Version 1.0.0: Tagr Data Science Experimentation Library

What is Tagr ?

A cloud agnostic data science productivity tool that will:

• help streamline the data science experimentation process
• allow data scientists to manage models and experiment data
• seamlessly integrate with different cloud storage providers. As of right now, v1.0.0 currently supports Amazon S3

Instructions

1. Import tagr

from tagr.tagging.artifacts import Tags
from tagr.config import EXP_OBJECTS, OBJECTS

2. After building your model and performing exploratory data analysis of your dataset, tag your training/testing/prediction datasets and model

x = tag.save(mock_df1, "X_train", "int")
y = tag.save(mock_df2, "y_train")
model = tag.save(RandomForestClassifier(max_depth=30), "model")
lin_model = tag.save(LinearRegression(), 'linmodel', 'model')
y_pred = tag.save(mock_df3, 'y_pred')

3. View what artifacts you have tagged so far

tag.inspect()

4. Push all your tagged artifacts to a cloud storage solution of your choice

# s3
tag.flush('waterflow-tagr', 'dev/eric', 'aws', 'demo')

# local
tag.flush('waterflow-tagr', 'eric', 'demo')