gokart

[](https://github.com/m3dev/gokart/actions?query=workflow%3ATest) [](https://gokart.readthedocs.io/en/latest/) [](https://pypi.org/project/gokart/) [](https://pypi.org/project/gokart/)  Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline. [Documentation](https://gokart.readthedocs.io/en/latest/) for the latest release is hosted on readthedocs. # About gokart Here are some good things about gokart. - The following meta 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 - Automatically rerun the pipeline if parameters of Tasks are changed. - Support GCS and S3 as a data store for intermediate results of Tasks in the pipeline. - 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](https://en.wikipedia.org/wiki/SOLID) principles as much as possible - Tasks are locked via redis even if they run in parallel **All the functions above are created for constructing Machine Learning batches. Provides an excellent environment for reproducibility and team development.** Here are some non-goal / downside of the gokart. - Batch execution in parallel is supported, but parallel and concurrent execution of task in memory. - Gokart is focused on reproducibility. So, I/O and capacity of data storage can become a bottleneck. - No support for task visualize. - Gokart is not an experiment management tool. The management of the execution result is cut out as [Thunderbolt](https://github.com/m3dev/thunderbolt). - Gokart does not recommend writing pipelines in toml, yaml, json, and more. Gokart is preferring to write them in Python. # Getting Started Within the activated Python environment, use the following command to install gokart. ``` pip install gokart ``` # Quickstart A minimal gokart tasks looks something like this: ```python import gokart class Example(gokart.TaskOnKart): def run(self): self.dump('Hello, world!') task = Example() output = gokart.build(task) print(output) ``` `gokart.build` return the result of dump by `gokart.TaskOnKart`. The example will output the following. ``` Hello, world! ``` This is an introduction to some of the gokart. There are still more useful features. Please See [Documentation](https://gokart.readthedocs.io/en/latest/) . Have a good gokart life. # Achievements Gokart is a proven product. - It's actually been used by [m3.inc](https://corporate.m3.com/en) for over 3 years - Natural Language Processing Competition by [Nishika.inc](https://nishika.com) 2nd prize : [Solution Repository](https://github.com/vaaaaanquish/nishika_akutagawa_2nd_prize) # Thanks gokart is a wrapper for luigi. Thanks to luigi and dependent projects! - [luigi](https://github.com/spotify/luigi)