Artificial Neural Networks (ANNs) have been demonstrated to be effective for many cases of supervised learning, but programming an ANN manually can be a challenging task. As a result, frameworks such as TensorFlow and PyTorch have been created to simplify the creation and use of ANNs.
With the increased interest in deep learning in recent years, there has been an explosion of machine learning tools. In recent years, deep learning frameworks such as PyTorch, TensorFlow, Keras, Chainer, and others have been introduced and developed at a rapid pace. These frameworks provide neural network units, cost functions, and optimizers to assemble and train neural network models.
Using artificial neural networks is an important approach for drawing inferences and making predictions when analyzing large and complex data sets. TensorFlow and PyTorch are two widely-used machine learning frameworks that support artificial neural network models.
This article describes the effectiveness and differences of these two frameworks based on current recent research to compare the training time, memory usage, and ease of use of the two frameworks. In particular, you will learn:
- Characteristics of PyTorch vs. TensorFlow
- Performance, Accuracy, Training, and Ease of Use
- Main Differences PyTorch vs. TensorFlow
Key Characteristics of TensorFlow and PyTorch
TensorFlow is a very popular end-to-end open-source platform for machine learning. It was originally developed by researchers and engineers working on the Google Brain team before it was open-sourced.
TensorFlow replaced Google’s DistBelief framework and runs on almost all available execution platforms (CPU, GPU, TPU, Mobile, etc.). TensorFlow is currently used by various international companies, such as Google, Uber, Microsoft, and a wide range of universities.
The TensorFlow Lite implementation is specially designed for edge-based machine learning. TF Lite is optimized to run various lightweight algorithms on various resource-constrained edge devices, such as smartphones, microcontrollers, and other chips.
- Support and library management: TensorFlow is backed by Google and has frequent releases with new features. It is popularly used in production environments.
- Open-sourced: TensorFlow is an open-source platform, available to a broad range of users and very popular.
- Data visualization: TensorFlow provides a tool called TensorBoard to visualize data graphically. It also allows easy debugging of nodes, reduces the effort of looking at the whole code, and effectively resolves the neural network.
- Keras compatibility: TensorFlow is compatible with Keras, which allows its users to code some high-level functionality sections and provides system-specific functionality to TensorFlow (pipelining, estimators, etc.).
- Very scalable: TensorFlow’s characteristic of being deployed on every machine allows its users to develop any kind of system.
- Architectural support: TensorFlow finds its use as a hardware acceleration library due to the parallelism of work models. It uses different distribution strategies in GPU and CPU systems. TensorFlow also has its architecture TPU, which performs computations faster than GPU and CPU. Therefore, models built using TPU can be easily deployed on a cloud at a cheaper rate and executed at a faster rate. However, TensorFlow’s architecture TPU only allows the execution of a model, not to train it.
- Benchmark tests: Computation speed is where TensorFlow is delaying behind when compared to its competitors. It has less usability in comparison to other frameworks.
- Dependency: Although TensorFlow reduces the length of code and makes it easier for a user to access it, it adds a level of complexity to its use. Every code needs to be executed using any platform for its support which increases the dependency for the execution.
- Symbolic loops: TensorFlow lags at providing the symbolic loops for indefinite sequences. It has its usage for definite sequences, which makes it a usable system. Hence it is referred to as a low-level API.
- GPU Support: TensorFlow has only NVIDIA support for GPU and Python support for GPU programming, which makes it a drawback as there is a hike of other languages in deep learning.
PyTorch was first introduced in 2016. Before PyTorch, deep learning frameworks have often focused on either speed or usability, but not both. PyTorch has become a popular tool in the deep learning research community by combining a focus on usability with careful performance considerations. It provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy, and is consistent with other popular scientific computing libraries while remaining efficient and supporting hardware accelerators such as GPUs.
PyTorch is a Python library that performs immediate execution of dynamic tensor computations with automatic differentiation and GPU acceleration and does so while maintaining performance comparable to the fastest current libraries for deep learning. Today, most of its core is written in C++, one of the primary reasons PyTorch can achieve much lower overhead compared to other frameworks.
As it stands now and for the foreseeable future as it moves from beta to production, PyTorch appears to be best suited for drastically shortening the design, training, and testing cycle for new neural networks for specific purposes. Hence it became very popular in the research communities.
Multiple popular deep learning software is built on top of PyTorch, including Tesla Autopilot or Uber’s Pyro.
- PyTorch is based on Python: PyTorch is Python-centric or “pythonic”, designed for deep integration in Python code instead of being an interface to a library written in some other language. Python is one of the most popular languages used by data scientists and is also one of the most popular languages used for building machine learning models and ML research.
- Easier to learn: Because its syntax is similar to conventional programming languages like Python, PyTorch is comparatively easier to learn than other deep learning frameworks.
- Debugging: PyTorch can be debugged using one of the many widely available Python debugging tools (for example, Python’s pdb and ipdb tools).
- Dynamic computational graphs: PyTorch supports dynamic computational graphs, which means the network behavior can be changed programmatically at runtime. This makes optimizing the model much easier and gives PyTorch a major advantage over other machine learning frameworks, which treat neural networks as static objects.
- Data parallelism: The data parallelism feature allows PyTorch to distribute computational work among multiple CPU or GPU cores. Although this parallelism can be done in other machine-learning tools, it’s much easier in PyTorch.
- Community: PyTorch has a very active community and forums (discuss.pytorch.org). Its documentation (pytorch.org) is very organized and helpful for beginners; it is kept up to date with the PyTorch releases and offers a set of tutorials. PyTorch is very simple to use, which also means that the learning curve for developers is relatively short.
- Lacks model serving in production: While this will change in the future, other frameworks have been more widely used for real production work (even if PyTorch becomes increasingly popular in the research communities). Hence, the documentation and developer communities are smaller compared to other frameworks.
- Limited monitoring and visualization interfaces: While TensorFlow also comes with a highly capable visualization tool for building the model graph (TensorBoard), PyTorch doesn’t have anything like this yet. Hence, developers can use one of the many existing Python data visualization tools or connect externally to TensorBoard.
- Not as extensive as TensorFlow: PyTorch is not an end-to-end machine learning development tool; the development of actual applications requires conversion of the PyTorch code into another framework such as Caffe2 to deploy applications to servers, workstations, and mobile devices.
Comparing PyTorch vs. TensorFlow
1.) Performance Comparison
The following performance benchmark aims to show an overall comparison of the single-machine eager mode performance of PyTorch by comparing it to the popular graph-based deep learning Framework TensorFlow.
The table shows the training speed for the two models using 32bit floats. Throughput is measured in images per second for the AlexNet, VGG-19, ResNet-50, and MobileNet models, in tokens per second for the GNMTv2 model, and in samples per second for the NCF model. The benchmark shows that the performance of PyTorch is better compared to TensorFlow, which can be attributed to the fact that these tools offload most of the computation to the same version of the cuDNN and cuBLAS libraries.
The TensorFlow Accuracy and the PyTorch Accuracy graphs (see below) show how similar the accuracies of the two frameworks are. For both models, the training accuracy constantly increases as the models start to memorize the information they are being trained on.
The validation accuracy indicates how well the model is actually learning through the training process. For both models, the validation accuracy of the models in both frameworks averaged about 78% after 20 epochs. Hence, both frameworks are able to implement the neural network accurately and are capable of producing the same results given the same model and data set to train on.
3.) Training Time and Memory Usage
The above figure shows the training times of TensorFlow and PyTorch. It indicates a significantly higher training time for TensorFlow (average of 11.19 seconds for TensorFlow vs. PyTorch with an average of 7.67 seconds).
While the duration of the model training times varies substantially from day to day on Google Colaboratory, the relative durations between TensorFlow and PyTorch remain consistent.
The memory usage during the training of TensorFlow (1.7 GB of RAM) was significantly lower than PyTorch’s memory usage (3.5 GB RAM). However, both models had a little variance in memory usage during training and higher memory usage during the initial loading of the data: 4.8 GB for TensorFlow vs. 5 GB for PyTorch.
4.) Ease of Use
PyTorch’s more object-oriented style made implementing the model less time-consuming. Also, the specification of data handling was more straightforward for PyTorch compared to TensorFlow.
On the other hand, TensorFlow indicates a slightly steeper learning curve due to the low-level implementations of the neural network structure. Hence, its low-level approach allows for a more customized approach to forming the neural network, allowing for more specialized features. Moreover, the very high-level Keras library runs on top of TensorFlow. So as a teaching tool, the very high-level Keras library can be used to teach basic concepts, and then TensorFlow can be used to further the understanding of the concepts by having to lay out more of the structure.
Differences of PyTorch vs. TensorFlow – Summary
TensorFlow and PyTorch implementations show equal accuracy. However, the training time of TensorFlow is substantially higher, but the memory usage was lower.
PyTorch allows quicker prototyping than TensorFlow, but TensorFlow may be a better option if custom features are needed in the neural network.
TensorFlow treats the neural network as a static object; if you want to change the behavior of your model, you have to start from scratch. With PyTorch, the neural network can be tweaked on the fly at run-time, making it easier to optimize the model.
Another major difference lies in how developers go about debugging. Effective debugging with TensorFlow requires a special debugger tool that enables you to examine how the network nodes are doing their calculations at each step. PyTorch can be debugged using one of the many widely available Python debugging tools.
Both PyTorch and TensorFlow provide ways to speed up model development and reduce amounts of boilerplate code. However, the core difference between PyTorch and TensorFlow is that PyTorch is more “pythonic” and based on an object-oriented approach. At the same time, TensorFlow provides more options to choose from, resulting in generally higher flexibility.
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