‘Man and machine together can be better than the human’.
All thanks to deep learning frameworks like PyTorch, Tensorflow, Keras, Caffe, and DeepLearning4j for making machines learn like humans with special brain-like architectures known as Neural Networks. The war of deep learning frameworks has two prominent competitors- PyTorch vs Tensorflow because the other frameworks have not yet been adopted widely. These frameworks simplify the process of humanizing machines with supremacy through accurate large-scale complex deep learning models. If you are new to machine learning, it means that you have been wheedled by this incredible field of study and its limitless possibilities of building applications that have never been implemented without human intervention, congratulations and welcome to the world of deep learning!
So, having played quite some time with neural networks you’ve finally decided to start your deep learning journey and become a deep learning engineer. Good call! Now, you probably want to dive into predicting who would take home the next Oscar, or are keen on generating faces of people who don’t exist at all with AI or simply want to detect apples and oranges on pictures. There are several machine learning and deep learning frameworks today that will help you achieve these goals. Each of these frameworks has a different purpose and is developed in a different manner for solving diverse business challenges. There are many deep learning frameworks but as a beginner, you will always have this question on “Which deep learning framework should I choose for my next machine learning project?’. The purpose of this article is to help you understand the similarities and differences between two of the most popular deep learning frameworks – PyTorch vs Tensorflow. It does not matter whether you are a data scientist, researcher, student, machine learning engineer, or just a deep learning enthusiast, you’re definitely going to find the comparison of PyTorch and TensorFlow very useful.
PyTorch and Tensorflow both are open-source frameworks with Tensorflow having a two-year head start to PyTorch. Tensorflow, based on Theano is Google’s brainchild born in 2015 while PyTorch, is a close cousin of Lua-based Torch framework born out of Facebook’s AI research lab in 2017.
You can read about the development of Tensorflow in the paper “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.” and PyTorch in this paper "Automatic Differentiation in PyTorch." PyTorch fits perfectly in the machine learning ecosystem as it is developed to be used in Python though it has a C++ interface. Compared to other declarative deep learning frameworks, PyTorch is popular for its imperative programming style which makes it more pythonic. PyTorch leverages the flexibility and popularity of the python programming language whilst maintaining the functionality and convenience of the native Torch library. In Tensorflow the computations are performed using high-performance C++ binaries but there are interfaces in several programming languages that ease the implementation of deep learning architectures for beginners. Tensorflow is in a relationship with TF 2.0 making its usability a lot more powerful. It comes with an intuitive high-level API tf.keras so one can enjoy all the low-level classes of TensorFlow together with the user-friendliness of Keras.
Just to show you a comprehensive picture of growth in demand and usage of PyTorch and Tensorflow deep learning frameworks, here is the Google trend graph for the keywords Tensorflow vs PyTorch -
You can also look at the number of job listings for PyTorch and Tensorflow to get a comprehensive picture of the demand for these deep learning skills –
Every deep learning framework depends on the generation of computation graphs for calculating the gradient values needed for gradient descent optimization. Having built the forward propagation graph, the deep learning frameworks tackle the backward differentiation. The reason for having computational graphs is to achieve parallelism and speed up the training process. There are usually two types of graphs – Static and Dynamic.
In a static computational graph, you just define the neural network architecture and run it through a bunch of data to train the model and make predictions while a dynamic computational graph is defined implicitly once the forward propagation is executed. One just needs to write the forward computation with all the features and the computational graph is built on-demand dynamically offering flexibility. Dynamic computational graphs are easy to debug, unlike static computational graphs. The computational graphs form an important design differentiating factor between TensorFlow and PyTorch. PyTorch makes use of dynamic computational graphs while TensorFlow creates optimized static computational graphs. However, TensorFlow 2.0 has eager execution by default that makes the graph generation similar to dynamic computational graphs wherein operations are evaluated immediately without generating a computational graph to be run later. So, as of today, you can have both static and dynamic modes of graph generation in both frameworks.
Suppose that you are developing a deep learning model for image classification with 98% desired accuracy. You will have to track the loss and accuracy of your deep learning model at every epoch. Also, you will want to profile the loss and accuracy of your model with various hyperparameters like optimizers, learning rate, drop out, and others to fine-tune the deep learning model. Deep learning models are pricey in terms of computation so to track the accuracy of the deep learning models on different data flow graphs with different values of hyperparameters, it is important to visualize the results and get a bird’s eye view of the overall training process of the model.
Both PyTorch and TensorFlow support visualization tools for quick analysis making it easier to review the training process of the deep learning models and ease the process of debugging. These visualization tools are also great for presenting the results. TensorFlow’s Tensorboard has an awesome in-built visualization tool with a suite of web apps for understanding a deep learning model through 5 different visualizations- graphs, scalars, audio, histograms, and images. PyTorch’s native visualization tool visdom is flexible, customized, lightweight, and easy to use with support for PyTorch tensors. Visdom is not interactive and has very limited and minimalistic features for overviewing data. However, the latest release of PyTorch 1.2.0 has PyTorch built-in Tensorboard.
Developing and training a deep learning model is just half the job done. Deploying these trained deep learning models into production and managing them is the most difficult part. TensorFlow has an in-built model deployment tool TensorFlow Serving which is used by most of the Google projects. Having been tested on over 1000 Google projects, TensorFlow Serving is capable of handling millions of requests per second. It is used for deploying machine learning models on specialized gRPC servers and provides remote access to them. Model deployment with TensorFlow Serving is highly flexible and it also integrates perfectly with Kubernetes and Docker. Using TensorFlow serving, it is easy to update a deployed model and also rollback any earlier versions of the deployed model without having to shut down the server. TensorFlow Serving is designed for industrial production environments and is a good choice where performance is a concern. While a 10% faster runtime might not mean a lot to a researcher working with these frameworks but it definitely helps organizations save millions.
PyTorch users have got TorchServe, a flexible model deployment tool in April 2020. The model deployment tool for PyTorch is in its infancy and in the experimental stage, unlike TensorFlow Serving that offers many functionalities. However, with a basic set of features like a model archiver tool, a server, metrics, logging, an API endpoint specification, and model snapshots, TorchServe attracts most of the business cases. The latest version of TorchServe also supports HuggingFace, Nvidia Waveglow, AWS Cloud Formation, and others.
Free access to solved code examples can be found here (these are ready-to-use for your ML projects)
There can be a huge difference in the performance of neural network models when managing devices. Both these frameworks apply neural networks perfectly, however, the way they execute is different. A TensorFlow operation has both GPU and CPU implementations but a GPU device would always be given a priority to run the operation unless you explicitly request it to run on another device. The GPU acceleration is automated in TensorFlow meaning there is no control over memory usage. So, even if one GPU is in use, it will consume the memory of all available GPUs.
On the contrary, PyTorch does not automate GPU usage and does not have a dedicated library for GPU users. It uses CUDA to specify the usage of CPU or GPU. The deep learning model does not run without CUDA specifications for CPU or GPU use so the training process enhances when using PyTorch as there is better control over the use of resources.
The learning curve for a deep learning framework depends on the previous expertise level and also on the purpose of using the framework. For machine learning beginners who are just getting started with deep learning, TensorFlow would be a little hard to comprehend because of the low-level implementations of the neural network structure. However, its very high-level Keras API makes it easy for beginners to learn the basic concepts, and later TensorFlow can be used for a better understanding of the implementations of neural network structure.
PyTorch is easy to learn when compared to TensorFlow because the syntax is very much similar to the traditional Python programming language and it is easier to experiment if you already know Python. With a more object-oriented style and straightforward data handling, the learning curve for PyTorch is easier compared to TensorFlow. Though it might not be feature-rich the ease of learning makes it one of the most loved deep learning frameworks among beginners, unlike TensorFlow where you need to understand the working of sessions and placeholders.
PyTorch users need not learn to use another debugger from scratch as debugging of PyTorch code can be done with the use of a standard python debugger. As we already discussed above that PyTorch defines computational graphs at run time so it is easier to use most of the regular python libraries and tools in PyTorch. So to debug PyTorch model code, one can use Python debugging tools like PyCharm, pdb, and ipdb. When it comes to debugging code in TensorFlow one needs to learn about the TF debugger and also about the variables that are requested from a TensorFlow session which is complex compared to debugging in PyTorch.
With a focussed community of developers, the documentation for both PyTorch and TensorFlow is very well organized and helpful for people getting started with these deep learning frameworks. There are tons of free tutorials, MOOCs, and example implementations available for both these frameworks. With lots of support and a huge community, both these frameworks are like magic wands to do some machine learning magic.
When you are searching for a term or a phrase on Google, it automatically suggests the next word you are most likely to type, it’s TensorFlow in action that predicts the next word for you. Whether it is Google Search, Google Translate, GMaps, Photos, Google Play, Android, or YouTube, TensorFlow is used by several internal Google projects and also for other applications like –
Common applications of PyTorch include -
TensorFlow and PyTorch have become the de facto standards for the research and development of innovative custom deep learning applications because of the heavy lifting features they offer in terms of managing the underlying hardware, computation, and visualization. There were some drastic differences between the two frameworks earlier but as of today each of these frameworks has adopted all the good features from each other and have fared well in the battle. Considering the similarities between the two frameworks today, it is now possible to easily transition back and forth among the two frameworks.
It is easy to learn PyTorch making it suitable for passion projects that require building prototypes in research while TensorFlow is a deep learning framework with production readiness. That being said, which deep learning framework should you choose to build your first deep learning model? The answer to this question depends on several factors but for people getting started with deep learning here is what our Project Advisors have to say – “Explore both Tensorflow and PyTorch before committing to learning any one of these deep learning frameworks in depth. Obviously, considering the best case you will be a master of both PyTorch and Tensorflow, however, at times it might not be possible to learn both. It is not necessary to know everything about each of these frameworks, just weigh your requirements before deciding to learn a deep learning framework in depth. Have fun while you discover the fantastic features of the framework rather than worrying about not being able to accomplish something too complex. Choose a deep learning framework based on the above listed best conditions for you to realize your deep learning projects”
If you are exploring a career in the deep learning industry get started with learning Tensorflow. However, if you are a researcher then it would be a good step to get started with PyTorch. Master real-world deep learning skills in Tensorflow and PyTorch with ProjectPro library of solved end-to-end deep learning projects that come with datasets, verified reusable solution code, 2-4 hours of video solution, documentation, and email technical support.