diff --git a/src/dataset_paths_cache.pkl b/src/dataset_paths_cache.pkl
new file mode 100644
index 0000000..010b852
Binary files /dev/null and b/src/dataset_paths_cache.pkl differ
diff --git a/src/main.py b/src/main.py
index ddc3485..b0da338 100644
--- a/src/main.py
+++ b/src/main.py
@@ -1,16 +1,15 @@
-import streamlit as st
-import streamlit.components.v1 as components
 import sys
 import os
 import subprocess
 
+import streamlit as st
+import streamlit.components.v1 as components
+
 from data_loader import load_music_engine, load_emoset_data, load_image_processor
 from tabs.tab_dataset import render_dataset_tab
 from tabs.tab_live import render_live_tab
 
-# ----------------------------
-# Запуск приложения
-# ----------------------------
+# Костыль для прямого запуска
 if __name__ == "__main__":
     if "STREAMLIT_RUN" not in os.environ:
         os.environ["STREAMLIT_RUN"] = "1"
@@ -18,33 +17,26 @@ if __name__ == "__main__":
         subprocess.run(cmd)
         sys.exit()
 
-# Автоматическое определение типа устройства через URL query parameters
-# Считывание происходит до set_page_config, что позволяет динамически менять layout
-viewport = st.query_params.get("viewport", "desktop")
-layout_mode = "centered" if viewport == "mobile" else "wide"
+viewport_mode = st.query_params.get("viewport", "desktop")
+page_layout = "centered" if viewport_mode == "mobile" else "wide"
 
-st.set_page_config(page_title="Thesis Demo", layout=layout_mode)
+st.set_page_config(page_title="Thesis Demo", layout=page_layout)
 
-# Внедрение легковесного JavaScript-детектора для определения ширины экрана
-# Перезагружает контекст Streamlit один раз при инициализации сессии, исключая циклическую перезагрузку
+# Определения ширины экрана и смены верстки
 components.html(
     """
     <script>
-    const width = window.parent.innerWidth;
-    const height = window.parent.innerHeight;
-    const currentUrl = new URL(window.parent.location.href);
+    const w = window.parent.innerWidth;
+    const h = window.parent.innerHeight;
+    const url = new URL(window.parent.location.href);
     
-    // Интерфейс признается мобильным, если экран находится в портретном режиме 
-    // (высота больше ширины, что актуально для вертикальных 2.5K мониторов)
-    // либо если абсолютная ширина физически мала.
-    const isPortrait = height > width;
-    const isSmallWidth = width < 768;
+    // Считаем мобилкой, если ушли в портретный режим или экран уже 768px
+    const isMobile = (h > w) || (w < 768);
+    const target = isMobile ? "mobile" : "desktop";
     
-    const targetViewport = (isPortrait || isSmallWidth) ? "mobile" : "desktop";
-    
-    if (currentUrl.searchParams.get("viewport") !== targetViewport) {
-        currentUrl.searchParams.set("viewport", targetViewport);
-        window.parent.location.href = currentUrl.href;
+    if (url.searchParams.get("viewport") !== target) {
+        url.searchParams.set("viewport", target);
+        window.parent.location.href = url.href;
     }
     </script>
     """,
@@ -52,43 +44,30 @@ components.html(
     width=0,
 )
 
-# Глобальная инъекция базовых CSS-стилей для адаптации медиаконтента
 st.markdown(
     """
     <style>
-    img {
-        max-width: 100%;
-        height: auto;
-        object-fit: contain;
-    }
-    [data-testid="stMetricValue"] {
-        font-size: 1.8rem;
-    }
+    img { max-width: 100%; height: auto; object-fit: contain; }
+    [data-testid="stMetricValue"] { font-size: 1.8rem; }
     </style>
     """,
     unsafe_allow_html=True
 )
 
-# ----------------------------
-# Инициализация движка и данных
-# ----------------------------
-matcher = load_music_engine()
-image_processor = load_image_processor()
-image_files, embeddings, labels_array, images_path = load_emoset_data()
+# Подгрузка ML-моделей и датасета
+music_matcher = load_music_engine()
+img_processor = load_image_processor()
+emoset_files, emoset_embeddings, emoset_labels, emoset_path = load_emoset_data()
 
-# ----------------------------
-# Интерфейс и Вкладки
-# ----------------------------
 st.title("Генератор саундтреков (Research Demo)")
 
-# Изменен порядок: Анализ событий стал первой активной вкладкой
-tab1, tab2 = st.tabs(["Анализ событий (Свои фото)", "Отладка (Датасет EmoSet)"])
+tab_live, tab_debug = st.tabs(["Анализ событий (Свои фото)", "Отладка (Датасет EmoSet)"])
 
-with tab1:
-    if image_processor:
-        render_live_tab(matcher, image_processor)
+with tab_live:
+    if img_processor:
+        render_live_tab(music_matcher, img_processor)
     else:
-        st.error("Система обработки изображений недоступна (не найдены веса ResNet).")
+        st.error("Ошибка загрузки: не найдены веса ResNet для image_processor.")
 
-with tab2:
-    render_dataset_tab(matcher, image_files, embeddings, labels_array, images_path)
\ No newline at end of file
+with tab_debug:
+    render_dataset_tab(music_matcher, emoset_files, emoset_embeddings, emoset_labels, emoset_path)
\ No newline at end of file
diff --git a/src/scripts/finetune.py b/src/scripts/finetune.py
new file mode 100644
index 0000000..8c33e20
--- /dev/null
+++ b/src/scripts/finetune.py
@@ -0,0 +1,314 @@
+import os
+import gc
+import pickle
+import random
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+from torch.utils.data import Dataset, DataLoader
+import torchvision.transforms as T
+import torchvision.io as tv_io
+from torch.amp import autocast, GradScaler
+from tqdm import tqdm
+import timm
+
+# ==========================================
+# 1. КОНФИГУРАЦИЯ И ПУТИ
+# ==========================================
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Используем устройство: {DEVICE}")
+
+# Путь к огромному датасету на NFS
+DATA_ROOT = Path("/home/zin/projects/Thesis/dataset/Original-2.41M")
+CACHE_PATH = Path("/home/zin/projects/Thesis/src/dataset_paths_cache.pkl")
+
+# Пути к моделям
+PREVIOUS_WEIGHTS = Path("/home/zin/projects/Thesis/src/emoset_resnet50_best.pth") # Старые веса (118K)
+RESUME_CHECKPOINT = Path("/home/zin/projects/Thesis/src/emoset_resnet50_resume.pth") # Файл для восстановления сессии
+SAVE_MODEL_PATH = Path("/home/zin/projects/Thesis/src/emoset_resnet50_finetuned_2.41M.pth") # Финальный файл
+
+EMO_MAP = {
+    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
+    "disgust": 4, "excitement": 5, "fear": 6, "sad": 7, "sadness": 7
+}
+
+# --- НАСТРОЙКИ ОБУЧЕНИЯ ---
+BATCH_SIZE = 82
+EPOCHS = 15
+LR = 5e-5 # Низкий LR, так как мы делаем Fine-Tuning
+NUM_TRAIN_WORKERS = 48
+NUM_VAL_WORKERS = 18
+
+# Настройки защиты от переобучения
+PATIENCE = 4
+best_val_loss = float('inf')
+epochs_no_improve = 0
+start_epoch = 1
+
+# ==========================================
+# 2. ПОДГОТОВКА ДАННЫХ (БЫСТРЫЙ КЭШ)
+# ==========================================
+if CACHE_PATH.exists():
+    print(f"Загрузка списка файлов из кэша: {CACHE_PATH}...")
+    with open(CACHE_PATH, 'rb') as f:
+        cache_data = pickle.load(f)
+        all_paths = cache_data['image_paths']
+        all_labels = cache_data['labels']
+    print(f"Готово! Моментально загружено {len(all_paths)} путей.")
+else:
+    print(f"Сканирование NFS директории {DATA_ROOT} (Выполняется один раз)...")
+    all_paths, all_labels = [], []
+    for img_path in DATA_ROOT.rglob('*.jpg'):
+        emotion_folder = img_path.parts[-3].lower()
+        if emotion_folder in EMO_MAP:
+            all_paths.append(str(img_path))
+            all_labels.append(EMO_MAP[emotion_folder])
+    
+    with open(CACHE_PATH, 'wb') as f:
+        pickle.dump({'image_paths': all_paths, 'labels': all_labels}, f)
+    print(f"Сохранено в кэш: {len(all_paths)} изображений.")
+
+# Разделение на Train / Validation (95% / 5%)
+random.seed(42) # Фиксируем сид, чтобы при перезапусках сплит не менялся
+combined = list(zip(all_paths, all_labels))
+random.shuffle(combined)
+all_paths, all_labels = zip(*combined)
+
+split_idx = int(len(all_paths) * 0.95)
+train_paths, train_labels = all_paths[:split_idx], all_labels[:split_idx]
+val_paths, val_labels = all_paths[split_idx:], all_labels[split_idx:]
+print(f"Трейн: {len(train_paths)} | Валидация: {len(val_paths)}")
+
+# ==========================================
+# 3. DATASET & DATALOADER
+# ==========================================
+class EmoSetDirectDataset(Dataset):
+    def __init__(self, image_paths, labels):
+        self.image_paths = image_paths
+        self.labels = labels
+        # На процессоре делаем только базовый ресайз
+        self.base_transform = T.Resize((256, 256), antialias=True)
+
+    def __len__(self): return len(self.image_paths)
+
+    def __getitem__(self, idx):
+        try:
+            image = tv_io.read_image(self.image_paths[idx], mode=tv_io.ImageReadMode.RGB)
+            image = image.to(torch.float32) / 255.0
+            image = self.base_transform(image)
+        except Exception:
+            # Отказоустойчивость для битых файлов из интернета
+            image = torch.zeros((3, 256, 256), dtype=torch.float32)
+        return image, self.labels[idx]
+
+# --- ИСПРАВЛЕННЫЕ ЗАГРУЗЧИКИ С PREFETCH ---
+train_loader = DataLoader(
+    EmoSetDirectDataset(train_paths, train_labels), 
+    batch_size=BATCH_SIZE, 
+    shuffle=True, 
+    num_workers=NUM_TRAIN_WORKERS, 
+    pin_memory=True, 
+    prefetch_factor=2,
+    persistent_workers=True
+)
+
+val_loader = DataLoader(
+    EmoSetDirectDataset(val_paths, val_labels), 
+    batch_size=BATCH_SIZE, 
+    shuffle=False, 
+    num_workers=NUM_VAL_WORKERS, 
+    pin_memory=True, 
+    prefetch_factor=2,
+    persistent_workers=True
+)
+
+# ==========================================
+# 4. АУГМЕНТАЦИИ НА GPU (СУПЕР СКОРОСТЬ)
+# ==========================================
+gpu_train_transforms = torch.nn.Sequential(
+    T.RandomCrop((224, 224)),
+    T.RandomHorizontalFlip(p=0.5),
+    T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),
+    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+).to(DEVICE)
+
+gpu_val_transforms = torch.nn.Sequential(
+    T.CenterCrop((224, 224)),
+    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+).to(DEVICE)
+
+# ==========================================
+# 5. ИНИЦИАЛИЗАЦИЯ МОДЕЛИ
+# ==========================================
+print("\nСоздание архитектуры ResNet-50...")
+model = timm.create_model('resnet50', pretrained=False, num_classes=8).to(DEVICE)
+
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)
+scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
+scaler = GradScaler()
+
+# --- ЛОГИКА БЕСШОВНОГО ВОССТАНОВЛЕНИЯ ---
+if RESUME_CHECKPOINT.exists():
+    print(f"ВОССТАНОВЛЕНИЕ СЕССИИ из: {RESUME_CHECKPOINT}")
+    checkpoint = torch.load(RESUME_CHECKPOINT, map_location=DEVICE)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+    scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
+    if 'scaler_state_dict' in checkpoint: 
+        scaler.load_state_dict(checkpoint['scaler_state_dict'])
+    if 'best_val_loss' in checkpoint: 
+        best_val_loss = checkpoint['best_val_loss']
+    start_epoch = checkpoint['epoch'] + 1
+    print(f"УСПЕХ: Продолжаем обучение с эпохи {start_epoch}")
+else:
+    print("Чекпоинт сессии не найден. Проверяем наличие базовых весов...")
+    if PREVIOUS_WEIGHTS.exists():
+        print(f"📥 Загрузка базовых весов (от 118K): {PREVIOUS_WEIGHTS}")
+        model.load_state_dict(torch.load(PREVIOUS_WEIGHTS, map_location=DEVICE))
+    else:
+        print("ВНИМАНИЕ: Базовые веса не найдены. Обучение начнется с нуля (ImageNet).")
+        model = timm.create_model('resnet50', pretrained=True, num_classes=8).to(DEVICE)
+
+# ==========================================
+# 6. ГЛАВНЫЙ ЦИКЛ ОБУЧЕНИЯ
+# ==========================================
+if start_epoch > EPOCHS:
+    print(f"\nОбучение уже было завершено (цель: {EPOCHS} эпох).")
+else:
+    print(f"\nСтарт обучения. Целевое количество эпох: {EPOCHS}")
+
+    try:
+        for epoch in range(start_epoch, EPOCHS + 1):
+            
+            # --- ФАЗА 1: ТРЕНИРОВКА ---
+            model.train()
+            running_loss, correct, total = 0.0, 0, 0
+            
+            pbar = tqdm(train_loader, desc=f"Эпоха {epoch}/{EPOCHS} [Тренировка]")
+            for inputs, labels in pbar:
+                try:
+                    # Перенос на GPU и применение быстрых аугментаций
+                    inputs = inputs.to(DEVICE, non_blocking=True)
+                    labels = labels.to(DEVICE, non_blocking=True)
+                    inputs = gpu_train_transforms(inputs)
+                    
+                    optimizer.zero_grad()
+                    
+                    # Смешанная точность (AMP) для экономии VRAM и ускорения
+                    with autocast(device_type="cuda"):
+                        outputs = model(inputs)
+                        loss = criterion(outputs, labels)
+                    
+                    scaler.scale(loss).backward()
+                    scaler.step(optimizer)
+                    scaler.update()
+                    
+                    running_loss += loss.item() * inputs.size(0)
+                    _, predicted = outputs.max(1)
+                    total += labels.size(0)
+                    correct += predicted.eq(labels).sum().item()
+                    
+                    pbar.set_postfix({'loss': f"{loss.item():.4f}", 'acc': f"{correct/total:.4f}"})
+                    
+                except RuntimeError as e:
+                    # Хендлер нехватки памяти (OOM)
+                    if "out of memory" in str(e).lower():
+                        print(f"\nВНИМАНИЕ: Нехватка VRAM! Очистка...")
+                        if 'outputs' in locals(): del outputs
+                        if 'loss' in locals(): del loss
+                        torch.cuda.empty_cache()
+                        optimizer.zero_grad()
+                        continue
+                    raise e
+                    
+            train_loss = running_loss / total if total > 0 else 0
+            train_acc = correct / total if total > 0 else 0
+
+            # Очистка мусора перед валидацией
+            gc.collect()
+            torch.cuda.empty_cache()
+
+            # --- ФАЗА 2: ВАЛИДАЦИЯ ---
+            model.eval()
+            val_loss, val_correct, val_total = 0.0, 0, 0
+            
+            with torch.no_grad():
+                for val_inputs, val_labels in tqdm(val_loader, desc=f"Эпоха {epoch}/{EPOCHS} [Валидация]", leave=False):
+                    val_inputs, val_labels = val_inputs.to(DEVICE), val_labels.to(DEVICE)
+                    val_inputs = gpu_val_transforms(val_inputs)
+                    
+                    with autocast(device_type="cuda"):
+                        val_outputs = model(val_inputs)
+                        v_loss = criterion(val_outputs, val_labels)
+                        
+                    val_loss += v_loss.item() * val_inputs.size(0)
+                    _, val_predicted = val_outputs.max(1)
+                    val_total += val_labels.size(0)
+                    val_correct += val_predicted.eq(val_labels).sum().item()
+
+            epoch_val_loss = val_loss / val_total if val_total > 0 else 0
+            epoch_val_acc = val_correct / val_total if val_total > 0 else 0
+            
+            scheduler.step()
+            print(f"🏁 Эпоха {epoch} | Train Loss: {train_loss:.4f} (Acc: {train_acc:.4f}) | Val Loss: {epoch_val_loss:.4f} (Acc: {epoch_val_acc:.4f})")
+
+            # --- ФАЗА 3: EARLY STOPPING И СОХРАНЕНИЕ ---
+            if epoch_val_loss < best_val_loss:
+                best_val_loss = epoch_val_loss
+                epochs_no_improve = 0
+                torch.save(model.state_dict(), "../emoset_resnet50_best_2_41M.pth")
+                print("Новая лучшая модель найдена! Веса сохранены.")
+            else:
+                epochs_no_improve += 1
+                print(f"Валидация не улучшается {epochs_no_improve}/{PATIENCE} эпох.")
+                if epochs_no_improve >= PATIENCE and epoch >= 15: # Даем модели минимум 15 эпох на раскачку
+                    print("\nСРАБОТАЛА ЗАЩИТА ОТ ПЕРЕОБУЧЕНИЯ (Early Stopping)!")
+                    break
+                    
+            # Сохранение полного состояния сессии
+            checkpoint_state = {
+                'epoch': epoch,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'scheduler_state_dict': scheduler.state_dict(),
+                'scaler_state_dict': scaler.state_dict(),
+                'best_val_loss': best_val_loss
+            }
+            torch.save(checkpoint_state, RESUME_CHECKPOINT)
+            
+            # Сохранение весов конкретной эпохи как бэкап
+            torch.save(model.state_dict(), f"../emoset_resnet50_finetuned_ep{epoch}.pth")
+            
+            gc.collect()
+            torch.cuda.empty_cache()
+
+    # ==========================================
+    # 7. ПЕРЕХВАТ РУЧНОЙ ОСТАНОВКИ (CTRL+C)
+    # ==========================================
+    except KeyboardInterrupt:
+        print("\n\nОБУЧЕНИЕ ПРЕРВАНО ВРУЧНУЮ (KeyboardInterrupt)!")
+        print(f"Экстренное сохранение состояния конвейера на эпохе {epoch}...")
+        
+        checkpoint_state = {
+            'epoch': epoch, 'model_state_dict': model.state_dict(),
+            'optimizer_state_dict': optimizer.state_dict(),
+            'scheduler_state_dict': scheduler.state_dict(), 'scaler_state_dict': scaler.state_dict(),
+            'best_val_loss': best_val_loss
+        }
+        torch.save(checkpoint_state, RESUME_CHECKPOINT)
+        
+        interrupted_weights_path = f"../emoset_resnet50_interrupted_ep{epoch}.pth"
+        torch.save(model.state_dict(), interrupted_weights_path)
+        print(f"Прогресс безопасно зафиксирован в файле {interrupted_weights_path}. Выходим.")
+
+    # ==========================================
+    # 8. ФИНАЛЬНОЕ СОХРАНЕНИЕ
+    # ==========================================
+    else:
+        if SAVE_MODEL_PATH.parent.exists():
+            torch.save(model.state_dict(), SAVE_MODEL_PATH)
+            print(f"\nОБУЧЕНИЕ УСПЕШНО ЗАВЕРШЕНО! Финальная модель: {SAVE_MODEL_PATH}")
+            if RESUME_CHECKPOINT.exists():
+                RESUME_CHECKPOINT.unlink() # Удаляем resume файл за ненадобностью
\ No newline at end of file
diff --git a/src/scripts/finetune_embeddings.ipynb b/src/scripts/finetune_embeddings.ipynb
new file mode 100644
index 0000000..179c048
--- /dev/null
+++ b/src/scripts/finetune_embeddings.ipynb
@@ -0,0 +1,467 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "71ef58af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Используем устройство: cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "import torchvision.transforms as T\n",
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "from tqdm.notebook import tqdm\n",
+    "import timm\n",
+    "\n",
+    "# Проверяем GPU\n",
+    "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(f\"Используем устройство: {DEVICE}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f4ae931c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# === НАСТРОЙКИ ДООБУЧЕНИЯ ===\n",
+    "\n",
+    "# Абсолютный путь к смонтированному NFS\n",
+    "DATA_ROOT = Path(\"/home/zin/projects/Thesis/NFS/Thesis/Emoset/Original-2.41M\")\n",
+    "\n",
+    "# Пути относительно src/scripts/\n",
+    "PREVIOUS_WEIGHTS = Path(\"../emoset_resnet50_best.pth\")\n",
+    "SAVE_MODEL_PATH = Path(\"../emoset_resnet50_finetuned_2.41M.pth\")\n",
+    "\n",
+    "# Маппинг эмоций в те же индексы (0-7), которые использовались при первоначальном обучении\n",
+    "EMO_MAP = {\n",
+    "    \"amusement\": 0,\n",
+    "    \"anger\": 1,\n",
+    "    \"awe\": 2,\n",
+    "    \"contentment\": 3,\n",
+    "    \"disgust\": 4,\n",
+    "    \"excitement\": 5,\n",
+    "    \"fear\": 6,\n",
+    "    \"sad\": 7,       # В твоем сообщении папка называется \"sad\"\n",
+    "    \"sadness\": 7    # На всякий случай оставляем и классическое название\n",
+    "}\n",
+    "\n",
+    "BATCH_SIZE = 96\n",
+    "EPOCHS = 15\n",
+    "LR = 5e-5\n",
+    "NUM_WORKERS = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "934cfe2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "import os\n",
+    "import torchvision.io as tv_io\n",
+    "\n",
+    "# Твои трансформации (только Resize, как мы сделали ранее)\n",
+    "train_transforms = T.Compose([\n",
+    "    T.Resize((256, 256), antialias=True)\n",
+    "])\n",
+    "\n",
+    "class EmoSetNestedDataset(Dataset):\n",
+    "    def __init__(self, root_dir, transform=None, cache_file=\"../dataset_cache_2.41M.pkl\"):\n",
+    "        self.root_dir = Path(root_dir)\n",
+    "        self.transform = transform\n",
+    "        self.image_paths = []\n",
+    "        self.labels = []\n",
+    "        \n",
+    "        # === ЛОГИКА КЭШИРОВАНИЯ ===\n",
+    "        if os.path.exists(cache_file):\n",
+    "            print(f\"📦 Загрузка списка файлов из локального кэша: {cache_file}...\")\n",
+    "            with open(cache_file, 'rb') as f:\n",
+    "                cache_data = pickle.load(f)\n",
+    "                self.image_paths = cache_data['image_paths']\n",
+    "                self.labels = cache_data['labels']\n",
+    "            print(f\"⚡ Готово! Моментально загружено {len(self.image_paths)} путей.\")\n",
+    "        else:\n",
+    "            print(f\"🔍 Сканирование NFS директории {self.root_dir}...\")\n",
+    "            print(\"Это займет около 8-10 минут. Выполняется один раз...\")\n",
+    "            \n",
+    "            for img_path in self.root_dir.rglob('*.jpg'):\n",
+    "                emotion_folder = img_path.parts[-3].lower()\n",
+    "                if emotion_folder in EMO_MAP:\n",
+    "                    self.image_paths.append(str(img_path))\n",
+    "                    self.labels.append(EMO_MAP[emotion_folder])\n",
+    "                    \n",
+    "            print(f\"💾 Сохранение результатов сканирования в кэш: {cache_file}...\")\n",
+    "            with open(cache_file, 'wb') as f:\n",
+    "                pickle.dump({'image_paths': self.image_paths, 'labels': self.labels}, f)\n",
+    "                \n",
+    "            print(f\"✅ Успешно найдено и закэшировано {len(self.image_paths)} изображений.\")\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.image_paths)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        img_path = self.image_paths[idx]\n",
+    "        label = self.labels[idx]\n",
+    "        \n",
+    "        try:\n",
+    "            image = tv_io.read_image(str(img_path), mode=tv_io.ImageReadMode.RGB)\n",
+    "            image = image.to(torch.float32) / 255.0\n",
+    "        except Exception as e:\n",
+    "            image = torch.zeros((3, 256, 256), dtype=torch.float32)\n",
+    "            \n",
+    "        if self.transform:\n",
+    "            image = self.transform(image)\n",
+    "            \n",
+    "        return image, label"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b10adc06",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📦 Загрузка списка файлов из локального кэша: ../dataset_paths_cache.pkl...\n",
+      "⚡ Готово! Моментально загружено 2048377 путей.\n",
+      "Батчей за одну эпоху: 21338\n",
+      "\n",
+      "Создание архитектуры ResNet50...\n",
+      "📝 Чекпоинт прерванной сессии не найден. Проверяем базовые веса...\n",
+      "УСПЕХ: Найдены предыдущие веса '../emoset_resnet50_best.pth' (из EmoSet-118K). Загружаем...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Путь к кэш-файлу (лучше положить его в src, рядом со скриптами, чтобы быстро читался)\n",
+    "CACHE_PATH = Path(\"../dataset_paths_cache.pkl\")\n",
+    "\n",
+    "# 1. Загрузка данных напрямую из папок (или из кэша!)\n",
+    "train_dataset = EmoSetNestedDataset(DATA_ROOT, transform=train_transforms, cache_file=CACHE_PATH)\n",
+    "\n",
+    "train_loader = DataLoader(\n",
+    "    train_dataset, \n",
+    "    batch_size=BATCH_SIZE, \n",
+    "    shuffle=True, \n",
+    "    num_workers=NUM_WORKERS,\n",
+    "    pin_memory=True,\n",
+    "    prefetch_factor=1,\n",
+    "    persistent_workers=True\n",
+    ")\n",
+    "\n",
+    "print(f\"Батчей за одну эпоху: {len(train_loader)}\")\n",
+    "\n",
+    "# Путь к файлу автоматического восстановления (чекпоинт полной сессии)\n",
+    "RESUME_CHECKPOINT = Path(\"../emoset_resnet50_checkpoint_latest.pth\")\n",
+    "\n",
+    "# 2. Инициализация архитектуры модели ResNet-50\n",
+    "print(\"\\nСоздание архитектуры ResNet50...\")\n",
+    "model = timm.create_model('resnet50', pretrained=False, num_classes=8)\n",
+    "model = model.to(DEVICE)\n",
+    "\n",
+    "# Инициализируем компоненты оптимизации\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=1e-4)\n",
+    "scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)\n",
+    "\n",
+    "# По умолчанию начинаем с 1-й эпохи\n",
+    "start_epoch = 1\n",
+    "\n",
+    "# === ЛОГИКА ВОССТАНОВЛЕНИЯ / СТАРТА ===\n",
+    "if RESUME_CHECKPOINT.exists():\n",
+    "    print(f\"🔄 ОБНАРУЖЕН ДЕЙСТВУЮЩИЙ ЧЕКПОИНТ: '{RESUME_CHECKPOINT}'\")\n",
+    "    print(\"Восстанавливаем полное состояние сессии...\")\n",
+    "    \n",
+    "    # Загружаем сохраненный словарь состояния\n",
+    "    checkpoint = torch.load(RESUME_CHECKPOINT, map_location=DEVICE)\n",
+    "    \n",
+    "    # Восстанавливаем всё до единого\n",
+    "    model.load_state_dict(checkpoint['model_state_dict'])\n",
+    "    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n",
+    "    scheduler.load_state_dict(checkpoint['scheduler_state_dict'])\n",
+    "    start_epoch = checkpoint['epoch'] + 1  # Начинаем со следующей эпохи\n",
+    "    \n",
+    "    print(f\"🚀 УСПЕХ: Сессия восстановлена! Продолжаем обучение с эпохи {start_epoch}\")\n",
+    "else:\n",
+    "    print(\"📝 Чекпоинт прерванной сессии не найден. Проверяем базовые веса...\")\n",
+    "    if PREVIOUS_WEIGHTS.exists():\n",
+    "        print(f\"УСПЕХ: Найдены предыдущие веса '{PREVIOUS_WEIGHTS}' (из EmoSet-118K). Загружаем...\")\n",
+    "        model.load_state_dict(torch.load(PREVIOUS_WEIGHTS, map_location=DEVICE))\n",
+    "    else:\n",
+    "        print(\"ВНИМАНИЕ: Базовые веса не найдены. Начинаем обучение с нуля (ImageNet pretrained).\")\n",
+    "        model = timm.create_model('resnet50', pretrained=True, num_classes=8).to(DEVICE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a7480834",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "⏰ Старт обучения. Целевое количество эпох: 15\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Эпоха 1/15 [Тренировка]:   0%|          | 0/21338 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Эпоха 1/15 [Тренировка]:  13%|█▎        | 2705/21338 [32:27<2:39:51,  1.94it/s, loss=1.6873, acc=0.3036] Corrupt JPEG data: 80 extraneous bytes before marker 0xd9\n",
+      "Эпоха 1/15 [Тренировка]:  17%|█▋        | 3623/21338 [43:15<4:46:57,  1.03it/s, loss=1.7141, acc=0.3102] Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]:  18%|█▊        | 3741/21338 [44:35<2:23:26,  2.04it/s, loss=1.7848, acc=0.3109] Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]:  19%|█▉        | 4109/21338 [49:12<2:17:42,  2.09it/s, loss=1.8072, acc=0.3133] Corrupt JPEG data: 485 extraneous bytes before marker 0xd9\n",
+      "Эпоха 1/15 [Тренировка]:  22%|██▏       | 4729/21338 [56:22<3:06:07,  1.49it/s, loss=1.6499, acc=0.3173] Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]:  38%|███▊      | 8033/21338 [1:35:20<2:21:51,  1.56it/s, loss=1.5897, acc=0.3338] Corrupt JPEG data: 41 extraneous bytes before marker 0xd9\n",
+      "Эпоха 1/15 [Тренировка]:  45%|████▌     | 9684/21338 [1:54:34<3:40:06,  1.13s/it, loss=1.6740, acc=0.3399] Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]:  50%|█████     | 10679/21338 [2:06:15<47:11,  3.76it/s, loss=1.6234, acc=0.3431]   Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]:  55%|█████▌    | 11802/21338 [2:19:39<1:50:22,  1.44it/s, loss=1.5677, acc=0.3463] Unknown Adobe color transform code 2\n",
+      "Эпоха 1/15 [Тренировка]:  67%|██████▋   | 14253/21338 [2:48:12<27:27,  4.30it/s, loss=1.7579, acc=0.3525]   Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]:  77%|███████▋  | 16377/21338 [3:13:17<1:06:23,  1.25it/s, loss=1.6855, acc=0.3572] Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]:  92%|█████████▏| 19575/21338 [3:51:14<11:13,  2.62it/s, loss=1.5876, acc=0.3631]  Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]:  92%|█████████▏| 19679/21338 [3:52:26<07:42,  3.59it/s, loss=1.7134, acc=0.3633]  Invalid SOS parameters for sequential JPEG\n",
+      "Эпоха 1/15 [Тренировка]: 100%|█████████▉| 21283/21338 [4:11:18<00:20,  2.70it/s, loss=1.4613, acc=0.3658]  Unknown Adobe color transform code 2\n",
+      "Эпоха 1/15 [Тренировка]: 100%|██████████| 21338/21338 [4:11:47<00:00,  1.41it/s, loss=1.6304, acc=0.3659]\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'val_loader' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 88\u001b[39m\n\u001b[32m     85\u001b[39m \u001b[38;5;66;03m# ВАЖНО: Если у тебя нет val_loader, создай его (откуси 5-10% от датасета)\u001b[39;00m\n\u001b[32m     86\u001b[39m \u001b[38;5;66;03m# На валидации мы НЕ применяем gpu_transforms (только нормализацию)\u001b[39;00m\n\u001b[32m     87\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m torch.no_grad():\n\u001b[32m---> \u001b[39m\u001b[32m88\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m val_inputs, val_labels \u001b[38;5;129;01min\u001b[39;00m \u001b[43mval_loader\u001b[49m:\n\u001b[32m     89\u001b[39m         val_inputs, val_labels = val_inputs.to(DEVICE), val_labels.to(DEVICE)\n\u001b[32m     91\u001b[39m         \u001b[38;5;66;03m# Валидация тоже идет в смешанной точности для скорости\u001b[39;00m\n",
+      "\u001b[31mNameError\u001b[39m: name 'val_loader' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "import gc\n",
+    "import torch\n",
+    "from torch.amp import autocast, GradScaler\n",
+    "from tqdm import tqdm\n",
+    "import torchvision.transforms as T\n",
+    "\n",
+    "# --- НАСТРОЙКИ EARLY STOPPING ---\n",
+    "PATIENCE = 4  # Сколько эпох ждем, если валидация не улучшается\n",
+    "best_val_loss = float('inf')\n",
+    "epochs_no_improve = 0\n",
+    "start_epoch = 1\n",
+    "\n",
+    "# --- ПЕРЕНОСИМ ТЯЖЕЛЫЕ АУГМЕНТАЦИИ НА GPU ---\n",
+    "gpu_transforms = torch.nn.Sequential(\n",
+    "    T.RandomCrop((224, 224)),\n",
+    "    T.RandomHorizontalFlip(p=0.5),\n",
+    "    T.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1),\n",
+    "    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
+    ").to(DEVICE)\n",
+    "# -------------------------------------------\n",
+    "\n",
+    "print(f\"⏰ Старт обучения. Целевое количество эпох: {EPOCHS}\")\n",
+    "scaler = GradScaler()\n",
+    "\n",
+    "try:\n",
+    "    for epoch in range(start_epoch, EPOCHS + 1):\n",
+    "        # ==========================================\n",
+    "        # 1. ТРЕНИРОВКА (TRAIN)\n",
+    "        # ==========================================\n",
+    "        model.train()\n",
+    "        running_loss = 0.0\n",
+    "        correct = 0\n",
+    "        total = 0\n",
+    "        \n",
+    "        pbar = tqdm(train_loader, desc=f\"Эпоха {epoch}/{EPOCHS} [Тренировка]\")\n",
+    "        for inputs, labels in pbar:\n",
+    "            try:\n",
+    "                inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)\n",
+    "                \n",
+    "                # Применяем аугментации на лету к батчу (на GPU)\n",
+    "                inputs = gpu_transforms(inputs)\n",
+    "                \n",
+    "                optimizer.zero_grad()\n",
+    "                \n",
+    "                with autocast(device_type=\"cuda\"): # Для PyTorch >= 2.0 лучше указывать \"cuda\"\n",
+    "                    outputs = model(inputs)\n",
+    "                    loss = criterion(outputs, labels)\n",
+    "                \n",
+    "                scaler.scale(loss).backward()\n",
+    "                scaler.step(optimizer)\n",
+    "                scaler.update()\n",
+    "                \n",
+    "                # Статистика\n",
+    "                running_loss += loss.item() * inputs.size(0)\n",
+    "                _, predicted = outputs.max(1)\n",
+    "                total += labels.size(0)\n",
+    "                correct += predicted.eq(labels).sum().item()\n",
+    "                \n",
+    "                pbar.set_postfix({'loss': f\"{loss.item():.4f}\", 'acc': f\"{correct/total:.4f}\"})\n",
+    "                \n",
+    "            except RuntimeError as e:\n",
+    "                # Обработчик OOM\n",
+    "                if \"out of memory\" in str(e).lower():\n",
+    "                    print(f\"\\n⚠️ ВНИМАНИЕ: Нехватка VRAM на батче! Выполняем экстренную очистку...\")\n",
+    "                    if 'outputs' in locals(): del outputs\n",
+    "                    if 'loss' in locals(): del loss\n",
+    "                    torch.cuda.empty_cache()\n",
+    "                    optimizer.zero_grad()\n",
+    "                    print(\"♻️ Кэш очищен. Батч пропущен. Продолжаем обучение...\")\n",
+    "                    continue\n",
+    "                else:\n",
+    "                    raise e\n",
+    "                    \n",
+    "        epoch_loss = running_loss / total if total > 0 else 0\n",
+    "        epoch_acc = correct / total if total > 0 else 0\n",
+    "        \n",
+    "        # ==========================================\n",
+    "        # 2. ВАЛИДАЦИЯ И EARLY STOPPING\n",
+    "        # ==========================================\n",
+    "        model.eval()\n",
+    "        val_loss = 0.0\n",
+    "        val_correct = 0\n",
+    "        val_total = 0\n",
+    "        \n",
+    "        # ВАЖНО: Если у тебя нет val_loader, создай его (откуси 5-10% от датасета)\n",
+    "        # На валидации мы НЕ применяем gpu_transforms (только нормализацию)\n",
+    "        with torch.no_grad():\n",
+    "            for val_inputs, val_labels in val_loader:\n",
+    "                val_inputs, val_labels = val_inputs.to(DEVICE), val_labels.to(DEVICE)\n",
+    "                \n",
+    "                # Валидация тоже идет в смешанной точности для скорости\n",
+    "                with autocast(device_type=\"cuda\"):\n",
+    "                    val_outputs = model(val_inputs)\n",
+    "                    v_loss = criterion(val_outputs, val_labels)\n",
+    "                    \n",
+    "                val_loss += v_loss.item() * val_inputs.size(0)\n",
+    "                _, val_predicted = val_outputs.max(1)\n",
+    "                val_total += val_labels.size(0)\n",
+    "                val_correct += val_predicted.eq(val_labels).sum().item()\n",
+    "                \n",
+    "        epoch_val_loss = val_loss / val_total if val_total > 0 else 0\n",
+    "        epoch_val_acc = val_correct / val_total if val_total > 0 else 0\n",
+    "        \n",
+    "        scheduler.step()\n",
+    "        print(f\"🏁 Эпоха {epoch} завершена | Train Loss: {epoch_loss:.4f} (Acc: {epoch_acc:.4f}) | Val Loss: {epoch_val_loss:.4f} (Acc: {epoch_val_acc:.4f})\")\n",
+    "        \n",
+    "        # --- ЛОГИКА РАННЕЙ ОСТАНОВКИ ---\n",
+    "        if epoch_val_loss < best_val_loss:\n",
+    "            best_val_loss = epoch_val_loss\n",
+    "            epochs_no_improve = 0\n",
+    "            # Сохраняем идеальные веса, пока сеть не переобучилась\n",
+    "            torch.save(model.state_dict(), \"../emoset_resnet50_best.pth\")\n",
+    "            print(\"🌟 Найдена лучшая модель! Веса сохранены.\")\n",
+    "        else:\n",
+    "            epochs_no_improve += 1\n",
+    "            print(f\"⚠️ Валидационный Loss не улучшается {epochs_no_improve}/{PATIENCE} эпох.\")\n",
+    "            \n",
+    "            # Условие: если переобучение длится долго И мы прошли хотя бы 15 эпох\n",
+    "            if epochs_no_improve >= PATIENCE and epoch >= 15:\n",
+    "                print(\"\\n🛑 СРАБОТАЛА ЗАЩИТА ОТ ПЕРЕОБУЧЕНИЯ (Early Stopping)!\")\n",
+    "                print(\"Модель начала запоминать данные вместо обобщения. Обучение досрочно завершено.\")\n",
+    "                break # Прерываем цикл эпох\n",
+    "        \n",
+    "        # ==========================================\n",
+    "        # 3. РЕГУЛЯРНОЕ СОХРАНЕНИЕ ПРОГРЕССА\n",
+    "        # ==========================================\n",
+    "        checkpoint_state = {\n",
+    "            'epoch': epoch,\n",
+    "            'model_state_dict': model.state_dict(),\n",
+    "            'optimizer_state_dict': optimizer.state_dict(),\n",
+    "            'scheduler_state_dict': scheduler.state_dict(),\n",
+    "            'scaler_state_dict': scaler.state_dict(),\n",
+    "            'loss': epoch_loss,\n",
+    "            'val_loss': epoch_val_loss\n",
+    "        }\n",
+    "        torch.save(checkpoint_state, RESUME_CHECKPOINT)\n",
+    "        \n",
+    "        # Сохранение весов конкретной эпохи (на всякий случай)\n",
+    "        epoch_weights_path = f\"../emoset_resnet50_finetuned_ep{epoch}.pth\"\n",
+    "        torch.save(model.state_dict(), epoch_weights_path)\n",
+    "        \n",
+    "        gc.collect()\n",
+    "        torch.cuda.empty_cache()\n",
+    "\n",
+    "# ==========================================\n",
+    "# 4. БЕЗОПАСНЫЙ ВЫХОД ПРИ РУЧНОМ ПРЕРЫВАНИИ\n",
+    "# ==========================================\n",
+    "except KeyboardInterrupt:\n",
+    "    print(\"\\n\\n🛑 ОБУЧЕНИЕ ПРЕРВАНО ВРУЧНУЮ (KeyboardInterrupt)!\")\n",
+    "    print(f\"💾 Экстренное сохранение состояния конвейера на эпохе {epoch}...\")\n",
+    "    \n",
+    "    # Сохраняем всё, чтобы потом можно было продолжить с этого же места\n",
+    "    checkpoint_state = {\n",
+    "        'epoch': epoch,\n",
+    "        'model_state_dict': model.state_dict(),\n",
+    "        'optimizer_state_dict': optimizer.state_dict(),\n",
+    "        'scheduler_state_dict': scheduler.state_dict(),\n",
+    "        'scaler_state_dict': scaler.state_dict()\n",
+    "    }\n",
+    "    torch.save(checkpoint_state, RESUME_CHECKPOINT)\n",
+    "    \n",
+    "    # Сохраняем промежуточные веса на момент остановки\n",
+    "    interrupted_weights_path = f\"../emoset_resnet50_interrupted_ep{epoch}.pth\"\n",
+    "    torch.save(model.state_dict(), interrupted_weights_path)\n",
+    "    \n",
+    "    print(f\"✅ Прогресс безопасно зафиксирован в файле {interrupted_weights_path}. Выходим.\")\n",
+    "\n",
+    "# Финальное сохранение (если цикл дошел до конца сам)\n",
+    "else:\n",
+    "    if SAVE_MODEL_PATH.parent.exists():\n",
+    "        torch.save(model.state_dict(), SAVE_MODEL_PATH)\n",
+    "        print(f\"\\n🎉 ОБУЧЕНИЕ УСПЕШНО ЗАВЕРШЕНО! Финальная модель: {SAVE_MODEL_PATH}\")\n",
+    "        if RESUME_CHECKPOINT.exists():\n",
+    "            RESUME_CHECKPOINT.unlink()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (thesis)",
+   "language": "python",
+   "name": "thesis"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/scripts/preprocessing.py b/src/scripts/preprocessing.py
new file mode 100644
index 0000000..f30e904
--- /dev/null
+++ b/src/scripts/preprocessing.py
@@ -0,0 +1,134 @@
+import os
+import random
+from pathlib import Path
+from tqdm.notebook import tqdm
+import webdataset as wds
+
+# --- НАСТРОЙКИ ---
+# Оригинальная папка с датасетом (на NFS)
+DATA_ROOT = Path("/home/zin/projects/Thesis/NFS/Thesis/Emoset/Original-2.41M")
+
+# Новая папка, куда мы сложим готовые .tar архивы (шарды)
+# Лучше создать её рядом с оригинальным датасетом на NFS
+SHARDS_DIR = Path("/home/zin/projects/Thesis/NFS/Thesis/Emoset/shards-2.41M")
+SHARDS_DIR.mkdir(parents=True, exist_ok=True)
+
+# Маппинг классов
+EMO_MAP = {
+    "amusement": 0, "anger": 1, "awe": 2, "contentment": 3,
+    "disgust": 4, "excitement": 5, "fear": 6, "sad": 7, "sadness": 7
+}
+
+# Размер одного архива. 10 000 картинок — идеальный баланс
+MAX_SAMPLES_PER_SHARD = 10000
+
+samples = []
+
+print(f"🔍 Сканирование директории {DATA_ROOT}...")
+# Используем os.walk, он часто работает быстрее rglob на сетевых дисках
+for root, dirs, files in os.walk(DATA_ROOT):
+    for file in files:
+        if file.lower().endswith('.jpg'):
+            full_path = os.path.join(root, file)
+            # Извлекаем эмоцию (зависит от структуры папок, берем предпоследнюю папку)
+            # Путь: .../amusement/0/image.jpg -> root_parts[-2] будет 'amusement'
+            path_parts = Path(full_path).parts
+            emotion_folder = path_parts[-3].lower()
+            
+            if emotion_folder in EMO_MAP:
+                samples.append((full_path, EMO_MAP[emotion_folder]))
+
+print(f"✅ Найдено изображений: {len(samples)}")
+
+# САМЫЙ ВАЖНЫЙ ШАГ: Глобальное перемешивание перед упаковкой
+print("🔀 Перемешиваем датасет...")
+random.shuffle(samples)
+print("✅ Перемешивание завершено!")
+
+import multiprocessing as mp
+from concurrent.futures import ProcessPoolExecutor, as_completed
+import webdataset as wds
+from PIL import Image
+import io
+from pathlib import Path
+
+# ВАЖНО: Импортируем базовый tqdm, а не notebook-версию. 
+# Notebook-версия в мультипроцессинге вызывает зависание графического интерфейса Jupyter.
+from tqdm import tqdm 
+
+# --- ПУТИ И НАСТРОЙКИ ---
+SHARDS_DIR = Path("../../dataset/EmoSet-2.41M-shards")
+SHARDS_DIR.mkdir(parents=True, exist_ok=True)
+
+NUM_WORKERS = 50
+
+# 1. Дробим наш список на чанки
+chunks = [samples[i:i + MAX_SAMPLES_PER_SHARD] for i in range(0, len(samples), MAX_SAMPLES_PER_SHARD)]
+
+print(f"📦 Подготовлено {len(chunks)} задач (шардов).")
+print(f"💾 Целевая папка (Локальный SSD): {SHARDS_DIR}")
+print(f"🚀 Запуск упаковки и сжатия в {NUM_WORKERS} потоков...\n")
+
+# Инициализация блокировки tqdm для мультипроцессинга (чтобы бары не съезжали)
+tqdm.set_lock(mp.RLock())
+
+# 2. Функция, которую выполняет каждый воркер
+def build_shard(args):
+    shard_idx, chunk = args
+    shard_path = SHARDS_DIR / f"emoset-{shard_idx:06d}.tar"
+    
+    success_count = 0
+    error_count = 0
+    
+    # ХИТРОСТЬ ЗДЕСЬ: position = остаток от деления + 1.
+    # Это гарантирует, что все 42 воркера поделят между собой 42 строчки на экране,
+    # и когда воркер берет новый шард, он обновляет свою старую строчку, а не создает новую.
+    # leave=False заставит бар исчезнуть, когда чанк докачается.
+    worker_pos = (shard_idx % NUM_WORKERS) + 1
+    
+    with wds.TarWriter(str(shard_path)) as sink:
+        # Рисуем прогресс-бар для текущего шарда
+        for i, (img_path, label) in enumerate(tqdm(chunk, desc=f"Шард {shard_idx:03d}", position=worker_pos, leave=False)):
+            try:
+                # --- МАГИЯ СЖАТИЯ ---
+                with Image.open(img_path) as img:
+                    img = img.convert("RGB")
+                    img = img.resize((256, 256), Image.Resampling.BILINEAR)
+                    
+                    with io.BytesIO() as img_byte_arr:
+                        img.save(img_byte_arr, format='JPEG', quality=85)
+                        image_data = img_byte_arr.getvalue()
+                # --------------------
+                
+                key = f"{shard_idx:06d}_{i:05d}"
+                
+                sink.write({
+                    "__key__": key,
+                    "jpg": image_data,
+                    "cls": label
+                })
+                success_count += 1
+                
+            except Exception:
+                error_count += 1
+                continue # Игнорируем битые файлы
+                
+    return shard_idx
+
+# 3. Запускаем армию воркеров
+with ProcessPoolExecutor(max_workers=NUM_WORKERS) as executor:
+    tasks = [(i, chunk) for i, chunk in enumerate(chunks)]
+    
+    # Отправляем задачи в пул
+    futures = [executor.submit(build_shard, task) for task in tasks]
+    
+    # ГЛАВНЫЙ прогресс-бар (position=0, всегда висит на самой первой строчке)
+    for future in tqdm(as_completed(futures), total=len(tasks), desc="📊 ОБЩИЙ ПРОГРЕСС", position=0, leave=True):
+        try:
+            future.result()
+        except Exception:
+            pass
+
+# Печатаем пару пустых строк, чтобы финальный текст не налез на бары
+print("\n" * (NUM_WORKERS + 2))
+print("🎉 ПАРАЛЛЕЛЬНАЯ УПАКОВКА И СЖАТИЕ ПОЛНОСТЬЮ ЗАВЕРШЕНЫ!")
\ No newline at end of file
diff --git a/src/scripts/preprocessing_2.1M.ipynb b/src/scripts/preprocessing_2.1M.ipynb
new file mode 100644
index 0000000..05e7c1e
--- /dev/null
+++ b/src/scripts/preprocessing_2.1M.ipynb
@@ -0,0 +1,919 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e6aa65e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import random\n",
+    "from pathlib import Path\n",
+    "from tqdm.notebook import tqdm\n",
+    "import webdataset as wds\n",
+    "\n",
+    "# --- НАСТРОЙКИ ---\n",
+    "# Оригинальная папка с датасетом (на NFS)\n",
+    "DATA_ROOT = Path(\"/home/zin/projects/Thesis/NFS/Thesis/Emoset/Original-2.41M\")\n",
+    "\n",
+    "# Новая папка, куда мы сложим готовые .tar архивы (шарды)\n",
+    "# Лучше создать её рядом с оригинальным датасетом на NFS\n",
+    "SHARDS_DIR = Path(\"/home/zin/projects/Thesis/NFS/Thesis/Emoset/shards-2.41M\")\n",
+    "SHARDS_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Маппинг классов\n",
+    "EMO_MAP = {\n",
+    "    \"amusement\": 0, \"anger\": 1, \"awe\": 2, \"contentment\": 3,\n",
+    "    \"disgust\": 4, \"excitement\": 5, \"fear\": 6, \"sad\": 7, \"sadness\": 7\n",
+    "}\n",
+    "\n",
+    "# Размер одного архива. 10 000 картинок — идеальный баланс\n",
+    "MAX_SAMPLES_PER_SHARD = 10000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "09d0e56c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔍 Сканирование директории /home/zin/projects/Thesis/NFS/Thesis/Emoset/Original-2.41M...\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 11\u001b[39m\n\u001b[32m      8\u001b[39m full_path = os.path.join(root, file)\n\u001b[32m      9\u001b[39m \u001b[38;5;66;03m# Извлекаем эмоцию (зависит от структуры папок, берем предпоследнюю папку)\u001b[39;00m\n\u001b[32m     10\u001b[39m \u001b[38;5;66;03m# Путь: .../amusement/0/image.jpg -> root_parts[-2] будет 'amusement'\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m11\u001b[39m path_parts = \u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfull_path\u001b[49m\u001b[43m)\u001b[49m.parts\n\u001b[32m     12\u001b[39m emotion_folder = path_parts[-\u001b[32m3\u001b[39m].lower()\n\u001b[32m     14\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m emotion_folder \u001b[38;5;129;01min\u001b[39;00m EMO_MAP:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:871\u001b[39m, in \u001b[36mPath.__new__\u001b[39m\u001b[34m(cls, *args, **kwargs)\u001b[39m\n\u001b[32m    869\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m Path:\n\u001b[32m    870\u001b[39m     \u001b[38;5;28mcls\u001b[39m = WindowsPath \u001b[38;5;28;01mif\u001b[39;00m os.name == \u001b[33m'\u001b[39m\u001b[33mnt\u001b[39m\u001b[33m'\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m PosixPath\n\u001b[32m--> \u001b[39m\u001b[32m871\u001b[39m \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_from_parts\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    872\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m._flavour.is_supported:\n\u001b[32m    873\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mcannot instantiate \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[33m on your system\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    874\u001b[39m                               % (\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m,))\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:509\u001b[39m, in \u001b[36mPurePath._from_parts\u001b[39m\u001b[34m(cls, args)\u001b[39m\n\u001b[32m    504\u001b[39m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[32m    505\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_from_parts\u001b[39m(\u001b[38;5;28mcls\u001b[39m, args):\n\u001b[32m    506\u001b[39m     \u001b[38;5;66;03m# We need to call _parse_args on the instance, so as to get the\u001b[39;00m\n\u001b[32m    507\u001b[39m     \u001b[38;5;66;03m# right flavour.\u001b[39;00m\n\u001b[32m    508\u001b[39m     \u001b[38;5;28mself\u001b[39m = \u001b[38;5;28mobject\u001b[39m.\u001b[34m__new__\u001b[39m(\u001b[38;5;28mcls\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m509\u001b[39m     drv, root, parts = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_parse_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    510\u001b[39m     \u001b[38;5;28mself\u001b[39m._drv = drv\n\u001b[32m    511\u001b[39m     \u001b[38;5;28mself\u001b[39m._root = root\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:502\u001b[39m, in \u001b[36mPurePath._parse_args\u001b[39m\u001b[34m(cls, args)\u001b[39m\n\u001b[32m    497\u001b[39m         \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    498\u001b[39m             \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[32m    499\u001b[39m                 \u001b[33m\"\u001b[39m\u001b[33margument should be a str object or an os.PathLike \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    500\u001b[39m                 \u001b[33m\"\u001b[39m\u001b[33mobject returning str, not \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    501\u001b[39m                 % \u001b[38;5;28mtype\u001b[39m(a))\n\u001b[32m--> \u001b[39m\u001b[32m502\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_flavour\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse_parts\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparts\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:67\u001b[39m, in \u001b[36m_Flavour.parse_parts\u001b[39m\u001b[34m(self, parts)\u001b[39m\n\u001b[32m     65\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m altsep:\n\u001b[32m     66\u001b[39m     part = part.replace(altsep, sep)\n\u001b[32m---> \u001b[39m\u001b[32m67\u001b[39m drv, root, rel = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43msplitroot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpart\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     68\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m sep \u001b[38;5;129;01min\u001b[39;00m rel:\n\u001b[32m     69\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mreversed\u001b[39m(rel.split(sep)):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/pathlib.py:241\u001b[39m, in \u001b[36m_PosixFlavour.splitroot\u001b[39m\u001b[34m(self, part, sep)\u001b[39m\n\u001b[32m    239\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34msplitroot\u001b[39m(\u001b[38;5;28mself\u001b[39m, part, sep=sep):\n\u001b[32m    240\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m part \u001b[38;5;129;01mand\u001b[39;00m part[\u001b[32m0\u001b[39m] == sep:\n\u001b[32m--> \u001b[39m\u001b[32m241\u001b[39m         stripped_part = part.lstrip(sep)\n\u001b[32m    242\u001b[39m         \u001b[38;5;66;03m# According to POSIX path resolution:\u001b[39;00m\n\u001b[32m    243\u001b[39m         \u001b[38;5;66;03m# http://pubs.opengroup.org/onlinepubs/009695399/basedefs/xbd_chap04.html#tag_04_11\u001b[39;00m\n\u001b[32m    244\u001b[39m         \u001b[38;5;66;03m# \"A pathname that begins with two successive slashes may be\u001b[39;00m\n\u001b[32m    245\u001b[39m         \u001b[38;5;66;03m# interpreted in an implementation-defined manner, although more\u001b[39;00m\n\u001b[32m    246\u001b[39m         \u001b[38;5;66;03m# than two leading slashes shall be treated as a single slash\".\u001b[39;00m\n\u001b[32m    247\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(part) - \u001b[38;5;28mlen\u001b[39m(stripped_part) == \u001b[32m2\u001b[39m:\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
+     ]
+    }
+   ],
+   "source": [
+    "samples = []\n",
+    "\n",
+    "print(f\"🔍 Сканирование директории {DATA_ROOT}...\")\n",
+    "# Используем os.walk, он часто работает быстрее rglob на сетевых дисках\n",
+    "for root, dirs, files in os.walk(DATA_ROOT):\n",
+    "    for file in files:\n",
+    "        if file.lower().endswith('.jpg'):\n",
+    "            full_path = os.path.join(root, file)\n",
+    "            # Извлекаем эмоцию (зависит от структуры папок, берем предпоследнюю папку)\n",
+    "            # Путь: .../amusement/0/image.jpg -> root_parts[-2] будет 'amusement'\n",
+    "            path_parts = Path(full_path).parts\n",
+    "            emotion_folder = path_parts[-3].lower()\n",
+    "            \n",
+    "            if emotion_folder in EMO_MAP:\n",
+    "                samples.append((full_path, EMO_MAP[emotion_folder]))\n",
+    "\n",
+    "print(f\"✅ Найдено изображений: {len(samples)}\")\n",
+    "\n",
+    "# САМЫЙ ВАЖНЫЙ ШАГ: Глобальное перемешивание перед упаковкой\n",
+    "print(\"🔀 Перемешиваем датасет...\")\n",
+    "random.shuffle(samples)\n",
+    "print(\"✅ Перемешивание завершено!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fe71d72",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "📦 Подготовлено 205 задач (шардов).\n",
+      "💾 Целевая папка: ../../dataset/EmoSet-2.41M-shards\n",
+      "🚀 Запуск упаковки в 42 потоков...\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Exception in thread Thread-4 (ui_thread_func):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/threading.py\", line 982, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/tmp/ipykernel_16083/731719608.py\", line 72, in ui_thread_func\n",
+      "  File \"/home/zin/projects/Thesis/.venv/lib/python3.11/site-packages/tqdm/notebook.py\", line 223, in __init__\n",
+      "    super().__init__(*args, **kwargs)\n",
+      "  File \"/home/zin/projects/Thesis/.venv/lib/python3.11/site-packages/tqdm/std.py\", line 1001, in __init__\n",
+      "    raise (\n",
+      "tqdm.std.TqdmKeyError: \"Unknown argument(s): {'color': 'blue'}\"\n",
+      "Process ForkProcess-39:\n",
+      "Process ForkProcess-35:\n",
+      "Process ForkProcess-28:\n",
+      "Process ForkProcess-29:\n",
+      "Process ForkProcess-43:\n",
+      "Process ForkProcess-38:\n",
+      "Process ForkProcess-36:\n",
+      "Process ForkProcess-37:\n",
+      "Process ForkProcess-34:\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-27:\n",
+      "Process ForkProcess-25:\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-30:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Process ForkProcess-41:\n",
+      "Process ForkProcess-31:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Process ForkProcess-22:\n",
+      "Process ForkProcess-32:\n",
+      "Process ForkProcess-40:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-42:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Process ForkProcess-20:\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-24:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Process ForkProcess-23:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Process ForkProcess-26:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Process ForkProcess-21:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-16:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-13:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Process ForkProcess-9:\n",
+      "Process ForkProcess-17:\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Process ForkProcess-6:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "Process ForkProcess-8:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Process ForkProcess-3:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Traceback (most recent call last):\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-10:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "Process ForkProcess-7:\n",
+      "Process ForkProcess-11:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-14:\n",
+      "Process ForkProcess-4:\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-5:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/threading.py\", line 982, in run\n",
+      "Process ForkProcess-2:\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-12:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-15:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Traceback (most recent call last):\n",
+      "Process ForkProcess-18:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 102\u001b[39m\n\u001b[32m    101\u001b[39m \u001b[38;5;28;01mwith\u001b[39;00m ProcessPoolExecutor(max_workers=NUM_WORKERS) \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[32m--> \u001b[39m\u001b[32m102\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuild_shard\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m    103\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mpass\u001b[39;49;00m \u001b[38;5;66;03m# Просто ждем завершения всех задач\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py:620\u001b[39m, in \u001b[36m_chain_from_iterable_of_lists\u001b[39m\u001b[34m(iterable)\u001b[39m\n\u001b[32m    615\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    616\u001b[39m \u001b[33;03mSpecialized implementation of itertools.chain.from_iterable.\u001b[39;00m\n\u001b[32m    617\u001b[39m \u001b[33;03mEach item in *iterable* should be a list.  This function is\u001b[39;00m\n\u001b[32m    618\u001b[39m \u001b[33;03mcareful not to keep references to yielded objects.\u001b[39;00m\n\u001b[32m    619\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43melement\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43miterable\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m    621\u001b[39m \u001b[43m    \u001b[49m\u001b[43melement\u001b[49m\u001b[43m.\u001b[49m\u001b[43mreverse\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:619\u001b[39m, in \u001b[36mExecutor.map.<locals>.result_iterator\u001b[39m\u001b[34m()\u001b[39m\n\u001b[32m    618\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m619\u001b[39m     \u001b[38;5;28;01myield\u001b[39;00m \u001b[43m_result_or_cancel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    620\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:317\u001b[39m, in \u001b[36m_result_or_cancel\u001b[39m\u001b[34m(***failed resolving arguments***)\u001b[39m\n\u001b[32m    316\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m317\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfut\u001b[49m\u001b[43m.\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    318\u001b[39m \u001b[38;5;28;01mfinally\u001b[39;00m:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:451\u001b[39m, in \u001b[36mFuture.result\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m    449\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.__get_result()\n\u001b[32m--> \u001b[39m\u001b[32m451\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_condition\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    453\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/threading.py:327\u001b[39m, in \u001b[36mCondition.wait\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m    326\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m327\u001b[39m     \u001b[43mwaiter\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    328\u001b[39m     gotit = \u001b[38;5;28;01mTrue\u001b[39;00m\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: ",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 101\u001b[39m\n\u001b[32m     99\u001b[39m \u001b[38;5;66;03m# 3. Запускаем 42 боевых ядра\u001b[39;00m\n\u001b[32m    100\u001b[39m tasks = [(i, chunk, queue) \u001b[38;5;28;01mfor\u001b[39;00m i, chunk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(chunks)]\n\u001b[32m--> \u001b[39m\u001b[32m101\u001b[39m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mProcessPoolExecutor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmax_workers\u001b[49m\u001b[43m=\u001b[49m\u001b[43mNUM_WORKERS\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m    102\u001b[39m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mexecutor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbuild_shard\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtasks\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m    103\u001b[39m \u001b[43m        \u001b[49m\u001b[38;5;28;43;01mpass\u001b[39;49;00m \u001b[38;5;66;03m# Просто ждем завершения всех задач\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/_base.py:647\u001b[39m, in \u001b[36mExecutor.__exit__\u001b[39m\u001b[34m(self, exc_type, exc_val, exc_tb)\u001b[39m\n\u001b[32m    646\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__exit__\u001b[39m(\u001b[38;5;28mself\u001b[39m, exc_type, exc_val, exc_tb):\n\u001b[32m--> \u001b[39m\u001b[32m647\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mshutdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwait\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m    648\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py:851\u001b[39m, in \u001b[36mProcessPoolExecutor.shutdown\u001b[39m\u001b[34m(self, wait, cancel_futures)\u001b[39m\n\u001b[32m    848\u001b[39m         \u001b[38;5;28mself\u001b[39m._executor_manager_thread_wakeup.wakeup()\n\u001b[32m    850\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._executor_manager_thread \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m wait:\n\u001b[32m--> \u001b[39m\u001b[32m851\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_executor_manager_thread\u001b[49m\u001b[43m.\u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    852\u001b[39m \u001b[38;5;66;03m# To reduce the risk of opening too many files, remove references to\u001b[39;00m\n\u001b[32m    853\u001b[39m \u001b[38;5;66;03m# objects that use file descriptors.\u001b[39;00m\n\u001b[32m    854\u001b[39m \u001b[38;5;28mself\u001b[39m._executor_manager_thread = \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/threading.py:1119\u001b[39m, in \u001b[36mThread.join\u001b[39m\u001b[34m(self, timeout)\u001b[39m\n\u001b[32m   1116\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mcannot join current thread\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m   1118\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1119\u001b[39m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_wait_for_tstate_lock\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1120\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   1121\u001b[39m     \u001b[38;5;66;03m# the behavior of a negative timeout isn't documented, but\u001b[39;00m\n\u001b[32m   1122\u001b[39m     \u001b[38;5;66;03m# historically .join(timeout=x) for x<0 has acted as if timeout=0\u001b[39;00m\n\u001b[32m   1123\u001b[39m     \u001b[38;5;28mself\u001b[39m._wait_for_tstate_lock(timeout=\u001b[38;5;28mmax\u001b[39m(timeout, \u001b[32m0\u001b[39m))\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/.pyenv/versions/3.11.7/lib/python3.11/threading.py:1139\u001b[39m, in \u001b[36mThread._wait_for_tstate_lock\u001b[39m\u001b[34m(self, block, timeout)\u001b[39m\n\u001b[32m   1136\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[32m   1138\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1139\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mlock\u001b[49m\u001b[43m.\u001b[49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43mblock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m   1140\u001b[39m         lock.release()\n\u001b[32m   1141\u001b[39m         \u001b[38;5;28mself\u001b[39m._stop()\n",
+      "\u001b[31mKeyboardInterrupt\u001b[39m: "
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Traceback (most recent call last):\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "Process ForkProcess-19:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "Traceback (most recent call last):\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 314, in _bootstrap\n",
+      "    self.run()\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/process.py\", line 108, in run\n",
+      "    self._target(*self._args, **self._kwargs)\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 103, in get\n",
+      "    res = self._recv_bytes()\n",
+      "          ^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/concurrent/futures/process.py\", line 249, in _process_worker\n",
+      "    call_item = call_queue.get(block=True)\n",
+      "                ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/connection.py\", line 216, in recv_bytes\n",
+      "    buf = self._recv_bytes(maxlength)\n",
+      "          ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/queues.py\", line 102, in get\n",
+      "    with self._rlock:\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/connection.py\", line 430, in _recv_bytes\n",
+      "    buf = self._recv(4)\n",
+      "          ^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/synchronize.py\", line 95, in __enter__\n",
+      "    return self._semlock.__enter__()\n",
+      "           ^^^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n",
+      "  File \"/home/zin/.pyenv/versions/3.11.7/lib/python3.11/multiprocessing/connection.py\", line 395, in _recv\n",
+      "    chunk = read(handle, remaining)\n",
+      "            ^^^^^^^^^^^^^^^^^^^^^^^\n",
+      "KeyboardInterrupt\n",
+      "KeyboardInterrupt\n"
+     ]
+    }
+   ],
+   "source": [
+    "import multiprocessing as mp\n",
+    "from concurrent.futures import ProcessPoolExecutor\n",
+    "import webdataset as wds\n",
+    "from PIL import Image\n",
+    "import io\n",
+    "import threading\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Включаем красивую версию tqdm специально для Jupyter\n",
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "# --- ПУТИ И НАСТРОЙКИ ---\n",
+    "SHARDS_DIR = Path(\"../../dataset/EmoSet-2.41M-shards\")\n",
+    "SHARDS_DIR.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "NUM_WORKERS = 42\n",
+    "MAX_SAMPLES_PER_SHARD = 10000\n",
+    "\n",
+    "# Дробим список на чанки\n",
+    "chunks = [samples[i:i + MAX_SAMPLES_PER_SHARD] for i in range(0, len(samples), MAX_SAMPLES_PER_SHARD)]\n",
+    "TOTAL_FILES = len(samples)\n",
+    "TOTAL_SHARDS = len(chunks)\n",
+    "\n",
+    "print(f\"📦 Подготовлено {TOTAL_SHARDS} задач (шардов).\")\n",
+    "print(f\"💾 Целевая папка: {SHARDS_DIR}\")\n",
+    "print(f\"🚀 Запуск упаковки в {NUM_WORKERS} потоков...\\n\")\n",
+    "\n",
+    "# --- ФУНКЦИЯ ДЛЯ ЯДЕР ПРОЦЕССОРА ---\n",
+    "def build_shard(args):\n",
+    "    shard_idx, chunk, queue = args\n",
+    "    shard_path = SHARDS_DIR / f\"emoset-{shard_idx:06d}.tar\"\n",
+    "    \n",
+    "    with wds.TarWriter(str(shard_path)) as sink:\n",
+    "        for i, (img_path, label) in enumerate(chunk):\n",
+    "            try:\n",
+    "                # Магия сжатия\n",
+    "                with Image.open(img_path) as img:\n",
+    "                    img = img.convert(\"RGB\")\n",
+    "                    img = img.resize((256, 256), Image.Resampling.BILINEAR)\n",
+    "                    with io.BytesIO() as img_byte_arr:\n",
+    "                        img.save(img_byte_arr, format='JPEG', quality=85)\n",
+    "                        image_data = img_byte_arr.getvalue()\n",
+    "                \n",
+    "                key = f\"{shard_idx:06d}_{i:05d}\"\n",
+    "                sink.write({\n",
+    "                    \"__key__\": key,\n",
+    "                    \"jpg\": image_data,\n",
+    "                    \"cls\": label\n",
+    "                })\n",
+    "                \n",
+    "                # Чтобы не перегружать очередь, отправляем отчет каждые 50 файлов\n",
+    "                if (i + 1) % 50 == 0:\n",
+    "                    queue.put((\"file\", 50))\n",
+    "                    \n",
+    "            except Exception:\n",
+    "                # Если файл битый, всё равно считаем его \"пройденным\", чтобы бар не застрял\n",
+    "                queue.put((\"file\", 1))\n",
+    "                continue\n",
+    "                \n",
+    "        # Сообщаем об остатке файлов в чанке, которые не попали в % 50\n",
+    "        remainder = len(chunk) % 50\n",
+    "        if remainder != 0:\n",
+    "            queue.put((\"file\", remainder))\n",
+    "            \n",
+    "    # Сообщаем, что целый шард готов\n",
+    "    queue.put((\"shard\", 1))\n",
+    "    return shard_idx\n",
+    "\n",
+    "# --- ФУНКЦИЯ ОТРИСОВКИ ИНТЕРФЕЙСА (Фоновый поток) ---\n",
+    "def ui_thread_func(q, total_files, total_shards):\n",
+    "    # Создаем две красивые независимые полоски\n",
+    "    pbar_files = tqdm(total=total_files, desc=\"🖼️ Сжато файлов\", color=\"blue\")\n",
+    "    pbar_shards = tqdm(total=total_shards, desc=\"📦 Готово архивов\", color=\"green\")\n",
+    "    \n",
+    "    while True:\n",
+    "        msg = q.get()\n",
+    "        if msg == \"DONE\":\n",
+    "            break\n",
+    "            \n",
+    "        msg_type, count = msg\n",
+    "        if msg_type == \"file\":\n",
+    "            pbar_files.update(count)\n",
+    "        elif msg_type == \"shard\":\n",
+    "            pbar_shards.update(count)\n",
+    "            \n",
+    "    pbar_files.close()\n",
+    "    pbar_shards.close()\n",
+    "\n",
+    "# === ГЛАВНЫЙ ЗАПУСК ===\n",
+    "if __name__ == '__main__':\n",
+    "    # 1. Создаем диспетчер очередей\n",
+    "    manager = mp.Manager()\n",
+    "    queue = manager.Queue()\n",
+    "    \n",
+    "    # 2. Запускаем фоновый поток отрисовки\n",
+    "    ui_thread = threading.Thread(target=ui_thread_func, args=(queue, TOTAL_FILES, TOTAL_SHARDS))\n",
+    "    ui_thread.start()\n",
+    "    \n",
+    "    # 3. Запускаем 42 боевых ядра\n",
+    "    tasks = [(i, chunk, queue) for i, chunk in enumerate(chunks)]\n",
+    "    with ProcessPoolExecutor(max_workers=NUM_WORKERS) as executor:\n",
+    "        for _ in executor.map(build_shard, tasks):\n",
+    "            pass # Просто ждем завершения всех задач\n",
+    "            \n",
+    "    # 4. Убиваем поток отрисовки и завершаем работу\n",
+    "    queue.put(\"DONE\")\n",
+    "    ui_thread.join()\n",
+    "    \n",
+    "    print(\"\\n🎉 ПАРАЛЛЕЛЬНАЯ УПАКОВКА И СЖАТИЕ ПОЛНОСТЬЮ ЗАВЕРШЕНЫ!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python (my-python-project)",
+   "language": "python",
+   "name": "my-python-project"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}