Both Caffe and PyTorch can be described as the building blocks required for deep learning applications. These lightweight frameworks are often used for building and researching AI products.
If you’re unfamiliar with deep learning frameworks, essentially they are a stack of many technologies and libraries taking place on different abstraction layers.
However, released in 2016, PyTorch is seen to have more benefits than Caffe, and any other machine learning framework for that matter, since it is considered to be more developer-friendly.
Thanks to its efficient memory usage and dynamic computational graph, PyTorch can become incredibly popular as opposed to other open-source frameworks.
With this in mind, this article will explore everything you need to know about Caffe and PyTorch to determine which is the best application for you.
What Is Caffe?
Caffe (otherwise known as Convolutional Architecture for Fast Feature Embedding) is described as an open-source deep learning framework that supports a variety of learning architectures, including RCNN, CNN, LSTM, and other networks fully connected.
Caffe is most known for its segmentation and classification tasks thanks to its out-of-the-box templates and graphics processing unit (GPU) support that make model setup and training simple.
With Caffe, you’re able to define solver, model, and optimization details using the configuration files due to its expressive architecture. Plus, you can also switch between central processing unit (CPU) computation and GPU by simply changing one flag under the configuration file.
When combined, these features remove the need for hard coding in your projects, which is typically required when undertaking deep learning frameworks.
In addition to this, Caffe is also considered to be among the fastest convolutional networks available on the market.
How Does It Work?
Blobs are used to process data in Caffe, these are described as N-dimensional arrays organized in a C-contiguous fashion.
Here, data is maintained in the form of data passed in the model as well as a diff – this is a gradient generated by the network.
Data layers are implemented to control how the data is refined within the Caffe model. Through configuring the data layer you can implement pre-processing and transformation, including mirroring, cropping, mean subtraction, and scaling.
Plus, the possibility of configuring multiple-input and prefetching can also be considered.
Caffe is made up of a library containing C++, and highlights a modular development interface; although, not every instance needs custom compilation.
Hence, Caffe provides its users with interfaces to be used every day in the form of a command line, MATLAB, and Python.
The solver in Caffe is required for learning – more specifically creating parameter updates that enhance the loss and model optimization.
In Caffe, there are several solvers offered, these include adaptive gradients, stochastic gradient descent, and RMSprop.
The solver is a separate configuration to optimize and decouple models.
The foundation of every Caffe deep learning model is dependent on the Caffe layers and parameters.
The bottom connection consists of the input data storage and the top connection is where, after computation, you’ll receive results.
In each layer, you’ll find three different computations forming: setup, forward and backward computations – hence, these make up the primary unit of computation.
A layer catalog in Caffe can be used to create state-of-the-art deep learning models. These include normalization layers, data layers, loss layers, utility layers, and activation layers.
Process over 60 million images per day using a single NVIDIA K40.
Easy To Use
In most cases, no coding is required, with GPU training, open-source frameworks, and ready-to-use templates.
Hard to experience new deep learning architectures not already implemented.
What Is PyTorch?
PyTorch can be described as an open-source library that takes place in a machine learning library generated through a Torch library to be used with python programming.
In January 2016, it was developed by Facebook’s AI Research lab to be used as a free and open-source library for deep learning, computer vision, and a natural language processing system.
Here, programmers are able to create complex neural networks without trouble by using PyTorch’s core data structure, Tensor – this is a multi-dimensions array, similar to Numpy arrays.
In both research communities and current industries, the use of PyTorch is increasing thanks to its speed, flexibility, and ease of use when getting a project started – making it one of the best deep learning systems.
Below, we have outlined the five major components that make up PyTorch, these include:
These are essentially a wrapper surrounding tensors that maintain the gradient. You can find the variable under torch.autograd as being a torch.autograd.Variable.
These components are known as the Multi-Dimensional array, like the Numpy array, and are available as a torch in Torch. For instance, torch.CharTen, IntTensor, torch.FloatTensor, etc.
Here, functions act as transform operations, and they don’t contain memory to store any matter. Similar to how log functions provide output in the form of log value, and how linear layers can’t function since it stores biases and weight values.
For instance, functions can take the form of torch.sum, torch.log, etc., and functions can be utilized through the torch.nn.functional.
These are essentially a wrapper surrounding the variable. Parameters are used when you want to use a tensor as a parameter of a particular module since, here, a variable isn’t available and Tensors don’t contain the appropriate gradient.
Hence, parameters can be used under torch.nn to be utilized as a torch.nn.Parameter.
Modules can be found as torch.nn.Module under Torch and are used for the base class covering all the neural networks.
A module can consist of other parameters, functions, and, of course, modules. Here, state and learnable weights can be stored. Essentially, these are a form of transformation that can place torch.nn.Linear, torch.nn.Conv2d, etc.
Easy To Learn
Provides users with simple codes, too.
In addition to this it is faster and provides optimizations.
These extend the PyTorch Libraries.
Since the application was released in 2016, it is comparatively newer when compared to others, hence, containing fewer users and isn’t widely known.
When compared to other frameworks, it contains a smaller community.
Absence Of Visualization And Monitoring tools
For instance, this would include a tensor board.
Both Caffe and PyTorch are excellent deep-learning platforms. However, the one you choose will be entirely dependent on your specific requirements.
When comparing the two, the obvious choice would be PyTorch since it has an easy-to-use interface and contains a dynamic computation graph as well as efficient memory usage.
That being said, Caffe is also a remarkable framework, containing excellent interfaces, data processing capabilities, layers, and solvers.
Hence, whichever one you choose, you are sure to be rewarded with a great experience. Hopefully, this guide has informed you on everything you need to know about Caffe and PyTorch and which framework is better for you.