{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/16"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/9"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/19"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Kubeflow SDK provides Python APIs to submit and manage distributed AI training jobs, hyperparameter optimization, and ML pipelines on Kubernetes without requiring direct Kubernetes knowledge.","skillfed_tags":["kubernetes","distributed-training","ml-orchestration"],"use_cases":["Submit distributed PyTorch or TensorFlow training jobs to a Kubernetes cluster without writing deployment manifests.","Run hyperparameter optimization sweeps using Katib, specifying search spaces and trial counts in Python.","Register and manage model versions in a Model Registry, tracking metadata and artifacts for production deployments.","Prototype training logic locally using ContainerBackend or LocalProcessBackend before deploying to Kubernetes.","Orchestrate multi-step ML pipelines with component definitions and run them on Kubeflow Pipelines.","Execute Spark data processing jobs on Kubernetes via SparkClient with configurable executors and resources."],"what_it_does":"Kubeflow SDK is a Python library that abstracts Kubernetes complexity to let you run distributed AI workloads\u2014training jobs, hyperparameter sweeps, model registry operations, and ML pipelines\u2014using familiar Python APIs. It unifies access to multiple Kubeflow components (Trainer, Katib, Model Registry, Spark Operator, Pipelines) through a single SDK, so you write Python code rather than YAML manifests or kubectl commands.\n\nThe SDK supports multiple execution backends: production Kubernetes clusters, local Docker/Podman containers for development, and Python subprocesses for quick prototyping. You define training jobs, optimization experiments, or pipelines in Python, submit them via client objects, and monitor their progress. Runtime dependencies include kubernetes (for cluster interaction), pydantic (for configuration validation), and requests (for HTTP calls).","worth_installing":"Yes. The SDK is actively maintained (release 4 days old), has no known vulnerabilities, uses a permissive Apache-2.0 license, and installs with low friction. It is the canonical way to interact with Kubeflow from Python if you are already running or planning to run Kubeflow on Kubernetes. Install it if you need to programmatically submit AI workloads to Kubeflow; skip it if you have no Kubernetes infrastructure or prefer direct kubectl/YAML workflows."},"id":"kubeflow","links":{"html":"https://skillfed.io/packages/kubeflow","md":"https://skillfed.io/packages/kubeflow.md","pypi":"https://pypi.org/project/kubeflow/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-10","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"kubeflow","python_support":"supports_current","summary":"Kubeflow Python SDK to manage ML workloads and to interact with Kubeflow APIs."},"popularity":{"monthly_downloads":123799,"position":11900,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.5.0"}