Comparison
PocketFlow vs awesome-AutoML
Verdict
Pick PocketFlow if pocketFlow automates deep learning model compression to enhance inference efficiency with minimal human effort by selecting optimal hyper-parameters for model development focusing on mobile applications; pick awesome-AutoML if curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Markdown twin · PocketFlow alternatives · awesome-AutoML alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | PocketFlow | awesome-AutoML |
|---|---|---|
| Maintenance | Dormant (1221d 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
- PocketFlow
- An Automatic Model Compression framework for developing smaller and faster AI applications
- awesome-AutoML
- Curating AutoML research and resources
Stars
- PocketFlow
- 2.9k
- awesome-AutoML
- 941
Forks
- PocketFlow
- 491
- awesome-AutoML
- 156
Open issues
- PocketFlow
- 75
- awesome-AutoML
- 1
Language
- PocketFlow
- Python
- awesome-AutoML
- -
Adopt for
- PocketFlow
- PocketFlow automates deep learning model compression to enhance inference efficiency with minimal human effort by selecting optimal hyper-parameters for model development focusing on mobile applications.
- awesome-AutoML
- Curates AutoML research across neural architecture search, hyperparameter optimization, and meta-learning.
Persona
- PocketFlow
- -
- awesome-AutoML
- -
Runtime
- PocketFlow
- -
- awesome-AutoML
- -
License
- PocketFlow
- Other
- awesome-AutoML
- GPL-3.0
Last pushed
- PocketFlow
- Mar 31, 2023
- awesome-AutoML
- Mar 24, 2026
Categories
- PocketFlow
- Inference & Serving, Model Training
- awesome-AutoML
- Model Training
Trust and health
Maintenance
- PocketFlow
- Dormant (18%)
- awesome-AutoML
- Slowing (36%)
Days since push
- PocketFlow
- 1221d
- awesome-AutoML
- 133d
Open issues (now)
- PocketFlow
- 75
- awesome-AutoML
- 1
Owner type
- PocketFlow
- Organization
- awesome-AutoML
- User
Full report
- PocketFlow
- Trust report
- awesome-AutoML
- Trust report
Choose PocketFlow if…
- License: PocketFlow is Other, awesome-AutoML is GPL-3.0.
- Tags unique to PocketFlow: computer-vision, deep-learning, mobile-app, model-compression.
- Also covers Inference & Serving.
- When you need to optimize TensorFlow models specifically for deployment on devices with limited computational resources like mobile phones
When NOT to use PocketFlow
- Avoid if your project does not require model compression and efficiency improvement for deployment
- Do not use if the TensorFlow-centric tools are irrelevant to your project, as PocketFlow integrates closely with TensorFlow APIs
Choose awesome-AutoML if…
- License: awesome-AutoML is GPL-3.0, PocketFlow is Other.
- Tags unique to awesome-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 (Tencent/PocketFlow) · observed Aug 4, 2026
- GitHub forks (Tencent/PocketFlow) · observed Aug 4, 2026
- Last push (Tencent/PocketFlow) · observed Mar 31, 2023
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 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: PocketFlow 2.9k · awesome-AutoML 941 (synced Aug 4, 2026).
Common questions
- What is the difference between PocketFlow and awesome-AutoML?
- PocketFlow: An Automatic Model Compression framework for developing smaller and faster AI applications. awesome-AutoML: Curating AutoML research and resources. See the comparison table for live GitHub stats and shared categories.
- When should I choose PocketFlow over awesome-AutoML?
- Choose PocketFlow over awesome-AutoML when License: PocketFlow is Other, awesome-AutoML is GPL-3.0; Tags unique to PocketFlow: computer-vision, deep-learning, mobile-app, model-compression; Also covers Inference & Serving; When you need to optimize TensorFlow models specifically for deployment on devices with limited computational resources like mobile phones.
- When should I choose awesome-AutoML over PocketFlow?
- Choose awesome-AutoML over PocketFlow when License: awesome-AutoML is GPL-3.0, PocketFlow is Other; Tags unique to awesome-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 PocketFlow?
- Avoid if your project does not require model compression and efficiency improvement for deployment Do not use if the TensorFlow-centric tools are irrelevant to your project, as PocketFlow integrates closely with TensorFlow APIs
- 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 PocketFlow or awesome-AutoML more popular on GitHub?
- PocketFlow has more GitHub stars (2,909 vs 941). Stars measure visibility, not whether either tool fits your constraints.
- Are PocketFlow and awesome-AutoML open source?
- Yes - both are open-source projects on GitHub (PocketFlow: Other, awesome-AutoML: GPL-3.0).
- Where can I find alternatives to PocketFlow or awesome-AutoML?
- GraphCanon lists graph-backed alternatives at PocketFlow alternatives and awesome-AutoML alternatives (PocketFlow 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, PocketFlow or awesome-AutoML?
- PocketFlow: Dormant. 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 PocketFlow and awesome-AutoML?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: PocketFlow trust report; awesome-AutoML trust report.