Top 10 Open Source Deep Learning Tools in 2022

Top 10 Open Source Deep Learning Tools

1. TensorFlow

  • 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

  • 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

  • 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

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