💠Why Covalent?#
Covalent overcomes computational and operational challenges inherent in AI/ML experimentation.
Computational Challenges |
Operational Challenges |
|---|---|
Advanced compute hardware is expensive, and access is often limited – shared with other researchers, for example. |
Proliferation of models, datasets, and hardware trials. |
You’d like to iterate quickly, but running large models takes time. |
Switching between development tools, including notebooks, scripts, and submission queues. |
Parallel computation speeds execution, but requires careful attention to data relationships. |
Tracking, repeating, and sharing results. |
With Covalent, you:
Assign functions to appropriate resources: Use advanced hardware (quantum computers, HPC clusters) for the heavy lifting and commodity hardware for bookkeeping.
Test functions on local servers before shipping them to advanced hardware.
Let Covalent’s services analyze functions for data independence and automatically parallelize them.
Run experiments from a Jupyter notebook (or whatever your preferred interactive Python environment is).
Track workflows and examine results in a browser-based GUI.