Hello everyone! Last week, we talked about different frameworks for Deep Learning. Today, we are going to compare and contrast two of the most used frameworks for Deep Learning, so that we can have a better understanding of how they work, and possibly better choose when to use which. Without any further ado, let’s compare PyTorch vs TensorFlow in 2022!
Metrics of Performance for PyTorch vs TensorFlow in 2022
PyTorch and TensorFlow have grown so swiftly in their brief lives that the debate over which one is better is ever-evolving.
While TensorFlow is known for being a framework for industry, and PyTorch is known for being a framework for research, we’ll discover that both of these perceptions are based on obsolete facts. Going into 2022, the debate over whether framework is better is considerably more complicated; let’s see how we are going to measure performance for different aspects of each framework.
We are going to focus on the following points:
- Definition of Dynamic Vs Static Graphic Mechanism
- Visualization
- Training
- Model Availability
- Deployment Infrastructure
Definition of Dynamic Vs Static Graphic Mechanism
A computational graph is a means to describe computations as a directed graph in an abstract form. A graph is a data structure made up of nodes (vertices) and edges (connections between them). It consists of a pair of vertices linked by directed edges.
The computation graphs in TensorFlow are defined statically when you run code. The tf.Session object and tf.Placeholder, which are tensors that will be replaced with external data at runtime, are used to communicate with the outside world. The main benefit of using a computational graph is that it allows for parallelism or dependency-driven scheduling, which speeds up and improves the efficiency of training.
PyTorch is made up of two main components:
- Imperative and dynamic computation graph construction.
- Autograds: Performs dynamic graph differentiation automatically.
There are no specific session interfaces or placeholders, so the graphs update and execute nodes as you go. Overall, the framework is more tightly interwoven with Python and, for the most part, seems more natural. As a result, PyTorch might feel more like a pythonic framework, while TensorFlow feels like an entirely new language.
Visualization
TensorFlow is definitely superior when it comes to training process visualization. Visualization allows you to keep track of the training process and debug more easily. TensorFlow has an integrated visualization library, Tensorboard. Visdom is used by PyTorch developers; however, Visdom’s capabilities are quite restricted, therefore TensorBoard wins in terms of displaying the training process.
Training
Data parallelism is a key feature that separates PyTorch from TensorFlow. PyTorch improves speed by utilizing Python’s natural capabilities for asynchronous execution. To support distributed training with TensorFlow, you’ll have to manually write and fine tune each operation to run on a given device. You can, however, reproduce everything in TensorFlow from PyTorch with a little extra work. The code sample below shows how easy it is to use PyTorch to perform distributed training for a model.
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel
dist.init_process_group(backend='gloo')
model = DistributedDataParallel(model)Model Availability
Implementing an effective Deep Learning model from the ground up can be tough, especially in applications like NLP where engineering and optimization are difficult. The increasing complexity of SOTA models makes training and tuning activities for small businesses unfeasible, if not impossible. GPT-3 from OpenAI contains about 175 billion parameters, whereas GPT-4 will have over 100 trillion. Access to pre-trained models for transfer learning, fine-tuning, or out-of-the-box inference is useful for startups and researchers alike.
PyTorch and TensorFlow differ dramatically in terms of model availability. As we’ll see in the Ecosystems section, both PyTorch and TensorFlow have their own official model libraries, but practitioners may choose to use models from other sources. Let’s have a look at the model availability for each framework in terms of numbers.
The HuggingFace Use case for PyTorch vs TensorFlow
In only a few lines of code, HuggingFace allows you to include trained and adjusted SOTA models into your processes.
When we compare the availability of HuggingFace models for PyTorch and TensorFlow, the results are astounding. The graph below shows the total number of models on HuggingFace that are either PyTorch or TensorFlow-only, or accessible for both frameworks. As can be seen, the number of models available for usage specifically in PyTorch much exceeds that of the competitors.
This is particularly important for researchers, and might be one of the contributing factors to PyTorch dominance in research. If you want more information on the subject of the PyTorch:Research = TensorFlow:Industry paradigm, read this article from the Gradient. It’s a bit dated, but it gives interesting figures and insights.
Deployment Infrastructure
From an inference standpoint, using SOTA models for cutting-edge outcomes is the holy grail of Deep Learning applications, but this ideal is not necessarily practicable or even viable to attain in an enterprise scenario. It’s worthless to have access to SOTA models if putting their wisdom to use is a time-consuming, error-prone procedure. As a result, it’s crucial to analyze the end-to-end Deep Learning process in each framework, in addition to which framework gives you access to the most dazzling models.
Since its introduction, TensorFlow has been the go-to framework for deployment-oriented applications, and for good reason. TensorFlow comes with a slew of companion tools that make the entire Deep Learning process simple and efficient. TensorFlow Serving and TensorFlow Lite, in particular, make it simple to deploy on clouds, servers, mobile devices, and IoT devices.
PyTorch used to be severely lacking in terms of deployment, but it has worked hard to close the gap in recent years. TorchServe, which was released last year, and PyTorch Live, which was released only a few weeks ago, have provided much-needed native deployment capabilities.
TensorFlow currently has the advantage in terms of deployment. Serving and TFLite are just more capable than PyTorch’s competitors, and the option to utilize TFLite for local AI with Google’s Coral devices is a must-have for many sectors. PyTorch Live, on the other hand, is solely for mobile devices, and TorchServe is still in its early stages. It will be fascinating to observe how the deployment landscape evolves in the future years, but for now, TensorFlow wins Round 2 of the PyTorch vs TensorFlow battle.
PyTorch Vs TensorFlow in 2022 – Final Words
As we as seen, both frameworks have their pros and cons, and that is completely fine and natural. However, as we look at the present, we also get an understanding of future trends both in the industry and in research. So let me tell you why I personally prefer PyTorch, and why I believe it will be the default framework for Deep Learning in the future. These same points are shared by the authors of the article cited above.
- It is simple. It’s comparable to numpy, but more pythonic, and it’s easy to connect with the rest of the Python environment. Debugging the model with TensorFlow is significantly more difficult.
- It performs well. Despite the fact that PyTorch’s dynamic graphs provide far fewer opportunities for optimization, anecdotal evidence suggests that PyTorch is as fast as, if not quicker than, TensorFlow. It’s unclear whether this is correct, but at the very least TensorFlow hasn’t acquired a significant edge in this area.
- It has an excellent API. The PyTorch API is preferred by most academics above TensorFlow’s API. This is partly due to PyTorch’s better design, and partly due to TensorFlow’s handicap of altering APIs so frequently.
So there you have it! What do you think, do you agree with the points outlined so far? What framework do you use?
