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HomeBlogData ScienceTensorFlow vs PyTorch: Deep Learning Frameworks [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 artificial neural networks (ANN). For many applications, shallow machine learning models and traditional data analysis approaches fail to reach the performance of deep learning models. Sharpen your skills with these online Data Science courses and learn to tackle complex Data Science problems.
Some of the common deep learning architectures include Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Belief Networks (DBN) and Deep Stacking Networks (DSN). This has been applied to Computer Vision (CV), audio and speech recognition and Natural Language Processing (NLP) use cases.
Deep learning (DL) frameworks offer developers, researchers, and data scientists 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, 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.
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.
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.
Is PyTorch better than TensorFlow? Let us see the point of differences between the two.
| Parameters | TensorFlow | PyTorch |
|---|---|---|
| Programming Language | Written in Python, C++ and CUDA | Written in Python, C++, CUDA and is based on Torch (written in Lua) |
| Developer | Facebook (now Meta AI) | |
| Graphs | Earlier TensorFlow 1.0 was based on the static graph. TensorFlow 2.0 with Keras integrated also supports dynamic graphs using eager execution | Dynamic |
| API Level | High and Low | Low |
| Installation | Complex GPU installation | Simple GPU installation |
| Debugging | Difficult to conduct debugging and requires the TensorFlow debugger tool | Easy to debug as it uses dynamic computational process. |
| Architecture | TensorFlow is difficult to use/implement but with Keras, it becomes bit easier. | Complex and difficult to read and understand. |
| Learning Curve | Steep and bit difficult to learn | Easy to learn. |
| Distributed Training | To allow distributed training, you must code manually and optimize every operation run on a specific device. | By relying on native support for asynchronous execution through Python it gains optimal performance in the area of data parallelism |
| APIs for Deployment/Serving Framework | TensorFlow serving. | TorchServe |
| Key Differentiator | Easy-to-develop models | Highly “Pythonic” and focuses on usability with careful performance considerations. |
| Eco System | Widely used at the production level in Industry | PyTorch is more popular in the research community. |
| Tools | TensorFlow Serving, TensorFlow Extended, TF Lite, TensorFlow.js, TensorFlow Cloud, Model Garden, MediaPipe and Coral | TorchVision, TorchText, TorchAudio, PyTorch-XLA, PyTorch Hub, SpeechBrain, TorchX, TorchElastic and PyTorch Lightning |
| Application/Utilization | Large-scale deployment | Research-oriented and rapid prototype development |
| Popularity | This library has garnered a lot of popularity among Deep Learning practitioners, developer community and is one of the widely used libraries | It has been gaining popularity in recent years and interest in PyTorch is growing rapidly. It has become the go-to tool for deep learning projects that rely on optimizing custom expressions, whether it’s academia projects or industries. |
| Projects | DeepSpeech, Magenta, StellarGraph | CycleGAN, FastAI, Netron |
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 them. 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.
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:
When we look at the data from Papers with Code, whose mission is to create a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables, we can observe the steady growth of papers utilizing PyTorch. Out of the 4021 repositories created this June 2022 quarter, 61% of them are implemented in PyTorch, with just 6% implemented in TensorFlow.
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.
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.
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.
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.
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).
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
Disadvantages
Advantages
Disadvantages
The debate on which one is better in PyTorch vs. TensorFlow does not have a single correct answer. It is more sensible to say that one framework is superior or preferable to another with respect to a specific use case.
Even though both are state-of-the-art deep learning frameworks, there are some key distinctions to consider between TensorFlow and PyTorch. PyTorch supports dynamic computation graphs and is easier to use in general. TensorFlow is more mature with a larger number of libraries, but it may require additional time to learn and understand the concepts.
It's important to decide what you're trying to accomplish in your deep learning projects before choosing a specific deep learning framework. If the objective is to have a fast-learning framework that doesn't require as much work, then mostly PyTorch is suited for that scenario. If the intent is to have a production-ready framework that can support heavy calculations, then maybe TensorFlow is the ideal choice.
PyTorch is the de facto research framework; hence most SOTA (state-of-the-art) models are available on PyTorch. So, if you are a researcher, you are certainly using PyTorch; for now, you should continue using it. PyTorch is equipped with features essential for research-oriented developers looking for a quick start on their projects. These features include great GPU capabilities, an easy-to-use API, scalability, and excellent debugging tools.
There are a few known exceptions to this rule, the most important one being that those in RL (Reinforcement Learning) should consider using TensorFlow. The reason is that TensorFlow has a native agent's library for RL, and also DeepMind’s RL research framework Acme is also implemented in TensorFlow.
If your work involves Deep Learning engineering in an industry setting, maybe you’re using TensorFlow and should probably continue with it for now. For those who want to productionize models, TensorFlow’s robust deployment framework and end-to-end TensorFlow Extended platform are invaluable. However, due to its higher level of abstraction, it takes longer to learn how to code with TensorFlow. Hence more knowledge about computer science and engineering is required for TensorFlow.
If you have a good reason, such as a need to access SOTA models that are only available in PyTorch, with the recent release of TorchServe, you can consider using PyTorch. But in this case, in order to deploy a converted PyTorch model within TensorFlow’s deployment workflows, you may have to employ ONNX.
Suppose you’re working on building applications for IoT (Internet of Things) devices or embedded systems that utilize machine learning or deep learning. In that case, you should be using TensorFlow, given the TFLite + Coral pipeline. If you are working on building mobile applications, you can prefer to use PyTorch, given the recent release of PyTorch Live, unless you require video or audio input; in that case, you should be using TensorFlow.
Wondering PyTorch vs TensorFlow for beginners, which one to choose? If you’re a beginner interested in learning Deep Learning and want to get started quickly, you can use Keras (that’s part of TensorFlow) or FastAI (a third-party high-level API for PyTorch). Using their high-level components, you can easily get started with understanding the basics of Deep Learning. When you are prepared to deep dive and want to start understanding the nuts and bolts of Deep Learning in detail, based on the above-discussed points, you can choose PyTorch or TensorFlow. To learn/improve data science skills with Python, join this KnowledgeHut’s Data Science with Python certification program.
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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 one can see, the PyTorch vs. TensorFlow debate is nuanced as their landscape is constantly changing.
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.
Both are comparable for small and medium-sized datasets. PyTorch is faster than TensorFlow as it allows quicker prototyping than TensorFlow.
PyTorch, an open-source deep learning framework, is used in computer vision and natural language processing tasks.
It depends. Learning Keras is a better choice for deep learning beginners due to its high-level API. However, if you already have some basic understanding of deep learning and have worked with Keras before, you can choose either of the two frameworks based on your project requirements. TensorFlow is good at deploying models in production to build AI products, while PyTorch is preferred in academia for research tasks. Thus, both TensorFlow and PyTorch are good frameworks to learn.
With the use of PyTorch, a lot of the complexities can be avoided, which are required for Neural Networks and Deep Learning technologies. You need much more experience to achieve the same functionality in TensorFlow. Many people generally opt for Keras over TensorFlow as an additional layer.
Yes, learning PyTorch is an excellent decision to improve one's deep learning skills. PyTorch is quite popular in the research community. It is also a part of the Python package ecosystem and hence, fully compatible with other popular Python libraries such as SciPy and NumPy.
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