https://github.com/agnostiqhq/covalent-gcpbatch-plugin
Executor plugin interfacing Covalent with GCP Batch
https://github.com/agnostiqhq/covalent-gcpbatch-plugin
covalent docker etl gcp-batch parallelization pipelines python python3 workflow
Last synced: about 1 year ago
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Executor plugin interfacing Covalent with GCP Batch
- Host: GitHub
- URL: https://github.com/agnostiqhq/covalent-gcpbatch-plugin
- Owner: AgnostiqHQ
- License: apache-2.0
- Created: 2023-02-28T04:17:33.000Z (over 3 years ago)
- Default Branch: develop
- Last Pushed: 2024-10-14T17:08:23.000Z (over 1 year ago)
- Last Synced: 2024-10-23T02:52:02.420Z (over 1 year ago)
- Topics: covalent, docker, etl, gcp-batch, parallelization, pipelines, python, python3, workflow
- Language: Python
- Homepage: https://agnostiq.ai/covalent
- Size: 433 KB
- Stars: 2
- Watchers: 8
- Forks: 2
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
## Covalent Google Cloud Platform (GCP) Batch Plugin
Covalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware. This executor plugin interfaces Covalent with [GCP Batch](https://cloud.google.com/batch).
## 1. Installation
To use this plugin with Covalent, install it using `pip`:
```sh
pip install covalent-gcpbatch-plugin
```
## 2. Usage Example
This is an example of how a workflow can be constructed to use the GCP 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](https://covalent.readthedocs.io/en/latest/concepts/concepts.html#depspip) which will be required to execute the electrons.
```python
from numpy.random import permutation
from sklearn import svm, datasets
import covalent as ct
deps_pip = ct.DepsPip(
packages=["numpy==1.22.4", "scikit-learn==1.1.2"]
)
executor = ct.executor.GCPBatchExecutor(
project_id='covalent_gcp_batch',
region='us-east1',
bucket_name='covalent-storage-bucket',
container_image_uri='us-east1-docker.pkg.dev/covalent_gcp_batch_/covalent/covalent-gcpbatch-executor',
service_account_email='covalentsaaccount@covalenttesting.iam.gserviceaccount.com',
vcpus = 2, # Number of vCPUs to allocate
memory = 512, # Memory in MB to allocate
time_limit = 300, # Time limit of job in seconds
poll_freq = 3, # Number of seconds to pause before polling for the job's status
)
# 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
)
return score_svm(
data=data,
clf=clf
)
# 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.
```sh
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 GCP Batch Resources](#-required-gcp-batch-resources).
## 3. Configuration
There are many configuration options that can be passed in to the class `ct.executor.GCPBatchExecutor` or by modifying the [covalent config file](https://covalent.readthedocs.io/en/latest/how_to/config/customization.html) under the section `[executors.gcpbatch]`.
For more information about all of the possible configuration values visit our [read the docs (RTD) guide](https://covalent.readthedocs.io/en/latest/api/executors/gcpbatch.html#overview-of-configuration) for this plugin.
## 4. Required GCP Resources
In order to run your workflows with covalent there are a few notable GCP resources that need to be provisioned first. The required resources are Google storage bucket, docker artifact registry and service account.
For more information regarding which cloud resources need to be provisioned visit our [read the docs (RTD) guide](https://covalent.readthedocs.io/en/latest/api/executors/gcpbatch.html#required-cloud-resources) for this plugin.
## Getting Started with Covalent
For more information on how to get started with Covalent, check out the project [homepage](https://github.com/AgnostiqHQ/covalent) and the official [documentation](https://covalent.readthedocs.io/en/latest/).
## Release Notes
Release notes are available in the [Changelog](https://github.com/AgnostiqHQ/covalent-gcpbatch-plugin/blob/main/CHANGELOG.md).
## 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](https://github.com/AgnostiqHQ/covalent-gcpbatch-plugin/blob/main/LICENSE) file or contact the [support team](mailto:support@agnostiq.ai) for more details.