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python3-fsspec+libarchive-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in libarchive extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+http-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in http extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+hdfs-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in hdfs extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+gs-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in gs extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+github-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in github extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+git-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in git extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+gcs-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in gcs extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+fuse-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in fuse extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+entrypoints-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in entrypoints extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+dask-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in dask extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec+arrow-2025.3.2-1.lbn36.noarch: This is a metapackage bringing in arrow extras requires for python3-fsspec. It makes sure the dependencies are installed.

python3-fsspec-2025.3.2-1.lbn36.noarch: Filesystem Spec is a project to unify various projects and classes to work with remote filesystems and file-system-like abstractions using a standard pythonic interface.

python3-e2b-code-interpreter-1.5.0-1.lbn36.noarch: What is E2B? E2B is an open-source infrastructure that allows you run to AI-generated code in secure isolated sandboxes in the cloud. To start and control sandboxes, use our JavaScript SDK or Python SDK. Run your first Sandbox 1. Install SDK pip install e2b-code-interpreter 2. Get your E2B API key Sign up to E2B here. Get your API key here. Set environment variable with your API key. E2B_API_KEY=e2b_*** 3. Execute code with code interpreter inside Sandbox from e2b_code_interpreter import Sandbox with Sandbox() as sandbox: sandbox.run_code("x = 1") execution = sandbox.run_code("x+=1; x") print(execution.text) # outputs 2 4. Check docs Visit E2B documentation. 5. E2B cookbook Visit our Cookbook to get inspired by examples with different LLMs and AI frameworks.

python3-e2b-1.4.0-1.lbn36.noarch: What is E2B? E2B is an open-source infrastructure that allows you to run AI-generated code in secure isolated sandboxes in the cloud. To start and control sandboxes, use our JavaScript SDK or Python SDK. Run your first Sandbox 1. Install SDK pip install e2b-code-interpreter 2. Get your E2B API key Sign up to E2B here. Get your API key here. Set environment variable with your API key E2B_API_KEY=e2b_*** 3. Execute code with code interpreter inside Sandbox from e2b_code_interpreter import Sandbox with Sandbox() as sandbox: sandbox.run_code("x = 1") execution = sandbox.run_code("x+=1; x") print(execution.text) # outputs 2 4. Check docs Visit E2B documentation. 5. E2B cookbook Visit our Cookbook to get inspired by examples with different LLMs and AI frameworks.

python3-datasets-3.6.0-1.lbn36.noarch: 🤗 Datasets is a lightweight library providing two main features: one-line dataloaders for many public datasets: one-liners to download and pre-process any of the major public datasets (image datasets, audio datasets, text datasets in 467 languages and dialects, etc.) provided on the HuggingFace Datasets Hub. With a simple command like squad_dataset = load_dataset("rajpurkar/squad"), get any of these datasets ready to use in a dataloader for training/evaluating a ML model (Numpy/Pandas/PyTorch/TensorFlow/JAX), efficient data pre-processing: simple, fast and reproducible data pre-processing for the public datasets as well as your own local datasets in CSV, JSON, text, PNG, JPEG, WAV, MP3, Parquet, etc. With simple commands like processed_dataset = dataset.map(process_example), efficiently prepare the dataset for inspection and ML model evaluation and training. 🎓 Documentation 🔎 Find a dataset in the Hub 🌟 Share a dataset on the Hub 🤗 Datasets is designed to let the community easily

python3-datapackage-1.15.2-1.lbn36.noarch: datapackage-py[![Travis]( [![Coveralls]( [![PyPi]( [![Github]( [![Gitter](

python3-cloudpickle-3.1.1-2.lbn36.noarch: cloudpickle makes it possible to serialize Python constructs not supported by the default pickle module from the Python standard library. cloudpickle is especially useful for cluster computing where Python expressions are shipped over the network to execute on remote hosts, possibly close to the data. Among other things, cloudpickle supports pickling for lambda expressions, functions and classes defined interactively in the __main__ module.

python3-clickhouse-connect-0.6.23-1.lbn36.x86_64: ClickHouse ConnectA high performance core database driver for connecting ClickHouse to Python, Pandas, and Superset * Pandas DataFrames * Numpy Arrays * PyArrow Tables * Superset Connector * SQLAlchemy 1.3 and 1.4 (limited feature set)ClickHouse Connect currently uses the ClickHouse HTTP interface for maximum compatibility. Installation pip install clickhouse-connect ClickHouse Connect...

python3-cassio-0.1.10-1.lbn36.noarch: cassIO A framework-agnostic Python library to seamlessly integrate Apache Cassandra with ML/LLM/genAI workloads. Note: this is currently an alpha release. Users Installation is as simple as: pip install cassio For example usages and integration with higher-level LLM frameworks such as LangChain, please visit cassio.org. CassIO developers Setup To develop cassio, we use poetry pip install poetry Use poetry to install dependencies poetry install Use cassio current code in other Poetry base projects If the integration is Poetry-based (e.g. LangChain itself), you should get this in your pyproject.toml: cassio = {path = "../../cassio", develop = true} Then you do poetry remove cassio # if necessary poetry lock --no-update poetry install -E all --with dev --with test_integration # or similar, this is for langchain Inspired from this. You also need a recent Poetry for this to work. Versioning We are still at 0.*. Occasional breaking changes are to be


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