PyTorch vs TensorFlow for Your Python Deep Learning Project
TensorFlow vs PyTorch: What's the difference? Graphs are used to compute numerically on data in both of these open-source Python libraries. Academic research and commercial code extensively use both of them. They're both integrated with APIs, cloud computing platforms, and model repositories.
Which one should you choose if they are so similar?
You'll learn these things in this tutorial:
TensorFlow versus PyTorch: what are the differences
What tools and resources are available for each
How to choose the best option for your specific use case
Before deciding which platform is suitable for your project, you'll examine the specifics of both, starting with the slightly older TensorFlow. Let's start!
What is Tensorflow?
A Google project called TensorFlow was released as open-source in 2015. The program originated from Google's own machine learning software, which was refactored and optimized for production use. TensorFlow is a way of organizing and operating data. TensorFlow and PyTorch both use tensors as their basic data structure. TensorFlow operates on the data stored in these tensors by creating a stateful dataflow graph, which is similar to a flowchart that remembers previous events.
Who uses Tensorflow?
The TensorFlow deep learning library is known for its production-grade capabilities. There is a huge, active user base and an abundance of official and third-party tools that can be used for training, deploying, and serving models.
In 2016, PyTorch was launched, which led to TensorFlow's decline in popularity. The machine learning community was renewed interest in TensorFlow 2.0, which simplifies and makes the library more user-friendly.
Code Styles And Function
Previously, TensorFlow's abstract syntax tree (the graph) was stitched together manually through tf.* API calls. The model was then manually compiled by passing input and output tensors to session.run().
It is possible to run TensorFlow operations using a Session object. In addition to evaluating Tensor objects and executing Operation objects, it can own resources such as tf.Variable objects. Context management is the most common use of a Session.
This method is still supported in TensorFlow 2.0, but eager execution is easier, which is how Python normally works. Python control flow can be used instead of graph control flow with eager execution since it evaluates operations immediately.
Let's see how each method multiplies two tensors to see how they differ. Using TensorFlow 1.0, here's an example:
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
x = tf.compat.v1.placeholder(tf.float32, name = "x")
y = tf.compat.v1.placeholder(tf.float32, name = "y")
multiply = tf.multiply(x, y)
with tf.compat.v1.Session() as session:
m = session.run(
multiply, feed_dict={x: [[2., 4., 6.]], y: [[1.], [3.], [5.]]}
)
print(m)
To access TensorFlow 1.x methods and disable eager execution, this code uses TensorFlow 2.x's tf.compat API.
Using tf.compat.v1.placeholder tensor objects, you declare the input tensors x and y. Your next step is to define the operation you will perform on them. A feed_dict facilitates the multiplication by feeding real values into placeholders with the context manager tf.Session. Lastly, you print() the result still within the session.
You can achieve the same result with eager execution in TensorFlow 2.0 by calling tf.multiply():
>>> import tensorflow as tf
>>> x = [[2., 4., 6.]]
>>> y = [[1.], [3.], [5.]]
>>> m = tf.multiply(x, y)
>>> m
<tf.Tensor: shape=(3, 3), dtype=float32, numpy=
array([[ 2., 4., 6.],
[ 6., 12., 18.],
[10., 20., 30.]], dtype=float32)>
As you can see, you declare your tensors using Python list notation, and tf.multiply() performs the element-wise multiplication immediately when you call it.
Keras is the recommended way to use TensorFlow if you don't need or want to build low-level components. The APIs are simpler, prefabricated components are created for common use cases, and the error messages are better than in TensorFlow.
What Is Pytorch?
The first public release of PyTorch in 2016 was developed by Facebook. This tool was created to make writing models easier while offering similar production optimizations to TensorFlow.
Python programmers immediately found PyTorch so natural to use that it became the inspiration for TensorFlow's 2.0, which added many of PyTorch's most popular features.
Who Uses Pytorch?
Researchers tend to use PyTorch more than manufacturers. The use of PyTorch has increased dramatically since its release the year after TensorFlow.
TensorFlow and PyTorch are among the most popular "Other Frameworks, Libraries, and Tools" according to the 2020 Stack Overflow Developer Survey. According to 2018, TensorFlow had a percentage of 7.6 percent and PyTorch had a percentage of just 1.6 percent.
In computer science, PyTorch is a popular choice, and Stanford's program teaches deep learning using it.
Code Style And Function
PyTorch is a Python wrapper for Torch, which is written in C and is a framework for fast computation. Models can be built using Torch's Lua wrapper.
A Python interface is wrapped around the same C back end as PyTorch. But it's more than that. Models can be written easily for Python programmers because of the way developers built it. C and C++ code is used to run Python code at low levels. By integrating the two, you get:
Optimization and better memory
management Sensible error messages
More precise control over model structure
More transparent behavior of the model
Improved NumPy compatibility
As a result, neural network component modules can be written directly in Python without relying on low-level Python functions.
Due to Python's eager execution, which enables tensor operations to be evaluated immediately and dynamically, TensorFlow 2.0 looks similar to Python's PyTorch.
PyTorch's data structures include the capability to convert NumPy objects to tensors. This allows for easy switching from torch.Tensor objects to numpy.array objects.
>>> import torch
>>> import numpy as np
>>> x = np.array([[2., 4., 6.]])
>>> y = np.array([[1.], [3.], [5.]])
>>> m = torch.mul(torch.from_numpy(x), torch.from_numpy(y))
>>> m.numpy()
array([[ 2., 4., 6.],
[ 6., 12., 18.],
[10., 20., 30.]])
The torch.Tensor.numpy() function gives you the result of matrix multiplication as a numpy.array object.
Unlike numpy.array objects, torch.Tensor objects have different methods and attributes, such as backward(), which computes the gradient, and CUDA capability.
PyTorch vs TensorFlow Decision Guide
Which library to use depends on your own style and preference, your data and model, and your project goal. When you start your project with a little research on which library best supports these three factors, you will set yourself up for success!
Style
Python programmers will find PyTorch easy to use. The program works as expected, right out of the box.
In contrast, TensorFlow supports more coding languages than PyTorch, which has a C++ API. TensorFlow can also be used in JavaScript and Swift. For common use cases, Keras abstracts away many of the details so you can build TensorFlow models without worrying about a lot of low-level code.
Data and Model
How do you model your data? You should research what your pretrained model is compatible with if you wish to use it, such as BERT or DeepDream. It is possible to find pretrained models in only one or both libraries, and in others it is possible to find them in both. There are also good resources to check out in Model Garden, PyTorch, and TensorFlow hubs.
Are you looking for any specific data? Preprocessed data may already be available in one or both libraries if you need it.
Conclusion
This tutorial introduced PyTorch and TensorFlow, examined who uses them and what APIs they support, and explained how to decide between PyTorch and TensorFlow. There are a variety of programming languages, tools, datasets, and models that each supports, and you learned how to choose the one that is right for your style and project.
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