Deep learning, a subset of machine learning, is a specialized research area that uses artificial neural networks to enable computers to imitate human behavior and thinking patterns. It is also an essential tool for Data Scientists to train highly accurate models that make dealing with big data faster and easier. To delve deeper into aspects of data science is a good choice.
Since deep learning is in great demand for predictive analytics, machine learning practitioners need to be familiar with various deep learning frameworks. TensorFlow, PyTorch, Keras, MXNet, Caffe, DL4J, and Chainer are a few currently used deep learning frameworks. There is a long ongoing debate as to which one is preferable, and each of these tools has its share of strong supporters. In this article, we will look at two extremely popular open-source deep learning frameworks: TensorFlow and PyTorch. We'll compare their features and performance, pros and cons, and the things to consider before choosing one of the two for your deep learning tasks.\
What Is TensorFlow at a Glance?
TensorFlow is a very popular and open-source library for machine learning. It was designed by researchers and engineers from the Google Brain team and was first released in 2015. TensorFlow has taken the position of Google's DistBelief framework and is now available on almost all execution platforms (CPU, GPU, TPU, etc.). The credit for its popularity goes to its distributed training assistance, scalable production, and deployment choices. It is also compatible with various devices, including mobile, desktop, web, and cloud.
TensorFlow is currently used by companies, startups, and business firms to automate things and develop new deep learning solutions. Even though it is a Python library, TensorFlow released an R interface for RStudio in 2017. Furthermore, the TensorFlow Lite (or TF Lite) implementation is designed for edge-based machine learning. TF Lite is developed to deliver lightweight algorithms on various resource-constrained edge devices, including smartphones, microcontrollers, etc.
Code Style and Function
- With the latest TensorFlow 2.0, eager execution (evaluates operations immediately) is supported, allowing the users to write the code using Python control flow rather than graph control flow. This approach is used to build low-level components.
- Keras is the recommended way of using TensorFlow due to its simpler APIs, availability of common use cases in the library, and better error message generating capabilities than base TensorFlow.
- Getting started with TensorFlow is faster as documentation, pre-trained models, and sample Google Colab notebooks are available.
- Several popular machine learning algorithms and datasets are available and ready to use in the TensorFlow. It is also possible to access Google Research datasets.
- Although the latest version, 'TensorFlow 2', is an improved version in terms of simplicity, and ease of use (supports eager execution, intuitive higher-level APIs, and flexible model building on any platform), users can face a limitation after updating from TensorFlow 1. x to TensorFlow 2.0. Several features in the version have changed, which might be challenging for the user familiar with the previous version. Additionally, upgrading code to this newer version is tedious and error-prone.
The TensorFlow extended ecosystem includes various tools, packages, and APIs to build production-grade AI models. Some notable parts of this ecosystem are:
- TensorFlow Hub: It is a library for reusable machine learning modules like BERT for NLP tasks and faster R-CNNs for Object Detection and Image classification.
- TensorBoard: A visualization tool to monitor model training and evaluate results.
- Keras: A high-level API for TensorFlow to build and train models.
- TensorFlow Lite: It is a set of tools that allows developers to run their models on mobile, embedded, and edge devices for on-device machine learning.
- Model Garden: This official repository provides a collection of sample implementations for SOTA models using the latest TensorFlow 2's high-level APIs.
What Is PyTorch at a Glance?
PyTorch is a machine learning library developed by the Facebook AI Research Lab. Initially released in 2016, it is offered free, open-source software under the BSD license.
PyTorch has a Python interface, and its features are implemented as Python classes, making it a part of the Python package ecosystem. Because it is a Python-based library, it is easy to expand its functionality using other Python libraries like SciPy and NumPy. However, as the library shares some C++ backend with Torch, the users can also code in C/C++. PyTorch is gaining popularity because of its ease of use, simplicity, dynamic computational graph, and efficient memory usage. Several popular deep learning software platforms, like Tesla Autopilot and Uber's Pyro, are built on PyTorch.
Code Style and Function
- Torch, a C-based framework for fast computation, serves as the foundation for PyTorch. PyTorch is a Python interface that encapsulates the same C back end.
- PyTorch offers better compatibility with NumPy. So, PyTorch allows easier conversion of NumPy objects to tensors.
- PyTorch uses eager execution, which evaluates tensor operations immediately and dynamically.
- PyTorch extends the Torch backend with a C++ package for automatic differentiation, which automatically calculates the gradient of the functions defined in the torch.nn (during backpropagation of neural networks).
- PyTorch's default compute mode is the eager mode which allows running a neural network line by line, making debugging easier. It also supports dynamic execution, i.e., creating neural networks with conditional execution.
PyTorch also offers a rich ecosystem of tools, libraries, and other resources to help facilitate, accelerate, and research AI development. Among several libraries and packages of the PyTorch extended ecosystem, some notable APIs, extensions, and useful libraries are:
- Fast.ai API: This is an extremely popular high-level API for PyTorch, which makes it exceedingly simple to create models in a short time.
- TorchServe: This is an open-source model server created in conjunction with AWS and Facebook.
- TorchElastic: This is for training deep neural networks at scale using Kubernetes.
- PyTorch Hub: This is an active community for sharing and expanding cutting-edge models.
- Albumentations: This is a very popular and fast image augmentation library and part of the PyTorch ecosystem. It also works with TensorFlow.
- ONNX Runtime: The ONNX (Open Neural Network eXchange) is an open standard format representing machine learning (ML) models. PyTorch offers a 'torch. onnx' module that can export PyTorch models to ONNX to accelerate the ML models. This way, a model trained in PyTorch can be deployed in a different framework like TensorFlow. ML developers can build models in their chosen framework and easily access hardware acceleration settings of different frameworks and runtime environments.
TensorFlow vs. PyTorch: The Differences
First, to understand their differences, explore what PyTorch and TensorFlow have in common in terms of graph definition. To begin with, they both take any model to be a DAG (Directed Acyclic Graph) and work with tensors. Tensors are mathematical expressions that describe the connections between groups of objects in a vector space.
TensorFlow allows the user to conduct tensor operations by building a stateful dataflow graph. Before the model can run, the computation graph is statically defined. On the other hand, PyTorch is more dynamic and allows the user to execute the nodes as the model runs. In other words, the computation graph is built at each point of execution, and the graph can be modified while it is running.
|General||TensorFlow is a very popular and open-source platform for machine learning developed by Google and has taken the position of Google's DistBelief framework|
PyTorch is a machine learning library created by Facebook AI Research Lab and is an open-source software under the BSD licence.
|Level of API’s||TensorFlow has both low- and high-level APIs.||Because of its low-level API, PyTorch focuses solely on array expression.|
|Architecture||TensorFlow is difficult to use but with Keras, it becomes easier.||PyTorch's architecture is complex and difficult to understand for beginners.|
|Training time and Framework||TensorFlow allows you to access GPUs, but it employs its own built-in GPU acceleration, so the time it takes to train these models will always vary depending on the framework you choose.||When training a dataset using PyTorch, you can speed up the process by using GPUs, which operate on CUDA (a C++ backend).|
|Speed||TensorFlow works at maximum speed, resulting in high performance.||PyTorch's performance and speed are similar to TensorFlow.|
|Model Deployment||TensorFlow Serving is a built-in model deployment tool that can be used to deploy machine learning models.||PyTorch released TorchServe (2020) for model deployment, which is a versatile and user-friendly solution for serving and scaling PyTorch models in production.|
|Visualization||TensorBoard is a TensorFlow visualization tool that provides a suite of web apps for evaluating and understanding your TensorFlow runs and graphs.||Visdom, a PyTorch visualization tool, creates rich visualizations of live data to assist researchers and developers in staying on top of their scientific experiments that are run on remote servers.|
|Applications||TensorFlow is mainly preferred for production applications. It is used for computer vision and NLP tasks.||PyTorch is used mainly in research for computer vision, NLP and reinforcement learning.|
|Top Projects||Magenta, Ludwig, Sonnet||CheXNet, Pyro, Horizon|
Advantages and Disadvantages of TensorFlow
- Regularly updated: Google supports and manages TensorFlow, and new features are released regularly.
- Open-source: TensorFlow is an open-source platform widely used and accessible to various users.
- Data visualization: TensorFlow includes a tool called TensorBoard that allows you to view data graphically. It also facilitates node debugging, minimizes the burden of inspecting the whole code, and effectively resolves the neural network.
- Compatibility: TensorFlow is compatible with Keras, allowing its users to create high-level functionality and giving TensorFlow system-specific functionality (pipelining, estimators, etc.).
- Very scalable: TensorFlow's ability to be installed on any computer enables its users to create any system.
- Hardware acceleration for models: TensorFlow is used as a hardware acceleration library because of the parallelism of work models. It employs several distribution mechanisms in GPU and CPU systems. TensorFlow also features a TPU architecture that does computations quicker than a GPU or CPU. As a result, models generated using TPU may be placed on the cloud at a lower cost and performed at a quicker pace. However, TensorFlow's TPU design only allows for model execution, not training.
- Lagging Performance: Benchmark tests show that TensorFlow lags behind its competitors in computation speed and performance.
- Dependency: The TensorFlow shortens code and makes it easier for users to access it. However, this approach adds complexity and might be challenging to use. Every code must be run utilizing any platform for support, increasing the reliance on the execution.
- Low-level API: TensorFlow falls behind in offering symbolic loops for indefinite sequences. It has a specific use for particular sequences, making it a usable system. As a result, it is referred to as a low-level API.
- GPU Support: TensorFlow only supports NVIDIA GPUs and Python for GPU programming, which is a disadvantage given the proliferation of alternative deep learning languages.
Advantages and Disadvantages of PyTorch
- Python-centric: PyTorch is known as "pythonic," designed for deep integration with Python programming rather than providing an interface to a library built in another language. This is an advantage as Python is extremely popular in the data science community and is widely used to develop machine learning models and ML research. You probably already know the basics of big data, data science, and deep learning, but if you're new to this topic and wish to earn a certificate, check out the data science course with Python certification.
- Learning curve: PyTorch is easier to understand than other deep learning frameworks since its syntax is comparable to traditional programming languages such as Python.
- Easier Debugging: PyTorch may be debugged using one of the numerous widely accessible Python debugging tools (such as Python's pdb and ipdb tools).
- Model Optimization: PyTorch supports dynamic computational graphs, implying network behavior may be altered dynamically at runtime. This simplifies model optimization and provides PyTorch with a significant edge over other machine learning frameworks that consider neural networks static objects.
- Data-parallelism: This PyTorch functionality distributes computational effort over numerous CPU or GPU cores. Although similar parallelism is possible in other machine-learning technologies, PyTorch makes it considerably more manageable.
- Active community: PyTorch has well-organized documentation useful for novices and a highly active community and forums. This documentation is regularly updated with PyTorch versions and includes many tutorials. PyTorch is incredibly easy to use, which implies that the learning curve for developers is low.
- Applicability in Production Environment: There is a lack of model serving in production. While this will change in the future, alternative frameworks have been more commonly adopted for real production work (even if PyTorch becomes increasingly popular in the research communities). As a result, the documentation and development communities are less than those in comparable frameworks.
- Limited monitoring and visualization interfaces: PyTorch does not have a visualization tool for developing the model graph like TensorBoard. As a result, developers may utilize one of the numerous current Python data visualization tools or connect to TensorBoard outside.
- Not as comprehensive as TensorFlow: PyTorch is not an end-to-end machine learning development platform; actual application development necessitates translation of PyTorch code into another framework to deploy the applications on the cloud.
TensorFlow vs PyTorch Decision Guide
We have explored the two frameworks' features, pros, and cons. In this section, let us consider some key points when choosing between TensorFlow and PyTorch. The following few things are to be kept in mind while making a decision -
- Project requirements: For research tasks, PyTorch is a better option since it is 'Pythonic' (Developed natively in Python), is easier to learn and debug, while if the goal is to deploy deep learning models, TensorFlow is a clear choice for production environments.
- Model availability: To train a State-of-the-Art (SOTA) model from scratch is simply not possible anymore. Many SOTA models are publicly available based on both TensorFlow and PyTorch frameworks. Not all pre-trained models are available for both TensorFlow and PyTorch. So, if there is a requirement to use SOTA models, first check model availability on Model Garden and PyTorch Hub and then decide.
- Training parameters, Model training, and Deployment: Both TensorFlow and PyTorch provide options to visualize the training parameters. TensorBoard is a TensorFlow feature for monitoring the behavior of model training parameters over time. Now PyTorch also supports integration with TensorBoard for the same.
- Ecosystems: Deep learning has a wider scope of application in different areas due to the advancements in AI. It is often expected that a framework should integrate well with a larger ecosystem and facilitate the development of mobile, local, and server applications. An example of this could be specialized Machine Learning hardware like Google's Edge TPU (Tensor Processing Unit), specifically developed for neural network machine learning. According to Google, these TPUs are 15 times to 30 times faster than contemporary GPUs and CPUs on production AI workloads utilizing neural network inference. So, if TPU power is to be used, TensorFlow is a better framework that can integrate well with this hardware.
Based on the above considerations, it can be decided whether TensorFlow or PyTorch is the right choice for the project.
Based on the comparison, declaring one framework as a clear winner is a tough choice. Both frameworks have their merits. The recent developments and the latest releases have narrowed the gap between these two. The TensorFlow vs. PyTorch debate remains inconclusive due to the constantly evolving AI landscape. As of 2022, both frameworks are mature frameworks with similar features and performance. Ultimately, selecting one of the two is more dependent on the relevant model availability and associated ecosystem, deployment time, and available infrastructure.
Both frameworks have excellent documentation, learning resources, and strong community support. PyTorch is favored as a research framework in academia, while TensorFlow remains an industry standard. Moreover, the number of available deep learning frameworks will continue to grow in 2022. Only time will tell if PyTorch becomes the new industry standard or if TensorFlow remains unrivaled in the coming future. If you’re interested in becoming a Data Scientist, check out KnowledgeHut’s Data Science with Python Certification today to get started.
Frequently Asked Questions (FAQs)
1. Is PyTorch Faster than TensorFlow?
Both are comparable for small and medium-sized datasets. PyTorch is faster than TensorFlow as it allows quicker prototyping than TensorFlow.
2. What is PyTorch Used for?
PyTorch, an open-source deep learning framework, is used in computer vision and natural language processing tasks.
3. Should I Learn PyTorch or TensorFlow First?
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.
4. Is TensorFlow Easier than PyTorch?
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.
5. Is PyTorch worth Learning?
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.