TensorFlow vs. PyTorch: What’s Better for a Deep Learning Project?

Author(s): Eashan Mahajan

Originally published on Towards AI.

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 you’re getting started with deep learning, you’ll find yourself overwhelmed with the amount of frameworks. However, you’ll 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 Tesla’s autopilot feature and OpenAI’s 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? Let’s answer that question.

What is TensorFlow?

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.

Functionality

One thing to note is the name “TensorFlow” tells you how you’re 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. Let’s 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:

1. 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”.

It’s 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, let’s take a look at PyTorch.

What is PyTorch

PyTorch 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.

Functionality

Like 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 PyTorch’s 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. Let’s take a look at a couple of ways PyTorch accomplishes this.

1. 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 processing
• PyTorch 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 Cases

We’ve 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: PyTorch’s 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 Python’s syntax, it makes it very easy for beginners to learn and use. PyTorch is used in academic courses often.

Concluding Thoughts

Let’s 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, don’t 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!

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Published via Towards AI