Comparison
tensorflow vs awesome-AutoML
Verdict
Pick tensorflow if open-source framework for building and deploying ML models with strong support for distributed computing and GPU acceleration; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · tensorflow alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | tensorflow | awesome-AutoML |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Slowing (133d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- tensorflow
- An Open Source Machine Learning Framework for Everyone
- awesome-AutoML
- Curating AutoML research and resources
Stars
- tensorflow
- 197k
- awesome-AutoML
- 941
Forks
- tensorflow
- 76k
- awesome-AutoML
- 156
Open issues
- tensorflow
- 3.0k
- awesome-AutoML
- 1
Language
- tensorflow
- C++
- awesome-AutoML
- -
Adopt for
- tensorflow
- Open-source framework for building and deploying ML models with strong support for distributed computing and GPU acceleration.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- tensorflow
- -
- awesome-AutoML
- -
Runtime
- tensorflow
- -
- awesome-AutoML
- -
License
- tensorflow
- Apache-2.0
- awesome-AutoML
- GPL-3.0
Last pushed
- tensorflow
- Aug 3, 2026
- awesome-AutoML
- Mar 24, 2026
Categories
- tensorflow
- LLM Frameworks, Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- tensorflow
- Very active (96%)
- awesome-AutoML
- Slowing (36%)
Days since push
- tensorflow
- 0d
- awesome-AutoML
- 133d
Open issues (now)
- tensorflow
- 3.0k
- awesome-AutoML
- 1
Owner type
- tensorflow
- Organization
- awesome-AutoML
- User
Full report
- tensorflow
- Trust report
- awesome-AutoML
- Trust report
Choose tensorflow if…
- License: tensorflow is Apache-2.0, awesome-AutoML is GPL-3.0.
- Tags unique to tensorflow: deep-learning, deep-neural-networks, distributed, machine-learning.
- Also covers LLM Frameworks.
- Need comprehensive tools for training deep neural networks
When NOT to use tensorflow
- Looking for simple model deployment without complex setup
- Preferring frameworks that integrate better with non-Python languages
- Requiring real-time processing guarantees not provided by TensorFlow's architecture
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, tensorflow is Apache-2.0.
- Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search.
- When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
When NOT to use awesome-AutoML
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides.
- When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (tensorflow/tensorflow) · observed Aug 3, 2026
- GitHub forks (tensorflow/tensorflow) · observed Aug 3, 2026
- Last push (tensorflow/tensorflow) · observed Aug 3, 2026
- License file (Apache-2.0) · observed Aug 3, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (windmaple/awesome-AutoML) · observed Aug 4, 2026
- GitHub forks (windmaple/awesome-AutoML) · observed Aug 4, 2026
- Last push (windmaple/awesome-AutoML) · observed Mar 24, 2026
- License file (GPL-3.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: tensorflow 197k · awesome-AutoML 941 (synced Aug 3, 2026).
Common questions
- What is the difference between tensorflow and awesome-AutoML?
- tensorflow: An Open Source Machine Learning Framework for Everyone. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose tensorflow over awesome-AutoML?
- Choose tensorflow over awesome-AutoML when License: tensorflow is Apache-2.0, awesome-AutoML is GPL-3.0; Tags unique to tensorflow: deep-learning, deep-neural-networks, distributed, machine-learning; Also covers LLM Frameworks; Need comprehensive tools for training deep neural networks.
- When should I choose awesome-AutoML over tensorflow?
- Choose awesome-AutoML over tensorflow when License: awesome-AutoML is GPL-3.0, tensorflow is Apache-2.0; Tags unique to awesome-AutoML: automl, hyperparameter-optimization, meta-learning, neural-architecture-search; When seeking comprehensive resources on diverse AutoML topics from recent and impactful research.
- When should I avoid tensorflow?
- Looking for simple model deployment without complex setup Preferring frameworks that integrate better with non-Python languages Requiring real-time processing guarantees not provided by TensorFlow's architecture
- When should I avoid awesome-AutoML?
- If looking for direct implementation advice as the repository focuses more on linking to resources rather than providing specific how-to guides. When requiring real-time or interactive AutoML features, since it's a curation hub rather than an application tool.
- Is tensorflow or awesome-AutoML more popular on GitHub?
- tensorflow has more GitHub stars (196,758 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are tensorflow and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (tensorflow: Apache-2.0, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to tensorflow or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at tensorflow alternatives and awesome-AutoML alternatives (tensorflow markdown twin, awesome-AutoML markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, tensorflow or awesome-AutoML?
- tensorflow: Very active. awesome-AutoML: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for tensorflow and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: tensorflow trust report; awesome-AutoML trust report.