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inspec-digitalocean-0.2.0-1.lbn36.noarch: This InSpec resource pack uses the Digital Ocean SDK and provides the required resources to write tests for resources in Digital Ocean. To use within you inspec installation; add the following to your profile's inspec.yml: depends: - name: inspec-digitalocean path: /usr/share/inspec-digitalocean supports: - platform: digitalocean

inspec-azure-1.118.43-1.lbn36.noarch: This InSpec resource pack uses the Azure SDK v0.8.0 and provides the required resources to write tests for resources in Azure. To install and use within you inspec installation; run the following $ inspec plugin install /usr/share/inspec-azure

inspec-aws-1.83.62-1.lbn36.noarch: This InSpec resource pack uses the AWS SDK and provides the required resources to write tests for resources in AWS. To install and use within you inspec installation; add the following to your profile's inspec.yml: depends: - name: inspec-aws path: /usr/share/inspec-aws supports: - platform: aws

python3-grpcio-status-1.59.0-1.lbn36.noarch: gRPC Python Status Proto Reference package for GRPC Python status proto mapping.Supported Python Versions - Python > 3.7Dependencies Depends on the grpcio package, available from PyPI via pip install grpcio.

python3-googleapis-common-protos+grpc-1.66.0-1.lbn36.noarch: This is a metapackage bringing in grpc extras requires for python3-googleapis-common-protos. It makes sure the dependencies are installed.

python3-googleapis-common-protos-1.66.0-1.lbn36.noarch: Common protobufs used in Google APIs.

amazon-ssm-agent-3.2.815.0-1.lbn36.x86_64: This package provides the Amazon SSM Agent for managing EC2 Instances using Amazon EC2 Systems Manager (SSM). The SSM Agent runs on EC2 or on-premise instances and enables you to quickly and easily execute remote commands or scripts against one or more instances. When you execute a command, the agent on the instance processes the document and configures the instance as specified. This collection of capabilities helps you automate management tasks such as collecting system inventory, applying operating system (OS) patches, automating the creation of Amazon Machine Images (AMIs), and configuring operating systems (OSs) and applications at scale. Systems Manager works with managed instances: Amazon EC2 instances, or servers and virtual machines (VMs) in your on-premises environment that are configured for Systems Manager.

amanda-server-3.5.4-15.lbn36.x86_64: The amanda-server package should be installed on the AMANDA server, the machine attached to the device(s) (such as a tape drive) where backups will be written. You will also need to install the amanda package on the AMANDA server machine. And, if the server is also to be backed up, the server also needs to have the amanda-client package installed.

amanda-libs-3.5.4-15.lbn36.x86_64: This package contains basic Amanda libraries, which are used by all Amanda programs.

amanda-client-3.5.4-15.lbn36.x86_64: The Amanda-client package should be installed on any machine that will be backed up by AMANDA (including the server if it also needs to be backed up). You will also need to install the amanda package on each AMANDA client machine.

amanda-3.5.4-15.lbn36.x86_64: AMANDA, the Advanced Maryland Automatic Network Disk Archiver, is a backup system that allows the administrator of a LAN to set up a single master backup server to back up multiple hosts to one or more tape drives or disk files. AMANDA uses native dump and/or GNU tar facilities and can back up a large number of workstations running multiple versions of Unix. Newer versions of AMANDA (including this version) can use SAMBA to back up Microsoft(TM) Windows95/NT hosts. The amanda package contains the core AMANDA programs and will need to be installed on both AMANDA clients and AMANDA servers. Note that you will have to install the amanda-client and/or amanda-server packages as well.

python3-tokenizers-0.15.1-1.lbn36.x86_64: Tokenizers Provides an implementation of today's most used tokenizers, with a focus on performance and versatility. Bindings over the Rust implementation. If you are interested in the High-level design, you can go check it there. Otherwise, let's dive in! Main features: Train new vocabularies and tokenize using 4 pre-made tokenizers (Bert WordPiece and the 3 most common BPE versions). Extremely fast (both training and tokenization), thanks to the Rust implementation. Takes less than 20 seconds to tokenize a GB of text on a server's CPU. Easy to use, but also extremely versatile. Designed for research and production. Normalization comes with alignments tracking. It's always possible to get the part of the original sentence that corresponds to a given token. Does all the pre-processing: Truncate, Pad, add the special tokens your model needs.

python3-starlette-0.46.1-2.lbn36.noarch: Starlette is a lightweight ASGI framework/toolkit, which is ideal for building async web services in Python. It is production-ready, and gives you the following: • A lightweight, low-complexity HTTP web framework. • WebSocket support. • In-process background tasks. • Startup and shutdown events. • Test client built on requests. • CORS, GZip, Static Files, Streaming responses. • Session and Cookie support. • 100% test coverage. • 100% type annotated codebase. • Few hard dependencies. • Compatible with asyncio and trio backends. • Great overall performance against independent benchmarks.

python3-snowflake-sqlalchemy-1.5.0-1.lbn36.noarch: This package includes the Snowflake SQLAlchemy, which supports Snowsql dialects for SQLAlchemy < Documentation is available at: < code is also available at: < Release Notes- v1.4.4(Nov 16, 2022) - Fixed a bug that percent signs in a non- compiled statement should not be interpolated with emtpy sequence when executed.- v1.4.3(Oct 17, 2022) - Fixed a bug that SnowflakeDialect.normalize_name and...

python3-snowflake-snowpark-python-1.31.1-1.lbn36.noarch: Snowflake Snowpark Python and Snowpark pandas APIs The Snowpark library provides intuitive APIs for querying and processing data in a data pipeline. Using this library, you can build applications that process data in Snowflake without having to move data to the system where your application code runs.

python3-scrapegraph-py-1.12.0-1.lbn36.noarch: 🌐 ScrapeGraph Python SDK Official Python SDK for the ScrapeGraph API - Smart web scraping powered by AI. 🚀 Features 🤖 AI-powered web scraping and search 🔄 Both sync and async clients 📊 Structured output with Pydantic schemas 🔍 Detailed logging ⚡ Automatic retries 🔐 Secure authentication

python3-scour-0.38.2-12.lbn36.noarch: Scour is an SVG optimizer/cleaner written in Python that reduces the size of scalable vector graphics by optimizing structure and removing unnecessary data. It can be used to create streamlined vector graphics suitable for web deployment, publishing/sharing or further processing. The goal of Scour is to output a file that renders identically at a fraction of the size by removing a lot of redundant information created by most SVG editors. Optimization options are typically lossless but can be tweaked for more aggressive cleaning.

python3-scikit-learn-1.6.1-1.lbn36.x86_64: Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts.

python3-scikit-image-0.25.2-1.lbn36.x86_64: The scikit-image SciKit (toolkit for SciPy) extends scipy.ndimage to provide a versatile set of image processing routines.

python3-scikit-build-core-0.11.0-1.lbn36.noarch: A next generation Python CMake adapter and Python API for plugins

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