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Added notebooks template

Browse files
Files changed (5) hide show
  1. .gitignore +0 -1
  2. notebooks/template-audio.ipynb +1351 -0
  3. notebooks/template-image.ipynb +416 -0
  4. notebooks/template-text.ipynb +1642 -0
  5. tasks/image.py +6 -2
.gitignore CHANGED
@@ -5,7 +5,6 @@ venv/
5
  __pycache__/
6
  .env
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  .ipynb_checkpoints
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- *ipynb
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  .vscode/
10
 
11
  eval-queue/
 
5
  __pycache__/
6
  .env
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  .ipynb_checkpoints
 
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  .vscode/
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  eval-queue/
notebooks/template-audio.ipynb ADDED
@@ -0,0 +1,1351 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# Text task notebook template\n",
8
+ "## Loading the necessary libraries"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "code",
13
+ "execution_count": 3,
14
+ "metadata": {},
15
+ "outputs": [
16
+ {
17
+ "name": "stderr",
18
+ "output_type": "stream",
19
+ "text": [
20
+ "[codecarbon WARNING @ 19:48:07] Multiple instances of codecarbon are allowed to run at the same time.\n",
21
+ "[codecarbon INFO @ 19:48:07] [setup] RAM Tracking...\n",
22
+ "[codecarbon INFO @ 19:48:07] [setup] CPU Tracking...\n",
23
+ "[codecarbon WARNING @ 19:48:09] We saw that you have a 13th Gen Intel(R) Core(TM) i7-1365U but we don't know it. Please contact us.\n",
24
+ "[codecarbon WARNING @ 19:48:09] No CPU tracking mode found. Falling back on CPU constant mode. \n",
25
+ " Windows OS detected: Please install Intel Power Gadget to measure CPU\n",
26
+ "\n",
27
+ "[codecarbon WARNING @ 19:48:11] We saw that you have a 13th Gen Intel(R) Core(TM) i7-1365U but we don't know it. Please contact us.\n",
28
+ "[codecarbon INFO @ 19:48:11] CPU Model on constant consumption mode: 13th Gen Intel(R) Core(TM) i7-1365U\n",
29
+ "[codecarbon WARNING @ 19:48:11] No CPU tracking mode found. Falling back on CPU constant mode.\n",
30
+ "[codecarbon INFO @ 19:48:11] [setup] GPU Tracking...\n",
31
+ "[codecarbon INFO @ 19:48:11] No GPU found.\n",
32
+ "[codecarbon INFO @ 19:48:11] >>> Tracker's metadata:\n",
33
+ "[codecarbon INFO @ 19:48:11] Platform system: Windows-11-10.0.22631-SP0\n",
34
+ "[codecarbon INFO @ 19:48:11] Python version: 3.12.7\n",
35
+ "[codecarbon INFO @ 19:48:11] CodeCarbon version: 3.0.0_rc0\n",
36
+ "[codecarbon INFO @ 19:48:11] Available RAM : 31.347 GB\n",
37
+ "[codecarbon INFO @ 19:48:11] CPU count: 12\n",
38
+ "[codecarbon INFO @ 19:48:11] CPU model: 13th Gen Intel(R) Core(TM) i7-1365U\n",
39
+ "[codecarbon INFO @ 19:48:11] GPU count: None\n",
40
+ "[codecarbon INFO @ 19:48:11] GPU model: None\n",
41
+ "[codecarbon INFO @ 19:48:11] Saving emissions data to file c:\\git\\submission-template\\notebooks\\emissions.csv\n"
42
+ ]
43
+ }
44
+ ],
45
+ "source": [
46
+ "from fastapi import APIRouter\n",
47
+ "from datetime import datetime\n",
48
+ "from datasets import load_dataset\n",
49
+ "from sklearn.metrics import accuracy_score\n",
50
+ "import random\n",
51
+ "\n",
52
+ "import sys\n",
53
+ "sys.path.append('../tasks')\n",
54
+ "\n",
55
+ "from utils.evaluation import AudioEvaluationRequest\n",
56
+ "from utils.emissions import tracker, clean_emissions_data, get_space_info\n",
57
+ "\n",
58
+ "\n",
59
+ "# Define the label mapping\n",
60
+ "LABEL_MAPPING = {\n",
61
+ " \"chainsaw\": 0,\n",
62
+ " \"environment\": 1\n",
63
+ "}"
64
+ ]
65
+ },
66
+ {
67
+ "cell_type": "markdown",
68
+ "metadata": {},
69
+ "source": [
70
+ "## Loading the datasets and splitting them"
71
+ ]
72
+ },
73
+ {
74
+ "cell_type": "code",
75
+ "execution_count": 4,
76
+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "668da7bf85434e098b95c3ec447d78fe",
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+ "version_major": 2,
83
+ "version_minor": 0
84
+ },
85
+ "text/plain": [
86
+ "README.md: 0%| | 0.00/5.18k [00:00<?, ?B/s]"
87
+ ]
88
+ },
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+ "metadata": {},
90
+ "output_type": "display_data"
91
+ },
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+ {
93
+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
96
+ "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\datasets--QuotaClimat--frugalaichallenge-text-train. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
97
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
98
+ " warnings.warn(message)\n"
99
+ ]
100
+ },
101
+ {
102
+ "data": {
103
+ "application/vnd.jupyter.widget-view+json": {
104
+ "model_id": "5b68d43359eb429395da8be7d4b15556",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "train.parquet: 0%| | 0.00/1.21M [00:00<?, ?B/s]"
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+ ]
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+ },
112
+ "metadata": {},
113
+ "output_type": "display_data"
114
+ },
115
+ {
116
+ "data": {
117
+ "application/vnd.jupyter.widget-view+json": {
118
+ "model_id": "140a304773914e9db8f698eabeb40298",
119
+ "version_major": 2,
120
+ "version_minor": 0
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+ },
122
+ "text/plain": [
123
+ "Generating train split: 0%| | 0/6091 [00:00<?, ? examples/s]"
124
+ ]
125
+ },
126
+ "metadata": {},
127
+ "output_type": "display_data"
128
+ },
129
+ {
130
+ "data": {
131
+ "application/vnd.jupyter.widget-view+json": {
132
+ "model_id": "6d04e8ab1906400e8e0029949dc523a5",
133
+ "version_major": 2,
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+ "version_minor": 0
135
+ },
136
+ "text/plain": [
137
+ "Map: 0%| | 0/6091 [00:00<?, ? examples/s]"
138
+ ]
139
+ },
140
+ "metadata": {},
141
+ "output_type": "display_data"
142
+ }
143
+ ],
144
+ "source": [
145
+ "request = AudioEvaluationRequest()\n",
146
+ "\n",
147
+ "# Load and prepare the dataset\n",
148
+ "dataset = load_dataset(request.dataset_name)\n",
149
+ "\n",
150
+ "# Split dataset\n",
151
+ "train_test = dataset[\"train\"].train_test_split(test_size=request.test_size, seed=request.test_seed)\n",
152
+ "test_dataset = train_test[\"test\"]"
153
+ ]
154
+ },
155
+ {
156
+ "cell_type": "markdown",
157
+ "metadata": {},
158
+ "source": [
159
+ "## Random Baseline"
160
+ ]
161
+ },
162
+ {
163
+ "cell_type": "code",
164
+ "execution_count": 5,
165
+ "metadata": {},
166
+ "outputs": [],
167
+ "source": [
168
+ "# Start tracking emissions\n",
169
+ "tracker.start()\n",
170
+ "tracker.start_task(\"inference\")"
171
+ ]
172
+ },
173
+ {
174
+ "cell_type": "code",
175
+ "execution_count": 6,
176
+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/plain": [
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1014
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1020
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1021
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1022
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1030
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1034
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1038
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1048
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1049
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1050
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1051
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1056
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1058
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1059
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1060
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1061
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1062
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1063
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1064
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1065
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1069
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1070
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1071
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1072
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1073
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1074
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1075
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1076
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1077
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1078
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1079
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1080
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1081
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1082
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1083
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1084
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1085
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1087
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1088
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1089
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1090
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1091
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1092
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1093
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1094
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1095
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1096
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1098
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1099
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1100
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1101
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1102
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1103
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1104
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1105
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1106
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1107
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1108
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1109
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1110
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1111
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1112
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1113
+ " 6,\n",
1114
+ " 0,\n",
1115
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1116
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1117
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1118
+ " 1,\n",
1119
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1120
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1121
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1122
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1123
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1124
+ " 2,\n",
1125
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1126
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1127
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1128
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1129
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1130
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1131
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1132
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1133
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1134
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1135
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1136
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1137
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1138
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1139
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1140
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1141
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1142
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1143
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1144
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1145
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1146
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1147
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1148
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1149
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1150
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1151
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1152
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1153
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1154
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1155
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1156
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1157
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1158
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1159
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1160
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1161
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1162
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1163
+ " 5,\n",
1164
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1165
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1166
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1167
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1168
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1169
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1170
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1171
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1172
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1173
+ " 5,\n",
1174
+ " 4,\n",
1175
+ " 2,\n",
1176
+ " 4,\n",
1177
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1178
+ " 5,\n",
1179
+ " 2,\n",
1180
+ " 7,\n",
1181
+ " ...]"
1182
+ ]
1183
+ },
1184
+ "execution_count": 6,
1185
+ "metadata": {},
1186
+ "output_type": "execute_result"
1187
+ }
1188
+ ],
1189
+ "source": [
1190
+ "\n",
1191
+ "#--------------------------------------------------------------------------------------------\n",
1192
+ "# YOUR MODEL INFERENCE CODE HERE\n",
1193
+ "# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
1194
+ "#-------------------------------------------------------------------------------------------- \n",
1195
+ "\n",
1196
+ "# Make random predictions (placeholder for actual model inference)\n",
1197
+ "true_labels = test_dataset[\"label\"]\n",
1198
+ "predictions = [random.randint(0, 1) for _ in range(len(true_labels))]\n",
1199
+ "\n",
1200
+ "predictions\n",
1201
+ "\n",
1202
+ "#--------------------------------------------------------------------------------------------\n",
1203
+ "# YOUR MODEL INFERENCE STOPS HERE\n",
1204
+ "#-------------------------------------------------------------------------------------------- "
1205
+ ]
1206
+ },
1207
+ {
1208
+ "cell_type": "code",
1209
+ "execution_count": 8,
1210
+ "metadata": {},
1211
+ "outputs": [
1212
+ {
1213
+ "name": "stderr",
1214
+ "output_type": "stream",
1215
+ "text": [
1216
+ "[codecarbon WARNING @ 19:53:32] Background scheduler didn't run for a long period (47s), results might be inaccurate\n",
1217
+ "[codecarbon INFO @ 19:53:32] Energy consumed for RAM : 0.000156 kWh. RAM Power : 11.755242347717285 W\n",
1218
+ "[codecarbon INFO @ 19:53:32] Delta energy consumed for CPU with constant : 0.000564 kWh, power : 42.5 W\n",
1219
+ "[codecarbon INFO @ 19:53:32] Energy consumed for All CPU : 0.000564 kWh\n",
1220
+ "[codecarbon INFO @ 19:53:32] 0.000720 kWh of electricity used since the beginning.\n"
1221
+ ]
1222
+ },
1223
+ {
1224
+ "data": {
1225
+ "text/plain": [
1226
+ "EmissionsData(timestamp='2025-01-21T19:53:32', project_name='codecarbon', run_id='908f2e7e-4bb2-4991-a0f6-56bf8d7eda21', experiment_id='5b0fa12a-3dd7-45bb-9766-cc326314d9f1', duration=47.736408500000834, emissions=4.032368007471064e-05, emissions_rate=8.444466886328872e-07, cpu_power=42.5, gpu_power=0.0, ram_power=11.755242347717285, cpu_energy=0.0005636615353475565, gpu_energy=0, ram_energy=0.00015590305493261682, energy_consumed=0.0007195645902801733, country_name='France', country_iso_code='FRA', region='île-de-france', cloud_provider='', cloud_region='', os='Windows-11-10.0.22631-SP0', python_version='3.12.7', codecarbon_version='3.0.0_rc0', cpu_count=12, cpu_model='13th Gen Intel(R) Core(TM) i7-1365U', gpu_count=None, gpu_model=None, longitude=2.3494, latitude=48.8558, ram_total_size=31.347312927246094, tracking_mode='machine', on_cloud='N', pue=1.0)"
1227
+ ]
1228
+ },
1229
+ "execution_count": 8,
1230
+ "metadata": {},
1231
+ "output_type": "execute_result"
1232
+ }
1233
+ ],
1234
+ "source": [
1235
+ "# Stop tracking emissions\n",
1236
+ "emissions_data = tracker.stop_task()\n",
1237
+ "emissions_data"
1238
+ ]
1239
+ },
1240
+ {
1241
+ "cell_type": "code",
1242
+ "execution_count": 9,
1243
+ "metadata": {},
1244
+ "outputs": [
1245
+ {
1246
+ "data": {
1247
+ "text/plain": [
1248
+ "0.10090237899917966"
1249
+ ]
1250
+ },
1251
+ "execution_count": 9,
1252
+ "metadata": {},
1253
+ "output_type": "execute_result"
1254
+ }
1255
+ ],
1256
+ "source": [
1257
+ "# Calculate accuracy\n",
1258
+ "accuracy = accuracy_score(true_labels, predictions)\n",
1259
+ "accuracy"
1260
+ ]
1261
+ },
1262
+ {
1263
+ "cell_type": "code",
1264
+ "execution_count": 10,
1265
+ "metadata": {},
1266
+ "outputs": [
1267
+ {
1268
+ "data": {
1269
+ "text/plain": [
1270
+ "{'submission_timestamp': '2025-01-21T19:53:46.639165',\n",
1271
+ " 'accuracy': 0.10090237899917966,\n",
1272
+ " 'energy_consumed_wh': 0.7195645902801733,\n",
1273
+ " 'emissions_gco2eq': 0.040323680074710634,\n",
1274
+ " 'emissions_data': {'run_id': '908f2e7e-4bb2-4991-a0f6-56bf8d7eda21',\n",
1275
+ " 'duration': 47.736408500000834,\n",
1276
+ " 'emissions': 4.032368007471064e-05,\n",
1277
+ " 'emissions_rate': 8.444466886328872e-07,\n",
1278
+ " 'cpu_power': 42.5,\n",
1279
+ " 'gpu_power': 0.0,\n",
1280
+ " 'ram_power': 11.755242347717285,\n",
1281
+ " 'cpu_energy': 0.0005636615353475565,\n",
1282
+ " 'gpu_energy': 0,\n",
1283
+ " 'ram_energy': 0.00015590305493261682,\n",
1284
+ " 'energy_consumed': 0.0007195645902801733,\n",
1285
+ " 'country_name': 'France',\n",
1286
+ " 'country_iso_code': 'FRA',\n",
1287
+ " 'region': 'île-de-france',\n",
1288
+ " 'cloud_provider': '',\n",
1289
+ " 'cloud_region': '',\n",
1290
+ " 'os': 'Windows-11-10.0.22631-SP0',\n",
1291
+ " 'python_version': '3.12.7',\n",
1292
+ " 'codecarbon_version': '3.0.0_rc0',\n",
1293
+ " 'cpu_count': 12,\n",
1294
+ " 'cpu_model': '13th Gen Intel(R) Core(TM) i7-1365U',\n",
1295
+ " 'gpu_count': None,\n",
1296
+ " 'gpu_model': None,\n",
1297
+ " 'ram_total_size': 31.347312927246094,\n",
1298
+ " 'tracking_mode': 'machine',\n",
1299
+ " 'on_cloud': 'N',\n",
1300
+ " 'pue': 1.0},\n",
1301
+ " 'dataset_config': {'dataset_name': 'QuotaClimat/frugalaichallenge-text-train',\n",
1302
+ " 'test_size': 0.2,\n",
1303
+ " 'test_seed': 42}}"
1304
+ ]
1305
+ },
1306
+ "execution_count": 10,
1307
+ "metadata": {},
1308
+ "output_type": "execute_result"
1309
+ }
1310
+ ],
1311
+ "source": [
1312
+ "# Prepare results dictionary\n",
1313
+ "results = {\n",
1314
+ " \"submission_timestamp\": datetime.now().isoformat(),\n",
1315
+ " \"accuracy\": float(accuracy),\n",
1316
+ " \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
1317
+ " \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
1318
+ " \"emissions_data\": clean_emissions_data(emissions_data),\n",
1319
+ " \"dataset_config\": {\n",
1320
+ " \"dataset_name\": request.dataset_name,\n",
1321
+ " \"test_size\": request.test_size,\n",
1322
+ " \"test_seed\": request.test_seed\n",
1323
+ " }\n",
1324
+ "}\n",
1325
+ "\n",
1326
+ "results"
1327
+ ]
1328
+ }
1329
+ ],
1330
+ "metadata": {
1331
+ "kernelspec": {
1332
+ "display_name": "base",
1333
+ "language": "python",
1334
+ "name": "python3"
1335
+ },
1336
+ "language_info": {
1337
+ "codemirror_mode": {
1338
+ "name": "ipython",
1339
+ "version": 3
1340
+ },
1341
+ "file_extension": ".py",
1342
+ "mimetype": "text/x-python",
1343
+ "name": "python",
1344
+ "nbconvert_exporter": "python",
1345
+ "pygments_lexer": "ipython3",
1346
+ "version": "3.12.7"
1347
+ }
1348
+ },
1349
+ "nbformat": 4,
1350
+ "nbformat_minor": 2
1351
+ }
notebooks/template-image.ipynb ADDED
@@ -0,0 +1,416 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# Image task notebook template\n",
8
+ "## Loading the necessary libraries"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "code",
13
+ "execution_count": 13,
14
+ "metadata": {},
15
+ "outputs": [],
16
+ "source": [
17
+ "from fastapi import APIRouter\n",
18
+ "from datetime import datetime\n",
19
+ "from datasets import load_dataset\n",
20
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
21
+ "\n",
22
+ "import random\n",
23
+ "\n",
24
+ "import sys\n",
25
+ "sys.path.append('../')\n",
26
+ "\n",
27
+ "from tasks.utils.evaluation import ImageEvaluationRequest\n",
28
+ "from tasks.utils.emissions import tracker, clean_emissions_data, get_space_info\n",
29
+ "from tasks.image import parse_boxes,compute_iou,compute_max_iou"
30
+ ]
31
+ },
32
+ {
33
+ "cell_type": "markdown",
34
+ "metadata": {},
35
+ "source": [
36
+ "## Loading the datasets and splitting them"
37
+ ]
38
+ },
39
+ {
40
+ "cell_type": "code",
41
+ "execution_count": 4,
42
+ "metadata": {},
43
+ "outputs": [
44
+ {
45
+ "data": {
46
+ "application/vnd.jupyter.widget-view+json": {
47
+ "model_id": "4f62b23ca587477d9f37430e687bf951",
48
+ "version_major": 2,
49
+ "version_minor": 0
50
+ },
51
+ "text/plain": [
52
+ "README.md: 0%| | 0.00/7.72k [00:00<?, ?B/s]"
53
+ ]
54
+ },
55
+ "metadata": {},
56
+ "output_type": "display_data"
57
+ },
58
+ {
59
+ "name": "stderr",
60
+ "output_type": "stream",
61
+ "text": [
62
+ "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\datasets--pyronear--pyro-sdis. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
63
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
64
+ " warnings.warn(message)\n"
65
+ ]
66
+ },
67
+ {
68
+ "data": {
69
+ "application/vnd.jupyter.widget-view+json": {
70
+ "model_id": "70735dd748e343119b5a7cd966dcd0f0",
71
+ "version_major": 2,
72
+ "version_minor": 0
73
+ },
74
+ "text/plain": [
75
+ "train-00000-of-00007.parquet: 0%| | 0.00/433M [00:00<?, ?B/s]"
76
+ ]
77
+ },
78
+ "metadata": {},
79
+ "output_type": "display_data"
80
+ },
81
+ {
82
+ "data": {
83
+ "application/vnd.jupyter.widget-view+json": {
84
+ "model_id": "903c3227c24649f1a0424e039d74d303",
85
+ "version_major": 2,
86
+ "version_minor": 0
87
+ },
88
+ "text/plain": [
89
+ "train-00001-of-00007.parquet: 0%| | 0.00/434M [00:00<?, ?B/s]"
90
+ ]
91
+ },
92
+ "metadata": {},
93
+ "output_type": "display_data"
94
+ },
95
+ {
96
+ "data": {
97
+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "8795b7696f124715b9d52287d5cd4ee0",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
102
+ "text/plain": [
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+ "train-00002-of-00007.parquet: 0%| | 0.00/432M [00:00<?, ?B/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
110
+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "4b6c1240bf024d61bf913584d13834f5",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "train-00003-of-00007.parquet: 0%| | 0.00/428M [00:00<?, ?B/s]"
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+ ]
119
+ },
120
+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
124
+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "cd5f8172a31f4fd79d489db96ede9c21",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "train-00004-of-00007.parquet: 0%| | 0.00/431M [00:00<?, ?B/s]"
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+ ]
133
+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
137
+ {
138
+ "data": {
139
+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "416af82dba3a4ab7ad13190703c90757",
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+ "version_major": 2,
142
+ "version_minor": 0
143
+ },
144
+ "text/plain": [
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+ "train-00005-of-00007.parquet: 0%| | 0.00/429M [00:00<?, ?B/s]"
146
+ ]
147
+ },
148
+ "metadata": {},
149
+ "output_type": "display_data"
150
+ },
151
+ {
152
+ "data": {
153
+ "application/vnd.jupyter.widget-view+json": {
154
+ "model_id": "6819ad85508641a1a64bea34303446ac",
155
+ "version_major": 2,
156
+ "version_minor": 0
157
+ },
158
+ "text/plain": [
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+ "train-00006-of-00007.parquet: 0%| | 0.00/431M [00:00<?, ?B/s]"
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+ ]
161
+ },
162
+ "metadata": {},
163
+ "output_type": "display_data"
164
+ },
165
+ {
166
+ "data": {
167
+ "application/vnd.jupyter.widget-view+json": {
168
+ "model_id": "90a7f85c802b4330b502c8bbd3cca7f9",
169
+ "version_major": 2,
170
+ "version_minor": 0
171
+ },
172
+ "text/plain": [
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+ "val-00000-of-00001.parquet: 0%| | 0.00/407M [00:00<?, ?B/s]"
174
+ ]
175
+ },
176
+ "metadata": {},
177
+ "output_type": "display_data"
178
+ },
179
+ {
180
+ "data": {
181
+ "application/vnd.jupyter.widget-view+json": {
182
+ "model_id": "b93f2f19aafb43e2b8db0fd7bb3ebd34",
183
+ "version_major": 2,
184
+ "version_minor": 0
185
+ },
186
+ "text/plain": [
187
+ "Generating train split: 0%| | 0/29537 [00:00<?, ? examples/s]"
188
+ ]
189
+ },
190
+ "metadata": {},
191
+ "output_type": "display_data"
192
+ },
193
+ {
194
+ "data": {
195
+ "application/vnd.jupyter.widget-view+json": {
196
+ "model_id": "c14c0f2cde184c959970dfccaa26b2d2",
197
+ "version_major": 2,
198
+ "version_minor": 0
199
+ },
200
+ "text/plain": [
201
+ "Generating val split: 0%| | 0/4099 [00:00<?, ? examples/s]"
202
+ ]
203
+ },
204
+ "metadata": {},
205
+ "output_type": "display_data"
206
+ }
207
+ ],
208
+ "source": [
209
+ "request = ImageEvaluationRequest()\n",
210
+ "\n",
211
+ "# Load and prepare the dataset\n",
212
+ "dataset = load_dataset(request.dataset_name)\n",
213
+ "\n",
214
+ "# Split dataset\n",
215
+ "train_test = dataset[\"train\"].train_test_split(test_size=request.test_size, seed=request.test_seed)\n",
216
+ "test_dataset = train_test[\"test\"]"
217
+ ]
218
+ },
219
+ {
220
+ "cell_type": "markdown",
221
+ "metadata": {},
222
+ "source": [
223
+ "## Random Baseline"
224
+ ]
225
+ },
226
+ {
227
+ "cell_type": "code",
228
+ "execution_count": 10,
229
+ "metadata": {},
230
+ "outputs": [],
231
+ "source": [
232
+ "# Start tracking emissions\n",
233
+ "tracker.start()\n",
234
+ "tracker.start_task(\"inference\")"
235
+ ]
236
+ },
237
+ {
238
+ "cell_type": "code",
239
+ "execution_count": 11,
240
+ "metadata": {},
241
+ "outputs": [],
242
+ "source": [
243
+ "\n",
244
+ "#--------------------------------------------------------------------------------------------\n",
245
+ "# YOUR MODEL INFERENCE CODE HERE\n",
246
+ "# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
247
+ "#-------------------------------------------------------------------------------------------- \n",
248
+ "\n",
249
+ "# Make random predictions (placeholder for actual model inference)\n",
250
+ "\n",
251
+ "predictions = []\n",
252
+ "true_labels = []\n",
253
+ "pred_boxes = []\n",
254
+ "true_boxes_list = [] # List of lists, each inner list contains boxes for one image\n",
255
+ "\n",
256
+ "for example in test_dataset:\n",
257
+ " # Parse true annotation (YOLO format: class_id x_center y_center width height)\n",
258
+ " annotation = example.get(\"annotations\", \"\").strip()\n",
259
+ " has_smoke = len(annotation) > 0\n",
260
+ " true_labels.append(int(has_smoke))\n",
261
+ " \n",
262
+ " # Make random classification prediction\n",
263
+ " pred_has_smoke = random.random() > 0.5\n",
264
+ " predictions.append(int(pred_has_smoke))\n",
265
+ " \n",
266
+ " # If there's a true box, parse it and make random box prediction\n",
267
+ " if has_smoke:\n",
268
+ " # Parse all true boxes from the annotation\n",
269
+ " image_true_boxes = parse_boxes(annotation)\n",
270
+ " true_boxes_list.append(image_true_boxes)\n",
271
+ " \n",
272
+ " # For baseline, make one random box prediction per image\n",
273
+ " # In a real model, you might want to predict multiple boxes\n",
274
+ " random_box = [\n",
275
+ " random.random(), # x_center\n",
276
+ " random.random(), # y_center\n",
277
+ " random.random() * 0.5, # width (max 0.5)\n",
278
+ " random.random() * 0.5 # height (max 0.5)\n",
279
+ " ]\n",
280
+ " pred_boxes.append(random_box)\n",
281
+ "\n",
282
+ "\n",
283
+ "#--------------------------------------------------------------------------------------------\n",
284
+ "# YOUR MODEL INFERENCE STOPS HERE\n",
285
+ "#-------------------------------------------------------------------------------------------- "
286
+ ]
287
+ },
288
+ {
289
+ "cell_type": "code",
290
+ "execution_count": null,
291
+ "metadata": {},
292
+ "outputs": [],
293
+ "source": [
294
+ "# Stop tracking emissions\n",
295
+ "emissions_data = tracker.stop_task()"
296
+ ]
297
+ },
298
+ {
299
+ "cell_type": "code",
300
+ "execution_count": 15,
301
+ "metadata": {},
302
+ "outputs": [],
303
+ "source": [
304
+ "import numpy as np\n",
305
+ "\n",
306
+ "# Calculate classification metrics\n",
307
+ "classification_accuracy = accuracy_score(true_labels, predictions)\n",
308
+ "classification_precision = precision_score(true_labels, predictions)\n",
309
+ "classification_recall = recall_score(true_labels, predictions)\n",
310
+ "\n",
311
+ "# Calculate mean IoU for object detection (only for images with smoke)\n",
312
+ "# For each image, we compute the max IoU between the predicted box and all true boxes\n",
313
+ "ious = []\n",
314
+ "for true_boxes, pred_box in zip(true_boxes_list, pred_boxes):\n",
315
+ " max_iou = compute_max_iou(true_boxes, pred_box)\n",
316
+ " ious.append(max_iou)\n",
317
+ "\n",
318
+ "mean_iou = float(np.mean(ious)) if ious else 0.0"
319
+ ]
320
+ },
321
+ {
322
+ "cell_type": "code",
323
+ "execution_count": 18,
324
+ "metadata": {},
325
+ "outputs": [
326
+ {
327
+ "data": {
328
+ "text/plain": [
329
+ "{'submission_timestamp': '2025-01-22T15:57:37.288173',\n",
330
+ " 'classification_accuracy': 0.5001692620176033,\n",
331
+ " 'classification_precision': 0.8397129186602871,\n",
332
+ " 'classification_recall': 0.4972677595628415,\n",
333
+ " 'mean_iou': 0.002819781629108398,\n",
334
+ " 'energy_consumed_wh': 0.779355299496116,\n",
335
+ " 'emissions_gco2eq': 0.043674291628462855,\n",
336
+ " 'emissions_data': {'run_id': '4e750cd5-60f0-444c-baee-b5f7b31f784b',\n",
337
+ " 'duration': 51.72819679998793,\n",
338
+ " 'emissions': 4.3674291628462856e-05,\n",
339
+ " 'emissions_rate': 8.445163379568943e-07,\n",
340
+ " 'cpu_power': 42.5,\n",
341
+ " 'gpu_power': 0.0,\n",
342
+ " 'ram_power': 11.755242347717285,\n",
343
+ " 'cpu_energy': 0.0006104993474311617,\n",
344
+ " 'gpu_energy': 0,\n",
345
+ " 'ram_energy': 0.00016885595206495442,\n",
346
+ " 'energy_consumed': 0.0007793552994961161,\n",
347
+ " 'country_name': 'France',\n",
348
+ " 'country_iso_code': 'FRA',\n",
349
+ " 'region': 'île-de-france',\n",
350
+ " 'cloud_provider': '',\n",
351
+ " 'cloud_region': '',\n",
352
+ " 'os': 'Windows-11-10.0.22631-SP0',\n",
353
+ " 'python_version': '3.12.7',\n",
354
+ " 'codecarbon_version': '3.0.0_rc0',\n",
355
+ " 'cpu_count': 12,\n",
356
+ " 'cpu_model': '13th Gen Intel(R) Core(TM) i7-1365U',\n",
357
+ " 'gpu_count': None,\n",
358
+ " 'gpu_model': None,\n",
359
+ " 'ram_total_size': 31.347312927246094,\n",
360
+ " 'tracking_mode': 'machine',\n",
361
+ " 'on_cloud': 'N',\n",
362
+ " 'pue': 1.0},\n",
363
+ " 'dataset_config': {'dataset_name': 'pyronear/pyro-sdis',\n",
364
+ " 'test_size': 0.2,\n",
365
+ " 'test_seed': 42}}"
366
+ ]
367
+ },
368
+ "execution_count": 18,
369
+ "metadata": {},
370
+ "output_type": "execute_result"
371
+ }
372
+ ],
373
+ "source": [
374
+ "\n",
375
+ "# Prepare results dictionary\n",
376
+ "results = {\n",
377
+ " \"submission_timestamp\": datetime.now().isoformat(),\n",
378
+ " \"classification_accuracy\": float(classification_accuracy),\n",
379
+ " \"classification_precision\": float(classification_precision),\n",
380
+ " \"classification_recall\": float(classification_recall),\n",
381
+ " \"mean_iou\": mean_iou,\n",
382
+ " \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
383
+ " \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
384
+ " \"emissions_data\": clean_emissions_data(emissions_data),\n",
385
+ " \"dataset_config\": {\n",
386
+ " \"dataset_name\": request.dataset_name,\n",
387
+ " \"test_size\": request.test_size,\n",
388
+ " \"test_seed\": request.test_seed\n",
389
+ " }\n",
390
+ "}\n",
391
+ "results"
392
+ ]
393
+ }
394
+ ],
395
+ "metadata": {
396
+ "kernelspec": {
397
+ "display_name": "base",
398
+ "language": "python",
399
+ "name": "python3"
400
+ },
401
+ "language_info": {
402
+ "codemirror_mode": {
403
+ "name": "ipython",
404
+ "version": 3
405
+ },
406
+ "file_extension": ".py",
407
+ "mimetype": "text/x-python",
408
+ "name": "python",
409
+ "nbconvert_exporter": "python",
410
+ "pygments_lexer": "ipython3",
411
+ "version": "3.12.7"
412
+ }
413
+ },
414
+ "nbformat": 4,
415
+ "nbformat_minor": 2
416
+ }
notebooks/template-text.ipynb ADDED
@@ -0,0 +1,1642 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "cells": [
3
+ {
4
+ "cell_type": "markdown",
5
+ "metadata": {},
6
+ "source": [
7
+ "# Text task notebook template\n",
8
+ "## Loading the necessary libraries"
9
+ ]
10
+ },
11
+ {
12
+ "cell_type": "code",
13
+ "execution_count": 3,
14
+ "metadata": {},
15
+ "outputs": [
16
+ {
17
+ "name": "stderr",
18
+ "output_type": "stream",
19
+ "text": [
20
+ "[codecarbon WARNING @ 19:48:07] Multiple instances of codecarbon are allowed to run at the same time.\n",
21
+ "[codecarbon INFO @ 19:48:07] [setup] RAM Tracking...\n",
22
+ "[codecarbon INFO @ 19:48:07] [setup] CPU Tracking...\n",
23
+ "[codecarbon WARNING @ 19:48:09] We saw that you have a 13th Gen Intel(R) Core(TM) i7-1365U but we don't know it. Please contact us.\n",
24
+ "[codecarbon WARNING @ 19:48:09] No CPU tracking mode found. Falling back on CPU constant mode. \n",
25
+ " Windows OS detected: Please install Intel Power Gadget to measure CPU\n",
26
+ "\n",
27
+ "[codecarbon WARNING @ 19:48:11] We saw that you have a 13th Gen Intel(R) Core(TM) i7-1365U but we don't know it. Please contact us.\n",
28
+ "[codecarbon INFO @ 19:48:11] CPU Model on constant consumption mode: 13th Gen Intel(R) Core(TM) i7-1365U\n",
29
+ "[codecarbon WARNING @ 19:48:11] No CPU tracking mode found. Falling back on CPU constant mode.\n",
30
+ "[codecarbon INFO @ 19:48:11] [setup] GPU Tracking...\n",
31
+ "[codecarbon INFO @ 19:48:11] No GPU found.\n",
32
+ "[codecarbon INFO @ 19:48:11] >>> Tracker's metadata:\n",
33
+ "[codecarbon INFO @ 19:48:11] Platform system: Windows-11-10.0.22631-SP0\n",
34
+ "[codecarbon INFO @ 19:48:11] Python version: 3.12.7\n",
35
+ "[codecarbon INFO @ 19:48:11] CodeCarbon version: 3.0.0_rc0\n",
36
+ "[codecarbon INFO @ 19:48:11] Available RAM : 31.347 GB\n",
37
+ "[codecarbon INFO @ 19:48:11] CPU count: 12\n",
38
+ "[codecarbon INFO @ 19:48:11] CPU model: 13th Gen Intel(R) Core(TM) i7-1365U\n",
39
+ "[codecarbon INFO @ 19:48:11] GPU count: None\n",
40
+ "[codecarbon INFO @ 19:48:11] GPU model: None\n",
41
+ "[codecarbon INFO @ 19:48:11] Saving emissions data to file c:\\git\\submission-template\\notebooks\\emissions.csv\n"
42
+ ]
43
+ }
44
+ ],
45
+ "source": [
46
+ "from fastapi import APIRouter\n",
47
+ "from datetime import datetime\n",
48
+ "from datasets import load_dataset\n",
49
+ "from sklearn.metrics import accuracy_score\n",
50
+ "import random\n",
51
+ "\n",
52
+ "import sys\n",
53
+ "sys.path.append('../tasks')\n",
54
+ "\n",
55
+ "from utils.evaluation import TextEvaluationRequest\n",
56
+ "from utils.emissions import tracker, clean_emissions_data, get_space_info\n",
57
+ "\n",
58
+ "\n",
59
+ "# Define the label mapping\n",
60
+ "LABEL_MAPPING = {\n",
61
+ " \"0_not_relevant\": 0,\n",
62
+ " \"1_not_happening\": 1,\n",
63
+ " \"2_not_human\": 2,\n",
64
+ " \"3_not_bad\": 3,\n",
65
+ " \"4_solutions_harmful_unnecessary\": 4,\n",
66
+ " \"5_science_unreliable\": 5,\n",
67
+ " \"6_proponents_biased\": 6,\n",
68
+ " \"7_fossil_fuels_needed\": 7\n",
69
+ "}"
70
+ ]
71
+ },
72
+ {
73
+ "cell_type": "markdown",
74
+ "metadata": {},
75
+ "source": [
76
+ "## Loading the datasets and splitting them"
77
+ ]
78
+ },
79
+ {
80
+ "cell_type": "code",
81
+ "execution_count": 4,
82
+ "metadata": {},
83
+ "outputs": [
84
+ {
85
+ "data": {
86
+ "application/vnd.jupyter.widget-view+json": {
87
+ "model_id": "668da7bf85434e098b95c3ec447d78fe",
88
+ "version_major": 2,
89
+ "version_minor": 0
90
+ },
91
+ "text/plain": [
92
+ "README.md: 0%| | 0.00/5.18k [00:00<?, ?B/s]"
93
+ ]
94
+ },
95
+ "metadata": {},
96
+ "output_type": "display_data"
97
+ },
98
+ {
99
+ "name": "stderr",
100
+ "output_type": "stream",
101
+ "text": [
102
+ "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\datasets--QuotaClimat--frugalaichallenge-text-train. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
103
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
104
+ " warnings.warn(message)\n"
105
+ ]
106
+ },
107
+ {
108
+ "data": {
109
+ "application/vnd.jupyter.widget-view+json": {
110
+ "model_id": "5b68d43359eb429395da8be7d4b15556",
111
+ "version_major": 2,
112
+ "version_minor": 0
113
+ },
114
+ "text/plain": [
115
+ "train.parquet: 0%| | 0.00/1.21M [00:00<?, ?B/s]"
116
+ ]
117
+ },
118
+ "metadata": {},
119
+ "output_type": "display_data"
120
+ },
121
+ {
122
+ "data": {
123
+ "application/vnd.jupyter.widget-view+json": {
124
+ "model_id": "140a304773914e9db8f698eabeb40298",
125
+ "version_major": 2,
126
+ "version_minor": 0
127
+ },
128
+ "text/plain": [
129
+ "Generating train split: 0%| | 0/6091 [00:00<?, ? examples/s]"
130
+ ]
131
+ },
132
+ "metadata": {},
133
+ "output_type": "display_data"
134
+ },
135
+ {
136
+ "data": {
137
+ "application/vnd.jupyter.widget-view+json": {
138
+ "model_id": "6d04e8ab1906400e8e0029949dc523a5",
139
+ "version_major": 2,
140
+ "version_minor": 0
141
+ },
142
+ "text/plain": [
143
+ "Map: 0%| | 0/6091 [00:00<?, ? examples/s]"
144
+ ]
145
+ },
146
+ "metadata": {},
147
+ "output_type": "display_data"
148
+ }
149
+ ],
150
+ "source": [
151
+ "request = TextEvaluationRequest()\n",
152
+ "\n",
153
+ "# Load and prepare the dataset\n",
154
+ "dataset = load_dataset(request.dataset_name)\n",
155
+ "\n",
156
+ "# Convert string labels to integers\n",
157
+ "dataset = dataset.map(lambda x: {\"label\": LABEL_MAPPING[x[\"label\"]]})\n",
158
+ "\n",
159
+ "# Split dataset\n",
160
+ "train_test = dataset[\"train\"].train_test_split(test_size=request.test_size, seed=request.test_seed)\n",
161
+ "test_dataset = train_test[\"test\"]"
162
+ ]
163
+ },
164
+ {
165
+ "cell_type": "markdown",
166
+ "metadata": {},
167
+ "source": [
168
+ "## Random Baseline"
169
+ ]
170
+ },
171
+ {
172
+ "cell_type": "code",
173
+ "execution_count": 5,
174
+ "metadata": {},
175
+ "outputs": [],
176
+ "source": [
177
+ "# Start tracking emissions\n",
178
+ "tracker.start()\n",
179
+ "tracker.start_task(\"inference\")"
180
+ ]
181
+ },
182
+ {
183
+ "cell_type": "code",
184
+ "execution_count": 6,
185
+ "metadata": {},
186
+ "outputs": [
187
+ {
188
+ "data": {
189
+ "text/plain": [
190
+ "[1,\n",
191
+ " 7,\n",
192
+ " 6,\n",
193
+ " 6,\n",
194
+ " 2,\n",
195
+ " 0,\n",
196
+ " 1,\n",
197
+ " 7,\n",
198
+ " 3,\n",
199
+ " 6,\n",
200
+ " 6,\n",
201
+ " 3,\n",
202
+ " 6,\n",
203
+ " 6,\n",
204
+ " 5,\n",
205
+ " 0,\n",
206
+ " 2,\n",
207
+ " 6,\n",
208
+ " 2,\n",
209
+ " 6,\n",
210
+ " 5,\n",
211
+ " 4,\n",
212
+ " 1,\n",
213
+ " 3,\n",
214
+ " 6,\n",
215
+ " 4,\n",
216
+ " 2,\n",
217
+ " 1,\n",
218
+ " 4,\n",
219
+ " 0,\n",
220
+ " 3,\n",
221
+ " 4,\n",
222
+ " 1,\n",
223
+ " 5,\n",
224
+ " 5,\n",
225
+ " 1,\n",
226
+ " 2,\n",
227
+ " 7,\n",
228
+ " 6,\n",
229
+ " 1,\n",
230
+ " 3,\n",
231
+ " 1,\n",
232
+ " 7,\n",
233
+ " 7,\n",
234
+ " 0,\n",
235
+ " 0,\n",
236
+ " 3,\n",
237
+ " 3,\n",
238
+ " 3,\n",
239
+ " 4,\n",
240
+ " 1,\n",
241
+ " 4,\n",
242
+ " 4,\n",
243
+ " 1,\n",
244
+ " 4,\n",
245
+ " 5,\n",
246
+ " 6,\n",
247
+ " 1,\n",
248
+ " 2,\n",
249
+ " 2,\n",
250
+ " 2,\n",
251
+ " 5,\n",
252
+ " 2,\n",
253
+ " 7,\n",
254
+ " 2,\n",
255
+ " 7,\n",
256
+ " 7,\n",
257
+ " 6,\n",
258
+ " 4,\n",
259
+ " 2,\n",
260
+ " 0,\n",
261
+ " 1,\n",
262
+ " 6,\n",
263
+ " 3,\n",
264
+ " 2,\n",
265
+ " 5,\n",
266
+ " 5,\n",
267
+ " 2,\n",
268
+ " 0,\n",
269
+ " 7,\n",
270
+ " 0,\n",
271
+ " 1,\n",
272
+ " 5,\n",
273
+ " 5,\n",
274
+ " 7,\n",
275
+ " 4,\n",
276
+ " 6,\n",
277
+ " 7,\n",
278
+ " 1,\n",
279
+ " 7,\n",
280
+ " 1,\n",
281
+ " 0,\n",
282
+ " 3,\n",
283
+ " 4,\n",
284
+ " 2,\n",
285
+ " 5,\n",
286
+ " 3,\n",
287
+ " 3,\n",
288
+ " 3,\n",
289
+ " 2,\n",
290
+ " 2,\n",
291
+ " 1,\n",
292
+ " 0,\n",
293
+ " 4,\n",
294
+ " 5,\n",
295
+ " 7,\n",
296
+ " 0,\n",
297
+ " 3,\n",
298
+ " 1,\n",
299
+ " 4,\n",
300
+ " 6,\n",
301
+ " 0,\n",
302
+ " 7,\n",
303
+ " 1,\n",
304
+ " 1,\n",
305
+ " 2,\n",
306
+ " 2,\n",
307
+ " 4,\n",
308
+ " 0,\n",
309
+ " 4,\n",
310
+ " 3,\n",
311
+ " 4,\n",
312
+ " 4,\n",
313
+ " 2,\n",
314
+ " 2,\n",
315
+ " 3,\n",
316
+ " 3,\n",
317
+ " 7,\n",
318
+ " 4,\n",
319
+ " 7,\n",
320
+ " 6,\n",
321
+ " 4,\n",
322
+ " 5,\n",
323
+ " 4,\n",
324
+ " 3,\n",
325
+ " 6,\n",
326
+ " 0,\n",
327
+ " 4,\n",
328
+ " 0,\n",
329
+ " 1,\n",
330
+ " 3,\n",
331
+ " 6,\n",
332
+ " 7,\n",
333
+ " 3,\n",
334
+ " 3,\n",
335
+ " 0,\n",
336
+ " 1,\n",
337
+ " 2,\n",
338
+ " 4,\n",
339
+ " 4,\n",
340
+ " 3,\n",
341
+ " 1,\n",
342
+ " 2,\n",
343
+ " 4,\n",
344
+ " 3,\n",
345
+ " 0,\n",
346
+ " 5,\n",
347
+ " 3,\n",
348
+ " 6,\n",
349
+ " 3,\n",
350
+ " 6,\n",
351
+ " 1,\n",
352
+ " 3,\n",
353
+ " 4,\n",
354
+ " 5,\n",
355
+ " 4,\n",
356
+ " 0,\n",
357
+ " 7,\n",
358
+ " 3,\n",
359
+ " 6,\n",
360
+ " 7,\n",
361
+ " 4,\n",
362
+ " 4,\n",
363
+ " 5,\n",
364
+ " 3,\n",
365
+ " 1,\n",
366
+ " 7,\n",
367
+ " 4,\n",
368
+ " 1,\n",
369
+ " 0,\n",
370
+ " 3,\n",
371
+ " 0,\n",
372
+ " 5,\n",
373
+ " 3,\n",
374
+ " 6,\n",
375
+ " 3,\n",
376
+ " 0,\n",
377
+ " 7,\n",
378
+ " 2,\n",
379
+ " 0,\n",
380
+ " 4,\n",
381
+ " 1,\n",
382
+ " 2,\n",
383
+ " 6,\n",
384
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1109
+ " 1,\n",
1110
+ " 0,\n",
1111
+ " 6,\n",
1112
+ " 4,\n",
1113
+ " 0,\n",
1114
+ " 5,\n",
1115
+ " 4,\n",
1116
+ " 6,\n",
1117
+ " 6,\n",
1118
+ " 7,\n",
1119
+ " 2,\n",
1120
+ " 6,\n",
1121
+ " 2,\n",
1122
+ " 6,\n",
1123
+ " 0,\n",
1124
+ " 3,\n",
1125
+ " 2,\n",
1126
+ " 2,\n",
1127
+ " 1,\n",
1128
+ " 5,\n",
1129
+ " 4,\n",
1130
+ " 7,\n",
1131
+ " 6,\n",
1132
+ " 6,\n",
1133
+ " 2,\n",
1134
+ " 5,\n",
1135
+ " 5,\n",
1136
+ " 5,\n",
1137
+ " 0,\n",
1138
+ " 3,\n",
1139
+ " 5,\n",
1140
+ " 4,\n",
1141
+ " 5,\n",
1142
+ " 7,\n",
1143
+ " 5,\n",
1144
+ " 0,\n",
1145
+ " 5,\n",
1146
+ " 0,\n",
1147
+ " 0,\n",
1148
+ " 2,\n",
1149
+ " 0,\n",
1150
+ " 2,\n",
1151
+ " 1,\n",
1152
+ " 0,\n",
1153
+ " 2,\n",
1154
+ " 4,\n",
1155
+ " 3,\n",
1156
+ " 4,\n",
1157
+ " 1,\n",
1158
+ " 7,\n",
1159
+ " 2,\n",
1160
+ " 1,\n",
1161
+ " 0,\n",
1162
+ " 3,\n",
1163
+ " 0,\n",
1164
+ " 3,\n",
1165
+ " 1,\n",
1166
+ " 1,\n",
1167
+ " 0,\n",
1168
+ " 5,\n",
1169
+ " 3,\n",
1170
+ " 1,\n",
1171
+ " 2,\n",
1172
+ " 5,\n",
1173
+ " 6,\n",
1174
+ " 7,\n",
1175
+ " 6,\n",
1176
+ " 7,\n",
1177
+ " 0,\n",
1178
+ " 2,\n",
1179
+ " 6,\n",
1180
+ " 3,\n",
1181
+ " 1,\n",
1182
+ " 5,\n",
1183
+ " 4,\n",
1184
+ " 2,\n",
1185
+ " 4,\n",
1186
+ " 6,\n",
1187
+ " 5,\n",
1188
+ " 2,\n",
1189
+ " 7,\n",
1190
+ " ...]"
1191
+ ]
1192
+ },
1193
+ "execution_count": 6,
1194
+ "metadata": {},
1195
+ "output_type": "execute_result"
1196
+ }
1197
+ ],
1198
+ "source": [
1199
+ "\n",
1200
+ "#--------------------------------------------------------------------------------------------\n",
1201
+ "# YOUR MODEL INFERENCE CODE HERE\n",
1202
+ "# Update the code below to replace the random baseline by your model inference within the inference pass where the energy consumption and emissions are tracked.\n",
1203
+ "#-------------------------------------------------------------------------------------------- \n",
1204
+ "\n",
1205
+ "# Make random predictions (placeholder for actual model inference)\n",
1206
+ "true_labels = test_dataset[\"label\"]\n",
1207
+ "predictions = [random.randint(0, 7) for _ in range(len(true_labels))]\n",
1208
+ "\n",
1209
+ "predictions\n",
1210
+ "\n",
1211
+ "#--------------------------------------------------------------------------------------------\n",
1212
+ "# YOUR MODEL INFERENCE STOPS HERE\n",
1213
+ "#-------------------------------------------------------------------------------------------- "
1214
+ ]
1215
+ },
1216
+ {
1217
+ "cell_type": "code",
1218
+ "execution_count": 8,
1219
+ "metadata": {},
1220
+ "outputs": [
1221
+ {
1222
+ "name": "stderr",
1223
+ "output_type": "stream",
1224
+ "text": [
1225
+ "[codecarbon WARNING @ 19:53:32] Background scheduler didn't run for a long period (47s), results might be inaccurate\n",
1226
+ "[codecarbon INFO @ 19:53:32] Energy consumed for RAM : 0.000156 kWh. RAM Power : 11.755242347717285 W\n",
1227
+ "[codecarbon INFO @ 19:53:32] Delta energy consumed for CPU with constant : 0.000564 kWh, power : 42.5 W\n",
1228
+ "[codecarbon INFO @ 19:53:32] Energy consumed for All CPU : 0.000564 kWh\n",
1229
+ "[codecarbon INFO @ 19:53:32] 0.000720 kWh of electricity used since the beginning.\n"
1230
+ ]
1231
+ },
1232
+ {
1233
+ "data": {
1234
+ "text/plain": [
1235
+ "EmissionsData(timestamp='2025-01-21T19:53:32', project_name='codecarbon', run_id='908f2e7e-4bb2-4991-a0f6-56bf8d7eda21', experiment_id='5b0fa12a-3dd7-45bb-9766-cc326314d9f1', duration=47.736408500000834, emissions=4.032368007471064e-05, emissions_rate=8.444466886328872e-07, cpu_power=42.5, gpu_power=0.0, ram_power=11.755242347717285, cpu_energy=0.0005636615353475565, gpu_energy=0, ram_energy=0.00015590305493261682, energy_consumed=0.0007195645902801733, country_name='France', country_iso_code='FRA', region='île-de-france', cloud_provider='', cloud_region='', os='Windows-11-10.0.22631-SP0', python_version='3.12.7', codecarbon_version='3.0.0_rc0', cpu_count=12, cpu_model='13th Gen Intel(R) Core(TM) i7-1365U', gpu_count=None, gpu_model=None, longitude=2.3494, latitude=48.8558, ram_total_size=31.347312927246094, tracking_mode='machine', on_cloud='N', pue=1.0)"
1236
+ ]
1237
+ },
1238
+ "execution_count": 8,
1239
+ "metadata": {},
1240
+ "output_type": "execute_result"
1241
+ }
1242
+ ],
1243
+ "source": [
1244
+ "# Stop tracking emissions\n",
1245
+ "emissions_data = tracker.stop_task()\n",
1246
+ "emissions_data"
1247
+ ]
1248
+ },
1249
+ {
1250
+ "cell_type": "code",
1251
+ "execution_count": 9,
1252
+ "metadata": {},
1253
+ "outputs": [
1254
+ {
1255
+ "data": {
1256
+ "text/plain": [
1257
+ "0.10090237899917966"
1258
+ ]
1259
+ },
1260
+ "execution_count": 9,
1261
+ "metadata": {},
1262
+ "output_type": "execute_result"
1263
+ }
1264
+ ],
1265
+ "source": [
1266
+ "# Calculate accuracy\n",
1267
+ "accuracy = accuracy_score(true_labels, predictions)\n",
1268
+ "accuracy"
1269
+ ]
1270
+ },
1271
+ {
1272
+ "cell_type": "code",
1273
+ "execution_count": 10,
1274
+ "metadata": {},
1275
+ "outputs": [
1276
+ {
1277
+ "data": {
1278
+ "text/plain": [
1279
+ "{'submission_timestamp': '2025-01-21T19:53:46.639165',\n",
1280
+ " 'accuracy': 0.10090237899917966,\n",
1281
+ " 'energy_consumed_wh': 0.7195645902801733,\n",
1282
+ " 'emissions_gco2eq': 0.040323680074710634,\n",
1283
+ " 'emissions_data': {'run_id': '908f2e7e-4bb2-4991-a0f6-56bf8d7eda21',\n",
1284
+ " 'duration': 47.736408500000834,\n",
1285
+ " 'emissions': 4.032368007471064e-05,\n",
1286
+ " 'emissions_rate': 8.444466886328872e-07,\n",
1287
+ " 'cpu_power': 42.5,\n",
1288
+ " 'gpu_power': 0.0,\n",
1289
+ " 'ram_power': 11.755242347717285,\n",
1290
+ " 'cpu_energy': 0.0005636615353475565,\n",
1291
+ " 'gpu_energy': 0,\n",
1292
+ " 'ram_energy': 0.00015590305493261682,\n",
1293
+ " 'energy_consumed': 0.0007195645902801733,\n",
1294
+ " 'country_name': 'France',\n",
1295
+ " 'country_iso_code': 'FRA',\n",
1296
+ " 'region': 'île-de-france',\n",
1297
+ " 'cloud_provider': '',\n",
1298
+ " 'cloud_region': '',\n",
1299
+ " 'os': 'Windows-11-10.0.22631-SP0',\n",
1300
+ " 'python_version': '3.12.7',\n",
1301
+ " 'codecarbon_version': '3.0.0_rc0',\n",
1302
+ " 'cpu_count': 12,\n",
1303
+ " 'cpu_model': '13th Gen Intel(R) Core(TM) i7-1365U',\n",
1304
+ " 'gpu_count': None,\n",
1305
+ " 'gpu_model': None,\n",
1306
+ " 'ram_total_size': 31.347312927246094,\n",
1307
+ " 'tracking_mode': 'machine',\n",
1308
+ " 'on_cloud': 'N',\n",
1309
+ " 'pue': 1.0},\n",
1310
+ " 'dataset_config': {'dataset_name': 'QuotaClimat/frugalaichallenge-text-train',\n",
1311
+ " 'test_size': 0.2,\n",
1312
+ " 'test_seed': 42}}"
1313
+ ]
1314
+ },
1315
+ "execution_count": 10,
1316
+ "metadata": {},
1317
+ "output_type": "execute_result"
1318
+ }
1319
+ ],
1320
+ "source": [
1321
+ "# Prepare results dictionary\n",
1322
+ "results = {\n",
1323
+ " \"submission_timestamp\": datetime.now().isoformat(),\n",
1324
+ " \"accuracy\": float(accuracy),\n",
1325
+ " \"energy_consumed_wh\": emissions_data.energy_consumed * 1000,\n",
1326
+ " \"emissions_gco2eq\": emissions_data.emissions * 1000,\n",
1327
+ " \"emissions_data\": clean_emissions_data(emissions_data),\n",
1328
+ " \"dataset_config\": {\n",
1329
+ " \"dataset_name\": request.dataset_name,\n",
1330
+ " \"test_size\": request.test_size,\n",
1331
+ " \"test_seed\": request.test_seed\n",
1332
+ " }\n",
1333
+ "}\n",
1334
+ "\n",
1335
+ "results"
1336
+ ]
1337
+ },
1338
+ {
1339
+ "cell_type": "markdown",
1340
+ "metadata": {},
1341
+ "source": [
1342
+ "## Development of the model"
1343
+ ]
1344
+ },
1345
+ {
1346
+ "cell_type": "code",
1347
+ "execution_count": 11,
1348
+ "metadata": {},
1349
+ "outputs": [
1350
+ {
1351
+ "data": {
1352
+ "application/vnd.jupyter.widget-view+json": {
1353
+ "model_id": "90f50ab19698484489f36976745efad3",
1354
+ "version_major": 2,
1355
+ "version_minor": 0
1356
+ },
1357
+ "text/plain": [
1358
+ "config.json: 0%| | 0.00/1.15k [00:00<?, ?B/s]"
1359
+ ]
1360
+ },
1361
+ "metadata": {},
1362
+ "output_type": "display_data"
1363
+ },
1364
+ {
1365
+ "name": "stderr",
1366
+ "output_type": "stream",
1367
+ "text": [
1368
+ "c:\\Users\\theo.alvesdacosta\\AppData\\Local\\anaconda3\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\theo.alvesdacosta\\.cache\\huggingface\\hub\\models--facebook--bart-large-mnli. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
1369
+ "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
1370
+ " warnings.warn(message)\n"
1371
+ ]
1372
+ },
1373
+ {
1374
+ "data": {
1375
+ "application/vnd.jupyter.widget-view+json": {
1376
+ "model_id": "6e3974d8ff284603821f7beca9bd353d",
1377
+ "version_major": 2,
1378
+ "version_minor": 0
1379
+ },
1380
+ "text/plain": [
1381
+ "model.safetensors: 0%| | 0.00/1.63G [00:00<?, ?B/s]"
1382
+ ]
1383
+ },
1384
+ "metadata": {},
1385
+ "output_type": "display_data"
1386
+ },
1387
+ {
1388
+ "data": {
1389
+ "application/vnd.jupyter.widget-view+json": {
1390
+ "model_id": "bc29cb379c644b00b1bdf61d5426d99d",
1391
+ "version_major": 2,
1392
+ "version_minor": 0
1393
+ },
1394
+ "text/plain": [
1395
+ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
1396
+ ]
1397
+ },
1398
+ "metadata": {},
1399
+ "output_type": "display_data"
1400
+ },
1401
+ {
1402
+ "data": {
1403
+ "application/vnd.jupyter.widget-view+json": {
1404
+ "model_id": "635503cf819747c9a83f22aa4f2f11db",
1405
+ "version_major": 2,
1406
+ "version_minor": 0
1407
+ },
1408
+ "text/plain": [
1409
+ "vocab.json: 0%| | 0.00/899k [00:00<?, ?B/s]"
1410
+ ]
1411
+ },
1412
+ "metadata": {},
1413
+ "output_type": "display_data"
1414
+ },
1415
+ {
1416
+ "data": {
1417
+ "application/vnd.jupyter.widget-view+json": {
1418
+ "model_id": "3a5f53e451e8483ca7c33f42245abd13",
1419
+ "version_major": 2,
1420
+ "version_minor": 0
1421
+ },
1422
+ "text/plain": [
1423
+ "merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
1424
+ ]
1425
+ },
1426
+ "metadata": {},
1427
+ "output_type": "display_data"
1428
+ },
1429
+ {
1430
+ "data": {
1431
+ "application/vnd.jupyter.widget-view+json": {
1432
+ "model_id": "84f922d1b68a4a0faa5e920d004efca0",
1433
+ "version_major": 2,
1434
+ "version_minor": 0
1435
+ },
1436
+ "text/plain": [
1437
+ "tokenizer.json: 0%| | 0.00/1.36M [00:00<?, ?B/s]"
1438
+ ]
1439
+ },
1440
+ "metadata": {},
1441
+ "output_type": "display_data"
1442
+ },
1443
+ {
1444
+ "name": "stderr",
1445
+ "output_type": "stream",
1446
+ "text": [
1447
+ "Device set to use cpu\n"
1448
+ ]
1449
+ }
1450
+ ],
1451
+ "source": [
1452
+ "from transformers import pipeline\n",
1453
+ "classifier = pipeline(\"zero-shot-classification\",\n",
1454
+ " model=\"facebook/bart-large-mnli\")\n"
1455
+ ]
1456
+ },
1457
+ {
1458
+ "cell_type": "code",
1459
+ "execution_count": 14,
1460
+ "metadata": {},
1461
+ "outputs": [],
1462
+ "source": [
1463
+ "sequence_to_classify = \"one day I will see the world\"\n",
1464
+ "\n",
1465
+ "candidate_labels = [\n",
1466
+ " \"Not related to climate change disinformation\",\n",
1467
+ " \"Climate change is not real and not happening\",\n",
1468
+ " \"Climate change is not human-induced\",\n",
1469
+ " \"Climate change impacts are not that bad\",\n",
1470
+ " \"Climate change solutions are harmful and unnecessary\",\n",
1471
+ " \"Climate change science is unreliable\",\n",
1472
+ " \"Climate change proponents are biased\",\n",
1473
+ " \"Fossil fuels are needed to address climate change\"\n",
1474
+ "]"
1475
+ ]
1476
+ },
1477
+ {
1478
+ "cell_type": "code",
1479
+ "execution_count": 15,
1480
+ "metadata": {},
1481
+ "outputs": [
1482
+ {
1483
+ "data": {
1484
+ "text/plain": [
1485
+ "{'sequence': 'one day I will see the world',\n",
1486
+ " 'labels': ['Fossil fuels are needed to address climate change',\n",
1487
+ " 'Climate change science is unreliable',\n",
1488
+ " 'Not related to climate change disinformation',\n",
1489
+ " 'Climate change proponents are biased',\n",
1490
+ " 'Climate change impacts are not that bad',\n",
1491
+ " 'Climate change solutions are harmful and unnecessary',\n",
1492
+ " 'Climate change is not human-induced',\n",
1493
+ " 'Climate change is not real and not happening'],\n",
1494
+ " 'scores': [0.16242119669914246,\n",
1495
+ " 0.15683825314044952,\n",
1496
+ " 0.1564282774925232,\n",
1497
+ " 0.14603719115257263,\n",
1498
+ " 0.12794046103954315,\n",
1499
+ " 0.10180754214525223,\n",
1500
+ " 0.0936085507273674,\n",
1501
+ " 0.0549185685813427]}"
1502
+ ]
1503
+ },
1504
+ "execution_count": 15,
1505
+ "metadata": {},
1506
+ "output_type": "execute_result"
1507
+ }
1508
+ ],
1509
+ "source": [
1510
+ "classifier(sequence_to_classify, candidate_labels)"
1511
+ ]
1512
+ },
1513
+ {
1514
+ "cell_type": "code",
1515
+ "execution_count": 26,
1516
+ "metadata": {},
1517
+ "outputs": [
1518
+ {
1519
+ "name": "stderr",
1520
+ "output_type": "stream",
1521
+ "text": [
1522
+ "[codecarbon WARNING @ 11:00:07] Already started tracking\n"
1523
+ ]
1524
+ },
1525
+ {
1526
+ "data": {
1527
+ "application/vnd.jupyter.widget-view+json": {
1528
+ "model_id": "5d66a13f76a4411d95b62d4a73012495",
1529
+ "version_major": 2,
1530
+ "version_minor": 0
1531
+ },
1532
+ "text/plain": [
1533
+ "0it [00:00, ?it/s]"
1534
+ ]
1535
+ },
1536
+ "metadata": {},
1537
+ "output_type": "display_data"
1538
+ },
1539
+ {
1540
+ "name": "stderr",
1541
+ "output_type": "stream",
1542
+ "text": [
1543
+ "[codecarbon WARNING @ 11:05:57] Background scheduler didn't run for a long period (349s), results might be inaccurate\n",
1544
+ "[codecarbon INFO @ 11:05:57] Energy consumed for RAM : 0.018069 kWh. RAM Power : 11.755242347717285 W\n",
1545
+ "[codecarbon INFO @ 11:05:57] Delta energy consumed for CPU with constant : 0.004122 kWh, power : 42.5 W\n",
1546
+ "[codecarbon INFO @ 11:05:57] Energy consumed for All CPU : 0.065327 kWh\n",
1547
+ "[codecarbon INFO @ 11:05:57] 0.083395 kWh of electricity used since the beginning.\n"
1548
+ ]
1549
+ },
1550
+ {
1551
+ "data": {
1552
+ "text/plain": [
1553
+ "EmissionsData(timestamp='2025-01-22T11:05:57', project_name='codecarbon', run_id='908f2e7e-4bb2-4991-a0f6-56bf8d7eda21', experiment_id='5b0fa12a-3dd7-45bb-9766-cc326314d9f1', duration=349.19709450000664, emissions=0.0002949120266226386, emissions_rate=8.445461750018632e-07, cpu_power=42.5, gpu_power=0.0, ram_power=11.755242347717285, cpu_energy=0.004122396676597424, gpu_energy=0, ram_energy=0.0011402244733631148, energy_consumed=0.005262621149960539, country_name='France', country_iso_code='FRA', region='île-de-france', cloud_provider='', cloud_region='', os='Windows-11-10.0.22631-SP0', python_version='3.12.7', codecarbon_version='3.0.0_rc0', cpu_count=12, cpu_model='13th Gen Intel(R) Core(TM) i7-1365U', gpu_count=None, gpu_model=None, longitude=2.3494, latitude=48.8558, ram_total_size=31.347312927246094, tracking_mode='machine', on_cloud='N', pue=1.0)"
1554
+ ]
1555
+ },
1556
+ "execution_count": 26,
1557
+ "metadata": {},
1558
+ "output_type": "execute_result"
1559
+ }
1560
+ ],
1561
+ "source": [
1562
+ "# Start tracking emissions\n",
1563
+ "tracker.start()\n",
1564
+ "tracker.start_task(\"inference\")\n",
1565
+ "\n",
1566
+ "from tqdm.auto import tqdm\n",
1567
+ "predictions = []\n",
1568
+ "\n",
1569
+ "\n",
1570
+ "\n",
1571
+ "# Option 1: Simple loop approach\n",
1572
+ "\n",
1573
+ "for i, text in tqdm(enumerate(test_dataset[\"quote\"])):\n",
1574
+ "\n",
1575
+ " result = classifier(text, candidate_labels)\n",
1576
+ "\n",
1577
+ " # Get index of highest scoring label\n",
1578
+ "\n",
1579
+ " pred_label = candidate_labels.index(result[\"labels\"][0])\n",
1580
+ "\n",
1581
+ " predictions.append(pred_label)\n",
1582
+ " if i == 100:\n",
1583
+ " break\n",
1584
+ "\n",
1585
+ "\n",
1586
+ "# Stop tracking emissions\n",
1587
+ "emissions_data = tracker.stop_task()\n",
1588
+ "emissions_data\n"
1589
+ ]
1590
+ },
1591
+ {
1592
+ "cell_type": "code",
1593
+ "execution_count": 28,
1594
+ "metadata": {},
1595
+ "outputs": [
1596
+ {
1597
+ "data": {
1598
+ "text/plain": [
1599
+ "0.4"
1600
+ ]
1601
+ },
1602
+ "execution_count": 28,
1603
+ "metadata": {},
1604
+ "output_type": "execute_result"
1605
+ }
1606
+ ],
1607
+ "source": [
1608
+ "# Calculate accuracy\n",
1609
+ "accuracy = accuracy_score(true_labels[:100], predictions[:100])\n",
1610
+ "accuracy"
1611
+ ]
1612
+ },
1613
+ {
1614
+ "cell_type": "code",
1615
+ "execution_count": null,
1616
+ "metadata": {},
1617
+ "outputs": [],
1618
+ "source": []
1619
+ }
1620
+ ],
1621
+ "metadata": {
1622
+ "kernelspec": {
1623
+ "display_name": "base",
1624
+ "language": "python",
1625
+ "name": "python3"
1626
+ },
1627
+ "language_info": {
1628
+ "codemirror_mode": {
1629
+ "name": "ipython",
1630
+ "version": 3
1631
+ },
1632
+ "file_extension": ".py",
1633
+ "mimetype": "text/x-python",
1634
+ "name": "python",
1635
+ "nbconvert_exporter": "python",
1636
+ "pygments_lexer": "ipython3",
1637
+ "version": "3.12.7"
1638
+ }
1639
+ },
1640
+ "nbformat": 4,
1641
+ "nbformat_minor": 2
1642
+ }
tasks/image.py CHANGED
@@ -2,7 +2,7 @@ from fastapi import APIRouter
2
  from datetime import datetime
3
  from datasets import load_dataset
4
  import numpy as np
5
- from sklearn.metrics import accuracy_score
6
  import random
7
  import os
8
 
@@ -138,8 +138,10 @@ async def evaluate_image(request: ImageEvaluationRequest):
138
  # Stop tracking emissions
139
  emissions_data = tracker.stop_task()
140
 
141
- # Calculate classification accuracy
142
  classification_accuracy = accuracy_score(true_labels, predictions)
 
 
143
 
144
  # Calculate mean IoU for object detection (only for images with smoke)
145
  # For each image, we compute the max IoU between the predicted box and all true boxes
@@ -157,6 +159,8 @@ async def evaluate_image(request: ImageEvaluationRequest):
157
  "submission_timestamp": datetime.now().isoformat(),
158
  "model_description": DESCRIPTION,
159
  "classification_accuracy": float(classification_accuracy),
 
 
160
  "mean_iou": mean_iou,
161
  "energy_consumed_wh": emissions_data.energy_consumed * 1000,
162
  "emissions_gco2eq": emissions_data.emissions * 1000,
 
2
  from datetime import datetime
3
  from datasets import load_dataset
4
  import numpy as np
5
+ from sklearn.metrics import accuracy_score, precision_score, recall_score
6
  import random
7
  import os
8
 
 
138
  # Stop tracking emissions
139
  emissions_data = tracker.stop_task()
140
 
141
+ # Calculate classification metrics
142
  classification_accuracy = accuracy_score(true_labels, predictions)
143
+ classification_precision = precision_score(true_labels, predictions)
144
+ classification_recall = recall_score(true_labels, predictions)
145
 
146
  # Calculate mean IoU for object detection (only for images with smoke)
147
  # For each image, we compute the max IoU between the predicted box and all true boxes
 
159
  "submission_timestamp": datetime.now().isoformat(),
160
  "model_description": DESCRIPTION,
161
  "classification_accuracy": float(classification_accuracy),
162
+ "classification_precision": float(classification_precision),
163
+ "classification_recall": float(classification_recall),
164
  "mean_iou": mean_iou,
165
  "energy_consumed_wh": emissions_data.energy_consumed * 1000,
166
  "emissions_gco2eq": emissions_data.emissions * 1000,