If you are reading this article on PyTorch vs TensorFlow, then you have likely started learning about the various machine learning libraries, particularly deep learning frameworks.
Recent times have witnessed a spike in the popularity of machine learning tools. A subset of machine learning, deep learning methods draw inspiration from the human brain to solve complex real-world problems through ‘human-like computers’. Deep learning algorithms involve a vast number of computation units to form an Artificial Neural Network (ANN) resembling the mesh of millions of neurons of the brain. Artificial Neural Networks are a critical resource for making predictions and drawing inferences when analyzing complex and large data sets.
PyTorch and TensorFlow are two widely-used deep learning frameworks that simplify the assembly and training of neural network models. In this article, we will take a look at these two popular frameworks and compare them.
What is PyTorch?
PyTorch is an end-to-end, open-source machine learning library developed and released by Facebook in 2016. Based on Python, PyTorch provides an imperative programming style. It runs immediately, and the programmer can check whether it is working before writing the entire code.
Besides immediate execution of tensor computations, PyTorch specializes in GPU acceleration and automatic differentiation. Further, PyTorch has much less overhead than other deep learning frameworks because most of its core is written in C++. It is very popular among research communities due to its ease of extendability and ability to significantly shorten neural networks’ designing, training, and testing cycle.
What is TensorFlow?
TensorFlow is also an open-source machine learning library developed and released by Google in 2015. It runs on almost all execution platforms (CPU, TPU, GPU, mobile, etc.) and has a reputation for distributed training support, deployment options, and scalable production.
Like PyTorch, the primary data structure of TensorFlow is a tensor. TensorFlow provides a robust platform for building and training AI models and deploying ML-powered applications with a flexible and comprehensive ecosystem of tools, libraries, and community resources. With the flexible architecture of TensorFlow, users can train and deploy ML models on-premises, in the cloud, or on devices, regardless of the language used.
PyTorch vs TensorFlow
With an overview of PyTorch and TensorFlow in mind, let’s draw a comparison between the two widely-used machine learning libraries.
The PyTorch framework comprises two core building blocks for the imperative and dynamic building of computational graphs and performing automatic differentiation. But when you run code in TensorFlow, the computation graphs are defined statically. While PyTorch has an in-built capability to implement dynamic graphs, TensorFlow uses a library called TensorFlow Fold that transforms batches of arbitrarily-shaped computation graphs to produce a static computation graph.
PyTorch does not provide any framework for the direct deployment of models on the web. Users will have to use either Django or Flask as the backend server. TensorFlow, however, is the clear winner when it comes to the deployment of trained models to production.
TensorFlow Serving is a framework that uses the REST Client API and can be used for directly deploying models in TensorFlow.
The PyTorch visualization tool Visdom comes with limited and minimalistic features such as the ability to handle callbacks, manage environments, and plot graphs and details. But here again, TensorFlow leads with its robust and feature-complete visualization tool TensorBoard. TensorBoard comes in handy for debugging, and comparison of different training runs. In addition, it can display model graphs, plot scalar variables, visualize images, visualize histograms and distributions, play audio, and visualize embeddings.
Data parallelism is a prominent feature that sets apart PyTorch from TensorFlow. Using PyTorch, users can wrap any module, and it will seamlessly be parallelized over batch dimension. Thus, with PyTorch, users can conveniently leverage multiple GPUs with minimum effort. In contrast, distributed training in TensorFlow requires more effort, and users have to code and fine-tune each operation manually. However, both PyTorch and TensorFlow support distributed execution while providing high-level interfaces for cluster definition.
In PyTorch, since computation graphs are defined at runtime, users can make use of Python debugging tools such as PyCharm debugger, pdb, ipdb, or print statements. TensorFlow, however, comes with a special tool called tfdbg that allows the evaluation of TensorFlow expressions at runtime and also lets browsing tensors and operations in session scope. However, users cannot debug any Python code with tfdbg and will need to use pdb separately.
Pros and Cons of PyTorch
Now that we have compared PyTorch and TensorFlow in terms of some critical aspects, let’s take a look into each of its advantages and disadvantages for a summary of what each has to offer.
Pros of PyTorch:
- Based on Python with Python-like coding
- Easier to learn
- Dynamic computational graphs
- Easier data parallelism
- Widely available Python debugging tools
- Organized documentation and active community support
Cons of PyTorch:
- Limited visualization capabilities
- Requires API server for production
Pros and Cons of TensorFlow
Now, let’s weigh out the benefits and limitations of TensorFlow.
Pros of TensorFlow
- Simple and high-level built-in API
- Feature-rich visualization tool
- Production-ready with TensorFlow Serving
- Scalable and deployable on every machine
- Good documentation and community support
Cons of TensorFlow
- Static graphs
- Limited debugging ability
- Only NVIDIA support for GPU and Python support for GPU programming
Wrapping up our discussion on PyTorch vs TensorFlow, it is pertinent to point out that no single machine learning is absolutely good or bad, and each comes with its own set of perks and flaws. On the one hand, TensorFlow is a sturdy tool with robust visualization capabilities, support for mobile development, and production-ready deployment options. On the contrary, PyTorch is a relatively younger framework, is more Python-friendly, and has stronger community involvement. Even though PyTorch is still gaining momentum, its fast and dynamic training support makes it ideal for research-oriented developers. In contrast, TensorFlow is an excellent choice for those looking to build AI-powered products and automate things faster.
If you want to learn more about deep learning frameworks, head to read a detailed comparison of Keras vs TensorFlow vs PyTorch.