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clucene-core-2.3.3.4-42.20130812.e8e3d20git.fc36.x86_64: CLucene is a C++ port of the popular Apache Lucene search engine (http://lucene.apache.org/java). CLucene aims to be a high-speed alternative to Java Lucene, its API is very similar to that of the Java version. CLucene has recently been brought up to date with Lucene 2.3.2. It contains most of the same functionality as the Java version.

clucene-contribs-lib-2.3.3.4-42.20130812.e8e3d20git.fc36.x86_64: Language specific text analyzers for clucene.

alembic-libs-1.8.5-2.lbn36.x86_64: Alembic is an open computer graphics interchange framework. Alembic distills complex, animated scenes into a non-procedural, application-independent set of baked geometric results. This 'distillation' of scenes into baked geometry is exactly analogous to the distillation of lighting and rendering scenes into rendered image data.

alembic-1.8.5-2.lbn36.x86_64: Alembic is an open computer graphics interchange framework. Alembic distills complex, animated scenes into a non-procedural, application-independent set of baked geometric results. This 'distillation' of scenes into baked geometry is exactly analogous to the distillation of lighting and rendering scenes into rendered image data.

python3-apache-airflow+ydb-2.10.5-1.lbn36.noarch: This is a metapackage bringing in ydb extras requires for python3-apache-airflow. It makes sure the dependencies are installed.

python3-apache-airflow+common-compat-2.10.5-1.lbn36.noarch: This is a metapackage bringing in common-compat extras requires for python3-apache-airflow. It makes sure the dependencies are installed.

python3-apache-airflow+cloudpickle-2.10.5-1.lbn36.noarch: This is a metapackage bringing in cloudpickle extras requires for python3-apache-airflow. It makes sure the dependencies are installed.

python3-apache-airflow+apache-iceberg-2.10.5-1.lbn36.noarch: This is a metapackage bringing in apache-iceberg extras requires for python3-apache-airflow. It makes sure the dependencies are installed.

python3-best-ec2-1.1.4-1.lbn36.noarch: Best EC2 Best EC2, the smart solution designed to optimize your Amazon EC2 instance type selection process. The app simplifies the challenge of choosing the optimal EC2 instance type that matches your specific requirements, balancing performance, cost, and computing needs. Prerequisites python >=3.8.0 AWS Credentials Install pip install best-ec2 The IAM policy grants minimal permissions necessary for the application { "Version": "2012-10-17", "Statement": [ { "Sid": "General", "Effect": "Allow", "Action": [ "ec2:DescribeInstanceTypes" ], "Resource": "*" }, { "Sid": "OnDemand", "Effect": "Allow", "Action": [ "pricing:GetProducts" ], "Resource": "*" }, { "Sid": "Spot", "Effect": "Allow", "Action": [ "ec2:DescribeAvailabilityZones",

python3-google-genai-1.14.0-1.lbn36.noarch: Google Gen AI SDK Documentation: https:/googleapis.github.io/python-genai/ Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. It supports the Gemini Developer API and Vertex AI APIs. Installation pip install google-genai Imports from google import genai from google.genai import types Create a client Please run one of the following code blocks to create a client for different services (Gemini Developer API or Vertex AI). client = genai.Client(api_key='GEMINI_API_KEY') client = genai.Client( vertexai=True, project='your-project-id', location='us-central1' ) (Optional) Using environment variables: You can create a client by configuring the necessary environment variables. Configuration setup instructions depends on whether you're using the Gemini Developer API or the Gemini API in Vertex AI. Gemini Developer A

python3-jupyterlab_pygments-0.1.2-5.fc36.noarch: This package contains a syntax coloring theme for pygments making use of the JupyterLab CSS variables.

python3-jupyter-sphinx-0.3.2-3.fc36.noarch: Jupyter-Sphinx enables running code embedded in Sphinx documentation and embedding output of that code into the resulting document. It has support for rich output such as images and even Jupyter interactive widgets.

python3-jupyter-polymake-0.16-18.20180129.7049940.fc36.noarch: This package contains a Jupyter kernel for polymake.

python3-jupyter-packaging-0.12.3-1.lbn36.noarch: Jupyter Packaging Tools to help build and install Jupyter Python packages that require a pre-build step that may include JavaScript build steps. Install pip install jupyter-packaging Usage There are three ways to use jupyter-packaging in another package. In general, you should not depend on jupyter_packaging as a runtime dependency, only as a build dependency. As a Build Requirement Use a pyproject.toml file as outlined in pep-518. An example: [build-system] requires = ["jupyter_packaging>=0.10,<2"] build-backend = "setuptools.build_meta" Below is an example setup.py using the above config. It assumes the rest of your metadata is in setup.cfg. We wrap the import in a try/catch to allow the file to be run without jupyter_packaging so that python setup.py can be run directly when not building. from setuptools import setup try: from jupyter_packaging import wrap_installers, npm_builder builder = npm_builder() cmdclass = wrap_installers(pre_develop=builder, pre_dist=builder)

python3-jupyter-packaging-0.12.3-1.lbn36.noarch: Jupyter Packaging Tools to help build and install Jupyter Python packages that require a pre-build step that may include JavaScript build steps. Install pip install jupyter-packaging Usage There are three ways to use jupyter-packaging in another package. In general, you should not depend on jupyter_packaging as a runtime dependency, only as a build dependency. As a Build Requirement Use a pyproject.toml file as outlined in pep-518. An example: [build-system] requires = ["jupyter_packaging>=0.10,<2"] build-backend = "setuptools.build_meta" Below is an example setup.py using the above config. It assumes the rest of your metadata is in setup.cfg. We wrap the import in a try/catch to allow the file to be run without jupyter_packaging so that python setup.py can be run directly when not building. from setuptools import setup try: from jupyter_packaging import wrap_installers, npm_builder builder = npm_builder() cmdclass = wrap_installers(pre_develop=builder, pre_dist=builder)

python3-jupyter-kernel-test-0.6.0-1.lbn36.noarch: jupyter_kernel_test is a tool for testing Jupyter kernels. It tests kernels for successful code execution and conformance with the Jupyter Messaging Protocol (currently 5.0). Install Install it with pip (python3.4 or greater required): pip3 install jupyter_kernel_test Usage To use it, you need to write a (python) unittest file containing code samples in the relevant language which test various parts of the messaging protocol. A short example is given below, and you can also refer to the test_ipykernel.py and test_irkernel.py files for complete examples. Some parts of the messaging protocol are relevant only to the browser-based notebook (rich display) or console interfaces (code completeness, history searching). Only parts of the spec for which you provide code samples are tested. Run this file directly using python, or use nosetests or py.test to find and run it. Example import unittest import jupyter_kernel_test class MyKernelTests(jupyter_kernel_test.KernelTests):

python3-jupyter-kernel-test-0.6.0-1.lbn36.noarch: jupyter_kernel_test is a tool for testing Jupyter kernels. It tests kernels for successful code execution and conformance with the Jupyter Messaging Protocol (currently 5.0). Install Install it with pip (python3.4 or greater required): pip3 install jupyter_kernel_test Usage To use it, you need to write a (python) unittest file containing code samples in the relevant language which test various parts of the messaging protocol. A short example is given below, and you can also refer to the test_ipykernel.py and test_irkernel.py files for complete examples. Some parts of the messaging protocol are relevant only to the browser-based notebook (rich display) or console interfaces (code completeness, history searching). Only parts of the spec for which you provide code samples are tested. Run this file directly using python, or use nosetests or py.test to find and run it. Example import unittest import jupyter_kernel_test class MyKernelTests(jupyter_kernel_test.KernelTests):

python3-jupyter-kernel-test-0.6.0-1.lbn36.noarch: jupyter_kernel_test is a tool for testing Jupyter kernels. It tests kernels for successful code execution and conformance with the Jupyter Messaging Protocol (currently 5.0). Install Install it with pip (python3.4 or greater required): pip3 install jupyter_kernel_test Usage To use it, you need to write a (python) unittest file containing code samples in the relevant language which test various parts of the messaging protocol. A short example is given below, and you can also refer to the test_ipykernel.py and test_irkernel.py files for complete examples. Some parts of the messaging protocol are relevant only to the browser-based notebook (rich display) or console interfaces (code completeness, history searching). Only parts of the spec for which you provide code samples are tested. Run this file directly using python, or use nosetests or py.test to find and run it. Example import unittest import jupyter_kernel_test class MyKernelTests(jupyter_kernel_test.KernelTests):

python3-jupyter-kernel-singular-0.9.9-8.fc36.noarch: This package contains a Jupyter kernel for Singular, to enable using Jupyter as the front end for Singular.

python3-jupyter-core-5.7.2-1.lbn36.noarch: There is no reason to install this package on its own.

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