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The Comparison Between PyTorch and Tensorflow: Which Framework is Best For Your AI

Artificial intelligence will change every industry. Experts predict a 14% growth in GDP by 2030 caused by advances in machine learning and AI.

There are plenty of off-the-shelf options for businesses that want to use AI. But to build your own AI systems for chatbots or other conversational AI use cases, you will need a machine learning library. However, it can be hard to choose without a good sense of each library's strengths and weaknesses.

Two of the most popular frameworks are PyTorch and Tensorflow, but even among those two, there's a lot to consider. With that in mind, let's explore what these two libraries are, the key differences between them, and find out which is best for your AI project.


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What Is PyTorch?

PyTorch is an open-source machine learning library used for training for deep learning. It was released in 2016 by an AI research team on Facebook and has been growing in popularity. The Python programming language is popular in data science because it's intuitive for learners, but the PyTorch library can also talk to C++ and CUDA.

Python's ease of use makes it possible to build, run and iterate on code quickly. On top of that, PyTorch integrates seamlessly with other Python-based data science tools. If you or your team have used NumPy, another popular data science tool, you're in a great position to start building with PyTorch.

Instead of using prefabricated graphs with predetermined features, PyTorch offers a framework for building computational graphs as we go and quickly iterating on them while they're running. It is helpful when you're running a machine learning project but aren't sure about your resource requirements, like how many gigabytes of memory you have available.

And PyTorch's capabilities are constantly being extended by a thriving developer community. They're building open-source tools and libraries that allow you to optimize PyTorch for computer vision, NLP, and other use-cases. Open-source tooling means that data isn't siloed for profit and code is easily adjustable. From powerful APIs to an Apache Hive to Databricks data integration, interoperability of tools is crucial to building data infrastructure that lasts.

What Is TensorFlow?

Another popular toolkit for machine learning infrastructure is Google's TensorFlow. The library was released by Google's AI division - Google Brain -  in 2015. While it was just an internal tool for Google engineers initially, it's now used in data science labs and huge businesses like Coca-Cola, Airbnb, and Twitter. These companies are using big data to provide personalized experiences at scale. 

TensorFlow enables teams to design and train ML models with quick iteration and even helps with debugging with user-friendly tools like Keras. Regardless of your chosen programming language, TensorFlow can train and deploy models in the cloud, on a company's on-site network, on hybrid integration platforms in the web browser, or on a single device.

It's important that your computer systems are up to date   to be able to install and run TensorFlow. If necessary, use legacy modernization to update your computer systems.

This is how Google can power features like recognizing objects from photos in their Pixel smartphones and IoT devices. The computationally intensive AI work is all able to happen on-device. TensorFlow is also used to autocomplete your searches in Google, suggesting the word you're most likely to input next. It's also an increasingly important part of products like Google Translate, Google Maps, Photos, Google Play, Android, and YouTube.


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The Differences Between Python and TensorFlow

Some differences between PyTorch and TensorFlow include their static vs. dynamic approaches to deployment pipelines, graph definition, visualization tools, default settings, their learning curve and debugging.

Model Deployment In Production

The task of creating and training a deep learning model is only half the battle. The most challenging aspect is managing and deploying these trained deep learning models as they're implemented and exposed to real user data. This involves different types of test cases in software testing.

TensorFlow's Serving library can handle deployment and training, which is how Google can test and deploy improved AI models at scale. TensorFlow Serving has been tested on over a thousand Google projects and can handle millions of requests per second.

TensorFlow offers users remote access to machine learning models that are deployed on dedicated servers. This makes it easy to update a deployed model and roll back any previous versions using TensorFlow Serving without shutting down the whole server and pausing service to users.

TensorFlow Serving is ideal when performance is a concern because it's specifically designed for industrial production scenarios. PyTorch's equivalent TorchServe is valuable for its fundamental capabilities and open-source tools, including a model archiver, server metrics, event logging, data virtualization, a definition of API endpoints, and snapshots of the machine learning model as it changes.

Graph Definition

Computations in machine learning are described using graphs. A data structure comprising nodes and connections is known as a graph. Graphs store the activations of the neural network during a forward pass in the training of neural networks.

PyTorch and TensorFlow both work with tensors. Tensors describe the connections between collections in the network. But they have two different graph definitions. TensorFlow enables the creation of a stateful dataflow graph before running the model.

On the other hand, PyTorch uses dynamic graphs compiled at runtime, which lets the user run the nodes as the model runs. In other words, the computation graph is generated at each execution stage, and changes can be made to the graph as needed.

For this reason, PyTorch is frequently chosen in research because it is better suited to the creation of bespoke models. Because PyTorch is dynamic, it may be simpler to interact with the internals of the models.

Dynamic graphs, a feature of TensorFlow announced in 2019, allow operations to be assessed at runtime without creating a graph that would be used afterward. Since the user can operate statically or dynamically on both frameworks, what was once a big difference between the two libraries is not as significant.


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The availability of visualization tools in PyTorch and TensorFlow helps with debugging and data orchestration meaning you can visualize data quickly and give stakeholders a quick understanding of the model training process.

PyTorch has Visdom, a straightforward tool for data visualization. Visdom can be utilized with PyTorch or NumPy. While it only offers a few basic capabilities, it is flexible, easy to use, and supports PyTorch tensors.

TensorFlow features Tensorboard-a suite of tools that let users understand the deep learning model through graphs, images, distributions, histograms, and scalars. Overall, Tensorboard is considered a more flexible tool than Visdom. Tensorboard integrates with PyTorch now to let both groups use these superior tools.

Default settings for devices

TensorFlow does not require the user to specify anything since the defaults are well set. For instance, it automatically assumes if the user wants to turn on the GPU if one is available. TensorFlow does have a downside when dealing with device management, unlike PyTorch, in that even if only one GPU is in use, it still consumes memory on all available GPUs. This is important for ETL pipelines, where you need to process and load vast amounts of data.

Documentation and debugging tools

Because PyTorch code can be debugged using a conventional Python debugger, users don't need to learn how to use another debugger from scratch. Since PyTorch defines computational graphs at runtime, it's simpler to use most of the standard Python debugging tools with PyTorch.

Debugging TensorFlow code is more complicated than PyTorch debugging since it requires knowledge of the TensorFlow debugger and the variables that are requested from a TensorFlow session. When using TensorFlow, the user must go in with a clear idea of the desired outcomes from a session and the library's own debugger.

The documentation for PyTorch and TensorFlow is very well organized and beneficial for new users of both deep learning frameworks because of the concentrated developer communities that support them. Both frameworks are supported by a wealth of free tutorials and online video courses.


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Steep vs. smooth learning curve

Because of the ‘low-level' implementation of its neural network tooling, TensorFlow is more challenging for new users than PyTorch, which could be a barrier to adoption. However, thanks to its sophisticated Keras API, even complete beginners can quickly pick up the basics.

Since PyTorch's syntax is similar to the standard Python programming language, it is easier to learn than TensorFlow. In comparison to TensorFlow, PyTorch has an easier learning curve thanks to its intuitive object-oriented design and uncomplicated data handling. Despite having fewer features than TensorFlow, it is one of the most popular deep learning frameworks among novices because of how simple it is to learn.

Choosing Between PyTorch and TensorFlow

When choosing between PyTorch and TensorFlow, think about the business needs of your AI project. The capabilities of the two frameworks have become more similar over time, but they still have built-in strengths and weaknesses because of their different backgrounds.

If this is more like a research project where you need easy tooling and room to explore, PyTorch might be more suited to your project. If you're looking to deploy a large-scale AI project across many devices, you likely need the industrial-scale power of TensorFlow used by businesses like Google every day.



Pohan Lin - Senior Web Marketing and Localizations Manager #1 

Pohan Lin 

Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data ingestion architecture, data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan Lin also published articles for domains such as SME-News.

Published Wednesday, July 20, 2022 7:33 AM by David Marshall
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