Deep Learning Frameworks

What is a Deep Learning Framework?

A deep learning framework is a software package used by researchers and data scientists to design and train deep learning models. The idea with these frameworks is to allow people to train their models without digging into the algorithms underlying deep learning, neural networks, and machine learning.

These frameworks offer building blocks for designing, training and validating models through a high level programming interface. Widely used deep learning frameworks such as PyTorch, TensorFlow, MXNet, and others can also use GPU-accelerated libraries such as cuDNN and NCCL to deliver high-performance multi-GPU accelerated training.

Why Use a Deep Learning Framework?

• They supply readily available libraries for defining layers, network types (CNNs, RNNs) and common model architectures
• They can support computer vision applications; image, speech and natural language processing
• They have familiar interfaces via popular programming languages such as Python, C, C++ and Scala
• Many deep learning frameworks are accelerated by NVIDIA deep learning libraries such as cuDNN, NCCl and cuBLAS for GPU accelerated deep learning training

Example Frameworks