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skylite
Advanced tools
The aim of this project is to create a minimalist approach to managing artifacts generated throughout the model development process.
Project: A Project is the single element that manages all of your tasks.
Store: A Store is the single element that manages all artifacts of a single type, across all projects.
The source code is currently hosted on GitHub at: https://github.com/jesse-sealand/skylite
The latest released version are available at Python Package Index (PyPI).
pip install skylite
from skylite import Exchange
# Settings
home_directory = '~/Documents'
settings = {
"exchange_name": "Exchange",
"available_stores": ["data-store", "model-store", "result-store", "project-store"],
}
# Setup
SkyLight = Exchange(home_directory, settings)
SkyLight.open_exchange()
# Create Project
PROJECT_NAME = 'aerial-imagery5'
SkyLight.create_project(PROJECT_NAME)
sky_proj = SkyLight.open_project(PROJECT_NAME)
# Start adding model artifacts when modeling
for i in range(0,10):
# Create new instance of Trial
sky_proj.create_trial()
"""
Perform Modeling / scoring / analysis
"""
# Store artifacts for this Trial
data_dict = {'train': df,
'test': df,
'score': df}
model_dict = {'model': clf}
results_dict = {'accuracy': 0.98,
'f1-score': 0.75
}
sky_proj.store_objects('data-store', data_dict)
sky_proj.store_objects('model-store', model_dict)
sky_proj.store_objects('result-store', results_dict)
# close instance of trial and save artifacts
sky_proj.close_trial()
See the change log for a detailed list of changes in each version.
This extension is licensed under the MIT License.
The official documentation is hosted on Read the Docs.
FAQs
Manage model artifacts throughout the model development process.
We found that skylite demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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