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https://github.com/m3dev/gokart
Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
https://github.com/m3dev/gokart
gokart luigi machine-learning mlops pipeline-framework
Last synced: 2 months ago
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Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
- Host: GitHub
- URL: https://github.com/m3dev/gokart
- Owner: m3dev
- License: mit
- Created: 2018-12-23T07:40:27.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-11-02T14:17:06.000Z (2 months ago)
- Last Synced: 2024-11-02T15:20:36.624Z (2 months ago)
- Topics: gokart, luigi, machine-learning, mlops, pipeline-framework
- Language: Python
- Homepage: https://gokart.readthedocs.io/en/latest/
- Size: 1.13 MB
- Stars: 315
- Watchers: 41
- Forks: 57
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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)