The TensorFlow is the most popular tool used in machine learning which is available where you can create and train models that is commonly known as deep neural networks where one can solve different of complex problems such as image classification, object detection and natural language programming. So, this is where the qpython tensorflow comes into picture and a library designed for those models in the mobile apps. There are tools where one can explore and accelerate the tensorflow workflows. Below are the tools used for tensorflow.
Colaboratory is a used in a jupyter notebook which requires no setup and runs entirely in the cloud. The use of Colaboratory one can write and execute code, save and share your analyses, and even access powerful computing resources which is free from the browser. Over here, to start working with colab you need to log in to your google account or one can create a new Jupyter notebook by clicking new Python 3 notebook or Python 2 notebook. So, on creating a new notebook, it will create a jupyter notebook with new name and save it your google drive which can be named as Colab Notebooks.
The TensorBoard is a suite of web applications for inspecting and understanding your tensorflow runs and graphs. So, with the tensorflow one can interpret the visualizations where the tensorboard provides. Just before running tensorboard make sure the one should have generated summary data in a log directory by creating a summary writer.
The MLPerf is a tool which measures how fast a system can train ML models. While the MLPerf is intended for a wide range of systems from mobile devices to servers. By measuring the benchmark suite will give valuable information on how quickly a trained neural network can process new data to provide useful insights. So, MLPerf consists of five benchmarks, but focused on three common ML tasks like image classification, object detection, machine Translation and also used in qpython tensorflow. The MLPerf provides a complete specification with the guidelines on the reference code and will track future results.
Apart from these try to search for other tools which can be used in qpython tensorflow. Hope that I have covered all the topics in my article about tools used for tensorflow. Thanks for reading!