Covalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware. Users can decorate their existing Python functions as electrons (tasks) or lattices (workflows) and then run these functions locally or dispatch them to various classical and quantum backends according to the hardware requirements. After submitting workflows, users can use the browser-based Covalent viewer to visualize dependencies and the workflow execution progress. User can view a variety of information about the workflow such as the status, errors, the workflow's dependency graph, and metadata, among other things. Covalent is designed to make it easy for users to keep track of their computationally heavy experiments by providing a simple and intuitive framework to store, modify, and re-analyze computational experiments. Covalent is rapidly expanding to include support for a variety of cloud interfaces, including HPC infrastructure tools developed by major cloud providers and emerging quantum APIs. It has never been easier to deploy your code on the world's most advanced computing hardware with Covalent. Read more in the official documentation.
With Covalent's UI, bring your workflows to life!
- Purely Pythonic : No need to learn any new syntax or mess around with YAML. Construct your complex workflow programmatically with native python functions. By just adding decorators to your functions, you can supercharge your experiments.
- Native parallelization : Covalent natively parallelizes parts of your workflow that are independent of each other.
- Monitor with UI : Covalent provides an intuitive and aesthetically beautiful browser-based user interface to monitor and manage your workflows.
- Abstracted dataflow : No need to worry about the details of the underlying data structures. Covalent automatically takes care of data dependencies in the background while you concentrate on understanding the big picture.
- Result management : Covalent automatically manages the results of your workflows. Whenever you need to modify parts of your workflow, from inputs to components, Covalent natively stores and saves the run of every experiment in a reproducible format.
- Little-to-no overhead : Covalent is designed to be as lightweight as possible and is optimized for the most common use cases. Covalent's overhead is less than 0.1% of the total runtime for typical high compute applications and often has a constant overhead of ~ 10-100μs -- and this is constantly being optimized.
- Interactive : Unlike other workflow tools, Covalent is interactive. You can view, modify, and re-submit workflows directly within a Jupyter notebook.
For a more in-depth description of Covalent's features and how they work, refer to the Concepts page.
Covalent is developed using Python version 3.8 on Linux and macOS. The easiest way to install Covalent is using the PyPI package manager:
pip install covaCovalent can also be run using Docker
docker pull public.ecr.aws/covalent/covalent
# Run the container as a server
docker run -d -p 48008:8080 covalent
# Or run the container as a developer environment
docker run -it --rm covalent bash
Refer to the Getting Started guide for more details on setting up.
Begin by starting the Covalent servers:
covalent startNavigate to the user interface at http://localhost:48008 to monitor workflow execution progress.
In your Python code, it's as simple as adding a few decorators! Consider the following example which uses a support vector machine (SVM) to classify types of iris flowers.
| Without Covalent | With Covalent |
|---|---|
from numpy.random import permutation
from sklearn import svm, datasets
def load_data():
iris = datasets.load_iris()
perm = permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
return iris.data, iris.target
def train_svm(data, C, gamma):
X, y = data
clf = svm.SVC(C=C, gamma=gamma)
clf.fit(X[90:], y[90:])
return clf
def score_svm(data, clf):
X_test, y_test = data
return clf.score(
X_test[:90],
y_test[:90]
)
def run_experiment(C=1.0, gamma=0.7):
data = load_data()
clf = train_svm(
data=data,
C=C,
gamma=gamma
)
score = score_svm(data=data, clf=clf)
return score
result=run_experiment(C=1.0, gamma=0.7) |
from numpy.random import permutation
from sklearn import svm, datasets
import covalent as ct
@ct.electron
def load_data():
iris = datasets.load_iris()
perm = permutation(iris.target.size)
iris.data = iris.data[perm]
iris.target = iris.target[perm]
return iris.data, iris.target
@ct.electron
def train_svm(data, C, gamma):
X, y = data
clf = svm.SVC(C=C, gamma=gamma)
clf.fit(X[90:], y[90:])
return clf
@ct.electron
def score_svm(data, clf):
X_test, y_test = data
return clf.score(
X_test[:90],
y_test[:90]
)
@ct.lattice
def run_experiment(C=1.0, gamma=0.7):
data = load_data()
clf = train_svm(
data=data,
C=C,
gamma=gamma
)
score = score_svm(
data=data,
clf=clf
)
return score
dispatchable_func = ct.dispatch(run_experiment)
dispatch_id = dispatchable_func(
C=1.0,
gamma=0.7
)
result = ct.get_result(dispatch_id) |
>>> print(result)
0.988888888 |
>>> print(f"""
... status = {result.status}
... input = {result.inputs}
... result = {result.result}
... """)
status = Status(STATUS='COMPLETED')
input = {'C': 1.0, 'gamma': 0.7}
result = 0.9666666666666667 |
For more examples, please refer to the Covalent tutorials.
The official documentation includes tips on getting started, some high level concepts, a handful of tutorials, and the API documentation. To learn more, please refer to the Covalent documentation.
To contribute to Covalent, refer to the Contribution Guidelines. We use GitHub's issue tracking to manage known issues, bugs, and pull requests. Get started by forking the develop branch and submitting a pull request with your contributions. Improvements to the documentation, including tutorials and how-to guides, are also welcome from the community. Participation in the Covalent community is governed by the Code of Conduct.
Release notes are available in the Changelog.
- Tensorflow isn't stable with M1 Macs right now due to which the Classifying discrete spacetimes by dimension tutorial does not work with M1 Macs.
Please use the following citation in any publications:
W. J. Cunningham, S. K. Radha, F. Hasan, J. Kanem, S. W. Neagle, and S. Sanand. Covalent. Zenodo, 2022. https://doi.org/10.5281/zenodo.5903364
Covalent is licensed under the GNU Affero GPL 3.0 License. Covalent may be distributed under other licenses upon request. See the LICENSE file or contact the support team for more details.