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PyTorch vs TensorFlow: Difference you need to know

Posted in Data Science
Pytorch and TensorFlow

Pytorch and TensorFlow are two of the most popular Python libraries for machine learning, and both are highly celebrated. However, for the newbie machine learning and artificial intelligence practitioner, it can be difficult to know which to pick. You might not even know the differences — and without that, you can’t make an informed decision. 

Here, we examine the Pytorch vs TensorFlow debate, which includes covering what they are exactly, the differences between them, and a concise head-to-head comparison summarizing both. With this knowledge, you’ll be able to answer the question of whether PyTorch is better than TensorFlow or vice versa.

Before we begin, here’s a quick head-to-head comparison of the differences between PyTorch and TensorFlow.

PyTorch vs TensorFlow: Head-to-Head Comparison

Features

PyTorch

TensorFlow

Developed By

Facebook

Google

Graphs

Dynamic

Static

Distinguishing Feature

Highly Pythonic

Easy to develop models

Learning Curve

Easy

Steep

Community

Comparatively small

Large

Device Deployment

Comparatively less support

Comparatively more support

Debugging

Dynamic computational process

Requires the TensorFlow debugger tool

Projects

cheXNet, PYRO, Horizon

Magenta, Sonnet, Ludwig

What is PyTorch?

Based on the Torch library, PyTorch is an open-source machine learning library. PyTorch was developed by Facebook’s AI Research lab, with the first release taking place in 2016. While Python is the most popular choice, PyTorch also has a C++ and CUDA interface. 

PyTorch is imperative, which means computations run immediately, thus users needn’t write the full code to check if it works. It allows you to efficiently run a part of the code and inspect it in real-time. 

The library is python-based and is built to provide flexibility as a deep learning development platform. 

The following features make a case for PyTorch as a deep learning development tool: 

  • Easy to use API 
  • Python support: PyTorch smoothly integrates with the python data science stack. It is similar to NumPy, so if you’re familiar with that, you will feel right at home.
  • Dynamic computation graphs: PyTorch provides a framework to build computational graphs as we go, and even change them during runtime instead of predefined graphs with specific functionalities. This service is valuable in scenarios where the memory requirements for creating a neural network are unknown.
  • TorchScript: Provides a seamless transition between graph mode and eager mode to accelerate the path to production.
  • Distributed training: It has a distributed backend. Torch enables performance optimization in research and production and scalable distributed training. 
  • Tools and libraries: A vibrant ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP, and more.
  • Companies that use PyTorch include Facebook, Microsoft, SalesForce, and JPMorganChase.

What is TensorFlow?

Google’s TensorFlow is a famous open-source deep-learning library for dataflow and differentiable programming across a range of tasks. Initially designed by Google’s Google Brain department for internal use, the platform has since gone on to be used by some of the world’s biggest companies, including Airbnb, Coca-Cola, Intel, and Twitter. The initial version was released in 2015.

TensorFlow is also a symbolic math library, with machine learning applications such as neural networks using it. Research and production are the primary use cases. 

The following are the most prominent features of TensorFlow: 

  • Secure model building: Using intuitive high-level APIs such as Keras, the library allows us to build and train ML models with quick model iteration and easy debugging. Check out our blog post to get a detailed comparison between Keras vs TensorFlow.
  • ML production anywhere: Trains and deploys models in the cloud, on-premises, in the browser, or on-device irrespective of the language the user uses.
  • Robust experimentation for research: A flexible and straightforward architecture to take new ideas from concept to code, including for state-of-the-art models, resulting in faster publishing.

The Components of TensorFlow

There are two major components of TensorFlow that are worth going over before we head to the difference between PyTorch and TensorFlow.

  • Tensor: Tensor is the core framework of the library that is responsible for all computations in TensorFlow. A tensor is a vector or matrix of n-dimensions that represents all types of data. 
  • Graph: TensorFlow uses the Graph framework. During the training, the graph gathers and describes all the series computations. This offers advantages such as running multiple CPUs or GPUs, and even mobile operating systems.

PyTorch vs TensorFlow: The Differences

Now that we have a basic idea of what TensorFlow and PyTorch are, let’s look at the difference between the two.

1. Original Developers

TensorFlow was developed by Google and is based on Theano (Python library), while PyTorch was developed by Facebook using the Torch library. 

2. Computational Graph Construction 

TensorFlow works on a static graph concept, which means the user has to first define the computation graph of the model and then run the ML model. PyTorch takes a dynamic graph approach that allows defining/manipulating the graph on the go. 

PyTorch offers an advantage with its dynamic nature of graph creation. These are built by interpreting the line of code corresponding to that particular aspect of the graph. However, in the case of TensorFlow, the construction is static - and the graph is required to go through compilation, after which it is executed.

3. Debugging

import numpy as np
import tensorflow as tf
import tensorflow.python.debug as tf_debug
xs = np.linspace(-0.5, 0.49, 100)
x = tf.placeholder(tf.float32, shape=[None], name="x")
y = tf.placeholder(tf.float32, shape=[None], name="y")
k = tf.Variable([0.0], name="k")
y_hat = tf.multiply(k, x, name="y_hat")
sse = tf.reduce_sum((y - y_hat) * (y - y_hat), name="sse")
train_op = tf.train.GradientDescentOptimizer(learning_rate=0.02).minimize(sse)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess = tf_debug.LocalCLIDebugWrapperSession(sess)
for _ in range(10):
sess.run(train_op, feed_dict={x: xs, y: 42 * xs})​

The TensorFlow debugger working on a linear equation through gradient descent

With PyTorch, the user does not need to learn another debugger as it uses a standard python debugger. For TensorFlow, debugging can be done in two ways: learn the TF debugger or request variables from the session. 

4. Features

# tensor 't' is [[[[ 0, 1, 2, 3],
# [ 4, 5, 6, 7],
# [ 8, 9, 10, 11]],
# [[12, 13, 14, 15],
# [16, 17, 18, 19],
# [20, 21, 22, 23]]]]
# tensor 't' shape is [1, 2, 3, 4]
# 'dims' is [3] or 'dims' is [-1]
reverse(t, dims) ==> [[[[ 3, 2, 1, 0],
[ 7, 6, 5, 4],
[ 11, 10, 9, 8]],
[[15, 14, 13, 12],
[19, 18, 17, 16],
[23, 22, 21, 20]]]]
# 'dims' is '[1]' (or 'dims' is '[-3]')
reverse(t, dims) ==> [[[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]
[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]]]]
# 'dims' is '[2]' (or 'dims' is '[-2]')
reverse(t, dims) ==> [[[[8, 9, 10, 11],
[4, 5, 6, 7],
[0, 1, 2, 3]]
[[20, 21, 22, 23],
[16, 17, 18, 19],
[12, 13, 14, 15]]]]​

Code to reverse the specific dimensions of a tensor

TensorFlow supports a higher level of functionality and offers a broad spectrum of options to work with by providing operations like: 

  • Flipping a tensor along with dimension.
  • Checking the Tensor for infinity and NaN.
  • Support for fast Fourier transforms.
  • Uses a package named contrib for the creation of models.

 PyTorch, on the other hand, has comparatively fewer features.

5. Serialization 

PyTorch serves a simple API that saves all the weights of the model or pickles the entire class.

TensorFlow also allows the entire graph to be saved as a protocol buffer, including parameters and operations. Other supported languages, such as C++ and Java, can load the graph; this is critical for deployment stacks where Python is not offered. It is also useful when the user changes the model source code and wants to run old models. 

6. Deployment 

Both ML frameworks are easy to wrap in case of small-scale server-side deployments. TensorFlow works well for mobile and embedded deployments. However, deploying to Android or iOS does require a non-trivial amount of work. Another noticeable feature that TensorFlow serves is that the models cannot be hot-swapped easily without bringing the service down. 

7. Visualization

An integration of TensorBoard with PyTorch

 Visualization plays a crucial role while presenting any project in an organization. TensorBoard visualizes machine learning models in TensorFlow, which helps during the training of the model and spots the errors quickly. TensorBoard is the real-time representation of the graphs of a model that depicts the graph representation and shows the accurate graphs in real-time. 

PyTorch also integrates with TensorBoard.

8. Device Management 

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 be on the GPU if one is available. TensorFlow does have a downside when it comes to device management, in that even if only one GPU is in use, it still consumes memory on all available GPUs. 

In contrast, PyTorch requires the user to move everything onto the device if CUDA is enabled explicitly. Also, in PyTorch, the code requires frequent checks for CUDA availability. 

9. Ecosystems

Large ecosystems have been built around both PyTorch and TensorFlow, and this one is close to call. TensorFlow has such things as the Model Garden, which makes available the source for SOTA models. It also has Vertex AI, TensorFlow.js, Colab, and Playground.

PyTorch has Hub, TorchVision, TorchText, TorchAudio, SpeechBrain, and many others — all popular toolkits. It also has PyTorch Lightning.

For the most part, we’d say this is equal, but TensorFlow does have a wide variety and the benefits of integrating with Google.

10. Available Models

PyTorch and TensorFlow both have their own set of available models, and this will factor into your consideration. For something like HuggingFace, you’ll notice that a great majority of models are exclusive to PyTorch. The same goes for research papers.

In this regard, PyTorch wins over TensorFlow, though this may not stay the same in the future. There are some developments happening in this regard, but PyTorch takes this one handily.

Conclusion: TensorFlow or PyTorch?

Both TensorFlow and PyTorch are useful and have a supportive community behind them. Their respective machine learning libraries are also both capable of executing various tasks. 

Now, choosing TensorFlow or PyTorch will come down to your own skill and needs. The former is a more mature platform that offers some advanced features. However, PyTorch is easy to learn and very Python-friendly. Ultimately, it really comes down to personal preference.

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The course will teach you how to develop deep learning models using Pytorch.

Frequently Asked Questions

1. Is PyTorch better than TensorFlow?

There’s no clear-cut answer to this question. They both have their strengths — for example, TensorFlow offers better visualization, but PyTorch is more Pythonic.

2. Which is better for deep learning: PyTorch or TensorFlow?

TensorFlow and PyTorch are both strong choices for deep learning. There is no clear-cut answer; it depends on the requirements of your project. 

3. Is PyTorch faster than TensorFlow?

This is dependent on several factors. Benchmarks have shown both PyTorch and TensorFlow as outperforming each other, depending on the computation.

4. Is PyTorch harder than TensorFlow?

No, PyTorch is much easier to get started with. Beginners are recommended to start with PyTorch.

5. Does Tesla use PyTorch or TensorFlow?

Tesla uses PyTorch for its self-driving implementation.

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Simran Kaur Arora

Simran Kaur Arora

Simran works at Hackr as a technical writer. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Traveling, sketching, and gardening are the hobbies that interest her. View all posts by the Author

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