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covalent-ec2-plugin-0.9.0rc0
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Covalent EC2 Plugin
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | covalent-ec2-plugin-0.9.0rc0 |
| نام | covalent-ec2-plugin |
| نسخه کتابخانه | 0.9.0rc0 |
| نگهدارنده | ['Agnostiq'] |
| ایمیل نگهدارنده | [] |
| نویسنده | Agnostiq |
| ایمیل نویسنده | support@agnostiq.ai |
| آدرس صفحه اصلی | https://github.com/AgnostiqHQ/covalent-ec2-plugin |
| آدرس اینترنتی | https://pypi.org/project/covalent-ec2-plugin/ |
| مجوز | GNU Affero GPL v3.0 |
<div align="center">
<img src="https://github.com/AgnostiqHQ/covalent-ec2-plugin/blob/main/assets/ec2_readme_banner.jpg" width=150%>
</div>
## Covalent EC2 Executor Plugin
Covalent is a Pythonic workflow tool used to execute tasks on advanced computing hardware.
This plugin allows tasks to be executed in an AWS EC2 instance (which is auto-created) when you execute your workflow with covalent.
## 1. Installation
To use this plugin with Covalent, simply install it using `pip`:
```
pip install covalent-ec2-plugin
```
## 2. Usage Example
This is a toy example of how a workflow can be adapted to utilize the EC2 Executor. Here we train a Support Vector Machine (SVM) and spin up an EC2 automatically to execute the `train_svm` electron. We also note we require [DepsPip](https://covalent.readthedocs.io/en/latest/concepts/concepts.html#depspip) to install the dependencies on the EC2 instance.
```python
from numpy.random import permutation
from sklearn import svm, datasets
import covalent as ct
deps_pip = ct.DepsPip(
packages=["numpy==1.23.2", "scikit-learn==1.1.2"]
)
executor = ct.executor.EC2Executor(
instance_type="t2.micro",
volume_size=8, #GiB
ssh_key_file="~/.ssh/id_rsa",
key_name="key_name" # EC2 Key Pair
)
# 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=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 however the above script should also output a value with the score of our model.
```
0.8666666666666667
```
## 3. Configuration
There are many configuration options that can be passed in to the class `ct.executor.EC2Executor` or by modifying the [covalent config file](https://covalent.readthedocs.io/en/latest/how_to/config/customization.html) under the section `[executors.ec2]`
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/awsec2.html) for this plugin.
## 4. Required AWS Resources
In order to run your workflows with covalent there are a few notable resources that need to be provisioned first.
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/awsec2.html) for this plugin.
The required resources include an EC2 Key Pair (which corresponds to the `key_name` config value), and optionally a VPC & Subnet that can be used instead of the EC2 executor automatically creating it.
## 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 for this plugin are available in the [Changelog](https://github.com/AgnostiqHQ/covalent-ec2-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 GNU Affero GPL 3.0 License. Covalent may be distributed under other licenses upon request. See the [LICENSE](https://github.com/AgnostiqHQ/covalent-executor-template/blob/main/LICENSE) file or contact the [support team](mailto:support@agnostiq.ai) for more details.
نحوه نصب
نصب پکیج whl covalent-ec2-plugin-0.9.0rc0:
pip install covalent-ec2-plugin-0.9.0rc0.whl
نصب پکیج tar.gz covalent-ec2-plugin-0.9.0rc0:
pip install covalent-ec2-plugin-0.9.0rc0.tar.gz