Python库 | cova-0.22.9.tar.gz

&nbsp; <div align="center">   &nbsp; [](https://github.com/AgnostiqHQ/covalent) [](https://www.python.org/downloads/release/python-380) [](https://github.com/AgnostiqHQ/covalent/actions/workflows/tests.yml) [](https://github.com/AgnostiqHQ/covalent/actions/workflows/publish_master.yml) [](https://covalent.readthedocs.io/en/latest/?badge=latest) [](https://codecov.io/gh/AgnostiqHQ/covalent) [](https://www.gnu.org/licenses/agpl-3.0.en.html) </div> ## 🤔 What is Covalent? 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](https://covalent.readthedocs.io/en/latest/). ## ✨ Features <div align="center">  <em>With Covalent's UI, bring your workflows to life! </em> </div> - **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](https://covalent.readthedocs.io/en/latest/concepts/concepts.html) page. ## 📦 Installation Covalent is developed using Python version 3.8 on Linux and macOS. The easiest way to install Covalent is using the PyPI package manager: ```console pip install cova ``` Refer to the [Getting Started](https://covalent.readthedocs.io/en/latest/getting_started/index.html) guide for more details on setting up. ## 📖 Example Begin by starting the Covalent servers: ```console covalent start ``` Navigate to the user interface at `http://localhost:47007` 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. <table style='margin-left: auto; margin-right: auto; word-wrap: break-word;'> <tr> <th style='text-align:center;'>Without Covalent</th> <th style='text-align:center;'>With Covalent</th> </tr> <tr> <td valign="top"> ``` python 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) ``` </td> <td valign="top"> ```python 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 dispatch_id = run_experiment.dispatch( C=1.0, gamma=0.7 ) result = ct.get_result(dispatch_id) ``` </td> </tr> <tr> <td valign="top"> ```python >>> print(result) 0.988888888 ``` </td> <td valign="top"> ```python >>> 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 ``` </td> </tr> </table> For more examples, please refer to the [Covalent tutorials](https://covalent.readthedocs.io/en/latest/tutorials/tutorials.html). ## 📚 Documentation 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](https://covalent.readthedocs.io/en/latest/). ## ✔️ Contributing To contribute to Covalent, refer to the [Contribution Guidelines](https://github.com/AgnostiqHQ/covalent/blob/master/CONTRIBUTING.md). We use GitHub's [issue tracking](https://github.com/AgnostiqHQ/covalent/issues) to manage known issues, bugs, and pull requests. Get star