An opinionated multithreaded Data Science framework
Project description
Hypergol
Hypergol is a Data Science productivity toolkit to accelerate small team projects into production at the shortest possible time while still maintaining high standard code. This is achieved by autogenerating structure and code in a form that is easy to extend, so only the project-specific code needs to be written.
Hypergol provides parallel execution capabilities with little restrictions. This enables a small team to accelerate time-consuming tasks by renting larger instances from a cloud provider, but with no additional infrastructure setup time and cost.
The toolkit provides a standard serialisation (autogenerated) and storage system that natively enables parallel processing. The resulting data is also easily accessible from any python code, including jupyter notebooks.
A Hypergol Project is connected to a git repo that acts as a version controller. Both code and generated data are linked to git branches and commits. Full data lineage can be retrieved, and the storage system is verifiable through SHA1 hashes.
Tensorflow model stubs can be generated that enables standardised model development and training that both connect to the storage system above, observe SOLID principles and enable autogenerated deployment with FastAPI.
See documentation for further details at: https://hypergol.readthedocs.io/en/latest/
Join our community at: Hypergol Slack Community
Quick Start
Install it with:
pip install hypergol
Create your first projects:
python3 -m hypergol.cli.create_project MyFirstProject
cd my_first_project
And follow instructions in the projects README.md
or the tutorial at: https://hypergol.readthedocs.io/en/latest/tutorial.html
Good Luck using Hypergol!
The authors wish that Hypergol frees you from tedious tasks so you can focus more on the core part of Machine Learning and generate everything else.
May the productivity be with you!
Join our community at: Hypergol Slack Community
All feedback is welcome at: hypergol.developer@gmail.com
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