To quickly install Covalent and run a short demo, follow the four steps below.
Before you start
Ensure you are using a compatible OS and Python version. See the Compatibility page for supported Python versions and operating systems.
Type the following in a terminal window:
$ pip install covalent
In the terminal window, type:
$ covalent start Covalent server has started at http://localhost:48008
Open a Jupyter notebook or Python console and run the following Python code:
import covalent as ct # Construct manageable tasks out of functions # by adding the @covalent.electron decorator @ct.electron def add(x, y): return x + y @ct.electron def multiply(x, y): return x*y # Note that electrons can be shipped to variety # of executors, for example, "local" computer @ct.electron(executor="local") def divide(x, y): return x/y # Construct the workflow by stitching together # the electrons defined earlier in a function with # the @covalent.lattice decorator @ct.lattice def workflow(x, y): r1 = add(x, y) r2 = [multiply(r1, y) for _ in range(4)] r3 = [divide(x, value) for value in r2] return r3 # Dispatch the workflow dispatch_id = ct.dispatch(workflow)(1, 2) result = ct.get_result(dispatch_id) print(result)
Navigate to the Covalent UI at http://localhost:48008 to see your workflow in the queue:
Click on the dispatch ID to view the workflow graph:
Note that the computed result is displayed in the Overview.
What to Do Next#
Read First Experiment for a more thorough discussion of the components of this simple workflow, including the important role of executors.
Read Concepts gain a deeper understanding of how Covalent works.
See the Tutorials to see how to apply Covalent to real-world machine learning problems in a variety of subject domains.
See the API Reference for usage information on
lattice, and ready-to-use executors.
See AWS Plugins to see how you can specify an executor to run this example on an AWS node using only two more lines of code.