{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/7"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/5"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/11"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Flytekit is the Python SDK for authoring, testing, deploying, and managing Flyte tasks, workflows, and launch plans\u2014a framework for building data and ML pipelines that run on the Flyte backend.","skillfed_tags":["workflow-orchestration","ml-pipeline","distributed-computing"],"use_cases":["Define and test ML training pipelines with task caching and automatic retry logic before deploying to a Flyte cluster.","Build data processing workflows that orchestrate multiple Python functions across distributed infrastructure with type-safe interfaces.","Schedule recurring data jobs using cron expressions and manage their execution state through Flyte launch plans.","Integrate with cloud storage backends (GCS, Azure Blob, S3) for reading and writing pipeline artifacts.","Author reusable task libraries with built-in versioning and dependency tracking for team collaboration."],"what_it_does":"Flytekit is the Python SDK for the Flyte workflow orchestration platform. It provides decorators and APIs to define tasks (atomic units of work) and workflows (compositions of tasks) with built-in support for caching, retries, and type safety. Tasks and workflows are authored in plain Python, then deployed to a Flyte backend for distributed execution. The package handles serialization, dependency management, and integration with cloud storage (GCS, Azure, S3) and container runtimes (Docker).\n\nThe library is designed for data engineers and ML practitioners building reproducible, scalable pipelines. It includes 39 runtime dependencies covering serialization (cloudpickle, msgpack, protobuf), scheduling (croniter), caching (diskcache, cachetools), and cloud integrations (gcsfs, adlfs). While Flyte 2 is now generally available, this version (1.16.27) remains actively maintained and suitable for existing Flyte 1.x deployments.","worth_installing":"Yes, if you are running Flyte 1.x or evaluating Flyte for workflow orchestration. The package is actively maintained, has low install friction, carries no known vulnerabilities, and is permissively licensed. However, note that Flyte 2 is now generally available\u2014new projects should evaluate whether Flyte 2's Python SDK is a better fit, and existing Flyte 1.x users should plan a migration timeline."},"id":"flytekit","links":{"html":"https://skillfed.io/packages/flytekit","md":"https://skillfed.io/packages/flytekit.md","pypi":"https://pypi.org/project/flytekit/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-10","license_spdx":null,"license_treatment":"permissive","name":"flytekit","python_support":"capped_below_current","summary":"Flyte SDK for Python"},"popularity":{"monthly_downloads":545779,"position":6075,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.16.27"}