Tensorflow created a buzz in AI and deep learning forum and TensorFlow is the most popular framework in the deep learning community.
As we know that to train deep learning models we need to compute power and this age of computation. Now we are moving with cloud computing along with edge computing. Edge computing is the need of today’s world because of innovation in the IoT domain and due to compliance and data protection laws enforcing companies to do computation and the edge side instead of computing model in the cloud and sending the result back to a client device is now the legacy.
As TensorFlow is the most popular deep-learning framework. It comes with its lite weight version for edge computation. Now a day’s mobile devices have good processing power but edge devices have less power.
Train deep learning model in less than 100KB.
The official definition of Tensorflow Lite:
“TensorFlow Lite is an open-source deep learning framework for on-device inference.”
Deploy machine learning models on mobile and IoT devices.
Tensorflow Lite is package of tools to help developers to run TensorFlow models on mobile, embedded devices, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
Tensorflow Lite is providing machine learning at the edge devices.
Edge computing means compute at local.
This diagram illustrates the standard flow for deploying the model using TensorFlow Lite.
Tensorflow Lite is not a separate deep learning framework, it is providing a set of tools that will help developers run TensorFlow models or any other deep learning models on mobile, embedded and IoT devices.
- Choose Model or develop your own model.
- Choose Model
- Convert the Model
- Deploy the Model
- Run the inference with the Model
- Optimize the Model and repeat the above steps.
Tensorflow Lite consists of two main components
- Converter:- Tensorflow Lite Converter converts the TensorFlow model into the TensorFlow lite model.
- Interpreter:- It is supporting a set of core operators that are optimized for on-device applications and with a small binary size. It is basically for inferencing the model.
Why Edge Computing?
Edge computing is really best to use case along with cloud computing. Nowadays cloud computing becomes crazy but there are a certain requirement where edge computation will beat cloud computing. Why edge computation is more important and what is advantage you will derive from this.
- Privacy:- No data needs to leave the device. Everything is there only.
- Latency:- There’s no back and forth request to a server.
- Connectivity:- Internet connection not required
- Power Consumption:- Connecting to a network requires power.
Tensorflow Lite is the one-stop solution to convert your deep learning model and deploy efficiently and enjoy inferencing. TensorFlow lite supports both mobile devices and microcontrollers.