PocketFlow
An Automatic Model Compression framework for developing smaller and faster AI applications
GraphCanon updated Oct 3, 2026 · GitHub synced Oct 3, 2026
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Decision brief
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.
Good fit when
- When you need to optimize TensorFlow models specifically for deployment on devices with limited computational resources like mobile phones
- If you want automated selection of optimal hyper-parameters for compressing deep learning models without manual tuning
Avoid when
- 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
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1281d since push)
- As of Oct 3, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 3, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install PocketFlowSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
PocketFlow is an open-source framework that automates deep learning model compression to improve inference efficiency with minimal human effort.
Capability facts
- Languages
- python
Source: github.language · Oct 3, 2026
Categories
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README
PocketFlow PocketFlow is an open source framework for compressing and accelerating deep learning models with minimal human effort. Deep learning is widely used in various areas, such as computer vision, speech recognition, and natural language translation. However, deep learning...
For agents
This page has a .md twin and JSON over the API.