cyberes gist felülvizsgálása . Revízióhoz ugrás
1 file changed, 3 insertions, 1 deletion
README.md
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| 1 | - | Data is available at | |
| 1 | + | Data is available at | |
| 2 | + | ||
| 3 | + | Use <https://github.com/elasticsearch-dump/elasticsearch-dump> to import into Elastic. | |
cyberes gist felülvizsgálása . Revízióhoz ugrás
2 files changed, 287 insertions
README.md(fájl létrehozva)
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| 1 | + | Data is available at | |
tally-costs.py(fájl létrehozva)
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| 1 | + | #!/usr/bin/env python3 | |
| 2 | + | import argparse | |
| 3 | + | import csv | |
| 4 | + | import locale | |
| 5 | + | import logging | |
| 6 | + | from datetime import datetime | |
| 7 | + | from typing import Union | |
| 8 | + | ||
| 9 | + | import elastic_transport | |
| 10 | + | import numpy as np | |
| 11 | + | from elasticsearch.helpers import scan | |
| 12 | + | from elasticsearch_dsl import Search, Q | |
| 13 | + | from pydantic import BaseModel | |
| 14 | + | from tqdm import tqdm | |
| 15 | + | ||
| 16 | + | from lib.config import ELASTIC_HOST, ELASTIC_INDEX, ELASTIC_API_KEY | |
| 17 | + | from lib.elastic.client import ElasticClient | |
| 18 | + | ||
| 19 | + | logging.basicConfig() | |
| 20 | + | logger = logging.getLogger('MAIN') | |
| 21 | + | logger.setLevel(logging.INFO) | |
| 22 | + | ||
| 23 | + | _IS_SERVICE = False | |
| 24 | + | _IS_TEST = False | |
| 25 | + | _WRITE_CSVS = False | |
| 26 | + | ||
| 27 | + | ||
| 28 | + | class Usage(BaseModel): | |
| 29 | + | type: str | |
| 30 | + | cost: Union[float, None] = None | |
| 31 | + | tokens: int | |
| 32 | + | ||
| 33 | + | ||
| 34 | + | API_COSTS_PER_1M = { | |
| 35 | + | # Input, output. | |
| 36 | + | 'turbo': np.mean([1.50, 2.00]), | |
| 37 | + | 'gpt4-turbo': np.mean([10.00, 30.00]), | |
| 38 | + | 'gpt4': np.mean([30.00, 60.00]), | |
| 39 | + | 'gpt4-32k': np.mean([60.00, 120.00]), | |
| 40 | + | 'gpt4o': np.mean([2.5, 10.00]), | |
| 41 | + | 'o1-mini': np.mean([3.00, 12.00]), | |
| 42 | + | 'o1': np.mean([15, 60]), | |
| 43 | + | 'claude': np.mean([8, 24]), | |
| 44 | + | 'claude-sonnet': np.mean([3, 15]), | |
| 45 | + | 'claude-opus': np.mean([15, 75]), | |
| 46 | + | 'gemini': np.mean([0.50, 1.50]), | |
| 47 | + | 'gemini-pro': np.mean([1.25, 5.00]), | |
| 48 | + | 'gemini-flash': np.mean([0.075, 0.30]), | |
| 49 | + | 'mistral-tiny': np.mean([2.5 / 4, 7.5 / 4]), | |
| 50 | + | 'mistral-small': np.mean([2.5 / 3, 7.5 / 3]), | |
| 51 | + | 'mistral-medium': np.mean([2.5 / 2, 7.5 / 2]), | |
| 52 | + | 'mistral-large': np.mean([2.5, 7.5]), | |
| 53 | + | 'aws-claude': np.mean([8, 24]), | |
| 54 | + | 'aws-claude-sonnet': np.mean([3, 15]), | |
| 55 | + | 'aws-claude-opus': np.mean([15, 75]), | |
| 56 | + | 'aws-mistral-small': np.mean([0.001 * 1000, 0.003 * 1000]), | |
| 57 | + | 'aws-mistral-large': np.mean([0.004 * 1000, 0.012 * 1000]), | |
| 58 | + | 'azure-gpt4-32k': np.mean([60.00, 120.00]), | |
| 59 | + | 'azure-gpt4': np.mean([30.00, 60.00]), | |
| 60 | + | 'azure-gpt4-turbo': np.mean([10.00, 30.00]), | |
| 61 | + | 'azure-gpt4o': np.mean([5, 15]), | |
| 62 | + | 'azure-turbo': np.mean([1.5, 2]), | |
| 63 | + | ||
| 64 | + | # These are ignored. | |
| 65 | + | # Not doing image models because the API returns the tokens, not the number of images generated. | |
| 66 | + | 'dall-e': 0, | |
| 67 | + | 'azure-dall-e': 0, | |
| 68 | + | 'palm-bison': 0, | |
| 69 | + | 'bison': 0, | |
| 70 | + | } | |
| 71 | + | ||
| 72 | + | ||
| 73 | + | def dynamic_print(msg: str): | |
| 74 | + | if _IS_SERVICE: | |
| 75 | + | logger.info(msg) | |
| 76 | + | else: | |
| 77 | + | tqdm.write(msg) | |
| 78 | + | ||
| 79 | + | ||
| 80 | + | def get_unique_urls(index_name="proxy_stats"): | |
| 81 | + | """ | |
| 82 | + | Retrieves all unique URLs from the specified Elasticsearch index. | |
| 83 | + | ||
| 84 | + | :param index_name: Name of the index to search (default: "proxy_stats") | |
| 85 | + | :return: A list of unique URLs | |
| 86 | + | """ | |
| 87 | + | s = Search(using=ElasticClient().client, index=index_name) | |
| 88 | + | s.aggs.bucket('unique_urls', 'terms', field='url.keyword', size=10000) | |
| 89 | + | s = s.extra(size=0) | |
| 90 | + | response = s.execute() | |
| 91 | + | unique_urls = [bucket.key for bucket in response.aggregations.unique_urls.buckets if bucket.key != 'proxy.chub-archive.evulid.cc'] | |
| 92 | + | return unique_urls | |
| 93 | + | ||
| 94 | + | ||
| 95 | + | def process_docs_by_url(index_name="proxy_stats", batch_size=1000): | |
| 96 | + | """ | |
| 97 | + | Processes documents for each unique URL in the index, sorted by timestamp. | |
| 98 | + | ||
| 99 | + | :param index_name: Name of the index to search (default: "proxy_stats") | |
| 100 | + | :param batch_size: Number of documents to fetch in each batch (default: 1000) | |
| 101 | + | """ | |
| 102 | + | es_client = ElasticClient().client | |
| 103 | + | unique_urls = get_unique_urls(index_name) | |
| 104 | + | grand_total = 0 | |
| 105 | + | ||
| 106 | + | main_pbar = tqdm(unique_urls, position=0, disable=_IS_SERVICE) | |
| 107 | + | ||
| 108 | + | for url in main_pbar: | |
| 109 | + | s = Search(using=es_client, index=index_name) | |
| 110 | + | s = s.query(Q('term', url__keyword=url)).sort({"timestamp": {"order": "asc"}}) | |
| 111 | + | doc_count = s.count() | |
| 112 | + | ||
| 113 | + | if _IS_SERVICE: | |
| 114 | + | logger.info(f'Processing {doc_count} documents for {url}') | |
| 115 | + | ||
| 116 | + | csv_rows = [] | |
| 117 | + | total_api_costs = {} | |
| 118 | + | total_cost = 0 | |
| 119 | + | previous_doc = None | |
| 120 | + | ||
| 121 | + | docs = scan(es_client, query=s.to_dict(), index=index_name, size=batch_size, preserve_order=True) | |
| 122 | + | pbar = tqdm(docs, total=doc_count, desc=url, disable=_IS_SERVICE, leave=True, position=1) | |
| 123 | + | ||
| 124 | + | i = 0 | |
| 125 | + | for current_doc in pbar: | |
| 126 | + | assert current_doc['_source']['url'] == url | |
| 127 | + | if _IS_TEST and i == 1400: | |
| 128 | + | previous_doc = None | |
| 129 | + | break | |
| 130 | + | i += 1 | |
| 131 | + | ||
| 132 | + | current_timestamp = current_doc['_source']['timestamp'] | |
| 133 | + | current_timestamp_d = datetime.fromtimestamp(current_timestamp) | |
| 134 | + | ||
| 135 | + | if previous_doc: | |
| 136 | + | previous_timestamp = previous_doc['_source']['timestamp'] | |
| 137 | + | previous_timestamp_d = datetime.fromtimestamp(previous_timestamp) | |
| 138 | + | if current_timestamp_d < previous_timestamp_d: | |
| 139 | + | raise Exception(f'ASSERT FAILED: {current_timestamp_d} < {previous_timestamp_d}') | |
| 140 | + | current_uptime = current_doc['_source']['uptime'] | |
| 141 | + | previous_uptime = previous_doc['_source']['uptime'] | |
| 142 | + | if previous_uptime > current_uptime: | |
| 143 | + | # Process and save the previous document's stats (which were before the proxy restarted). | |
| 144 | + | new_costs = determine_costs(previous_doc['_source']) | |
| 145 | + | costs = combine_model_stats(total_api_costs, new_costs) | |
| 146 | + | total_cost += calc_total_cost(costs) | |
| 147 | + | timestamp = previous_timestamp_d.strftime('%m-%d-%Y %H:%M') | |
| 148 | + | csv_rows.append([new_costs, timestamp, previous_uptime]) | |
| 149 | + | ||
| 150 | + | previous_doc = current_doc | |
| 151 | + | pbar.set_postfix({ | |
| 152 | + | 'Timestamp': current_timestamp_d.strftime('%m-%d-%Y %H:%M'), | |
| 153 | + | 'Total Cost': f'{locale.currency(total_cost, grouping=True)}' | |
| 154 | + | }) | |
| 155 | + | main_pbar.set_postfix({ | |
| 156 | + | 'Grand Total': f'{locale.currency(grand_total, grouping=True)}' | |
| 157 | + | }) | |
| 158 | + | ||
| 159 | + | # Process the last document | |
| 160 | + | if previous_doc: | |
| 161 | + | new_costs = determine_costs(previous_doc['_source']) | |
| 162 | + | costs = combine_model_stats(total_api_costs, new_costs) | |
| 163 | + | total_cost += calc_total_cost(costs) | |
| 164 | + | timestamp = datetime.fromtimestamp(previous_doc['_source']['timestamp']).strftime('%m-%d-%Y %H:%M') | |
| 165 | + | csv_rows.append([new_costs, timestamp, previous_doc['_source']['uptime']]) | |
| 166 | + | ||
| 167 | + | grand_total += total_cost | |
| 168 | + | if _WRITE_CSVS and len(csv_rows): | |
| 169 | + | write_to_csv(csv_rows, filename=f'{url.replace(".", "-")}.csv') | |
| 170 | + | if _IS_SERVICE: | |
| 171 | + | logger.info(f'Total cost for {url}: {locale.currency(total_cost, grouping=True)}') | |
| 172 | + | ||
| 173 | + | return grand_total | |
| 174 | + | ||
| 175 | + | ||
| 176 | + | def determine_costs(doc): | |
| 177 | + | costs = {} | |
| 178 | + | for k, v in doc.items(): | |
| 179 | + | if isinstance(v, dict) and v.get('usage'): | |
| 180 | + | usage = Usage(type=k, cost=v['usage']['cost'] if v['usage']['cost'] > 0 else None, tokens=v['usage']['tokens']) | |
| 181 | + | if k not in costs: | |
| 182 | + | costs[k] = [] | |
| 183 | + | costs[k].append(usage) | |
| 184 | + | return calculate_costs(costs) | |
| 185 | + | ||
| 186 | + | ||
| 187 | + | def calculate_costs(data: dict): | |
| 188 | + | results = {} | |
| 189 | + | for k, v in data.items(): | |
| 190 | + | if k not in results: | |
| 191 | + | results[k] = { | |
| 192 | + | 'cost': 0, | |
| 193 | + | 'tokens': 0, | |
| 194 | + | } | |
| 195 | + | for x in v: | |
| 196 | + | if x.cost: | |
| 197 | + | results[k]['cost'] = results[k]['cost'] + x.cost | |
| 198 | + | results[k]['tokens'] = results[k]['tokens'] + x.tokens | |
| 199 | + | ||
| 200 | + | for api_type, v in results.items(): | |
| 201 | + | if v['cost'] == 0: | |
| 202 | + | v['cost'] = (v['tokens'] / 1_000_000) * API_COSTS_PER_1M[api_type] | |
| 203 | + | ||
| 204 | + | return results | |
| 205 | + | ||
| 206 | + | ||
| 207 | + | def calc_total_cost(data: dict): | |
| 208 | + | total = 0 | |
| 209 | + | for k, v in data.items(): | |
| 210 | + | total += v['cost'] | |
| 211 | + | return np.round(total, 2) | |
| 212 | + | ||
| 213 | + | ||
| 214 | + | def combine_model_stats(dict1: dict, dict2: dict): | |
| 215 | + | result = {} | |
| 216 | + | all_models = set(dict1.keys()) | set(dict2.keys()) | |
| 217 | + | for model in all_models: | |
| 218 | + | result[model] = { | |
| 219 | + | 'cost': (dict1.get(model, {}).get('cost', 0) + | |
| 220 | + | dict2.get(model, {}).get('cost', 0)), | |
| 221 | + | 'tokens': (dict1.get(model, {}).get('tokens', 0) + | |
| 222 | + | dict2.get(model, {}).get('tokens', 0)) | |
| 223 | + | } | |
| 224 | + | return result | |
| 225 | + | ||
| 226 | + | ||
| 227 | + | def write_to_csv(data: list, filename: str): | |
| 228 | + | all_models = set() | |
| 229 | + | for row in data: | |
| 230 | + | all_models.update(row[0].keys()) | |
| 231 | + | ||
| 232 | + | header = ['timestamp', 'uptime_seconds'] + [f'{model}_{metric}' for model in sorted(all_models) for metric in ['cost', 'tokens']] | |
| 233 | + | ||
| 234 | + | with open(filename, 'w', newline='') as csvfile: | |
| 235 | + | writer = csv.writer(csvfile) | |
| 236 | + | writer.writerow(header) | |
| 237 | + | ||
| 238 | + | for row in data: | |
| 239 | + | model_data = row[0] | |
| 240 | + | timestamp = row[1] | |
| 241 | + | ||
| 242 | + | if isinstance(timestamp, (int, float)): | |
| 243 | + | timestamp = datetime.fromtimestamp(timestamp).strftime('%m-%d-%Y %H:%M') | |
| 244 | + | ||
| 245 | + | csv_row = [timestamp] | |
| 246 | + | ||
| 247 | + | # Add uptime (if present, otherwise use 0) | |
| 248 | + | csv_row.append(row[2] if len(row) > 2 else 0) | |
| 249 | + | ||
| 250 | + | for model in sorted(all_models): | |
| 251 | + | if model in model_data: | |
| 252 | + | csv_row.extend([model_data[model].get('cost', 0), model_data[model].get('tokens', 0)]) | |
| 253 | + | else: | |
| 254 | + | csv_row.extend([0, 0]) # Add zeros for missing models | |
| 255 | + | ||
| 256 | + | writer.writerow(csv_row) | |
| 257 | + | ||
| 258 | + | dynamic_print(f"Data has been written to {filename}") | |
| 259 | + | ||
| 260 | + | ||
| 261 | + | def main(args): | |
| 262 | + | try: | |
| 263 | + | ElasticClient.initialise(ELASTIC_HOST, ELASTIC_INDEX, ELASTIC_API_KEY) | |
| 264 | + | except elastic_transport.ConnectionError as e: | |
| 265 | + | logger.critical(f'Failed to connect to Elastic: {e}') | |
| 266 | + | quit(1) | |
| 267 | + | ||
| 268 | + | locale.setlocale(locale.LC_ALL, '') | |
| 269 | + | ||
| 270 | + | logger.info('Fetching URLs from Elastic...') | |
| 271 | + | unique_urls = get_unique_urls() | |
| 272 | + | logger.info(f'Found {len(unique_urls)} unique URLs.') | |
| 273 | + | grand_total = process_docs_by_url() | |
| 274 | + | dynamic_print(f'Total wasted....... {locale.currency(grand_total, grouping=True)}') | |
| 275 | + | ||
| 276 | + | ||
| 277 | + | if __name__ == '__main__': | |
| 278 | + | parser = argparse.ArgumentParser() | |
| 279 | + | parser.add_argument('-s', '--service', action='store_true', help='Run as a service.') | |
| 280 | + | parser.add_argument('-t', '--test', action='store_true') | |
| 281 | + | parser.add_argument('--write-csv', action='store_true', help='Write the CSVs.') | |
| 282 | + | args = parser.parse_args() | |
| 283 | + | _IS_SERVICE = args.service | |
| 284 | + | _IS_TEST = args.test | |
| 285 | + | _WRITE_CSVS = args.write_csv | |
| 286 | + | main(args) | |
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