import io import json from argparse import ArgumentParser import click from huggingface_hub import HfApi, get_token from loguru import logger from sklearn.metrics import get_scorer_names from . import BaseCompetitionsCommand COMPETITION_DESC = """Sample competition description""" DATASET_DESC = """Sample dataset description""" SUBMISSION_DESC = """Sample submission description""" SOLUTION_CSV = """ id,pred,split 0,1,public 1,0,private 2,0,private 3,1,private 4,0,public 5,1,private 6,1,public 7,1,private 8,0,public 9,0,private 10,0,private 11,0,private 12,1,private 13,0,private 14,1,public 15,1,private 16,1,private 17,0,private 18,0,private 19,0,public 20,0,private 21,0,private 22,1,private 23,1,public 24,0,private 25,0,private 26,0,public 27,1,private 28,1,private 29,0,private 30,0,public """ SOLUTION_CSV = SOLUTION_CSV.strip() DOCKERFILE = """ FROM huggingface/competitions:latest CMD uvicorn competitions.app:app --host 0.0.0.0 --port 7860 --workers 1 """ DOCKERFILE = DOCKERFILE.replace("\n", " ").replace(" ", "\n").strip() README = """ --- title: My Competition emoji: 🤗 colorFrom: indigo colorTo: gray sdk: docker pinned: false --- """ README = README.strip() def create_command_factory(args): return CreateCompetitionAppCommand() class CreateCompetitionAppCommand(BaseCompetitionsCommand): @staticmethod def register_subcommand(parser: ArgumentParser): create_project_parser = parser.add_parser("create", description="✨ Create a new competition") create_project_parser.set_defaults(func=create_command_factory) def _create_readme(self, competition_name): _readme = "---\n" _readme += f"title: {competition_name}\n" _readme += "emoji: 🚀\n" _readme += "colorFrom: green\n" _readme += "colorTo: indigo\n" _readme += "sdk: docker\n" _readme += "pinned: false\n" _readme += "duplicated_from: autotrain-projects/autotrain-advanced\n" _readme += "---\n" _readme = io.BytesIO(_readme.encode()) return _readme def run(self): competition_name_text = "Competition name. Must be unqiue and contain only letters, numbers & hypens." competition_name = click.prompt(competition_name_text, type=str) competition_name = competition_name.lower().replace(" ", "-") competition_name = competition_name.replace("_", "-") competition_name = competition_name.replace(".", "-") competition_name = competition_name.replace("/", "-") competition_name = competition_name.replace("\\", "-") competition_name = competition_name.replace(":", "-") competition_name = competition_name.replace(";", "-") competition_name = competition_name.replace(",", "-") competition_name = competition_name.replace("!", "-") competition_name = competition_name.replace("?", "-") competition_name = competition_name.replace("'", "-") competition_name = competition_name.replace('"', "-") competition_name = competition_name.replace("`", "-") competition_name = competition_name.replace("~", "-") competition_name = competition_name.replace("@", "-") competition_name = competition_name.replace("#", "-") competition_org_text = "Competition organization. Choose one of the organizations you are a part of." competition_org = click.prompt(competition_org_text, type=str) competition_type_text = "Competition type. Choose one of 'generic', 'script'" competition_type = click.prompt(competition_type_text, type=str) if competition_type not in ["generic", "script"]: raise ValueError(f"Competition type {competition_type} not found in ['generic', 'script']") if competition_type == "script": time_limit = click.prompt("Time limit in seconds", type=int) else: time_limit = 10 hardware_choices = [ "cpu-basic", "cpu-upgrade", "t4-small", "t4-medium", "zero-a10g", "a10g-small", "a10g-large", "a10g-largex2", "a10g-largex4", "a100-large", ] hardware_text = f"Hardware. Choose one of {hardware_choices}" hardware = click.prompt(hardware_text, type=str) if hardware not in hardware_choices: raise ValueError(f"Hardware {hardware} not found in {hardware_choices}") metric_choices = get_scorer_names() metric_text = f"Metric. Choose one of {metric_choices}" metric = click.prompt(metric_text, type=str) if metric not in metric_choices: raise ValueError(f"Metric {metric} not found in {metric_choices}") eval_higher_text = "Is higher metric better? Enter 1, if yes" eval_higher = click.prompt(eval_higher_text, type=int) if eval_higher not in [0, 1]: raise ValueError("Invalid value for eval_higher. Must be 0 or 1") submission_limit_text = "Daily submission limit" submission_limit = click.prompt(submission_limit_text, type=int) if submission_limit < 1: raise ValueError("Submission limit must be positive integer, greater than 0") end_date_text = "End date. Format: YYYY-MM-DD. Private leaderboard will be available on this date." end_date = click.prompt(end_date_text, type=str) submission_id_col_text = "Submission ID column name. This column will be used to identify submissions." submission_id_col = click.prompt(submission_id_col_text, type=str) submission_cols_text = "Submission columns. Enter comma separated column names, including id column." submission_cols = click.prompt(submission_cols_text, type=str) submission_rows_text = "Submission rows. How many rows are allowed in a submission, exluding header?" submission_rows = click.prompt(submission_rows_text, type=int) competition_logo_text = "Competition logo. Enter URL to logo." competition_logo = click.prompt(competition_logo_text, type=str) conf_json = { "COMPETITION_TYPE": competition_type, "SUBMISSION_LIMIT": submission_limit, "TIME_LIMIT": time_limit, "SELECTION_LIMIT": 2, "HARDWARE": hardware, "END_DATE": end_date, "EVAL_HIGHER_IS_BETTER": eval_higher, "SUBMISSION_ID_COLUMN": submission_id_col, "SUBMISSION_COLUMNS": submission_cols, "SUBMISSION_ROWS": submission_rows, "EVAL_METRIC": metric, "LOGO": competition_logo, } teams_json = {} user_team_json = {} logger.info(f"Creating competition: {competition_name}") api = HfApi() api.create_repo( repo_id=f"{competition_org}/{competition_name}", repo_type="dataset", private=True, ) conf_json = json.dumps(conf_json, indent=4) conf_json_bytes = conf_json.encode("utf-8") conf_json_buffer = io.BytesIO(conf_json_bytes) api.upload_file( path_or_fileobj=conf_json_buffer, path_in_repo="conf.json", repo_id=f"{competition_org}/{competition_name}", repo_type="dataset", ) teams_json = json.dumps(teams_json, indent=4) teams_json_bytes = teams_json.encode("utf-8") teams_json_buffer = io.BytesIO(teams_json_bytes) api.upload_file( path_or_fileobj=teams_json_buffer, path_in_repo="teams.json", repo_id=f"{competition_org}/{competition_name}", repo_type="dataset", ) user_team_json = json.dumps(user_team_json, indent=4) user_team_json_bytes = user_team_json.encode("utf-8") user_team_json_buffer = io.BytesIO(user_team_json_bytes) api.upload_file( path_or_fileobj=user_team_json_buffer, path_in_repo="user_team.json", repo_id=f"{competition_org}/{competition_name}", repo_type="dataset", ) comp_desc = io.BytesIO(COMPETITION_DESC.encode()) api.upload_file( path_or_fileobj=comp_desc, path_in_repo="COMPETITION_DESC.md", repo_id=f"{competition_org}/{competition_name}", repo_type="dataset", ) dataset_desc = io.BytesIO(DATASET_DESC.encode()) api.upload_file( path_or_fileobj=dataset_desc, path_in_repo="DATASET_DESC.md", repo_id=f"{competition_org}/{competition_name}", repo_type="dataset", ) submission_desc = io.BytesIO(SUBMISSION_DESC.encode()) api.upload_file( path_or_fileobj=submission_desc, path_in_repo="SUBMISSION_DESC.md", repo_id=f"{competition_org}/{competition_name}", repo_type="dataset", ) solution_csv = io.BytesIO(SOLUTION_CSV.encode()) api.upload_file( path_or_fileobj=solution_csv, path_in_repo="solution.csv", repo_id=f"{competition_org}/{competition_name}", repo_type="dataset", ) # create competition space api.create_repo( repo_id=f"{competition_org}/{competition_name}", repo_type="space", space_sdk="docker", space_hardware="cpu-basic" if competition_type == "script" else hardware, private=True, ) api.add_space_secret(repo_id=f"{competition_org}/{competition_name}", key="HF_TOKEN", value=get_token()) api.add_space_secret( repo_id=f"{competition_org}/{competition_name}", key="COMPETITION_ID", value=f"{competition_org}/{competition_name}", ) readme = self._create_readme(competition_name) api.upload_file( path_or_fileobj=readme, path_in_repo="README.md", repo_id=f"{competition_org}/{competition_name}", repo_type="space", ) _dockerfile = io.BytesIO(DOCKERFILE.encode()) api.upload_file( path_or_fileobj=_dockerfile, path_in_repo="Dockerfile", repo_id=f"{competition_org}/{competition_name}", repo_type="space", ) logger.info( "Created private dataset and competition space. To make competition public, you should make the space private. Please note that the dataset should always be kept private." )