker in the cluster nodes interface
- Clean up unused imports and form processing

- Add missing set_* function imports to admin.py config route
- Resolve NameError when saving admin configuration

- Change container max-width to 95% for better use of screen space
- Maintain centered layout for the cluster nodes table

- Show individual select forms for every worker in the driver modal
- Update API to handle per-worker driver selection for local nodes
- Maintain compatibility with existing backend switching logic

- Add --config <file> argument to load config from custom path
- Modify config loader to use custom config file if specified
- Fix cluster nodes interface to only show available GPU backends for workers
- Differentiate between local and remote node driver selection

- Removed the 'Settings' link from the admin navigation menu
- Settings page route and template still exist but are no longer accessible from navbar
- Admin navbar now shows: Cluster Tokens, Cluster Nodes (no Settings)

- Added workers array to local node API response for modal population
- Fixed table colspan values to match 13 columns
- Removed debug console.log statements
- Modal should now open and show worker driver selection options

- Fixed JavaScript template literal issue preventing button clicks from working
- Changed from inline onclick with template variables to data attributes + event delegation
- Added event listener for .set-driver-btn class buttons
- Buttons now properly read hostname and token from data attributes
- Modal should now open when clicking Set Driver buttons

- Removed brand-specific filtering that only allowed NVIDIA GPUs
- Now detects any GPU that can actually perform CUDA or ROCm operations
- Functional test determines if GPU should be included, not brand
- GPUs are shown with correct system indices (Device 0, 1, etc.)
- AMD GPUs that support ROCm will be shown if functional
- CUDA GPUs from any vendor will be shown if functional

- Updated GPU VRAM detection to use torch.cuda.get_device_properties(i).total_memory / 1024**3
- Same method as used in /api/stats endpoint for consistency
- Still filters out non-NVIDIA and non-functional GPUs
- Now shows correct VRAM amounts (e.g., 24GB for RTX 3090 instead of hardcoded 8GB)
- Fixed both worker-level and node-level GPU detection

- Added debug output to see what CUDA device names are detected
- Will help identify why AMD GPU is still being counted as CUDA device
- Debug output shows device names and functional test results
- User can now see what devices PyTorch is detecting

- Modified detect_gpu_backends() to perform functional tests on GPUs
- CUDA detection now verifies devices can actually perform tensor operations
- ROCm detection now tests device functionality before counting
- Only NVIDIA GPUs are counted for CUDA, and only functional devices
- Prevents counting of non-working GPUs like old AMD cards misreported as CUDA
- Example: System with old AMD GPU (device 0) + working CUDA GPU (device 1) now correctly shows only the functional CUDA GPU
- Total VRAM calculation now reflects only actually usable GPUs
- Both PyTorch and nvidia-smi/rocm-smi detection paths updated

- Modified local node GPU memory calculation to only count GPUs that are actually available for supported backends
- Previously counted all GPUs in system, now only counts CUDA GPUs if CUDA is available and ROCm GPUs if ROCm is available
- Fixes issue where unsupported GPUs (like old AMD GPUs without ROCm support) were incorrectly included in VRAM totals
- Example: System with old AMD GPU (8GB, no ROCm) and CUDA GPU (24GB) now correctly shows 24GB total instead of 32GB
- Ensures accurate GPU resource reporting in cluster nodes interface