The popularity and success of deep learning has been motivated by the creation of many popular and open source deep learning frameworks that can be used for training and inference of neural networks. Caffe was one of the first popular deep learning frameworks. It was created by Yangqing Jia at UC Berkeley for his PhD thesis and released to the public at the end of 2013. It was primarily written in C++ and provided a C++ API. Caffe also provided a rudimentary Python API wrapped around the C++ API. The Caffe framework created networks using layers. Users created networks by listing down and describing its layers in a text file commonly referred to as a prototxt.

Following the popularity of Caffe, universities, corporations, and individuals created and launched many deep learning frameworks. Some of the popular ones today are Caffe2, TensorFlow, MXNet, and PyTorch. TensorFlow is driven by Google, MXNet has the support of Amazon, and PyTorch was primarily developed by Facebook.

Caffe's creator, Yangqing Jia, moved to Facebook, where he created a follow-up to Caffe called Caffe2. Compared to the other deep learning frameworks, Caffe2 was designed to focus on scalability, high performance, and portability. Written in C++, it has both a C++ API and a Python API.

Caffe2 and PyTorch are both popular DL frameworks, maintained and driven by Facebook. PyTorch originates from the Torch DL framework. It is characterized by a Python API that is easy for designing different network structures and experimenting with training parameters and regimens on them. While PyTorch could be used for inference in production applications on the cloud and in the edge, it is not as efficient when it comes to this.

Caffe2 has a Python API and a C++ API. It is designed for practitioners who tinker with existing network structures and use pre-trained models from PyTorch, Caffe, and other DL frameworks, and ready them for deployment inside applications, local workstations, low-power devices at the edge, mobile devices, and in the cloud.

Having observed the complementary features of PyTorch and Caffe2, Facebook has a plan to merge the two projects. As we will see later, Caffe2 source code is already organized as a subdirectory under the PyTorch Git repository. In the future, expect more intermingling of these two projects, with a final goal of fusing the two together to create a single DL framework that is easy to experiment with and tinker, efficient to train and deploy, and that can scale from the cloud to the edge, from general-purpose processors to special-purpose accelerators.

Working with deep learning models, especially the training process, requires a lot of computing power. While you could train a popular neural network on the CPU, it could typically take many hours or days, depending on the complexity of the network. Using GPUs for training is highly recommended since they typically reduce the training time by an order of magnitude or more compared to CPUs. Caffe2 uses CUDA to access the parallel processing capabilities of NVIDIA GPUs. CUDA is an API that enables developers to use the parallel computation capabilities of an NVIDIA GPU, so you will need to use an NVIDIA GPU. You can either install an NVIDIA GPU on your local computer, or use a cloud service provider such as Amazon AWS that provides instances with NVIDIA GPUs. Please take note of the running costs of such cloud instances before you use them for extended periods of training.

Once you have trained a model using Caffe2, you can use CPUs, GPUs, or many other processors for inference. We will explore a few such options in Chapter 6, Deploying Models to Accelerators for Inference, and Chapter 7, Caffe2 at the Edge and in the cloud, later in the book.

A major portion of deep learning research and development is currently taking place on Linux computers. Ubuntu is a distribution of Linux that happens to be very popular for deep learning research and development. We will be using Ubuntu as the operating system of choice in this book. If you are using a different flavor of Linux, you should be able to search online for commands similar to Ubuntu commands for most of the operations described here. If you use Windows or macOS, you will need to replace the Linux commands in this book with equivalent commands. All the code samples should work on Linux, Windows, and macOS with zero or minimal changes.