update web dataset file
wangyuzhan
parent
8845d9170c
commit
dfe1e5c6c7
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@ -2,6 +2,7 @@ import { openTab as _openTab } from './ui/tabs.js';
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import { initTrainForm } from './train/form.js';
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import { startProcessPolling, stopProcessPolling, loadProcesses } from './processes/list.js';
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import { loadLogFiles } from './logfiles/list.js';
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import { refreshLog } from './processes/logs.js';
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const hooks = {
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onEnterProcesses: () => {
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@ -337,6 +338,11 @@ window.confirmFileSelection = confirmFileSelection;
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window.navigateToParent = navigateToParent;
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window.selectCurrentDirectory = selectCurrentDirectory;
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// 将进程管理函数暴露到全局作用域
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window.refreshProcesses = loadProcesses;
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window.refreshLogs = loadLogFiles;
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window.refreshLog = refreshLog;
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window.addEventListener('load', () => {
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initTrainForm();
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startProcessPolling();
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@ -5,6 +5,46 @@ import { el, clearChildren } from '../utils/dom.js';
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import { showLogs, refreshLog, clearLogTimerFor } from './logs.js';
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// 计算训练进度信息
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function calculateRemainingTime(current, total, logText) {
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// 尝试从日志中提取时间信息
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const timePatterns = [
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/remaining[\s:=]\s*(\d+)[\s:]?(\d+)?[\s:]?(\d+)?/i, // remaining: 1:30:45 or remaining: 90
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/ETA[\s:=]\s*(\d+):(\d+):(\d+)/i, // ETA: 1:30:45
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/预计剩余[\s:=]\s*(\d+)[\s小时]*[\s:]?(\d+)?[\s分钟]*/i, // 预计剩余: 1小时30分钟
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/剩余时间[\s:=]\s*(\d+)[\s小时]*[\s:]?(\d+)?[\s分钟]*/i, // 剩余时间: 1小时30分钟
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/time left[\s:=]\s*(\d+)[\s:]?(\d+)?[\s:]?(\d+)?/i, // time left: 1:30:45
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/还需[\s:=]\s*(\d+)[\s小时]*[\s:]?(\d+)?[\s分钟]*/i // 还需: 1小时30分钟
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];
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for (const pattern of timePatterns) {
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const match = logText.match(pattern);
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if (match) {
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const hours = parseInt(match[1]) || 0;
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const minutes = parseInt(match[2]) || 0;
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const seconds = parseInt(match[3]) || 0;
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if (hours > 0 || minutes > 0 || seconds > 0) {
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const parts = [];
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if (hours > 0) parts.push(`${hours}小时`);
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if (minutes > 0) parts.push(`${minutes}分钟`);
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if (seconds > 0 && hours === 0 && minutes === 0) parts.push(`${seconds}秒`);
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return parts.join('');
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}
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}
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}
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// 如果没有找到时间信息，根据进度估算
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if (current > 0 && current < total) {
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const remainingEpochs = total - current;
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// 假设每个epoch大约需要一定时间，这里使用简单的线性估算
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// 实际应用中可以根据历史数据更准确地估算
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return `约${remainingEpochs}个epoch`;
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}
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return '计算中...';
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}
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function calculateProgress(process) {
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const defaultProgress = {
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percentage: 0,
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@ -32,29 +72,91 @@ function calculateProgress(process) {
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};
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}
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// 尝试从日志中提取进度信息（简化版本）
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// 尝试从日志中提取进度信息（增强版本）
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if (process.logs) {
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const logText = process.logs.slice(-1000); // 取最近1000字符
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const logText = process.logs.slice(-2000); // 取最近2000字符以获取更多上下文
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// 提取epoch信息
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const epochMatch = logText.match(/epoch\s+(\d+)\/(\d+)/i);
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if (epochMatch) {
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const current = parseInt(epochMatch[1]);
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const total = parseInt(epochMatch[2]);
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const percentage = total > 0 ? Math.round((current / total) * 100) : 0;
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// 提取epoch信息 - 支持多种格式
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const epochPatterns = [
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/epoch\s+(\d+)\s*\/\s*(\d+)/i, // epoch 3/10
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/Epoch\s+(\d+)\s*of\s*(\d+)/i, // Epoch 3 of 10
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/\[(\d+)\/(\d+)\]/i, // [3/10]
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/epoch\s*[:：]\s*(\d+)\s*\/\s*(\d+)/i, // epoch: 3/10
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/第\s*(\d+)\s*轮\s*\/\s*共\s*(\d+)\s*轮/i // 第3轮/共10轮
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];
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// 提取loss信息
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const lossMatch = logText.match(/loss[\s:=]\s*([\d.]+)/i);
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const currentLoss = lossMatch ? parseFloat(lossMatch[1]).toFixed(4) : null;
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let current = 0;
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let total = 0;
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let percentage = 0;
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for (const pattern of epochPatterns) {
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const match = logText.match(pattern);
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if (match) {
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current = parseInt(match[1]);
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total = parseInt(match[2]);
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percentage = total > 0 ? Math.round((current / total) * 100) : 0;
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break;
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}
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}
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// 提取loss信息 - 支持多种格式
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const lossPatterns = [
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/loss[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)/i, // loss: 4.32 or loss = 4.32
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/training_loss[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)/i, // training_loss: 4.32
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/train_loss[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)/i, // train_loss: 4.32
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/Loss[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)/i, // Loss: 4.32
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/训练损失[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)/i, // 训练损失: 4.32
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/损失[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)/i, // 损失: 4.32
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/\s+([\d.]+(?:e[+-]?\d+)?)\s*loss/i, // 4.32 loss
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/\s+([\d.]+(?:e[+-]?\d+)?)\s*训练损失/i, // 4.32 训练损失
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/(?:loss|损失|training_loss|train_loss)\s*=\s*([\d.]+(?:e[+-]?\d+)?)/i // loss = 4.32
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];
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let currentLoss = null;
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for (const pattern of lossPatterns) {
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const matches = [...logText.matchAll(pattern)];
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if (matches.length > 0) {
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// 取最后一个匹配的loss值
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const lastMatch = matches[matches.length - 1];
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const lossValue = parseFloat(lastMatch[1]);
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if (!isNaN(lossValue) && lossValue > 0 && lossValue < 100) { // 合理的loss范围
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currentLoss = lossValue.toFixed(4);
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break;
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}
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}
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}
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// 提取学习率信息
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const lrPatterns = [
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/lr[\s:=]\s*([\d.e+-]+)/i, // lr: 1e-4
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/learning_rate[\s:=]\s*([\d.e+-]+)/i, // learning_rate: 1e-4
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/LR[\s:=]\s*([\d.e+-]+)/i, // LR: 1e-4
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/学习率[\s:=]\s*([\d.e+-]+)/i // 学习率: 1e-4
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];
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let currentLr = null;
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for (const pattern of lrPatterns) {
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const matches = [...logText.matchAll(pattern)];
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if (matches.length > 0) {
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const lastMatch = matches[matches.length - 1];
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const lrValue = parseFloat(lastMatch[1]);
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if (!isNaN(lrValue) && lrValue > 0 && lrValue < 1) { // 合理的lr范围
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currentLr = lrValue.toExponential(2);
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break;
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}
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}
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}
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// 如果找到了有效的epoch信息，返回进度
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if (total > 0) {
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return {
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percentage,
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current,
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total,
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remaining: '计算中...',
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remaining: calculateRemainingTime(current, total, logText),
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loss: currentLoss,
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epoch: `${current}/${total}`,
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lr: null
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lr: currentLr
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};
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}
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}
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@ -88,24 +88,70 @@ def calculate_training_progress(process_id, process_info):
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current_lr = None
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for line in reversed(lines): # 从最新日志开始
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# 提取epoch信息 (格式: epoch 3/10)
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line = line.strip()
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if not line:
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continue
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# 提取epoch信息 - 支持多种格式
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if not total_epochs:
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epoch_match = re.search(r'epoch\s+(\d+)\s*/\s*(\d+)', line, re.IGNORECASE)
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if epoch_match:
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current_epoch = int(epoch_match.group(1))
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total_epochs = int(epoch_match.group(2))
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# 格式: epoch 3/10, Epoch 3 of 10, [3/10], 第3轮/共10轮
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epoch_patterns = [
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r'epoch\s+(\d+)\s*/\s*(\d+)',
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r'Epoch\s+(\d+)\s*of\s*(\d+)',
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r'\[(\d+)/(\d+)\]',
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r'epoch\s*[:：]\s*(\d+)\s*/\s*(\d+)',
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r'第\s*(\d+)\s*轮\s*/\s*共\s*(\d+)\s*轮'
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]
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# 提取loss信息 (格式: loss: 4.32 或 loss = 4.32)
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for pattern in epoch_patterns:
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match = re.search(pattern, line, re.IGNORECASE)
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if match:
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current_epoch = int(match.group(1))
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total_epochs = int(match.group(2))
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break
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# 提取loss信息 - 支持多种格式
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if not current_loss:
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loss_match = re.search(r'loss[\s:=]\s*([\d.]+)', line, re.IGNORECASE)
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if loss_match:
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current_loss = float(loss_match.group(1))
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# 格式: loss: 4.32, training_loss: 4.32, train_loss: 4.32, Loss: 4.32, 训练损失: 4.32
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loss_patterns = [
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r'loss[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)', # loss: 4.32
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r'training_loss[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)', # training_loss: 4.32
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r'train_loss[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)', # train_loss: 4.32
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r'Loss[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)', # Loss: 4.32
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r'训练损失[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)', # 训练损失: 4.32
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r'损失[\s:=]\s*([\d.]+(?:e[+-]?\d+)?)', # 损失: 4.32
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r'\s+([\d.]+(?:e[+-]?\d+)?)\s*loss', # 4.32 loss
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r'\s+([\d.]+(?:e[+-]?\d+)?)\s*训练损失', # 4.32 训练损失
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r'(?:loss|损失|training_loss|train_loss)\s*=\s*([\d.]+(?:e[+-]?\d+)?)' # loss = 4.32
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]
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# 提取学习率信息 (格式: lr: 1e-4 或 learning_rate: 1e-4)
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for pattern in loss_patterns:
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matches = re.findall(pattern, line, re.IGNORECASE)
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if matches:
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# 取最后一个匹配的loss值
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loss_value = float(matches[-1])
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if 0 < loss_value < 100: # 合理的loss范围
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current_loss = loss_value
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break
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# 提取学习率信息 - 支持多种格式
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if not current_lr:
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lr_match = re.search(r'(?:lr|learning_rate)[\s:=]\s*([\d.e-]+)', line, re.IGNORECASE)
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if lr_match:
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current_lr = lr_match.group(1)
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# 格式: lr: 1e-4, learning_rate: 1e-4, LR: 1e-4, 学习率: 1e-4
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lr_patterns = [
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r'lr[\s:=]\s*([\d.e+-]+)',
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r'learning_rate[\s:=]\s*([\d.e+-]+)',
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r'LR[\s:=]\s*([\d.e+-]+)',
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r'学习率[\s:=]\s*([\d.e+-]+)'
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]
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for pattern in lr_patterns:
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matches = re.findall(pattern, line, re.IGNORECASE)
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if matches:
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# 取最后一个匹配的lr值
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lr_value = float(matches[-1])
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if 0 < lr_value < 1: # 合理的lr范围
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current_lr = f"{lr_value:.2e}"
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break
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# 如果已经收集到足够信息，提前退出
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if total_epochs and current_loss and current_lr:
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@ -116,11 +162,57 @@ def calculate_training_progress(process_id, process_info):
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if total_epochs > 0:
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percentage = min(100, max(0, int((current_epoch / total_epochs) * 100)))
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# 估算剩余时间（简化计算）
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# 估算剩余时间（增强计算）
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remaining_time = '计算中...'
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if current_epoch > 0 and total_epochs > current_epoch:
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# 从日志中提取时间信息
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for line in reversed(lines):
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# 格式: remaining: 1:30:45, ETA: 1:30:45, 预计剩余: 1小时30分钟
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time_patterns = [
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r'remaining[\s:=]\s*(\d+):(\d+):(\d+)', # remaining: 1:30:45
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r'ETA[\s:=]\s*(\d+):(\d+):(\d+)', # ETA: 1:30:45
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r'预计剩余[\s:=]\s*(\d+)[\s小时]*[\s:]?(\d+)?[\s分钟]*', # 预计剩余: 1小时30分钟
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r'剩余时间[\s:=]\s*(\d+)[\s小时]*[\s:]?(\d+)?[\s分钟]*', # 剩余时间: 1小时30分钟
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r'time left[\s:=]\s*(\d+)[\s:]?(\d+)?[\s:]?(\d+)?', # time left: 1:30:45
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r'还需[\s:=]\s*(\d+)[\s小时]*[\s:]?(\d+)?[\s分钟]*' # 还需: 1小时30分钟
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]
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for pattern in time_patterns:
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match = re.search(pattern, line, re.IGNORECASE)
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if match:
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groups = match.groups()
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if len(groups) >= 3 and all(groups[:3]):
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# 小时:分钟:秒格式
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hours = int(groups[0])
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minutes = int(groups[1])
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seconds = int(groups[2])
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if hours > 0 or minutes > 0 or seconds > 0:
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parts = []
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if hours > 0: parts.append(f"{hours}小时")
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if minutes > 0: parts.append(f"{minutes}分钟")
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if seconds > 0 and hours == 0 and minutes == 0:
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parts.append(f"{seconds}秒")
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remaining_time = ''.join(parts)
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break
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elif len(groups) >= 2:
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# 小时和分钟格式
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hours = int(groups[0])
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minutes = int(groups[1]) if groups[1] else 0
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if hours > 0 or minutes > 0:
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parts = []
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if hours > 0: parts.append(f"{hours}小时")
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if minutes > 0: parts.append(f"{minutes}分钟")
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remaining_time = ''.join(parts)
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break
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if remaining_time != '计算中...':
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break
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# 如果没有找到时间信息，根据进度估算
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if remaining_time == '计算中...':
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# 假设每epoch时间大致相同
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elapsed_time = time.time() - process_info.get('start_timestamp', time.time())
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if current_epoch > 0:
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time_per_epoch = elapsed_time / current_epoch
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remaining_epochs = total_epochs - current_epoch
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remaining_seconds = remaining_epochs * time_per_epoch
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