fix model mappings,dash updates, trading
This commit is contained in:
Dobromir Popov
203
web/tensorboard_integration.py
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203
web/tensorboard_integration.py
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#!/usr/bin/env python3
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"""
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TensorBoard Integration for Dashboard
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This module provides integration between the trading dashboard and TensorBoard,
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allowing training metrics to be visualized in real-time.
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"""
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import os
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import sys
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import subprocess
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import threading
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import time
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import logging
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import webbrowser
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from pathlib import Path
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from typing import Optional, Dict, Any
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logger = logging.getLogger(__name__)
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class TensorBoardIntegration:
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"""
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TensorBoard integration for dashboard
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Provides methods to start TensorBoard server and access training metrics
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"""
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def __init__(self, log_dir: str = "runs", port: int = 6006):
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"""
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Initialize TensorBoard integration
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Args:
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log_dir: Directory containing TensorBoard logs
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port: Port to run TensorBoard on
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"""
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self.log_dir = log_dir
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self.port = port
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self.process = None
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self.url = f"http://localhost:{port}"
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self.is_running = False
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self.latest_metrics = {}
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# Create log directory if it doesn't exist
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os.makedirs(log_dir, exist_ok=True)
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def start_tensorboard(self, open_browser: bool = False) -> bool:
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"""
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Start TensorBoard server in a separate process
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Args:
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open_browser: Whether to open browser automatically
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Returns:
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bool: True if TensorBoard was started successfully
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"""
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if self.is_running:
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logger.info("TensorBoard is already running")
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return True
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try:
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# Check if TensorBoard is available
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try:
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import tensorboard
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logger.info(f"TensorBoard version {tensorboard.__version__} available")
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except ImportError:
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logger.warning("TensorBoard not installed. Install with: pip install tensorboard")
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return False
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# Check if log directory exists and has content
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log_dir_path = Path(self.log_dir)
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if not log_dir_path.exists():
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logger.warning(f"Log directory {self.log_dir} does not exist")
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os.makedirs(self.log_dir, exist_ok=True)
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logger.info(f"Created log directory {self.log_dir}")
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# Start TensorBoard process
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cmd = [
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sys.executable,
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"-m",
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"tensorboard.main",
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"--logdir", self.log_dir,
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"--port", str(self.port),
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"--reload_interval", "5", # Reload data every 5 seconds
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"--reload_multifile", "true" # Better handling of multiple log files
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]
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logger.info(f"Starting TensorBoard: {' '.join(cmd)}")
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# Start process without capturing output
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self.process = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True
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)
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# Wait a moment for TensorBoard to start
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time.sleep(2)
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# Check if process is running
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if self.process.poll() is None:
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self.is_running = True
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logger.info(f"TensorBoard started at {self.url}")
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# Open browser if requested
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if open_browser:
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try:
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webbrowser.open(self.url)
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logger.info("Browser opened automatically")
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except Exception as e:
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logger.warning(f"Could not open browser: {e}")
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# Start monitoring thread
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threading.Thread(target=self._monitor_process, daemon=True).start()
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return True
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else:
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stdout, stderr = self.process.communicate()
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logger.error(f"TensorBoard failed to start: {stderr}")
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return False
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except Exception as e:
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logger.error(f"Error starting TensorBoard: {e}")
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return False
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def _monitor_process(self):
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"""Monitor TensorBoard process and capture output"""
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try:
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while self.process and self.process.poll() is None:
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# Read output line by line
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for line in iter(self.process.stdout.readline, ''):
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if line:
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line = line.strip()
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if line:
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logger.debug(f"TensorBoard: {line}")
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time.sleep(0.1)
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# Process has ended
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self.is_running = False
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logger.info("TensorBoard process has ended")
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except Exception as e:
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logger.error(f"Error monitoring TensorBoard process: {e}")
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def stop_tensorboard(self):
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"""Stop TensorBoard server"""
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if self.process and self.process.poll() is None:
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try:
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self.process.terminate()
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self.process.wait(timeout=5)
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logger.info("TensorBoard stopped")
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except subprocess.TimeoutExpired:
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self.process.kill()
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logger.warning("TensorBoard process killed after timeout")
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except Exception as e:
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logger.error(f"Error stopping TensorBoard: {e}")
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self.is_running = False
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def get_tensorboard_url(self) -> str:
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"""Get TensorBoard URL"""
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return self.url
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def is_tensorboard_running(self) -> bool:
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"""Check if TensorBoard is running"""
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if self.process:
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return self.process.poll() is None
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return False
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def get_latest_metrics(self) -> Dict[str, Any]:
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"""
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Get latest training metrics from TensorBoard
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This is a placeholder - in a real implementation, you would
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parse TensorBoard event files to extract metrics
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"""
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# In a real implementation, you would parse TensorBoard event files
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# For now, return placeholder data
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return {
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"training_active": self.is_running,
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"tensorboard_url": self.url,
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"metrics_available": self.is_running
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}
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# Singleton instance
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_tensorboard_integration = None
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def get_tensorboard_integration(log_dir: str = "runs", port: int = 6006) -> TensorBoardIntegration:
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"""
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Get TensorBoard integration singleton instance
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Args:
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log_dir: Directory containing TensorBoard logs
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port: Port to run TensorBoard on
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Returns:
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TensorBoardIntegration: Singleton instance
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"""
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global _tensorboard_integration
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if _tensorboard_integration is None:
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_tensorboard_integration = TensorBoardIntegration(log_dir, port)
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return _tensorboard_integration
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