Tworzenie zadań na SprawdzAI

sai ds

sai ds create [nazwa]

sai ds unlink [nazwa] --notebook/--checker (-n/-c) --valid/--test (-v/-t)

sai ds link [nazwa] --notebook/--checker (-n/-c) --valid/--test (-v/-t)

sai ds link [nazwa] --test

sai ds send [nazwa]

sai ds send [nazwa] --no-zip (-n)

sai ds pull [nazwa]

sai ds pull --missing (-m)

sai ds link [id] --notebook/--checker (-n/-c) --valid/--test (-v/-t)

sai ds link 28d12307-3880-4bdc-bbf6-68fefb3a434d

sai ds rename [nazwa] [nowa nazwa]

sai ds create train sai ds create valid --checker --valid sai ds create test --checker --test

import os import subprocess os.remove('initiator.py') subprocess.run(['python', '-u', 'submission.py'])

import os import subprocess import shutil os.makedirs('/home/runner/.cache/torch/hub/checkpoints', exist_ok=True) shutil.move('./resnet18-f37072fd.pth', '/home/runner/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth') os.remove('initiator.py') subprocess.run(['python', '-u', 'submission.py'])

from checklib import IS_VALID, NOTEBOOK_EXECUTION_TIME, NOTEBOOK_TIME_LIMIT, CHECKER_TIME_LIMIT from checklib import check_file_exists, linear_grade, safe_score, debug, format_time, print_tree, run_pickle from checklib import SubmissionError, TimeError

def check() -> float | tuple[float, str]: punkty = ... notatki = 'Ta wiadomość będzie widoczna dla użytkowników' return punkty, notatki

@safe_score def check() -> float | tuple[float, str]: return punkty, notatki

def check() -> float | tuple[float, str]: raise SubmissionError('Ta wiadomość będzie widoczna dla użytkowników')

def check() -> float | tuple[float, str]: raise SubmissionError()

def check(): with open('wynik.txt', 'r') as f: res = f.read() try: wynik = int(res) except Exception: raise SubmissionError('Wysłany plik wynik.txt nie jest liczbą całkowitą') if wynik < 0 or wynik > 100: raise SubmissionError('Wynik musi być z zakresu od 0 do 100') return wynik

from checklib import IS_VALID, NOTEBOOK_EXECUTION_TIME, NOTEBOOK_TIME_LIMIT, CHECKER_TIME_LIMIT from checklib import write_ok, write_time_error, write_submission_error from checklib import SubmissionError, TimeError from checker import check import os import shutil print('This message is automatically printed by checkrun.py before running check():') print(f'IS_VALID: {IS_VALID}') print(f'NOTEBOOK_EXECUTION_TIME: {NOTEBOOK_EXECUTION_TIME}') print(f'NOTEBOOK_TIME_LIMIT: {NOTEBOOK_TIME_LIMIT}') print(f'CHECKER_TIME_LIMIT: {CHECKER_TIME_LIMIT}') print('checkrun.py will first remove: checker.py, checklib.py, checkrun.py and __pycache__') os.remove('checker.py') os.remove('checklib.py') os.remove('checkrun.py') shutil.rmtree('__pycache__') print('checkrun.py will now run check()...') try: result = check() if isinstance(result, tuple): score, notes = result write_ok(score, notes) else: write_ok(result) except SubmissionError as e: write_submission_error(str(e)) except TimeError as e: write_time_error(str(e))

try: import cloudpickle import numpy as np with open('nb/predict.pkl', 'rb') as f: try: predict = cloudpickle.load(f) except Exception as e: raise Exception('Nie udało się załadować pliku cloudpickle (plik został uszkodzony?) ' + repr(e)) try: predicted = predict(np.load('snails_test.npy')) except Exception as e: raise Exception('Wystąpił błąd podczas uruchamiania pliku cloudpickle: ' + repr(e)) try: if hasattr(predicted, 'cpu'): predicted = predicted.cpu() np.save('predictions.npy', predicted) except Exception as e: raise Exception('Nie udało się zapisać odpowiedzi do pliku (czy na pewno zwracasz numpy array lub tensora?) ' + repr(e)) except Exception as e: with open('error.txt', 'w', encoding='UTF-8') as f: f.write(str(e) if type(e).__name__ == 'Exception' else repr(e)) raise # raise so full logs will be visible on stderr

try: predicted = pickle_function() except Exception as e: import traceback tb = traceback.extract_tb(e.__traceback__)[-1] raise Exception('Wystąpił błąd podczas uruchamiania pliku cloudpickle: ' + repr(e) + ('' if tb.line is None) else ' ' + str(tb.line))

try: predicted = pickle_function() except Exception as e: import traceback raise Exception(traceback.format_exc())

from checklib import IS_VALID, NOTEBOOK_EXECUTION_TIME, NOTEBOOK_TIME_LIMIT, CHECKER_TIME_LIMIT from checklib import check_file_exists, linear_grade, safe_score, debug, format_time, print_tree, run_pickle from checklib import SubmissionError, TimeError import numpy as np import os @safe_score def check() -> float | tuple[float, str]: pickle_file = 'predict.pkl' # Load all test labels (files you don't want pickle function to access) into RAM and REMOVE THOSE FILES! labels = np.load('labels_test.npy') os.remove('labels_test.npy') print_tree() # This prints all files, useful for checking if there are only files that the subprocess will need # Check if all files that the notebook should generate are actually here if not check_file_exists(f'nb/{pickle_file}'): raise SubmissionError(f'Nie znaleziono pliku {pickle_file}') # Run pickle by calling subprocess_script.py # If it creates error.txt file, it will be raised as SubmissionError() # If running pickle exceeds timeout, it will raise TimeError() # You can handle these yourself to give additional error messages like in this example total_execution_time = NOTEBOOK_EXECUTION_TIME # This will be the 'Sumaryczny czas uruchomienia' total_time_limit = NOTEBOOK_TIME_LIMIT # This will be the time limit for total_execution_time total_execution_time += run_pickle(timeout = total_time_limit - total_execution_time) # Now just check the generated files. # Do not assume anything about the files, handle every exception! # If you don't run any pickles inside checker, just start from this section and remove all sections above. try: preds = np.load('predictions.npy') except Exception as e: print(e) raise SubmissionError('Funkcja predict() nie zwróciła numpy array lub tensora') if preds.shape != labels.shape: raise SubmissionError(f'Oczekiwano, że wyjście z funkcji pliku {pickle_file} będzie miało kształt {labels.shape}, ale otrzymano {preds.shape}') acc = (preds == labels).astype(float).mean() return linear_grade(acc, 0.5, 0.95, True), f'Dokładność klasyfikacji: {acc * 100 :.1f}%, Sumaryczny czas uruchomienia: {format_time(total_execution_time)}'

try: total_execution_time += run_pickle(timeout = total_time_limit - total_execution_time) except SubmissionError as e: raise SubmissionError('Możesz tu dodać dodatkową wiadomość: ' + str(e)) except TimeError as e: raise TimeError('Wiadomość gdy wystąpi przekroczenie czasu')