<jupyter_start><jupyter_text>--- gokart task run---running sample task<jupyter_code>import os
os.environ['TASK_WORKSPACE_DIRECTORY'] = './resources'
!cat ./task.py
# sample task run
!rm -rf ./resources
!python task.py sample.SampleTask --name='EXAMPLE' --number=1 --local-scheduler 2> /dev/null
!python task.py sample.SampleTask --name='EXAMPLE' --number=2 --local-scheduler 2> /dev/null
!python task.py sample.SampleTask --name='EXAMPLE' --number=3 --local-scheduler 2> /dev/null
!python task.py sample.SampleTask --name='TEMP' --number=1 --local-scheduler 2> /dev/null
!python task.py sample.SampleTask --name='TEMP' --number=2 --local-scheduler 2> /dev/null
!python task.py sample.SecondTask --name='TEMP' --number=2 --param='RUN' --local-scheduler --local-temporary-directory='./resource' 2> /dev/null
!tree ./resources/<jupyter_output>[34m./resources/[00m
├── [34m__main__[00m
│   ├── SampleTask_84b0b9c5a39bce072271599c9f730660.pkl
│   ├── SampleTask_944fc52ef5011b71b5839f035f4d7e48.pkl
│   ├── SampleTask_d05a2ab961781d3d8eca3e2e5f0d608b.pkl
│   ├── SampleTask_d57cff8074e2560896974850e5d3174d.pkl
│   ├── SampleTask_e883bcfad65f5fb68259d1cd4691f384.pkl
│   └── SecondTask_ea1806322904199b2455d6e115c525ea.pkl
└── [34mlog[00m
    ├── [34mmodule_versions[00m
    │   ├── SampleTask_84b0b9c5a39bce072271599c9f730660.txt
    │   ├── SampleTask_944fc52ef5011b71b5839f035f4d7e48.txt
    │   ├── SampleTask_d05a2ab961781d3d8eca3e2e5f0d608b.txt
    │   ├── SampleTask_d57cff8074e2560896974850e5d3174d.txt
    │   ├── SampleTask_e883bcfad65f5fb68259d1cd4691f384.txt
    │   └── SecondTask_ea1806322904199b2455d6e115c525ea.txt
    ├── [34mprocessing_time[00m
    │   ├── SampleTask_84b0b9c5a39bce072271599c9f730660.pkl
    │   ├── SampleTask_944fc52ef5011b71b5839f035f4d7e48.pkl
    │   ├── SampleTask_d05a2ab961781d3d8eca3e2e5f0d6[...]<jupyter_text>--- Init Thunderbolt---using thunderbolt<jupyter_code>from thunderbolt import Thunderbolt
# 1st arg is gokart's output directory (default: $TASK_WORKSPACE_DIRECTORY)
# 2nd arg is `task_filters` (for fast)

tb = Thunderbolt()<jupyter_output><empty_output><jupyter_text>Check tasks paramchecking thunderbolt's task_id<jupyter_code>df = tb.get_task_df()
import pandas as pd
pd.set_option("display.max_colwidth", 200)
df<jupyter_output><empty_output><jupyter_text>thunderbolt filter2nd arg 'task_filters' is str or list. So fast.task_filters is partial match word for example:  - 'Tag' -> HogeTag, NormalizeHogeTag, TagTask, ... - ['Train', 'Tag'] -> TrainModel, TrainData, HogeTag, NormalizeHogeTag, TagTask, ...<jupyter_code>tb = Thunderbolt(task_filters='Sample') 
# example: all_data=True

tb.get_task_df(all_data=True)<jupyter_output><empty_output><jupyter_text>--- Data Load---using load method- arg: thunderbolt's task_id- return : data list<jupyter_code>x = tb.load(task_id=3)
print(x)<jupyter_output>this is sample output. model number: 1<jupyter_text>newest data load example<jupyter_code>tb.get_data('SampleTask')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1359255566633433, 'fraction_numerical': 0.11997341309405118, 'mean_word_length': 5.748878923766816, 'pattern_counts': {'":': 0, '<': 23, '<?xml version=': 0, '>': 31, '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': '6', 'dir': 'jupyter-structured-clean-dedup', 'id': '398972', 'license': 'MIT', 'n_tokens_mistral': 1456, 'n_tokens_neox': 1264, 'n_words': 208, 'path': 'examples/example.ipynb', 'repo_name': 'qqhann/thunderbolt', 'size': '3009'}