Metadata-Version: 2.1
Name: tgml
Version: 0.1.2
Summary: ML Workbench
Home-page: https://github.com/TigerGraph-DevLabs/tgml
Author: Bill Shi
Author-email: bill.shi@tigergraph.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/TigerGraph-DevLabs/tgml/issues
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: requests
Requires-Dist: python-dotenv
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: torch
Requires-Dist: torch-sparse
Requires-Dist: torch-scatter
Requires-Dist: torch-geometric
Requires-Dist: boto3
Requires-Dist: google-cloud-storage

# TigerGraph ML Workbench

`tgml` provides a python toolkit for machine learning practitioners to integrate TigerGraph into their existing workflow. The core component of `tgml` is the graph loader, which behaves like a data loader for typical machine learning tasks. Putting differently, users can write their model training code as before but only replace the previous data loader with our graph loader; they will get batches of graph data for training as if the data is read from their local disk. `tgml` also provides syntactic sugar to the graph data processing APIs, so users can run algorithms such as PageRank on their graphs in TG as calling a normal Python function. Under the hood, `tgml` takes care of all the communications with the Graph Data Processing Service and convert the final output to a format that users need (dataframes and PyG graphs for now).

See the [tutorial notebooks](https://github.com/TigerGraph-DevLabs/MLWorkbench-Tutorials) on how to use the package. For `tgml` to work, the [Graph Data Processing Service](https://github.com/TigerGraph-DevLabs/GDPS) has to be running on the TigerGraph server. 

### Getting Started
Install from github:
```
pip install git+https://github.com/TigerGraph-DevLabs/tgml.git -f https://data.pyg.org/whl/torch-1.10.0+cpu.html
```


