Tensorflow vs PyTorch: Which is better for your application development?

TensorFlow is developed by Google Brain and actively used at Google both for research and production needs. Its closed-source predecessor is called DistBelief.

PyTorch is a cousin of lua-based Torch framework which was developed and used at Facebook. However, PyTorch is not a simple set of wrappers to support popular language, it was rewritten and tailored to be fast and feel native.

PyTorch

Based on the Torch library, PyTorch is an open-source machine learning library. PyTorch is imperative, which means computations run immediately, which means the user need not wait to write the full code before checking if it works or not. We can efficiently run a part of the code and inspect it in real-time. The library is a python based built for providing flexibility as a deep learning development platform. Enlisted below are the features that justify PyTorch as a deep learning model:

• Easy to use API
• Python support — PyTorch smoothly integrates with the python data science stack. It is similar to NumPy, so if you are already using that, you will feel right at home.
• Dynamic computation graphs — PyTorch provides a framework for us to build computational graphs as we go, and even change them during runtime instead of predefined graphs with specific functionalities. This service is valuable for situations where we don’t know the requirement of memory for creating a neural network.

Other key strengths of the machine learning framework include:

• TorchScript: Provides a seamless transition between graph mode and eager mode to accelerate the path to production.
• Distributed Training: Distributed backend. Torch enables performance optimization in research and production and scalable distributed training.
• Tools and Libraries: A vibrant ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP, and more.

TensorFlow

Google’s TensorFlow is a famous open-source deep-learning library for dataflow and differentiable programming across a range of tasks. It is also a symbolic math library and machine learning applications such as neural networks also use this library. Research and production are the primary uses of the library. Enlisted are the features of TensorFlow:

• Secure Model Building: Using intuitive high-level APIs such as Keras, the library allows us to build and train ML models, which makes for quick model iteration and easy debugging.
• ML Production Anywhere: Trains and deploys models in the cloud, on-prem, in the browser, or on-device irrespective of the language the user uses.
• Robust Experimentation for Research: A flexible and straightforward architecture to take new ideas from concept to code, to state-of-the-art models, and publication faster

Components of TensorFlow:

• Tensor

Tensor is the core framework of the library that is responsible for all computations in TensorFlow. A tensor is a vector or matrix of n-dimensions that represents all types of data. Values have identical data types with a known shape in a Tensor. The dimensionality of the matrix or array is the shape of the data. An input data or the result of computation generally originates a tensor. A graph conducts all operations in the TensorFlow, which a set of computations that takes place successively. Op node is the term for each operation conducted, and nodes are connected.

The graph is responsible for outlining the ops and connections between the nodes, but it does not display the values.

• Graph

TensorFlow uses Graph framework. During the training, the graph gathers and describes all the series computations. The graph offers advantages like

Running multiple CPUs or GPUs and even mobile operating systems.

The portability feature allows us to preserve the computation for immediate or later use.

The computations in the graph are done by connecting tensor, with the help of a node and an edge. The node is responsible for carrying out the mathematical operation, and the node produces endpoints outputs while the edges explain the input/output relationships between nodes.

Difference between Pytorch vs Tensorflow

Let us weigh the two frameworks below:

• Development Wizards

TensorFlow was developed by Google and is based on Theano (Python library), whereas Facebook developed PyTorch using the Torch library.

• Computational Graph Construction

Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go.

PyTorch offers an advantage with its dynamic nature of creating graphs. The graphs are built, interpreting the line of code corresponding to that particular aspect of the graph. However, in the case of TensorFlow, as the construction is static and the graph is required to go through compilation and then executed on the execution engine.

Debugging

By making use of a standard python debugger, the user does not require learning to make use of another debugger from scratch. Debugging can be done in two ways when TensorFlow is concerned: a) required to learn TF debugger. b) requesting variables that are requested from the session.

• Coverage

TensorFlow supports a higher level of functionality and gives a broad spectrum of options to work with by providing certain operations like:

• Flipping a tensor along with dimension
• Checking the Tensor for infinity and NaN
• Providing support for fast Fourier transforms

Uses a package named contrib, for the creation of models.

PyTorch, on the other hand, has fewer features comparatively.

• Serialization

PyTorch serves a simple API that saves all the weights of the model or pickles the entire class. TensorFlow also offers a significant advantage in that the entire graph can be saved as a protocol buffer, including parameters and operations as well. Also, other supported languages such as C++ and Java can load the graph; this is critical for deployment stacks where Python is not offered. It is also useful when the user changes the model source code and but wants to run old models.

• Deployment

Both frameworks are easy to wrap in case of small-scale server-side deployments. TensorFlow works well for mobile and embedded deployments. However, deploying to Android or iOS does require a non-trivial amount of work. Another noticeable feature that TensorFlow serves is that the models cannot be hot-swapped easily without bringing the service down.

• Visualization

Visualization plays a crucial role while presenting any project in an organization. TensorBoard visualizes machine learning models in TensorFlow, which helps during the training of the model and spot the errors quickly. TensorBoard is the real-time representation of the graphs of a model that depicts the graph representation and shows the accurate graphs in real-time.

• Device Management

TensorFlow does not require the user to specify anything since the defaults are well set. For instance, it automatically assumes if the user wants to on the GPU if one is available. In contrast, PyTorch requires the user to move everything onto the device if CUDA is enabled explicitly. TensorFlow has a negative side in device management that even if one GPU is in use, it still consumes all the memory on available GPUs.

Conclusion

Both frameworks are useful and have a huge community behind them. Both provide machine learning libraries to achieve various tasks and get the job done. TensorFlow is a powerful and deep learning tool with active visualization and debugging capabilities. TensorFlow also offers serialization benefits as the entire graph is saved as a protocol buffer. It also has support for mobile platforms and offers production-ready deployment. PyTorch, on the flip side, is still gaining momentum and attracting Python developers as it’s more Python-friendly. In summary, TensorFlow is used to make things faster and build AI-related products, while research-oriented developers prefer PyTorch.