<jupyter_start><jupyter_text>`tqdm` essentially does this already, learning how long each iteration takesand knowing the number of iterations<jupyter_code>for _ in tqdm.tqdm(range(10)):
    sleepy()<jupyter_output>100%|██████████| 10/10 [00:10<00:00,  1.01s/it]<jupyter_text>In order to predict remaining time on launch, we need the run times topersist.<jupyter_code># with noise, time remaining won't be perfect but should improve with progress
for _ in tqdm.tqdm(range(50)):
    sleepy(add_noise=True)<jupyter_output>100%|██████████| 50/50 [00:25<00:00,  2.30it/s]
{'fraction_non_alphanumeric': 0.14664310954063603, 'fraction_numerical': 0.0706713780918728, 'mean_word_length': 6.56, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 7, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '105964', 'license': 'MIT', 'n_tokens_mistral': 249, 'n_tokens_neox': 211, 'n_words': 54, 'path': 'prototyping.ipynb', 'repo_name': 'stuartmac/octo-winner', 'size': '566'}