Spaces:
Sleeping
Sleeping
File size: 11,510 Bytes
6cc8ae1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# \ud83d\udc04 Cattle Breed Classifier \u2014 Colab Runner\n",
"\n",
"This notebook sets up the environment on **Google Colab** and runs all training notebooks sequentially.\n",
"\n",
"### What this does\n",
"1. Verifies GPU availability\n",
"2. Clones the repo (dataset included)\n",
"3. Installs missing dependencies\n",
"4. Runs each notebook in order:\n",
" - `00_data_audit` \u2192 `01_mlp_baseline` \u2192 `02_cnn_from_scratch` \u2192 `03_resnet_transfer_learning` \u2192 `04_vit_transfer_learning` \u2192 `05_model_comparison`\n",
"\n",
"> \u26a0\ufe0f **Runtime**: Select **GPU** runtime before running: *Runtime \u2192 Change runtime type \u2192 T4 GPU*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 0. Check GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"print(f'PyTorch version: {torch.__version__}')\n",
"print(f'CUDA available: {torch.cuda.is_available()}')\n",
"if torch.cuda.is_available():\n",
" print(f'GPU: {torch.cuda.get_device_name(0)}')\n",
" print(f'Memory: {torch.cuda.get_device_properties(0).total_mem / 1024**3:.1f} GB')\n",
"else:\n",
" print('\u26a0\ufe0f No GPU detected. Go to Runtime \u2192 Change runtime type \u2192 T4 GPU')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Clone Repository"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"REPO_URL = 'https://github.com/ajitsingh98/cattle-breed-classifier-webapp.git'\n",
"REPO_DIR = '/content/cattle-breed-classifier-webapp'\n",
"\n",
"if not os.path.exists(REPO_DIR):\n",
" !git clone {REPO_URL} {REPO_DIR}\n",
"else:\n",
" print(f'Repo already cloned at {REPO_DIR}')\n",
" !cd {REPO_DIR} && git pull\n",
"\n",
"os.chdir(REPO_DIR)\n",
"print(f'\\nWorking directory: {os.getcwd()}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Install Dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Colab already has PyTorch, torchvision, numpy, pandas, matplotlib, seaborn, PIL, scikit-learn.\n",
"# Install only the missing packages.\n",
"!pip install -q PyYAML gdown aiofiles aiohttp nest_asyncio"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Set Up Python Path"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"\n",
"PROJECT_ROOT = Path(REPO_DIR).resolve()\n",
"if str(PROJECT_ROOT) not in sys.path:\n",
" sys.path.insert(0, str(PROJECT_ROOT))\n",
"\n",
"# Create artifact directories\n",
"for d in ['ml/artifacts/manifests', 'ml/artifacts/checkpoints',\n",
" 'ml/artifacts/figures', 'ml/artifacts/logs', 'ml/artifacts/reports']:\n",
" (PROJECT_ROOT / d).mkdir(parents=True, exist_ok=True)\n",
"\n",
"# Verify dataset exists\n",
"data_dir = PROJECT_ROOT / 'Cattle_Resized'\n",
"class_dirs = sorted([d for d in data_dir.iterdir() if d.is_dir()])\n",
"total_images = sum(len(list(d.glob('*'))) for d in class_dirs)\n",
"print(f'Dataset: {len(class_dirs)} breeds, {total_images} images')\n",
"print(f'Project root: {PROJECT_ROOT}')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Run All Notebooks\n",
"\n",
"Each notebook is executed in order using `nbconvert`. Output is displayed inline.\n",
"\n",
"You can also **skip this cell** and open each notebook individually from the file browser on the left:\n",
"> `cattle-breed-classifier-webapp/ml/notebooks/`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install -q papermill jupyter ipykernel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import subprocess, time\n",
"\n",
"NOTEBOOKS = [\n",
" '00_data_audit.ipynb',\n",
" '01_mlp_baseline.ipynb',\n",
" '02_cnn_from_scratch.ipynb',\n",
" '03_resnet_transfer_learning.ipynb',\n",
" '04_vit_transfer_learning.ipynb',\n",
" '05_model_comparison.ipynb',\n",
"]\n",
"\n",
"NOTEBOOK_DIR = PROJECT_ROOT / 'ml' / 'notebooks'\n",
"results = {}\n",
"\n",
"for nb_name in NOTEBOOKS:\n",
" nb_path = NOTEBOOK_DIR / nb_name\n",
" print(f'\\n{\"=\" * 60}')\n",
" print(f'\u25b6 Running: {nb_name}')\n",
" print(f'{\"=\" * 60}')\n",
"\n",
" start = time.time()\n",
" # We use papermill because it supports streaming cell outputs (--log-output)\n",
" result = subprocess.run(\n",
" [\n",
" \"papermill\",\n",
" str(nb_path),\n",
" str(nb_path), # overwrite inplace so output is saved in the notebook\n",
" \"--log-output\",\n",
" \"--kernel\", \"python3\"\n",
" ],\n",
" cwd=str(PROJECT_ROOT),\n",
" env={**os.environ, \"PYTHONPATH\": str(PROJECT_ROOT)},\n",
" )\n",
" elapsed = time.time() - start\n",
"\n",
" if result.returncode == 0:\n",
" status = '\u2705 PASSED'\n",
" else:\n",
" status = '\u274c FAILED'\n",
"\n",
" results[nb_name] = {'status': status, 'time': elapsed}\n",
" print(f'{status} ({elapsed:.1f}s)')\n",
"\n",
"# Summary\n",
"print(f'\\n{\"=\" * 60}')\n",
"print('Summary')\n",
"print(f'{\"=\" * 60}')\n",
"for nb, info in results.items():\n",
" print(f\" {info['status']} {nb:45s} {info['time']:6.1f}s\")\n",
"total_time = sum(r['time'] for r in results.values())\n",
"print(f'\\nTotal time: {total_time/60:.1f} minutes')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Download Artifacts (Optional)\n",
"\n",
"After training completes, download the best model checkpoint and reports."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# List generated artifacts\n",
"import glob\n",
"\n",
"print('=== Checkpoints ===')\n",
"for f in sorted(glob.glob(str(PROJECT_ROOT / 'ml/artifacts/checkpoints/*.pth'))):\n",
" size_mb = os.path.getsize(f) / 1024 / 1024\n",
" print(f' {Path(f).name:40s} {size_mb:8.1f} MB')\n",
"\n",
"print('\\n=== Figures ===')\n",
"for f in sorted(glob.glob(str(PROJECT_ROOT / 'ml/artifacts/figures/*.png'))):\n",
" print(f' {Path(f).name}')\n",
"\n",
"print('\\n=== Reports ===')\n",
"for f in sorted(glob.glob(str(PROJECT_ROOT / 'ml/artifacts/reports/*.json'))):\n",
" print(f' {Path(f).name}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Zip and download artifacts\n",
"!cd {REPO_DIR} && zip -r /content/artifacts.zip ml/artifacts/\n",
"\n",
"from google.colab import files\n",
"files.download('/content/artifacts.zip')\n",
"print('\\n\u2705 Download started! Check your browser downloads.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"### \ud83d\udca1 Running Notebooks Individually\n",
"\n",
"Instead of the automated runner above, you can open each notebook directly:\n",
"\n",
"1. In the **Colab file browser** (left panel), navigate to: \n",
" `cattle-breed-classifier-webapp/ml/notebooks/`\n",
"2. Double-click any `.ipynb` file to open it in a new tab\n",
"3. Run cells with `Shift+Enter`\n",
"\n",
"**Important**: Each notebook auto-detects the project root, so they work both from the automated runner and when opened individually."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.10.0"
},
"colab": {
"provenance": [],
"gpuType": "T4"
},
"accelerator": "GPU"
},
"nbformat": 4,
"nbformat_minor": 4
} |