# tensorflow/ecosystem

Integration of TensorFlow with other open-source frameworks

Repository: https://github.com/tensorflow/ecosystem
Canonical: https://ross.abutalabs.com/products/ecosystem
Language: Scala
License: Apache-2.0
License Family: permissive
Archived: true
Last push: 2024-09-25T14:32:01+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3610, "days_push": 707, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1377, forks 390 (observed 2026-08-28T04:04:33.467036+00:00)

## What it is
A collection of templates and connectors for integrating TensorFlow with other open-source frameworks such as Kubernetes, Kubeflow, Spark, Hadoop, and Mesos. It provides Docker configurations, cluster manager templates, and data connectors like the Spark TensorFlow Connector for TFRecord I/O.

## Use cases
- run distributed TensorFlow training on Kubernetes
- train TensorFlow models on a Spark cluster
- read and write TFRecord files from Hadoop MapReduce or Spark
- deploy TensorFlow Serving with Docker and Kubernetes templates
- run TensorFlow jobs via Marathon on Mesos
- set up Jupyter images for TensorFlow on Kubeflow

## When to choose
- you need to run distributed TensorFlow training on cluster managers like Kubernetes or Mesos
- you want to move data between Spark/Hadoop and TensorFlow via TFRecord
- you need ready-made templates for TensorFlow Serving deployments on Kubernetes

## When to avoid
- you use PyTorch or other frameworks instead of TensorFlow
- you want a modern managed training platform rather than templates
- you need active development - the repo is largely in maintenance mode with minimal updates

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, container-orchestration, etl, deployment, infrastructure-as-code
- domain: machine-learning, deep-learning, big-data, cloud-computing, microservices
- platform: python, jvm, cloud, self-hosted
- tags: tensorflow, spark, hadoop, kubeflow, distributed-training, mesos, marathon, tfrecord, containers, kubernetes, docker

## Member repositories
- tensorflow/ecosystem (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:33.467036+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:40:28.028771+00:00, confidence not recorded.
  - readme: https://github.com/tensorflow/ecosystem (fetched 2026-08-28T04:04:33.467036+00:00, sha 508c46cb80b2)
- Data as of 2026-08-30T08:39:29.467469+00:00.