Top 10 Open Source Deep Learning Tools in 2022

The Tech Trend
2 min readApr 1, 2022


Alexa playing your favorite song on your sideboard, autonomous cars driving along the roads, Netflix suggesting shows based upon what you’ve watched…deep learning has the power to change everything! This field is home to many open source deep learning tools. Let’s take a look at some of their best.

Top 10 Open Source Deep Learning Tools

1. TensorFlow

TensorFlow ( ) is an open source deep learning framework that allows the creation of machine learning models. It was founded on November 9, 2015.


  • This model provides an entire end-to-end solution, from construction to deployment
  • Also supports model deployment on embedded devices and mobile devices
  • Documentation and support from the community are key.
  • Support for multiple GPUs
  • Queues and graph visualization
  • Image processing, computer vision, and speech recognition are supported

2. Keras

Keras (, an open-source deep-learning library for Python, was released on March 27, 2015.


  • Developer guides and extensive documentation
  • It is easy to use and learn
  • Clear and complete error messages
  • Models for mobile devices, web, and Java Virtual Machine support
  • Distributed deep learning models for graphics processing units (GPUs), and tensor processor units (TPUs).
  • Both in the industry and in research, this principle is widely accepted

3. PyTorch

PyTorch (, an open-source machine learning library, was released in September 2016. It was created by Adam Paszke and Sam Gross, Soumith Chantala, Gregory Chanan, and Gregory Chanan.


  • It is widely accepted on major cloud platforms for its ease of development and scalability.
  • Facilitates end-to-end pipeline flow from Python development to mobile device installation (iOS or Android).
  • Industry developers and active researchers provide strong ecosystem libraries that allow development in multiple domains, from reinforcement learning to computer vision.
  • Direct interface to ONNX (open neural network exchange ) compatible systems and environments. Models can be exported in the ONNX standard format
  • Multiple GPU support is available for deep learning models.
  • C++ Interface Supports High Performance and Low Latency Applications
  • Data sharing with libraries externally supported

Originally published on The Tech Trend



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