PyTorch vs. TensorFlow: Which Should You Use?

Deep learning frameworks provide a layer of abstraction between the user and underlying models, allowing developers to create powerful applications without worrying much about the core logic. By laying the groundwork, deep learning frameworks save time, enabling you to focus on implementing app-specific features.

PyTorch and TensorFlow are two open-source deep learning frameworks you can use for your project, allowing you to explore numerous advanced features without additional costs. However, PyTorch and TensorFlow have their differences and similarities, which can affect their use in different projects.

In this article, we’ll walk you through the thought process of selecting a deep learning framework by comparing PyTorch and TensorFlow.

How to choose a deep learning framework

Picking a deep learning framework boils down to a few key selection criteria:

• Machine learning models. A huge part of AI development revolves around the quality of the models available. As the industry has evolved, so too have the complexity and number of machine learning models available for use. Depending on your needs, you may want to look at which models are supported by a given framework.
• Deployment infrastructure. It’s great if your deep learning project works on your individual machine, but will that AI-powered app work as well when deployed at scale to the cloud? Pick frameworks that reduce the time to deploy and work well with common deployment infrastructure.
• Community ecosystem. Nothing speeds up the learning process like an active developer community—GitHub stars, Stack Overflow questions, and plenty of documentation and tutorials. Beyond that, industry adoption of certain deep learning frameworks can simplify your development process.
• Big-name backers. The developer landscape is constantly evolving. The level of investment from a major tech company with a good developer reputation can indicate how long a framework is likely to stick around.
• Technology. Does a framework give an edge in performance, deployment, rapid prototyping, or developer friendliness? Which languages do you prefer to work with?

For the mathematically curious, this post on backpropagation can get you up to speed on the philosophy behind graphs and their relevance to machine learning.

What is PyTorch?

Are you an academic who enjoys using Python to crunch numbers? PyTorch provides a high level of abstraction for ease of use, but it also allows for fine-grained control over models.

This makes working directly with array expressions easy. It also supports NumPy and integrates into a Python developer’s workflow.

PyTorch uses tensors as its specialized data structures, which are similar to matrices and arrays. But in this case, they can be optimized to work on GPUs or other hardware accelerators. This boosts performance and facilitates faster data operations.

PyTorch also offers TorchServe, a flexible platform that allows developers to deploy PyTorch models to production efficiently. This tool makes it easy to manage and update machine learning models. As a result, you can dedicate more time toward the actual development of machine learning applications.

Among PyTorch’s highlights, it:

• Is the framework of choice for academic researchers
• Is supported by Meta’s artificial intelligence research group
• Supports dynamic computational graphs by default
• Is easy to debug models line by line
• Exports models via pickle (a Python-based file type)

Because the dynamic approach lets you define models that can change during runtime, PyTorch is well suited to the development of recurrent neural networks (RNNs). If you want low-level control, customizability, ease of use, and dynamic graphs, PyTorch is a good pick.

What is TensorFlow?

Are you a Python developer looking to write production-ready deep learning models that perform efficiently at scale? TensorFlow is for you. TensorFlow benefits from the largest, most active community among machine learning frameworks.

TensorFlow 2, the latest iteration, brings more advanced features to the machine learning development workflow. It uses the Keras API, which makes it easier to build and deploy models. TensorFlow also supports more intensive experimentation, making it a valuable tool for researchers.

Additionally, TensorFlow 2 has increased compatibility, allowing you to deploy models on different platforms and operating systems. With TensorFlow Lite, you can optimize models for mobile platforms like Android and iOS, microcontrollers like Raspberry Pi, and edge devices. You can also deploy machine learning models in JavaScript environments using TensorFlow.js, which is compatible with Node.js.

Among its valuable features, TensorFlow:

• Is supported by Google Brain
• Supports static computational graphs
• Provides access, via TensorFlow Serving, to Google’s battle-tested server infrastructure for your projects
• Gives you a great visualization tool (TensorBoard) for picturing the graphs, nodes, and edges that represent your algorithms
• Lets you use dynamic graphs with TensorFlow via TensorFlow Fold
• Provides a Python control flow with easier debugging via eager execution

In the static graph approach to machine learning, you specify the sequence of computations you want to use and then flow data through the application. The advantage of this approach is it makes distributed training of models easier.

PyTorch vs. TensorFlow: key differences

In this section, we’ll look at the key differences between Tensorflow and PyTorch.

Programming language

When you dissect TensorFlow and PyTorch, you’ll find they both use the Python programming language. Python has simple syntax, contributing to its ease of use and flat learning curve. As a result, many individuals, including beginners, can master the basics of Python and start working with these deep learning frameworks. You can find many open-source libraries to speed up your software development process.

Although PyTorch primarily uses Python, it also supports C++ and Java programming languages. On the other hand, TensorFlow is compatible with more languages, including C++, Java, and JavaScript.

The TensorFlow.js model allows JavaScript developers to access crucial deep learning features directly from their browsers. The TensorFlow community has also helped support languages like Scala, Julia, Ruby, R, and Haskell.

Static vs. dynamic computational graphs

Computational graphs are directed graphs from graph theory in which operations are represented by nodes and the data flowing between operations are represented by edges.

Frameworks that favor static graphs encourage you to build fixed, reusable structures you can repeatedly run data through. This can give you a performance boost, especially for larger data sets because you don’t have to worry about mutations at runtime. TensorFlow uses static graphs because they’re easier to deploy at scale and optimize for higher performance in production.

Frameworks that favor dynamic graphs place programmer flexibility front and center. They’re well suited for situations where the graph must deal with inputs of varying size (e.g., processing a few words versus a paragraph in natural language processing).

A dynamic graph is implicitly defined by the logic you write and can change at runtime, following the define-by-run convention that’s more natural to programmers. PyTorch uses dynamic graphs for their flexibility and ease of use.

Learning curve

TensorFlow is generally considered to have a more difficult learning curve than PyTorch, particularly for users who are new to deep learning. TensorFlow has a more complex API and requires more explicit programming, which can make it harder to learn and use.

PyTorch has a more intuitive API and is easier to learn and use, especially for users already  familiar with Python. Additionally, PyTorch includes more debugging information in its error messages, which can make troubleshooting problems easier.

Overall, PyTorch might be a better choice for users new to deep learning and want something that’s easy to learn and use, while TensorFlow may be better suited for users who are more experienced and have specific performance or production requirements.

Models

As the deep learning ecosystem continues to mature, we’re starting to see a plethora of large state-of-the-art (SOTA) models made publicly available for machine learning projects. We’re also starting to see a noteworthy divergence in model availability between the two most popular deep learning frameworks: TensorFlow and Pytorch.

PyTorch is the clear winner here. From Papers With Code to Hugging Face, most of the SOTA models available on the web support PyTorch. Models either support both TensorFlow and PyTorch or are exclusive to PyTorch. Because a lot of machine learning research is driven by academics, PyTorch has cornered the market for available SOTA models.

TensorFlow and PyTorch support transformers and automatic differentiation, which are essential for building neural network architectures. Transformers are effective in handling sequential data and NLP tasks. ChatGPT is a good example of AI applications built on top of transformer models. Automatic differentiation facilitates efficient computation of gradients, which boosts the training of neural networks.

Pretrained models are available in both TensorFlow and PyTorch frameworks. With these models, you can perform specific functions like generating text, translating languages, or retrieving specific information—out of the box—rather than having to train models from scratch.

This saves time, effort, and various resources. For instance, PyTorch offers models like AlexNet, VGG, SqueezeNet, and Vision Transformer that are pretrained for image classification tasks.

Data parallelism

Data parallelism is a technique for distributing the training of a deep learning model across multiple devices, such as GPUs. Both TensorFlow and PyTorch support data parallelism, but they have different approaches to implementing it.

In TensorFlow, data parallelism is implemented using the tf.distribute.Strategy API, which allows users to specify how the model should be distributed across devices. TensorFlow supports a variety of strategies for distributing the model, including tf.distribute.MirroredStrategy, which supports model parallelism by creating copies of the model on different devices, and tf.distribute.experimental.MultiWorkerMirroredStrategy, which supports model parallelism across multiple machines.

In PyTorch, data parallelism is implemented using the torch.nn.DataParallel class, which wraps a model and parallelizes the forward pass using multiple GPUs. The DataParallel class is designed to be used with models that have a single input and a single output, and it uses the device parallelism provided by the Compute Unified Device Architecture (CUDA) library to parallelize the forward pass.

PyTorch also supports model parallelism using the torch.nn.parallel.DistributedDataParallel class, which is designed to be used with models with multiple inputs and outputs and can be distributed across multiple devices and machines.

Both TensorFlow and PyTorch provide tools for implementing data parallelism, but they have different approaches and capabilities. It’s much easier to implement data parallelism in PyTorch because you can take advantage of native support for asynchronous execution in Python. TensorFlow requires manual fine-tuning of every operation for each device you wish to distribute your workload.

Visualization: Visdom vs. Tensorboard

Both PyTorch and TensorFlow have tools that allow users to visualize the computation graph, track training progress, and monitor various metrics.

Visdom

PyTorch’s native visualization tool—Visdom—is less a specific tool for visualizing your training progress and more a lightweight Python library for plotting data visualizations that can be viewed in your browser. It’s similar to Matplotlib.

TensorBoard

TensorBoard provides a suite of visualization tools for exploring and understanding TensorFlow runs. Its suite of web apps includes visualizations for graphs, scalars, audio, histograms, and images. To use TensorBoard with TensorFlow, write code to log relevant data and then run TensorBoard in a separate process.‍

Overall, TensorFlow and PyTorch provide ways to visualize machine learning projects. TensorFlow provides a stand-alone tool called TensorBoard for visualization, while PyTorch has the lighter-weight minimalist Visdom. But since you aren’t limited to out-of-the-box features, a variety of visualization tools are available for both frameworks. In fact, you can even use TensorBoard with PyTorch.

Debugging

TensorFlow and PyTorch have different approaches to debugging; each has its own set of tools for debugging deep learning models.

In TensorFlow, debugging can be challenging, especially for users who are new to the framework. TensorFlow has a number of tools for debugging, including the TensorFlow Debugger ( tfdbg), which allows users to pause the execution of the model and examine intermediate results. TensorFlow also provides a high-level API called tf.debugging, which includes a number of functions for debugging TensorFlow programs. However, TensorFlow’s debugging tools can be difficult to use and may not provide as much information as some users would like.

In PyTorch, debugging is generally easier, thanks to the framework’s more intuitive API and more descriptive error messages. PyTorch integrates with the pdb Python debugger, which allows users to pause the execution of the model and examine intermediate results. PyTorch also has the torch.autograd module that can be used for debugging, including .gradcheck() and .debug_anomaly() functions. Additionally, PyTorch’s dynamic computation graph and ability to execute models incrementally make it easier to understand and debug model behavior.

Overall, PyTorch might be easier to debug than TensorFlow due to its ability to use native Python debugging tools, its more intuitive API, and its more descriptive error messages. However, TensorFlow might have an edge in debugging larger-scale applications due to its use of static graphs. Of course, debugging ease for large projects is still largely dependent on how well you design your projects.

Performance and optimization

TensorFlow and PyTorch are both powerful frameworks that have different use cases. However, they differ when it comes to high-performance computing and optimization capabilities.

TensorFlow 2.0 relies on structured pruning and quantization techniques to optimize model performance. These methods make it possible to reduce payload and parameter size, allowing models to be deployed even on platforms with limited memory. TensorFlow 2.0 also comes with eager execution, which facilitates faster computing capabilities, easier debugging, and accelerated training.

Additionally, TensorFlow supports high performance—out of the box—through distributed computing across multiple machines and GPUs. AI professionals can take advantage of TensorFlow’s distributed training and data parallelism to train models faster.

PyTorch utilizes a dynamic computational graph that makes it faster to update graph structures, experiment with models, and perform debugging in case of errors. There’s also the eager model that allows PyTorch to execute operations as soon as they’re encountered, which can translate to improved performance.

Similar to TensorFlow, PyTorch supports distributed training and multiprocess parallelism. These features allow PyTorch models to scale quickly by taking advantage of different machines and GPUs.

Community

TensorFlow and PyTorch are both popular deep learning frameworks. Each has its own community of users and developers. Consider a few key differences between the TensorFlow and PyTorch communities:

• Origins. TensorFlow was developed by Google as an open-source deep learning framework and released in 2015. PyTorch was developed by Facebook and eventually released as an open-source project on GitHub in 2017.
• Focus. The TensorFlow community tends to focus more on production and deployment, while the PyTorch community tends to focus more on research and development. TensorFlow has a number of tools and features designed for deploying models to production and monitoring their performance. In contrast, PyTorch has a more flexible and modular design that’s well suited to rapid prototyping and experimentation.
• Language. The TensorFlow community is more diverse, with users and developers working in a variety of languages, including Python, C++, and Java. The PyTorch community is primarily centered on the Python language.

Overall, the TensorFlow and PyTorch communities are both active and supportive, but they have different areas of focus and cater to different audiences.

PyTorch vs. TensorFlow use cases

PyTorch and TensorFlow can fit different projects like object detection, computer vision, image classification, and NLP.

TensorFlow

• Spotify. Spotify uses TensorFlow for its music recommendation system. Specifically, it uses reinforcement learning to solve sequential recommendation problems. TensorFlow’s flexibility and powerful features allow Spotify to quickly train, test, and deploy models to production environments.
• Mobile apps. Developers can use TensorFlow Lite to create AI-powered apps for Android and iOS. For example, TensorFlow Lite can be used to power multiplayer apps like board games.
• CircularNet. TensorFlow is helping recycling companies in building advanced models for sorting waste materials into different categories like paper, plastic, and metal.

PyTorch

• PathAI. PathAI uses PyTorch models to improve patient outcomes. Specifically, PathAI creates AI-powered applications that aid in disease diagnosis and outbreak prediction. The company also uses AI to measure the effectiveness of various medications and provides valuable information to medical practitioners to aid in decision-making.
• Amazon Ads. Alongside other AI technologies, Amazon uses PyTorch to assist businesses in building their brands by advertising products to their target audience effectively.
• Agricultural robotics. Blue River Technology uses PyTorch to build models to power its smart machines. PyTorch models allow these robots to differentiate between plants and weeds and spray fields efficiently—and with minimal human intervention.

Should I use PyTorch or TensorFlow?

In this article, we took a quick look at two popular deep learning platforms: PyTorch and TensorFlow.

Your choice of tool really boils down to which computational graph you prefer and if there’s a high-level front-end API available to make your job easier. The other major deciding factor is the level of community support and the specific SOTA models you wish to work with.

Google has heavily invested in Keras/TensorFlow, while Facebook has backed PyTorch. Both frameworks have thriving open-source communities. It’s the confluence of these factors that can give you confidence that a given framework is worth the long-term investment. Choose the best deep learning framework that meets your needs.

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PyTorch and TensorFlow are versatile deep learning frameworks that can fit various projects. They both use the Python programming language, which is relatively easy to understand. PyTorch has a large community of users and is suitable for prototyping and experimentation. TensorFlow is a more mature framework and more suitable for large-scale projects. TensorFlow can also be ported to mobile, embedded, and edge devices.

To take effective advantage of TensorFlow and PyTorch, you need specific technical skills that require time to master. Consider working with deep learning experts on Upwork to help you harness the power of AI technologies.

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