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Integrations
Kangas integrates with a few popular ML libraries and platforms. If there is a library you'd like Kangas to integrate with that you can't find below, please feel free to open a ticket. We prioritize integrations in order of community demand, so your feedback is very helpful.
The Kangas Python library can deploy hosted instances of Kangas to Hugging Face Spaces, prepopulated with your DataGrid. This is achieved with the kangas.integrations.huggingface.deploy_to_huggingface
method:
def deploy_to_huggingface(path, name):
"""
Deploy DataGrid as part of a hosted Kangas instance on
HuggingFace Spaces. Overwrites any existing DataGrids with
identical names.
Args:
path: (str) Name of repository to create
name: (str) DataGrid to use
"""
Example:
from kangas.integrations.huggingface import deploy_to_huggingface
deploy_to_huggingface(path="YOUR-HF-SPACE", name="YOUR-DATA.datagrid")
The Kangas CLI can import/export Hugging Face Datasets directly. For more details, see https://github.com/comet-ml/kangas/wiki/Kangas-CLI#kangas-importexport
The Kangas CLI can import/export Comet Experiments directly. For more details, see https://github.com/comet-ml/kangas/wiki/Kangas-CLI#kangas-importexport
Kangas DataGrid is completely open source; sponsored by Comet ML
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Home
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- Constructing DataGrids - building from scratch
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- Kangas Command-Line Interface
- Kangas Python API
- Integrations - with Hugging Face and Comet
- User Interface
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- Under the Hood
- Security - issues related to security
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- Roadmap - plans and known issues
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