Compare PyTorch vs. TensorFlow for AI and machine learning
PyTorch and TensorFlow are both dependable open source frameworks for AI and machine learning. Deciding which to use for your project comes down to your use case and priorities.
At first glance, PyTorch and TensorFlow seem almost identical: They're both free, open source machine learning frameworks that make extensive use of Python; they both benefit from large, dynamic developer communities; and they're both widely used in modern AI projects.
But a closer look reveals important differences between PyTorch and TensorFlow. These distinctions don't make one framework overall better than the other -- but their unique features and design philosophies mean that each is more suited to certain use cases.
What is PyTorch?
PyTorch is an open source framework for developing deep learning models. As its name implies, it's based on Torch, a machine learning library first introduced in 2002. Initially developed by Facebook, now Meta, and the Linux Foundation, PyTorch was launched in 2016 and became a public open source project in 2017.
PyTorch's key features include the following:
- Dynamic computation graphs. PyTorch uses dynamic computation graphs, also known as define-by-run. This lets developers modify neural network behavior on the fly, making debugging and experimentation more intuitive.
- Tensors. In PyTorch, tensors are n-dimensional arrays used to represent model inputs, outputs and intermediate data. They support a variety of operations and are easy to manipulate.
- Neural network module. The torch.nn module simplifies the process of building neural networks by providing predefined layers, loss functions and optimizers, streamlining model construction and training.
- GPU support. PyTorch integrates with CUDA, a parallel computing platform developed by Nvidia, to enable efficient tensor computations on GPUs. This can significantly speed up training and inference.
What is TensorFlow?
TensorFlow is an open source machine learning framework. Originally developed at Google, it became publicly available in 2015.
TensorFlow's key features include the following:
- Static and dynamic computation graphs. In previous versions, TensorFlow primarily used static computation graphs, known as TensorFlow 1.x, which are optimized for production and deployment. Modern versions of TensorFlow now also support a feature called eager execution, which enables dynamic graphs similar to PyTorch that make experimentation more flexible.
- Tensors. Like PyTorch, TensorFlow uses tensors as its fundamental data structure. Tensors in TensorFlow are multidimensional arrays that can run on CPUs or GPUs, making them suitable for high-performance computing.
- Keras integration. TensorFlow includes Keras, a user-friendly, high-level API that simplifies building and training neural networks. Historically, developers tended to view TensorFlow as more complicated to use than PyTorch, but the introduction of Keras arguably changed this by providing a simpler interface for machine learning development. While Keras is tightly integrated with TensorFlow, it's also compatible with PyTorch.
- CUDA support. Like PyTorch, TensorFlow offers support for CUDA, enabling developers to use GPUs to accelerate training and inference.
Where does Keras fit in?
Keras is a Python-based API designed to simplify interactions with machine learning frameworks like PyTorch, TensorFlow and JAX.
Although developers can use these frameworks directly, Keras streamlines neural network implementation by serving as a high-level front end: Developers interact with Keras while the underlying framework does the computational heavy lifting in the background.
In many respects, Keras is easier to use than these frameworks' native interfaces -- especially TensorFlow, whose syntax is arguably more complex than PyTorch's. This makes it ideal for developers new to machine learning, as well as for quick experimentation or prototyping where fine-grained control isn't necessary.
However, Keras' simplicity comes at the cost of reduced control. Because developers don't interact directly with the underlying framework, customization is limited, which could be problematic for more complex use cases.
PyTorch vs. TensorFlow compared
Compared with TensorFlow, PyTorch's main advantages include the following:
- Dynamic computation graphs. PyTorch offers first-class support for dynamic computation graphs. While TensorFlow offers a similar feature, its support for dynamically changing graphs is limited, making experimentation more challenging in some cases.
- Deep Python integration. PyTorch is natively built on Python. Its simple, consistent syntax makes the overall PyTorch interface more user-friendly and approachable for beginners. Although TensorFlow is also Python-based, its syntax is less consistent, and newcomers might find it less intuitive and harder to learn.
- Highly active community. While it's difficult to quantify community engagement, PyTorch benefits from an especially vibrant and dynamic community that users typically feel is more active than TensorFlow's. This is likely due in part to the fact that developers tend to view PyTorch as more user-friendly, which helps to engender more widespread adoption.
In contrast, TensorFlow has several strengths compared with PyTorch:
- Static computation graphs. Although TensorFlow's static computation graphs can make experimentation more challenging, they can also be easier to optimize. Because developers know what the graph will look like from the start of a project, they can take steps to improve its structure and resource use during development. This isn't possible with a PyTorch dynamic computation graph because developers don't know the graph's exact structure until it's already implemented.
- Multilanguage support. PyTorch and TensorFlow both support Python, but TensorFlow also supports other languages, such as C++ and Java. This is an advantage for developers who work in diverse coding environments or who want to integrate TensorFlow projects into non-Python codebases.
- Deployment tooling. TensorFlow provides a built-in tool called TensorFlow Serving for deploying models after development. This makes it easier to deploy models in TensorFlow than in PyTorch, which typically relies on external frameworks like Flask or FastAPI to serve models in production.
PyTorch vs. TensorFlow: What to use when
The use cases for PyTorch and TensorFlow overlap considerably; developers can use either framework to create virtually any type of deep learning module. However, each framework's strengths make it a better fit for certain scenarios.
When to choose PyTorch
PyTorch is typically the best choice when the following are priorities:
- Ease of use. PyTorch's intuitive interface is typically considered easier to learn for those new to machine learning.
- Rapid experimentation. PyTorch's dynamic computation graphs are more flexible, making it a good fit for prototyping and iterative experimentation.
- "Pythonic" design. PyTorch's deep integration with Python means that it looks more like a standard Python framework than TensorFlow, providing a workflow that will feel familiar to Python developers.
When to choose TensorFlow
Alternatively, TensorFlow is the better choice when developers need to prioritize the following:
- Model performance. TensorFlow's static computation graphs provide more optimization opportunities during development, potentially leading to better model performance in production.
- Single framework uniformity. TensorFlow enables the use of native tools for model development, training and deployment, making for a more streamlined workflow that doesn't require developers to bring in outside frameworks.
- Languages other than Python. As noted, TensorFlow supports additional languages other than Python. For projects that require integration with non-Python environments, TensorFlow's ability to handle languages like C++ and Java is a significant advantage.
Chris Tozzi is a freelance writer, research adviser, and professor of IT and society who has previously worked as a journalist and Linux systems administrator.
