Tensorflow Lite vs Tensorpack

Tensorflow Lite vs Tensorpack: What are the differences?

Tensorflow Lite: Deploy machine learning models on mobile and IoT devices. It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size; Tensorpack: A neural network training interface based on TensorFlow. It is a Neural Net Training Interface on TensorFlow, with focus on speed + flexibility. It is a training interface based on TensorFlow, which means: you’ll use mostly tensorpack high-level APIs to do training, rather than TensorFlow low-level APIs.

Tensorflow Lite and Tensorpack can be categorized as "Machine Learning" tools.

Some of the features offered by Tensorflow Lite are:

• Lightweight solution for mobile and embedded devices
• Enables low-latency inference of on-device machine learning models with a small binary size
• Fast performance

On the other hand, Tensorpack provides the following key features:

• Training interface based on TensorFlow
• Focus on training speed
• Focus on large datasets

Tensorpack is an open source tool with 5.36K GitHub stars and 1.64K GitHub forks. Here's a link to Tensorpack's open source repository on GitHub.