codeflare-sdk
Python SDK for codeflare client
Decision gist · record as of 2026-08-14
Yes, if you need to submit batch jobs or provision compute resources on Kubernetes from Python. The low install friction, active maintenance, permissive license, and lack of known vulnerabilities make it a solid choice. The substantial dependency footprint (9 runtime packages) is justified by the abstraction it provides. Not necessary if you already have a working Kubernetes workflow or do not need programmatic cluster provisioning.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or 3.12; requires a Kubernetes cluster endpoint and valid authentication credentials (auto-detected from kubeconfig or in-cluster service account by default).
- Low friction installation as a pure Python wheel.
- Active maintenance with a release 24 days ago.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and proprietary projects without viral obligations, though derivative works must retain license attribution.
last release 2026-07-21 (24 days) · last repo commit 2026-08-11 · 35 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 108,249 downloads/mo, #12,568 on PyPI
Alternatives
Verify before relying
pip install codeflare-sdk
from codeflare_sdk import Cluster, ClusterConfiguration
cluster = Cluster(ClusterConfiguration(
name='my-cluster',
num_workers=2,
))
cluster.apply()- Whether the package works with Kubernetes versions beyond what the fact sheet specifies.
- Performance characteristics and scalability limits for large job submissions.
- Whether Ray integration requires Ray cluster pre-deployment or if CodeFlare manages that.
What it is and what it does
CodeFlare SDK is a Python client library that abstracts Kubernetes cluster management and batch job submission, letting developers request compute resources and submit work without writing raw Kubernetes manifests. It handles authentication through multiple methods (auto-detection, OIDC, OpenShift OAuth, token-based, kubeconfig) via its kube-authkit dependency, and provides a high-level Cluster and ClusterConfiguration API for resource provisioning.
The package is designed for developers who need to access high-performance compute on Kubernetes—either cloud-hosted or on-premises—without deep Kubernetes expertise. It depends on kubernetes, ray, pydantic, and rich for its core functionality, and includes demo notebooks and a migration path from deprecated authentication classes. Maintenance is active, with a recent release and no known security vulnerabilities.
Use it for
- Submit distributed machine learning training jobs to a Kubernetes cluster without writing YAML manifests.
- Provision compute clusters on-demand for batch processing workloads in a Python notebook or script.
- Authenticate to OpenShift or cloud Kubernetes clusters using OIDC or token-based methods.
- Monitor and manage job lifecycle (submission, observation, resource cleanup) from Python code.
- Migrate legacy Kubernetes authentication code to modern kube-authkit patterns.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need to submit batch jobs or provision compute resources on Kubernetes from Python.
The low install friction, active maintenance, permissive license, and lack of known vulnerabilities make it a solid choice. The substantial dependency footprint (9 runtime packages) is justified by the abstraction it provides. Not necessary if you already have a working Kubernetes workflow or do not need programmatic cluster provisioning.
Install
codeflare-sdk on PyPI
Before you install
Low friction installation as a pure Python wheel. Active maintenance with a release 24 days ago. Depends on 9 runtime packages including kubernetes, ray, and kube-authkit; all are established libraries, though the dependency footprint is substantial.
Requires Python 3.11 or 3.12; requires a Kubernetes cluster endpoint and valid authentication credentials (auto-detected from kubeconfig or in-cluster service account by default).
License in practice
Apache-2.0 permissive license allows use in commercial and proprietary projects without viral obligations, though derivative works must retain license attribution.
Quickstart
pip install codeflare-sdk
from codeflare_sdk import Cluster, ClusterConfiguration
cluster = Cluster(ClusterConfiguration(
name='my-cluster',
num_workers=2,
))
cluster.apply()Verify before relying
- Whether the package works with Kubernetes versions beyond what the fact sheet specifies.
- Performance characteristics and scalability limits for large job submissions.
- Whether Ray integration requires Ray cluster pre-deployment or if CodeFlare manages that.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <4.0,>=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagescryptographyexecutingipywidgetskube-authkitkubernetesopenshift-clientpydanticrayrich |
| Maintenance | Actively maintained 24 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 108,249 / month, #12,568 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12 |
Evidence: codeflare_sdk-0.38.2-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “kubernetes batch job submission”
- codeflare-sdkCodeFlare SDK provides a Python interface for submitting and managing…
- torchxTorchX is a universal job launcher that submits PyTorch training and…
- submititSubmitit wraps Slurm job submission and monitoring, letting you…
Give your agent the search over MCP, or paste the wish link into any chat.
More Distributed Computing packages
gRPC Python is an HTTP/2-based RPC framework that enables you to define and call remote procedures across network boundaries using protocol buffers for serialization.
Install it if you need RPC communication in a distributed system or are integrating with existing gRPC services.
execnet lets you spawn and communicate with Python interpreters across local processes, remote hosts, and different platforms, using a simple API for task distribution and inter-process messaging.
However, the aging maintenance status (275 days since last release) means you should verify it meets your concurrency and performance needs before committing to a…
Cloudpickle extends Python's standard pickle module to serialize lambda functions, interactively-defined functions and classes, and other constructs that the default pickle cannot handle, making it suitable for cluster computing and remote code execution.
Install it if you need to serialize lambda functions, interactively-defined code, or non-standard Python constructs for cluster computing or distributed execution.
Provides a unified, open()-compatible Python API for streaming large files from remote storage (S3, GCS, Azure, HDFS, SFTP, HTTP) and local filesystems, with transparent compression support.
Install it if you work with large files on cloud storage or remote systems and want to avoid writing boilerplate around multiple SDKs.
Portalocker provides cross-platform file locking with support for exclusive and shared locks, plus Redis-based distributed locks and process-aware PID file locking.
Install it if you need file or process coordination; the optional extras (pywin32, redis) are only required for specific lock types.
Ray is a distributed computing framework that scales Python applications from a single machine to multi-node clusters, providing abstractions for parallel tasks, stateful actors, and shared objects.
See also pulumi-kubernetes · argo-workflows · aws-cdk.aws-batch · k8s-agent-sandbox · azure-batch · submitit · awslabs.eks-mcp-server · openshift · aws-parallelcluster · pykube-ng