TensorFlow vs. PyTorch: Whats Better for a Deep Learning Project?
Source: tai_blog

Deep learning. A subset of machine learning utilizing multilayered neural networks  otherwise known as deep neural networks. Allowing society to simulate the decision-making prowess the human brain possesses  deep learning exists within some of the AI applications we use in our lives today.   If youre getting started with deep learning  youll find yourself overwhelmed with the amount of frameworks. However  youll see two frameworks stand at the top: PyTorch and TensorFlow. Possessing their own strengths and weaknesses  both these frameworks are powerful deep learning tools. PyTorch powers Teslas autopilot feature and OpenAIs ChatGPT  while TensorFlow is used in Google search and Uber.   Both TensorFlow and PyTorch are both relied on heavily in research and commercial code. APIs and cloud computing platforms extend the usage of both frameworks. If both of them have so much support and usage  how do you decide which one to use? Lets answer that question.   TensorFlow is an end-to-end platform for machine learning  a prominent open-source library dedicated to accomplishing a wide range of machine and deep learning tasks. Developed by Google in 2015  TensorFlow boasts extensive capabilities  resulting in the tool being used often for research purposes or companies using it for their programming purposes. It can also be used in a variety of languages  such as Python  C++  JavaScript  and Java.   FunctionalityOne thing to note is the name TensorFlow tells you how youre going to work with this framework. The basic data structure for TensorFlow are tensors. A tensor is an algebraic object detailing the multilinear relationship between sets of algebraic objects with respect to a vector space. There are many types of tensors  with some of the most popular ones being scalars and vectors  the 2 simplest tensors.   Now  a big focus for TensorFlow is on production and scalability. It becomes obvious when you take a look at its robust architecture and enormous support for deploying models on a variety of platforms. Lets take a look at what other reasons makes TensorFlow so reliable for production and scalability.   Production:   1. TensorFlow Extended (TFX):   End-to-End Pipeline: Providing a variety of tools and libraries for production-ready machine learning pipelines  TFX takes care of the entire lifecycle from data ingestion and validation to model training  evaluation  and deployment.Component Integration: TFX has components such as TensorFlow Data Validation  Transform  Model Analysis  and Serving. All of these components work well together and ensure a reliable production workflow.2. TensorFlow Serving:   Model Deployment: TensorFlow serving was specifically reated for deploying machine learning models in production. Supporting features such as model versioning  it allows for updates to be implemented easily.High Performance: TensorFlow has been optimized for low-latency and high-throughput serving  making it suitable for real-time interference applications.3. TensorFlow Lite:   Edge Deployment: TensorFlow Lite allows for you to deploy your models on mobile and other embedded devices. Optimizing models for performance and resource usage  it ensures efficient performance on resource-constrained devices.Hardware Acceleration: In addition  it supports various hardware accelerators  such as GPUs and TPUs  allowing for a performance boost on edge devices.Scalability:   Distributed Training:Multi-GPU and Multi-TPU Support: TensorFlow allows for groups to train models across multiple GPUs and TPUs  decreasing training time.Multi-Machine Training: It also facilitates training across several machines  enabling the handling of very large datasets and complex models.2. Docker and Kubernetes:   Containerization: TensorFlow allows for its models to be containerized using Docker  making it significantly easier to deploy  scale  and manage applications in various environments.Orchestration: You can also use Kubernetes to create TensorFlow workloads  which enables the automatic scaling  management of containerized applications  and deployment.3. Cloud Integration:   Google Cloud AI Platform: Integrating well with the Google Cloud API  TensorFlow can provide managed services for training and serving models.Other Cloud Providers: TensorFlow works well with other cloud platforms such as AWS and Azure  supporting scalable deployment and training in cloud environments.From this  it becomes obvious how TensorFlow prioritizes production and scalability. Even with all of this functionality and support  TensorFlow has something else that makes users fall in love with it: Keras.   Keras is an open-source deep-learning framework with a popularity stemming from its user-friendly interface. A high-level  user-friendly API  Keras allows you to build  train  and deploy deep-learning models very minimal code.   In TensorFlow 2.0  Keras was added in to the TensorFlow package as tf.keras  making it officially an API of TensorFlow. This integration allows users to access the simplicity of Keras whilst also leverging the pwoer and flexibility that TensorFlow offers. Any of the advanced features of TensorFlow  such as custom training loops and the TensorFlow Data API can be utilized whilst using tf.keras.   Its also very easy for beginners to start with deep learning through tf.keras because of the simplicity. At the same time  it gives advanced users the flexibility to build more complicated models.   Keras brings more life to TensorFlow  giving it a significant boost in popularity when the API was introduced to it. Now  with all these features  it may look like TensorFlow is the clear choice. TensorFlow has so much support and flexibility for designing deep learning models  so why ishere a need to look at a different framework? Well the answer is quite simple. PyTorch offers a dynamic experience whilst designing your deep learning models. So  lets take a look at PyTorch.   What is PyTorchPyTorch is an open-source deep learning framework developed by Facebook and released in 2016. Facebook released the framework with the intention of matching the production of TensorFlow while making it easier to write code for models. Since python programmers found it easy to use  PyTorch gained popularity at a rapid rate. PyTorch has an emphasis on providing a high-level user friendly interface while possessing immense power and flexibility for any deep learning task.   FunctionalityLike TensorFlow  the unit of data for PyTorch remains the tensor. However  PyTorch is based on Torch  a framework designed for fast computations which was written in the language Lua. Torch provided implementations of deep learning algorithms and tools  which heavily inspired PyTorchs design and fucntionality.   Now although PyTorch has an emphasis on easy usage and readability  it retains the power needed for users to accomplish complicated deep learning tasks. This allows for beginnners to easily learn thew framework while allowing more advanced users to build more complex models. Lets take a look at a couple of ways PyTorch accomplishes this.   Comprehensive Libraries and Tools:Torchvision: Library that provides datasets  model architectures  and image transformations.TorchText: Library for natural language processing (NLP). Offers datasets  tokenizers  and pre-trained word vectors.TorchAudio: Library for audio processingPyTorch Lightning: Framework for structuring PyTorch code  which makes it easier to manage training loops and logging.2. Dynamic Computation Graphs:   Eager Execution: PyTorch builds computation graphs as operations are executed. This dynamic nature makes PyTorch more flexible  allowing for debugging and modification.Immediate Feedback: Since operations are executed immediately  PyTorch gives immediate feedback  which makes it easier to experiment with different architectures and strategies.3. Production-Ready:   TorchScript: Allows you to run PyTorch models independent of Python. Easier to deploy models in production environments.ONNX (Open Neural Network Exchange): PyTorch supports exporting models to the ONNX format  which allows for interoperability with other frameworks and deployment on other platforms.4. Research and Prototyping:   Flexibility: The dynamic nature of PyTorch makes it perfect for research and prototyping. Researchers can implement and test new ideas without being concerned about static-graph constraints.Active Community: PyTorch has an active community of researchers and developers who are constantly contributing to its development.5. Visualization and Debugging:   TensorBoard Integration: Integrating with TensorBoard allows PyTorch to access visualizations of training metrics  model graphs  and other information.Advanced Debugging Tools: The dynamic nature of PyTorch simplifies debugging  allowing people to use the standard Python debugging tools.Use CasesWeve talked about the individual strengths of PyTorch and TensorFlow  but what about their use cases? When it is it most appropriate to implement one or the other? The use cases for TensorFlow are:   Production Deployment: With components such as TensorFlow Serving and Lite  TensorFlow is very well-suited for deploying machine learning models in production. TensorFlow provides a high performance serving system for models while allowing the user to deploy on mobile and embedded devices.Large -Scale Machine Learning: TensorFlow has built-in support for training across several GPUs and machines. This makes it very suitable for large-scale machine learning tasks.Applications: TensorFlow integrates well with commercial and enterprise applications such as Google Cloud  where TensorFlow can use the AI Platform  BigQuery  and Cloud Storage.As for PyTorch  they are as follows:   Research: PyTorchs dynamic computation graph allows it work well for researching and prototyping purposes. This allows for more intuitive and flexible model development.Computer Vision and NLP: Utilizing torchvision with PyTorch  you will have access to tools for computer vision  including pre-trained models  datasets  and image transformations. TorchText offers datasets  tokenizers  and pre-trained embeddings for natural language processing.Education: As PyTorch follows Pythons syntax  it makes it very easy for beginners to learn and use. PyTorch is used in academic courses often.Concluding ThoughtsLets recap  TensorFlow and PyTorch are powerful frameworks for deep learning. TensorFlow is often used for deployment purposes  while PyTorch is used for research. Based on what your task is  you can then choose either PyTorch or TensorFlow.   However  dont just stop with learning just one of the frameworks. Try and learn both. Both have their weaknesses and strengths and for a task where PyTorch may not work  TensorFlow could. For a task where TensorFlow may struggle  PyTorch may excel. Both frameworks are great at what they do and have made machine learning and deep learning much more accessible for everyone. I hope you enjoyed this article  and thank you for reading it!