A unified platform for Accelerated Computing
Covalent is a Pythonic workflow orchestration platform for scaling workloads from your laptop to any compute backend
Scale to any backend, on any cloud, with a single line of code
import covalent as ct
# Ship your task to any device
@ct.electron("awsbatch")
def training(model,X,y):
return model.train(X,y)
# Combine them however you want !
@ct.lattice
def compare(X,y):
model=...
c_model=training(model,X,y)
return plot_performance(c_model,q_model)
run_id=ct.dispatch(compare)(X_train,y_train)
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A simple, unified framework
Covalent unifies all of your hardware, software, and cloud infrastructure into a single scalable platform
Cloud abstraction
Focus on solving mission critical problems while Covalent handles all of the cloud and DevOps overhead
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Heterogenous hardware
Easily connect to – and iterate across – diverse hardware platforms without disrupting end user workflows
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Native parallelization
Leverage Covalent's dependency inspection capabilities to natively run independent tasks in parallel
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Intuitive user interface
Visualize workflow dependencies, monitor job progress, and easily reproduce workflows from a convenient browser-based UI
Learn moreBuilt for heterogeneous workflows
Easily mix and match languages, clouds, and hardware backends
Partners
Covalent partners with leading teams and organizations around the world
Industry & Vertical Agnostic
Machine Learning
Rapidly prototype computationally intensive ML models, and seamlessly scale to any hardware or cloud – all from your Python environment.
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Bio & Life Sciences
Prototype and scale workflows from image analysis to drug discovery across multiple cloud platforms effortlessly from a Jupyter notebook
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Chemistry
Build dynamic workflows that scale with the size of molecular systems and flexibly match software to hardware resources.
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Quantum Computing
Compare emerging quantum platforms and algorithms with ease. Covalent integrates quantum into the broader compute ecosystem and lowers the barrier to entry for quantum-curious organizations
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Scientific Computing
Rapidly iterate over models, collect and organize large datasets, and collaborate with other scientists. Covalent reduces the time from hypothesis to publication by eliminating operational overheads.
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HPC
Deploy and dynamically balance workloads across federated HPC clusters without needing to interact with individual file systems or schedulers.
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