Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
Gokart solves reproducibility, task dependencies, constraints of good code, and ease of use for Machine Learning Pipeline.
Documentation for the latest release is hosted on readthedocs.
Here are some good things about gokart.
pkl
file with hash value
pandas.DataFrame
type and column checking during I/OAll 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.
Within the activated Python environment, use the following command to install gokart.
pip install gokart
A minimal gokart tasks looks something like this:
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 .
Have a good gokart life.
Gokart is a proven product.
gokart is a wrapper for luigi. Thanks to luigi and dependent projects!