Packages
s2sphere provides Python bindings to work with S2 geometry—a spherical geometry library for spatial indexing and geometric calculations on the Earth's surface.
However, abandonment since 2017-11-16 means no bug fixes, security patches, or compatibility updates are forthcoming—verify it runs on your target Python version…
s3cmd is a command-line tool for uploading, downloading, and managing files on Amazon S3 and CloudFront, with support for rsync-like backups and GPG encryption.
However, do not rely on it for new projects requiring active support—consider AWS CLI or boto3 instead if you need ongoing compatibility with AWS API changes.
s3fs provides a Python filesystem interface to Amazon S3, allowing you to read, write, and navigate S3 buckets using standard file operations.
Install it if you need filesystem-like access to S3 from Python; the standard file interface will be more intuitive than raw boto3 calls for most use cases.
S3Path provides a pathlib-like interface for working with AWS S3 buckets and objects, letting you navigate, query, and manipulate S3 storage using familiar filesystem operations.
s3pathlib provides a pathlib-like object-oriented interface to AWS S3, letting you construct, query, and manipulate S3 objects and directories using familiar Python path syntax.
Converts audio into discrete semantic speech tokens using a PyTorch implementation of the S3Tokenizer model, supporting batch inference and online extraction during model training.
Provides PyTorch dataset primitives and checkpoint interfaces for efficient data loading and model checkpointing directly from Amazon S3, supporting both map-style and iterable-style datasets.
Internal S3 client implementation providing optimized data loading and checkpoint management for PyTorch training jobs accessing Amazon S3.
s3transfer manages concurrent uploads and downloads to Amazon S3, handling multipart transfers, retries, and bandwidth throttling through a high-level interface built on botocore.
However, if you are building production systems, either pin to a specific minor version or use boto3's stable S3 interface instead, as the maintainers explicitly warn…
Parse and build Amazon S3 URLs in multiple formats, extracting or constructing bucket names, keys, regions, and optional credential names.
Provides a Python wheel distribution that installs the s5cmd executable, a fast S3 and local filesystem tool with wildcard and tab-completion support for bulk file operations.
SacreBLEU computes BLEU, chrF, and TER scores for machine translation evaluation with automatic test set management and reproducible, comparable results across systems.
Install it if you work with machine translation evaluation, benchmark scoring, or need comparable BLEU/chrF/TER metrics across systems.
Sacremoses provides tokenization, detokenization, truecasing, and punctuation normalization for text processing, primarily for machine translation and NLP pipelines.
Install it if you need Moses-style tokenization or truecasing for machine translation or similar tasks.
SAE Lens trains and analyzes sparse autoencoders for mechanistic interpretability research, with built-in support for PyTorch models and deep integration with TransformerLens and Hugging Face Transformers.
Safe Init wraps AWS Lambda handlers to provide automatic error handling, execution tracing, and integration with monitoring services like Sentry, Datadog, and Slack.
Provides a safer subclass of Python's standard netrc parser that enforces strict file permissions and supports custom netrc file paths via environment variable.
Provides SHA-3, SHAKE, and Keccak hash functions for Python 3.9–3.13 via a PEP 247 compatible interface, with optional monkey-patching of the hashlib module.
Wraps httpx.AsyncClient.get() with DNS validation and DNS rebinding protections to prevent Server Side Request Forgery (SSRF) attacks.
Install it if SSRF is a real threat in your application; skip it if you are not accepting untrusted URLs or are not using async code.
Serializes and deserializes tensors to and from a safe, standardized binary format designed for secure model storage and sharing.
Install it if you work with tensor serialization or model checkpoints.
Safety CLI scans Python project dependencies for known vulnerabilities and malicious packages, then reports findings and can automatically update vulnerable dependencies to secure versions.
Provides Pydantic models and schemas for Safety tools, enabling structured validation and serialization of safety policy files and configuration data.
SageAttention provides quantized attention kernels for transformer inference that replace the standard scaled dot-product attention with 8-bit or 4-bit quantized variants, designed to accelerate inference on specific GPU architectures.
SageMaker Python SDK is a library for training and deploying machine learning models on Amazon SageMaker, supporting frameworks like PyTorch and MXNet as well as Amazon's built-in algorithms.
Provides tools to build Docker containers compatible with Amazon SageMaker for model training and inference, handling script execution, hyperparameter mapping, and environment variable management.
sagemaker-core provides an object-oriented Python interface to Amazon SageMaker resources with full API parity, resource chaining, and type hints for building and deploying machine learning models.
However, the Alpha status means the API may change; pin a version and monitor releases.
Computes ML-relevant statistical summaries of datasets, integrating with Amazon SageMaker to surface data characteristics for model development and debugging.
No—not recommended for new projects.
Provides a Python library for Amazon SageMaker Data Wrangler, enabling data preparation and transformation workflows within the SageMaker ecosystem.
Tracks machine learning experiments, trials, and trial components in AWS SageMaker training jobs, processing jobs, and notebooks.
Ingest Spark DataFrames into Amazon SageMaker FeatureStore's online and offline stores, and load feature definitions from schema.
Connects Apache Spark DataFrames to Amazon SageMaker FeatureStore for ingesting feature data into online and offline stores, with automatic feature definition loading.
A PySpark connector for Amazon SageMaker FeatureStore that ingests data from Spark DataFrames into FeatureStore's online and offline stores, with automatic feature definition loading.
However, proceed with caution: maintenance is dormant (554 days since last release), so compatibility with recent SageMaker or Spark updates is uncertain.
Provides a model serving stack for deploying machine learning models in Docker containers on Amazon SageMaker, built on Multi Model Server.
However, the abandoned repository status (last commit 2023-11-20) means you should verify compatibility with your target SageMaker version and Python runtime before…
Integrates MLflow with Amazon SageMaker by signing requests with AWS IAM credentials and enabling model registration to the SageMaker Model Registry.
Provides high-level orchestration for Amazon SageMaker workflows, including pipeline definitions, step implementations, and model building utilities that coordinate training, serving, and core SageMaker components.
Provides pre-built schema inference artifacts and task sample inputs/outputs for Hugging Face models in SageMaker workflows.
However, dormant maintenance status means you should verify that included schemas match your target versions before relying on it in production.
Extends scikit-learn with additional estimators and preprocessing tools designed to support SageMaker Autopilot, including dimension reduction, feature extraction, imputation, and encoding transformers.
Provides model serving and deployment functionality for machine learning models on Amazon SageMaker, integrating with SageMaker's core training and inference infrastructure.
However, verify that its API and deployment model match your specific serving requirements before committing, as the fact sheet does not detail its exact interface or…
A Python SDK for accessing Amazon SageMaker Unified Studio resources—domains, projects, connections, databases, and tables—through a unified interface with minimal code.
Trains and deploys machine learning models on Amazon SageMaker using popular frameworks like Apache MXNet, TensorFlow, and Amazon's built-in algorithms, or custom Docker containers.
However, verify the license terms (Apache 2.0 is mentioned in the description but not confirmed in metadata) and ensure your AWS account has the necessary IAM…
Integrates training scripts into Docker containers for use with Amazon SageMaker, handling environment setup, hyperparameter passing, and entry point execution within containerized training jobs.