PyTorch vs TensorFlow: Which is Better in 2024?

As technology is evolving rapidly today, both Predictive Analytics and Machine Learning are imbibed in most business operations and have proved to be quite integral. Deep learning is a machine learning type based on ANN. For many applications, shallow machine learning models and traditional data analysis approaches fail to reach the performance of deep learning models. 

Deep learning (DL) frameworks offer the building blocks for designing, training, and validating deep neural networks through a high-level programming interface. These frameworks provide superior performance and better management of dependencies. 

Today, let's discuss the key differences between PyTorch vs TensorFlow. We have numerous frameworks at our disposal that allow us to develop compact and robust tools that can offer a better abstraction and simplify difficult programming challenges. 

PyTorch vs TensorFlow [Head-to-Head Comparison] 

Is PyTorch better than TensorFlow? Let us see the point of differences between the two.  

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What is PyTorch? 

From the definition as per the official website, PyTorch is an open-source machine learning framework that accelerates the path from research prototyping to production deployment. It is a development tool that removes cognitive overhead involved in building, training and deploying neural networks.  

The PyTorch framework runs on Python and is based on the Torch library (Lua-based deep learning framework). Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan authored PyTorch, and Meta AI primarily develops it. Given the PyTorch framework’s architectural style, one can tell the entire deep modeling process is far more transparent and straightforward when compared with Torch. 

What is TensorFlow? 

As per the definition from the official website, TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. TensorFlow is by far one of the most popular deep learning frameworks. It is developed by Google Brain and supports languages like Python, C++ and R.  

TensorFlow uses dataflow graphs to process data. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. As you build these neural networks, you can look at how the data flows through the neural network.   

Difference Between TensorFlow and PyTorch: Detailed Comparison 

TensorFlow and PyTorch are inarguably the two most popular Deep Learning frameworks today. Though both are open-source libraries, it might not be easy to figure out the difference between PyTorch and TensorFlow. Both frameworks are extensively used by data scientists, ML engineers, researchers and developers in commercial code and academic research.    

Both frameworks work on the fundamental data type called a tensor. A tensor is a multidimensional array, as shown in the below picture. 

Source: tensorflow.org 

There has always been a contentious debate over which framework is superior, with each camp having its share of ardent supporters. The debate landscape is ever evolving as PyTorch and TensorFlow have developed quickly over their relatively short lifetimes. It is important to note that since incomplete or outdated information is abundant, the conversation about which framework reigns premier is much more nuanced as of 2024 - let’s explore these differences in detail.  

Just to show you a broad picture of growth in usage and demand of TensorFlow and PyTorch deep learning frameworks, Google's worldwide trend graph for the search keywords TensorFlow vs. PyTorch across the last 5 years is as below:

Google search trends

1. PyTorch vs TensorFlow: Performance Comparison 

Even though both PyTorch and TensorFlow provide similar fast performance when it comes to speed, both frameworks have advantages and disadvantages in specific scenarios.   

The performance of Python is faster for PyTorch. Despite that, due to TensorFlow’s greater support for symbolic manipulation that allows users to perform higher-level operations, programming models can be less flexible in PyTorch as compared to TensorFlow. 

In general, for most cases, because of its ability to take advantage of any GPU(s) connected to your system, TensorFlow should ideally provide better performance than PyTorch. Training deep learning models using Autograd that require significantly less memory is one of the exceptions where PyTorch performs better than TensorFlow in terms of training times.   

The following benchmark shows that TensorFlow exhibits better training performance on CNN models, while PyTorch is better on BERT and RNN models (except for GNMT). Looking at the difference % column, it is noticeable that the performance between TensorFlow and PyTorch is very close. 

2. PyTorch vs TensorFlow: Training Time and Memory Usage 

For PyTorch and TensorFlow, time taken for training and memory usage vary based on the dataset used for training, device type and neural network architecture.    

We can observe from the diagram below that the training time for PyTorch is significantly higher than TensorFlow on the CPU.

Source

From the below diagram, we can see that for CNN architecture training time for PyTorch is significantly higher than TensorFlow on GPU. But, for LSTM architecture, except for “Many things” dataset, training time for PyTorch is significantly lower than TensorFlow on GPU.

As we can see from the following diagram, memory consumption is slightly higher for PyTorch on CPU compared to that of TensorFlow. 

And as we can see from the following diagram, memory consumption is significantly higher for TensorFlow on GPU compared to that of PyTorch.
 

 3. PyTorch vs TensorFlow: Accuracy 

For a good number of models, the best possible accuracy attained during training can be the same for PyTorch and TensorFlow for a given model. But hyperparameters used could be different between these frameworks including parameters such as number of epochs, training time, etc. From the below diagram, we can see that the validation accuracy of the models in both frameworks averaged about 78% after 20 epochs. 

In Spite of all sorts of hyperparameter tuning, the best possible accuracy achieved could differ between PyTorch and TensorFlow, and one might beat another one in accuracy - for a given dataset (CIFAR, MNIST, etc.), device (CPU, GPU, TPU etc.), type of neural network (CNN, RNN, LSTM, etc.), type of CNN (Faster R-CNN, Efficientnet, etc.). These differences arise due to various reasons including optimization methods, backend libraries used, computation methods used, etc.

From the below diagram, we can see that for MNIST, both TensorFlow and PyTorch achieve an accuracy of ~98%. While for CIFAR-10, TensorFlow achieved an accuracy of ~80%, but PyTorch could get ~72% only. For CIFAR-100, PyTorch archives ~48% but TensorFlow could score ~42% only, whereas Keras gets ~54%. 

For the below diagram, we can observe that PyTorch experiences a significant performance jump after the 30th epochs to reach a peak accuracy of 51.4% at the 48th epochs, while TensorFlow achieves peak accuracy of 63% at the 40th epochs. 

4. PyTorch vs TensorFlow: Debugging 

As PyTorch uses a standard python debugger, the user does not need to learn another debugger. Since PyTorch uses immediate execution (i.e., eager mode), it is said to be easier to use than TensorFlow when it comes to debugging. Hence in the case of PyTorch, you can use Python debugging tools such as PDB, ipdb, and PyCharm debugger.  

For TensorFlow, there are two ways to go about debugging: you must request the variables from the session or learn the TF debugger. Either way, TensorFlow requires you to execute your code before you can debug it explicitly. You must write code for the nodes in your graph to be able to run your program in debug mode. To find the problems related to memory allocation or errors at runtime that require more advanced debugging features such as stack traces and watches, you’ll have to use TF debugger). 

5. PyTorch versus TensorFlow: Mechanism: Graph Definition 

As TensorFlow works on a static graph concept, the user must first define the computation graph and then run the machine learning model. So basically, TensorFlow has its graphs pre-constructed at the beginning of training. Next, the graph must go through compilation, executing computations against these graphs.   

PyTorch gives an edge with its dynamic computational graph construction, which means the graph is constructed as the operations are executed. The main advantage of this approach is that - graphs can be less complex than those in other frameworks since graphs are built on demand (i.e., graphs are built by interpreting the line of code corresponding to that particular aspect of the graph). Since data doesn't need to be passed around to intermediate nodes when it's not required, complexity can be reduced here. 

Advantages and Disadvantages of TensorFlow 

Advantages 

1. Data Visualization: TensorFlow provides a tool called TensorBoard that helps with the graphical visualization of data. By reducing the effort of looking at the whole code, the tool facilitates easy node debugging and effectively helps with an easy resolution of the neural network. The tool lets you see and observe multiple aspects of the machine learning model, such as the model graph and loss curve. 
2. Compatibility: TensorFlow is compatible with many programming languages. It provides a stable Python API and APIs without a backward compatibility guarantee for languages such as Javascript, C++, and Java. It provides third-party language binding packages for C#, Haskell, Julia, MATLAB, R, Scala, Rust, OCaml, and Crystal.  
3. Scalability: The scalability offered by TensorFlow is high as it was built to be production-ready and can easily handle large datasets.  
4. Architectural Support: The TensorFlow architecture uses an application-specific AI accelerator called TPU (Tensor Processing Unit), which offers faster computation than that of GPUs and CPUs. Deep learning models built on top of TPUs can be easily deployed over clouds, and they work faster than the other two.  
5. Model Building: Using intuitive high-level APIs such as Keras, the TensorFlow library allows us to build and train machine learning models with quick model iteration and easy debugging.   
6. Deployment: Since its inception, it has been the go-to framework for deployment-oriented applications. TensorFlow, equipped with the arsenal of associated tools, makes the end-to-end Deep Learning process easy and efficient. For deployment specifically, robust tools such as TensorFlow Serving and TensorFlow Lite allow you to painlessly deploy on clouds, servers, mobile, and IoT devices.  
7. ML Production: We can train and deploy the models in the cloud, on-premises, in the browser, or on a device, irrespective of the language the user makes use of.  
8. Open Source: Any user can employ the TensorFlow module whenever and wherever required, as it is free of cost to anyone who wants to work with it or utilize it.  
9. Integration and EcoSystem: TensorFlow can easily integrate with Google’s services if you use Google Cloud. For example, saving a TF Lite model onto its Firestore account and delivering the model to a mobile application. Another example is the ability to use TFLite for local AI in conjunction with Google’s Coral devices, a must-have for many industries.  

Disadvantages 

1. Backward Compatibility: The life of researchers is difficult with TensorFlow as there are backward compatibility issues between old research in TensorFlow 1 and new research in TensorFlow 2.  
2. Training Loops: In TensorFlow, the procedure to create training loops is slightly complex and not very intuitive.  
3. Frequent Updates: As TensorFlow gets updates very often, it becomes overhead for a user to maintain the project as it involves uninstallation and reinstallation from time to time so that it can bind and be blended with its latest updates.  
4. Symbolic Loops: TensorFlow lags at providing symbolic loops for indefinite sequences. Its support for definite sequences makes it a useful resource.   
5. Inconsistency: TensorFlow’s contents include some homonyms as names, making it difficult for users to remember to use them. Since the same name gets used for various purposes, it can get confusing more often.  
6. Computation Speed: Benchmark tests show that TensorFlow lags in computation speed compared to its competitors. Also, it has less usability in comparison to other frameworks.  

Advantages and Disadvantages of PyTorch 

Advantages 

1. Pythonic in Nature: Most of the code deployed in PyTorch is pythonic, which means the procedural coding is similar to most of the elements of Python. PyTorch smoothly integrates with the python data science stack. PyTorch functionalities can easily be implemented with other libraries such as Numpy, Scipy, and Cython.  
2. Ease of Use and Flexibility: PyTorch is very simple and provides easy-to-use APIs. PyTorch is constructed in a way that is intuitive to understand and easy to develop machine learning projects.  
3. Easier to Learn: PyTorch is relatively easier to learn than other deep learning frameworks, as its syntax is similar to conventional programming languages like Python.  
4. Dynamic Computation Graph: PyTorch supports Dynamic Graphs. This feature is especially useful for changing the network behavior programmatically at runtime. When you cannot pre-determine the allocation of memory or other details for the particular computation, dynamically created graphs are most useful.  
5. Documentation: PyTorch’s documentation is very organized and helpful for beginners, and it is kept up to date with the PyTorch releases. PyTorch has one of the best documentations that is helpful to get a hold of a majority of the essential concepts. They have a detailed description where one can understand most of the core topics such as torch.Tensor, torch.autograd, Tensor Attributes, Tensor Views, and so much more. 
6. Model Availability: Since PyTorch currently dominates the research landscape and the community has widely adopted it, most publications/available models use PyTorch.  
7. Community Support: PyTorch has a very active community and forums (discuss.PyTorch.org). Apart from the default documentation, the entire community highly supports PyTorch and related projects. Working, sharing, and developing PyTorch projects is easier while working on a research project. 

Disadvantages 

1. Visualization Techniques: PyTorch does not have as great an option for visualization, and developers can connect externally to TensorBoard or use one of the existing Python data visualization tools.  
2. Model Serving in Production: For PyTorch serving, even though we have TorchServe, which is easy to use and flexible, it does not have the same compactness as its TensorFlow counterpart. In terms of serving in production, PyTorch has a long way to go before it can compete with the superior deployment tool. While this will change in the future, other frameworks have been more widely used for real production work.  
3. Not as extensive as TensorFlow: The development of actual applications might involve converting the PyTorch code or model into another framework, as PyTorch is not an end-to-end machine learning development tool. 

Which is Better in 2024: PyTorch or TensorFlow? 

The debate on PyTorch vs. TensorFlow doesn't have a definitive answer. Each framework is superior for specific use cases. Both are state-of-the-art, but they have key distinctions. PyTorch supports dynamic computation graphs and is generally easier to use. TensorFlow is more mature with extensive libraries but may require more learning time.

Decide based on your project needs. For quick learning and ease of use, PyTorch is preferable. For production-ready frameworks supporting heavy calculations, TensorFlow may be ideal.

1. For a Researcher 

PyTorch is the de facto research framework with most SOTA models. It offers features essential for research, like GPU capabilities, an easy API, scalability, and excellent debugging tools. However, in Reinforcement Learning (RL), TensorFlow might be better due to its native agents' library and DeepMind’s Acme.

2. For an Industry Professional 

For deep learning engineering in industry, TensorFlow’s robust deployment framework and end-to-end platform are invaluable, though it requires more learning. If accessing SOTA models in PyTorch, consider using TorchServe. For deploying PyTorch models within TensorFlow workflows, ONNX might be needed. For IoT or embedded systems, use TensorFlow with the TFLite + Coral pipeline. For mobile applications, prefer PyTorch unless you need video or audio input, then use TensorFlow.

3. For a Beginner 

Beginners should start with Keras (part of TensorFlow) or FastAI (for PyTorch) to quickly learn Deep Learning basics. As you advance, choose based on the discussed points. 

Conclusion

As both PyTorch and TensorFlow have their merits, declaring one framework as a clear winner is always a tough choice. Picking TensorFlow or PyTorch will come down to one’s skill and needs. Overall, both frameworks offer great speed and come equipped with strong Python APIs. 

As of 2024, both TensorFlow and PyTorch are very mature and stable frameworks, and there is a significant and visible overlap with their core Deep Learning features. Today, the practical considerations of each framework supersede their technical differences. These considerations include time to deploy, model availability, associated ecosystems, etc.  

Both frameworks have good documentation, active communities, and many learning resources, so you’re not making a mistake choosing either framework. While TensorFlow remains the go-to industry framework, and after its explosive adoption by the research community, PyTorch has become the go-to research framework, there are certainly use cases for each in both domains. 

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