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Description:

covalentazurebatchplugin 0.17.0

Covalent Azure Batch Plugin
Covalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware. This executor plugin interfaces Covalent with Microsoft Azure Batch.
1. Installation
To use this plugin with Covalent, install it using pip:
pip install covalent-azurebatch-plugin

2. Usage Example
This is an example of how a workflow can be constructed to use the Azure Batch executor. In the example, we train a Support Vector Machine (SVM) and use an instance of the executor to execute the train_svm electron. Note that we also require DepsPip which will be required to execute the electrons.
from numpy.random import permutation
from sklearn import svm, datasets
import covalent as ct

from covalent.executor import AzureBatchExecutor

deps_pip = ct.DepsPip(
packages=["numpy==1.22.4", "scikit-learn==1.1.2"]
)

executor = AzureBatchExecutor(
tenant_id="tenant-id",
client_id="client-id",
client_secret="client-secret",
batch_account_url="https://covalent.eastus.batch.azure.com",
batch_account_domain="batch.core.windows.net",
storage_account_name="covalentbatch",
storage_account_domain="blob.core.windows.net",
base_image_uri="covalent.azurecr.io/covalent-executor-base:latest",
pool_id="covalent-pool",
retries=3,
time_limit=300,
cache_dir="/tmp/covalent",
poll_freq=10
)

# Use executor plugin to train our SVM model
@ct.electron(
executor=executor,
deps_pip=deps_pip
)
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 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 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

# Dispatch the workflow.
dispatch_id = ct.dispatch(run_experiment)(
C=1.0,
gamma=0.7
)

# Wait for our result and get result value
result = ct.get_result(dispatch_id, wait=True).result

print(result)

During the execution of the workflow, one can navigate to the UI to see the status of the workflow. Once completed, the above script should also output a value with the score of our model.
0.8666666666666667

In order for the above workflow to run successfully, one has to provision the required cloud resources as mentioned in the section Required Microsoft Azure Batch Resources.
3. Configuration
There are many configuration options that can be passed in to the class ct.executor.AzureBatchExecutor or by modifying the covalent config file under the section [executors.azurebatch].
For more information about all of the possible configuration values visit our read the docs (RTD) guide for this plugin.
4. Required Microsoft Azure Resources
In order to run your workflows with covalent there are a few notable Microsoft Azure resources that need to be provisioned first.
The required Azure resources are:
1. Batch account

2. Storage account

3. Resource group

4. Container registry (custom images only)

5. Pool of compute nodes

For more information regarding which cloud resources need to be provisioned visit our read the docs (RTD) guide for this plugin.
Getting Started with Covalent
For more information on how to get started with Covalent, check out the project homepage and the official documentation.
Release Notes
Release notes are available in the Changelog.
Citation
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

License
Covalent is licensed under the Apache License 2.0. See the LICENSE file or contact the support team for more details.