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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Visualization
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.
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ABOUT
THE AUTHOR
Pohan Lin -
Senior Web Marketing and Localizations Manager #1
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.