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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)

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

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Built for heterogeneous workflows

Easily mix and match languages, clouds, and hardware backends

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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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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Deploy and dynamically balance workloads across federated HPC clusters without needing to interact with individual file systems or schedulers.

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Ready to Get Started?

Open Source

Join a fast growing community and run your first workflow in less than 2 minutes


Effortlessly move from prototype to production without any of the operational overhead


Unify, manage and scale all of your computing resources – from on-prem to multi-cloud in a custom manner

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