Hypergol achieves compounding acceleration by providing opinionated tools in all areas of the Data Science stack.
Treat your data as first-class citizens. Handle everything in python classes. Autogenerate the code from the command line to avoid this tedious and error-prone task.
This simplifies:
Your data is the most utilised part of your project. Rather than defining it as columns in pandas dataframes or dictionaries that lack validation, create standardised and composable structures everywhere.
Store all your data in the same way with the data model defined above by using Hypergol’s own storage format.
This enables:
The storage format enables processing larger than machine memory data sizes and parallel processing by sharding. Hypergol uses the same SHA-1 checksum and compression git uses to create data lineage.
You will always know how your data was created and process it as efficiently as possible.
Parallelise your data processing with Hypergol’s own no-infra task scheduler. All you need is just a really large clound instance. Using its custom data format you are able to process huge datasets multithreaded just as easily as on a single thread.
No schedulers, no DAGs, no containers, no clusters. Just organised computing power.
Hypergol’s Deep Learning Framework will enable you to create models that are easily maintainable. Treat machine learning as programming by adapting a well-structured system.
This enables:
Hypergol’s batch processing classes enable seamless integration with Hypergol’s storage format to simplify training and evaluation.
Hypergol’s integrated framework enables deployment code generation with FastAPI and uvicorn.
Using your datamodel you will be able to generate typed APIs for your models automatically through pydantic.
Because FastAPI uses Swagger your code is immediately self-documented. Deploy your models in minutes!
All you need to do is `pip install hypergol`
Preemptable instances are cheaper than normal ones but can automatically disappear. To deal with it, you not only need to make sure your data pipeline saves your progress at regular checkpoints but also to get notified when this unfortunate event happens.
Step-by-step instruction on how to create an instance, install the right tools easily and start using Hypergol.
How to get your usual environment on a new VM in no time at all? Including scripts to start Jupyter notebook server, create virtual environments and git autocomplete in command line.