The following data for each Task is stored separately in a pkl file with hash value
* task output data
* imported all module versions
* task processing time
* random seed in task
* displayed log
* all parameters set as class variables in the task

If change parameter of Task, rerun spontaneously.
* The above file will be generated with a different hash value
* The hash value of dependent task will also change and both will be rerun

> Support GCS or S3
> The above output is exchanged between tasks as an intermediate file, which is memory-friendly
> pandas.DataFrame type and column checking during I/O
> Directory structure of saved files is automatically determined from structure of script
> Seeds for numpy and random are automatically fixed
> Can code while adhering to SOLID principles as much as possible
> Tasks are locked via redis even if they run in parallel