Tensorflow and pytorch

Tensorflow and pytorch DEFAULT

Disney, Blue River Technology and Datarock preferred PyTorch over Google’s TensorFlow deep learning framework because of its relative ease of use.

Disney has been using PyTorch since 2019 to recognize characters’ faces in its cartoons. (credit: Disney) Deep learning is a sub-category of machine learning, which uses neural networks to automate historically complex tasks. Examples are image recognition and natural language processing. TensorFlow was released in 2015 by Google and has been widely used in research and manufacturing. But PyTorch, which left Facebook in 2016, quickly caught up thanks to community-made improvements in terms of ease of use and deployment and its ability to cover a wider range of scenarios.

PyTorch has found applications in autonomous driving systems like Tesla and Lyft Level 5, where it’s been adopted particularly widely. The framework is also used to categorize and recommend content in media companies or to operate robots in industrial applications. 

Disney: Identifying Faces in Animated Film and Cartoons

Since 2012, engineers and data scientists at media giant Disney have been working to build what the company calls the “Content Genome,” a knowledge graph that pulls together content data to power machine learning-based search and personalization applications across Disney’s huge content library. Disney invested in a huge amount of content annotation work. They asked their data scientists to develop deep learning algorithms to identify millions of images of people, characters, and locations.

To start, Disney engineers tested different frameworks, including TensorFlow. But ultimately, in 2019, their choice fell on PyTorch. . After dealing with hundreds of faces, the new model has already been able to identify faces in all three types of use. Also, the object detector has been in production since January 2020.

To process such a large amount of video data to train and run the model in parallel, engineers wanted Expensive, high-performance GPUs to speed up training and updating models. 

Additionally, the distribution of the results to Disney groups was sped up, and processing time for a feature film went from an hour to five or ten minutes. It is also made very simple for the engineering team since the adoption of PyTorch was simple and fast. Besides, Disney engineers were helped by the very active PyTorch community when they encountered certain issues or bottlenecks using PyTorch.

Blue River Technology: Robotic Weed Killers

The amazing robot developed by Blue River Technology combines digital tracking, integrated cameras and computer vision to spray weeds with a herbicide in near-real time without damaging crops. It allows farmers to save on the high price of herbicide while at the same time conserving the environment. Cotton plants can be a challenge, as they can sometimes be mistaken for weeds.

Agronomists have trained and tagged the images with PyTorch and have used the tagged images to train a CNN. 

What are the differences between TensorFlow and PyTorch?

Analyzing TensorFlow vs. PyTorch

In general, a simple Neural Network has three layers. The input is provided to the Embedding Layer, the Global Average Pooling Layer provides the output, and the predictions are the Dense Layer’s output.

TensorFlow

TensorFlow models are generally created by using Keras. Keras is used because Keras is an open-source library that, unlike TensorFlow, mostly uses high-level APIs.

Subclassing, Functional API or Sequential model API can be used to develop models in Keras.

Subclassing – the Keras. Model class, which is more extended than other classes, enables you to develop completely customizable models and in which the forward pass is implemented in the call method. In contrast, the layers are defined in the _init_ method.

Functional API – it is a very user-friendly approach compared to Subclassing as the developers’ community recommends. This approach requires less coding as the previous layer’s input is passed on as soon as the layer is defined. The model is instantiated through input and output tensor(s).

Sequential model API is a shortcut to a trainable model with only a few common layers, and thus it is a compact way to define a model. On the other hand, this approach performs extremely well when it comes to creating simple Neural Networks, but complex Neural Networks become very hard,

Python

In comparison to TensorFlow or Keras, PyTorch only supports two modeling approaches: sequential and subclassing.

Subclassing – subclassing is done with this approach, much like TensorFlow (Keras). The layers are defined with the _init_() method, but the forward pass is created with the forward method instead of TensorFlow’s call (Keras). There is a need in PyTorch to have the exact kernel size so that it can be used as global average-pooling since there is only one average-pooling layer available.

Sequential – also very much similar to how it is done in TensorFlow (Keras) and done through the Sequential module.

Subclassing approach instead of Sequential approach is widely recommended for many recurrent layers, viz. RNN, LSTM that cannot be used with an.Sequential in PyTorch.

Datarock also stated a 4x increase in inference performance with PyTorch and Detectron2 when running the GPUs’ models and a 3x increase on CPUs.

Truong has mentioned the Python ecosystem’s growing community, well-designed interface, simplicity, and better debugging as reasons for switching to PyTorch and noted that although the interfaces are quite different, TensorFlow knowledge is quite sufficient to switch, especially if you know Python.”

Sours: https://triassicsolutions.com/why-companies-are-switching-from-tensorflow-to-pytorch/

Pytorch vs Tensorflow 2021

Tensorflow/Keras & Pytorch are by far the 2 most popular major machine learning libraries. Tensorflow is maintained and released by Google while Pytorch is maintained and released by Facebook. In this article, I want to compare them in terms of:

  • What's new in the latest released versions
  • Which one to use & why (based on 2 years of doing ML projects)

Tensorflow 2.x:

There are multiple changes between Tensorflow 1 and Tensorflow 2.x, I am going to try to pinpoint the most important ones.

The first one is the release of Tensorflow.js. With web applications being more and more dominant, the need for deploying models on browsers has grown quite a lot. With Tensorflow.js you can run existing python models in the browser using Node, retrain existing models, and build & train your own models fully using Javascript (you don’t need python).

The other release in Tensorflow 2.x is Tensorflow Lite, a “Lightweight library to deploy models on mobile and embedded devices”. This makes sense since mobile & web applications are the 2 most dominant types of applications. With Tensorflow Lite you can simply convert your existing models into a “compressed flat buffer” and load that buffer into a mobile or any other embedded device. The main optimization process that happens is converting the 32-bit floats into 8-bits which is better suited for embedded devices (less memory usage).

The final major release is Tensorflow Extended (TFX) which is an end-to-end platform for deploying production ML pipelines. I have to admit they have done a good job pinpointing the 3 most significant areas in machine learning (web apps, mobile apps & production management). ML production pipelines still need a lot of research and development. TFX helps with the classic software production challenges such as scalability, maintainability, and modularity. Moreover, it helps with machine learning specific challenges such as continuous online learning, data validation, data management, and much more.

Pytorch 1.8:

Similar to Tensorflow Lite, Pytorch has also improved their existing Pytorch Mobile. A framework quantizes, traces, optimizes, and saves models for both Android and iOS. They have also released a prototype of the Pytorch Lite Interpreter which reduces the binary runtime size on mobile devices.

Furthermore, they have added more support for distributed training with more concrete error handling and pipeline parallelism. Lastly, they introduced Pytorch Profiler which is a tool for debugging and troubleshooting large-scale deep learning models.

Although Pytorch lightning isn’t part of Pytorch 1.8, I think it's worth mentioning. Pytorch lightning has been released to make coding neural networks much simpler. You can think of it as the Keras of Pytorch. It has been getting a lot of traction. The reason I thought it was worth mentioning is that Keras has always improved Tensorflow significantly as it makes implementing models much easier and shorter. Pytorch lightning does the same thing to Pytorch.

Which one to use?

The next question always on everyone’s mind is which one to use. I have been wondering about this question each time I have started a machine learning project over the last 2 years. I am going to give you a quick reflection of my experience along with tips on how to choose between them.

Essentially, the 2 libraries are quite good, they are very close in performance and in the features that they provide. Overall, you have to realize that there is a difference in the coding style between the 2 and this is what actually makes a difference when you are working on a machine learning project.

Pytorch is quite famous for its OOP (Object Oriented Programming) style. For instance, when you are creating a custom model or a custom dataset, you will most likely create a new class that inherits the default PyTorch libraries and then adapt your own methods. Personally, I am not a big fan of OOP. Although it provides a structure somehow to the code, it makes the implementations much longer in terms of the number of lines of code.

On the other hand, when you are using Tensorflow, you will most likely be using Keras. I have almost always found Keras's implementation to be shorter than Pytorch’s when doing Kaggle competitions (supervised image classification, object detection, image segmentation, NLP, etc…). Which is very nice to have as a beginner/intermediate since you don't have to spend a lot of time reading and breaking down lines of code.

In some scenarios, you will be looking for quite a specific model within a specific machine learning subfield. One very helpful tip from my experience is that in such a scenario you will most likely find more support for that model in only one of the libraries. Simply because it was firstly implemented there and tutorials just piled on from that first implementation. In that scenario just the library with more support because it is going to make your life easier. For instance, when I was doing an object detection competition and I wanted to implement DETR (Facebook’s Data-Efficient transformer), most of the resources that I have found were written in Pytorch (obviously) and thus it was just much easier to use Pytorch in that case.

Also, one final reflection is that I have found that Pytorch’s implementations are longer because they seem to cover a lot of low-level details. This is both an advantage and a disadvantage. It's a pro because when you are a beginner, it’s better to learn those low-level details and then move to a more high-level API such as Keras. However, it is a con because you will find yourself being lost in a lot of details and fairly long pieces of code. So essentially, if you are working on a tight deadline, it’s better to go with Keras over Pytorch.

Final Thoughts

I hope you have liked this article and found my tips helpful. I know there might be some disagreements about them, but that is simply because people have had different experiences with those 2 libraries. But, I have always found beginners/intermediates to have had quite similar experiences with those 2 libraries. Since the 2 libraries are quite similar in performance, I think the library of choice is likely to be just a simple case of preference.

If you want to receive regular paper reviews about the latest papers in AI & Machine learning, add your email here & Subscribe!

https://artisanal-motivator-8249.ck.page/5524b8f934

Sours: https://towardsdatascience.com/pytorch-vs-tensorflow-2021-d403504d7bc3
  1. Cadillac coupe 2006
  2. E3s text guide
  3. Your precious bouquet
  4. N64 emulator 2020

PyTorch and Tensorflow are among the most popular libraries for deep learning, which is a subfield of machine learning. Similarly to the way human brains process information, deep learning structures algorithms into layers creating deep artificial neural networks, which it can learn and make decisions on its own.

This article invites you to take a closer look at these deep learning frameworks to understand their features, key differences and how to choose between PyTorch and TensorFlow.

Table of contents

What is PyTorch?
What is TensorFlow?
PyTorch vs TensorFlow
    ➤  Mechanism: Graph Definition
    ➤  Model Deployment
    ➤  Visualization
    ➤  Debugging
    ➤  Comparison Table
PyTorch vs TensorFlow: how to choose?
Conclusion

What is PyTorch?

PyTorch is a machine learning library that Facebook AI Research Lab developed. It was first introduced in 2016 and is distributed on the BSD license as free, open-source software.

As previously explained in one of our articles, Python has a vast number of Data Science packages. PyTorch has a Python interface, and its functionalities are built as Python classes, thus being part of the Python package ecosystem. Being a Python-based library makes it easier to extend its functionalities with other Python libraries, such as SciPy and NumPy. However, PyTorch users can also program in C/C++ since the library shares some C++ backend with Torch, the deep learning framework.

What is TensorFlow?

TensorFlow is an open-source machine learning library created by the Google Brain team. Its initial release was in 2015, and it is written in Python, C++, and CUDA.

Similarly to PyTorch, TensorFlow also has a high focus on deep neural networks and enables the user to create and combine different types of deep learning models and generate graphs of the model’s performance during training. Even though it is a Python library, in 2017, TensorFlow additionally introduced an R interface for the RStudio.

PyTorch vs TensorFlow

Both PyTorch and Tensorflow are very popular frameworks regarding the application of neural networks. In fact, they are often considered by project managers and data scientists the go-to libraries when handling the development of innovative deep learning applications or even research.

PyTorch vs TensorFlow Popularity

Further, PyTorch and Tensorflow still have (some) distinct ways to operate, even though they used to have a lot more differences! Over time, they have become so competitive that pretty much all the good features are currently included in both frameworks.

Mechanism: Graph Definition

Graphs are used to describe computations. A graph is a data structure that consists of nodes and edges. During the training process of deep neural networks, computation graphs store the activations of the neural network during a forward pass. A back-propagation step then uses the computation graphs to calculate updates to each of the network's weights, thus training the network.

To understand their differences, let's first understand what PyTorch and TensorFlow have in common regarding their graph definition. First, they both view any model as a DAG (Directed Acyclic Graph) and operate on tensors. Tensors describe the relationships between sets of objects to a vector space. Nonetheless, they have very distinct graph definitions.

TensorFlow allows the user to perform operations on tensors by creating a stateful dataflow graph. The computation graph is defined statically before the model can run.

Contrarily, PyTorch is more dynamic and enables the user to execute the nodes as the model runs. In other words, the computation graph is created at every point of execution, and it is possible to perform modifications in the graph during run-time.

For this reason, PyTorch is often prefered in research since it is more suitable to create custom models, and being dynamic might make it easier to interact with the models' internals.

However, in 2019, TensorFlow 2.0 also introduced dynamic graphs, allowing operations to be evaluated at run-time without generating a graph to be run later. Therefore, what used to be one of the major differences between both libraries, is not as significant considering that the user can operate dynamically or statically on both frameworks.

Model Deployment

In 2020, PyTorch introduced TorchServe, which is a model deployment tool. This tool provides the basic set of features, such as metrics, an API endpoint specification, a model archiver tool, and so on.

In turn, TensorFlow has the TensorFlow Serving, which is a built-in model deployment tool used to deploy machine learning models as well as gRPC servers. Plus, it also enables remote access to the gRPC servers. Overall, TensorFlow Serving allows the user to deploy new algorithms while preserving the same server architecture and APIs. This tool has been tested on many Google projects, and it is designed for production environments.

Visualization

PyTorch and TensorFlow support visualization tools, which facilitates debugging and allows the user to visualize the results quickly and have a broad view of the model's training process.

On the one hand, PyTorch does not necessarily have a dedicated visualization tool, but it does have Visdom, a minimalistic visualization tool. Visdom can be used with Numpy or PyTorch. It provides limited basic features, but it is also reasonably easy to use, flexible and supports PyTorch tensors.

On the other hand, TensorFlow has Tensorboard, which offers a suite of apps that allow the user to comprehend the deep learning model through five different visualizations: (1) graphs; (2) audio; (3) images; (4)distributions and histograms; (5) scalars.

All in all, Tensorboard is considered a more versatile visualization tool than Visdom, which is why PyTorch 1.2.0 version has made it possible to integrate Tensorboard as well.

Debugging

Debugging in Python can be performed with Python's standard debuggers (e.g., PyCharm debugger and pdb). In fact, since PyTorch defines graphs dynamically at run time, most Python tools can easily be integrated.

It is more complex than PyTorch to debug the TensorFlow model code. In this case, the user must learn the library's debugger - tfdbg - as well as the requested variables from a session.

Comparison table

FrameworkPyTorchTensorFlow

Origin

PyTorch is a machine learning library that Facebook AI Research Lab developed.

TensorFlow is an open-source machine learning library created by the Google Brain team.

Mechanism: Graph Definition

Dynamic Graphs - enables the user to execute the nodes as the model runs.

Stateful dataflow graph - However, in 2019, TensorFlow 2.0 also introduced dynamic graphs.

Model Deployment

In 2020 PyTorch introduced TorchServe.

TensorFlow has the TensorFlow Serving, which is a built-in model deployment tool used to deploy machine learning models as well as gRPC servers.

Visualization

Visdom - PyTorch 1.2.0 version has made it possible to integrate Tensorboard as well.

Tensorboard - offers a suite of apps that allow the user to comprehend the deep learning model through five different visualizations.

Debugging

Python's standard debuggers - e.g., PyCharm debugger and pdb.

tfdbg - the user must learn the library's debugger.

PyTorch vs TensorFlow: how to choose?

PyTorch and TensorFlow are both incredible tools; otherwise, they would not be so popular. In fact, they have made so many improvements over the years that choosing between both has never been more challenging.

When it comes to deciding between PyTorch and TensorFlow, it is first crucial to have a vast understanding of how neural networks will help build better deep learning models for specific purposes. The tools will follow that knowledge and not the other way around.

A useful and very simple approach to choose between both tools is that if the user is accustomed to using Python as a programming language, then PyTorch is a good choice since it is very Python friendly.

PyTorch has gained a lot of popularity among research-oriented developers, supporting dynamic training. It is also an excellent choice for a more straightforward debugging experience.

TensorFlow provides various options for high-level model development and is usually considered a more mature library than PyTorch. Moreover, this framework offers support for mobile platforms.

In terms of community, both frameworks are well organized and provide comprehensive documentation with many resources and free tutorials.

Conclusion

PyTorch and TensorFlow are both aiming for excellence regarding deep neural networks. Both frameworks have continuously been improving and copying each other's top features - making it very challenging to choose which one is better.

While TensorFlow is considered a more mature library; PyTorch, has also proved to be incredibly powerful. Usually, Python enthusiasts prefer PyTorch, but it has mostly gained popularity in the research field, while TensorFlow is more often associated with building Artificial Intelligence products.

Grow your revenue and user engagement by running a UX Audit! - Book a call

Found this article useful? You might like these ones too!

Sours: https://www.imaginarycloud.com/blog/pytorch-vs-tensorflow/
PyTorch and Keras TensorFlow for the Next 3 Years, Reflections from GTC 2021

PyTorch vs TensorFlow — spotting the difference

The best way to compare two frameworks is to code something up in both of them. I’ve written a companion jupyter notebook for this post and you can get it here. All code will be provided in the post too.

First, let’s code a simple approximator for the following function in both frameworks:

We will try to find unknown parameter phi given data x and function values f(x). Yes, using stochastic gradient descent for this is an overkill and analytical solution may be found easily, but this problem will serve our purpose well as a simple example.

We will solve this with PyTorch first:

If you have some experience in deep learning frameworks you may have noticed that we are implementing gradient descent by hand. Not very convenient, huh? Gladly, PyTorch has module which contains implementations of popular optimization algorithms such as RMSProp or Adam. We will use SGD with momentum

As you can see, we quickly inferred true exponent from training data. And now let’s go on with TensorFlow:

As you can see, implementation in TensorFlow works too (surprisingly 🙃). It took more iterations to recover the exponent, but I am sure that the cause is I did not fiddle with optimiser’s parameters enough to reach comparable results.

Now we are ready to explore some differences.

Currently, TensorFlow is considered as a to-go tool by many researchers and industry professionals. The framework is well documented and if the documentation will not suffice there are many extremely well-written tutorials on the internet. You can find hundreds of implemented and trained models on github, start here.

PyTorch is relatively new compared to its competitor (and is still in beta), but it is quickly getting its momentum. Documentation and official tutorials are also nice. PyTorch also include several implementations of popular computer vision architectures which are super-easy to use.

Both frameworks operate on tensors and view any model as a directed acyclic graph (DAG), but they differ drastically on how you can define them.

TensorFlow follows ‘data as code and code is data’ idiom. In TensorFlow you define graph statically before a model can run. All communication with outer world is performed via object and which are tensors that will be substituted by external data at runtime.

In PyTorch things are way more imperative and dynamic: you can define, change and execute nodes as you go, no special session interfaces or placeholders. Overall, the framework is more tightly integrated with Python language and feels more native most of the times. When you write in TensorFlow sometimes you feel that your model is behind a brick wall with several tiny holes to communicate over. Anyways, this still sounds like a matter of taste more or less.

However, those approaches differ not only in a software engineering perspective: there are several dynamic neural network architectures that can benefit from the dynamic approach. Recall RNNs: with static graphs, the input sequence length will stay constant. This means that if you develop a sentiment analysis model for English sentences you must fix the sentence length to some maximum value and pad all smaller sequences with zeros. Not too convenient, huh. And you will get more problems in the domain of recursive RNNs and tree-RNNs. Currently Tensorflow has limited support for dynamic inputs via Tensorflow Fold. PyTorch has it by-default.

Since computation graph in PyTorch is defined at runtime you can use our favorite Python debugging tools such as pdb, ipdb, PyCharm debugger or old trusty print statements.

This is not the case with TensorFlow. You have an option to use a special tool called tfdbg which allows to evaluate tensorflow expressions at runtime and browse all tensors and operations in session scope. Of course, you won’t be able to debug any python code with it, so it will be necessary to use pdb separately.

Tensorboard is awesome when it comes to visualization 😎. This tool comes with TensorFlow and it is very useful for debugging and comparison of different training runs. For example, consider you trained a model, then tuned some hyperparameters and trained it again. Both runs can be displayed at Tensorboard simultaneously to indicate possible differences. Tensorboard can:

  • Display model graph
  • Plot scalar variables
  • Visualize distributions and histograms
  • Visualize images
  • Visualize embeddings
  • Play audio

Tensorboard can display various summaries which can be collected via module. We will define summary operations for our toy exponent example and use to save them to disk.

To launch Tensorboard execute. This tool is very convenient to use on cloud instances since it is a webapp.

Tensorboard competitor from the PyTorch side is visdom. It is not as feature-complete, but a bit more convenient to use. Also, integrations with Tensorboard do exist. Also, you are free to use standard plotting tools — matplotlib and seaborn.

If we start talking about deployment TensorFlow is a clear winner for now: is has TensorFlow Serving which is a framework to deploy your models on a specialized gRPC server. Mobile is also supported.

When we switch back to PyTorch we may use Flask or another alternative to code up a REST API on top of the model. This could be done with TensorFlow models as well if gRPC is not a good match for your usecase. However, TensorFlow Serving may be a better option if performance is a concern.

Tensorflow also supports distributed training which PyTorch lacks for now.

One of the biggest features that distinguish PyTorch from TensorFlow is declarative data parallelism: you can use to wrap any module and it will be (almost magically) parallelized over batch dimension. This way you can leverage multiple GPUs with almost no effort.

On the other hand, TensorFlow allows you to fine tune every operation to be run on specific device. Nonetheless, defining parallelism is way more manual and requires careful thought. Consider the code that implements something like in TensorFlow:

def make_parallel(fn, num_gpus, **kwargs):
in_splits = {}
for k, v in kwargs.items():
in_splits[k] = tf.split(v, num_gpus)out_split = []
for i in range(num_gpus):
with tf.device(tf.DeviceSpec(device_type="GPU", device_index=i)):
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
out_split.append(fn(**{k : v[i] for k, v in in_splits.items()}))return tf.concat(out_split, axis=0)def model(a, b):
return a + bc = make_parallel(model, 2, a=a, b=b)

That being said, when using TensorFlow you can achive everything you can do in PyTorch, but with more effort (you have more control as a bonus).

Also it is worth noting that both frameworks support distributed execution and provide high level interfaces for defining clusters.

Let’s build a CNN classifier for handwritten digits. Now PyTorch will really start to look like a framework. Recall that a programming framework gives us useful abstractions in certain domain and a convenient way to use them to solve concrete problems. That is the essence that separates a framework from a library.

Here we introduce module which contains wrappers for popular datasets used to benchmark deep learning architectures. Also is used to build a custom convolutional neural network classifier. is a building block PyTorch gives us to create complex deep learning architectures. There are large amounts of ready to use modules in package that we can use as a base for our model. Notice how PyTorch uses object oriented approach to define basic building blocks and give us some 'rails' to move on while providing ability to extend functionality via subclassing.

Here goes a slightly modified version of https://github.com/pytorch/examples/blob/master/mnist/main.py:

Plain TensorFlow feels a lot more like a library rather than a framework: all operations are pretty low-level and you will need to write lots of boilerplate code even when you might not want to (let’s define those biases and weights again and again and …).

As the time as passed a whole ecosystem of high-level wrappers started to emerge around TensorFlow. Each of those aims to simplify the way you work with the library. Many of them are currently located at module (which is not considered a stable API) and some started to migrate to the main repository (see ).

So, you have a lot of freedom on how to use TensorFlow and what framework will suit the task best: TFLearn, tf.contrib.learn, Sonnet, Keras, plain , etc. To be honest, Keras deserves another post but is currently out of the scope of this comparison.

Here we will use and to build our CNN classifier. The code follows the official tutorial on tf.layers:

So, both TensorFlow and PyTorch provide useful abstractions to reduce amounts of boilerplate code and speed up model development. The main difference between them is that PyTorch may feel more “pythonic” and has an object-oriented approach while TensorFlow has several options from which you may choose.

Personally, I consider PyTorch to be more clear and developer-friendly. It’s gives you the ability to define reusable modules in an OOP manner and I find this approach very flexible and powerful. Later you can compose all kind of modules via (hi Keras ✋🏻). Also, you have all built-in modules in a functional form, which can be very convenient. Overall, all parts of the API play well together.

Of course, you can write very clean code in plain TensorFlow but it just takes more skill and trial-and-error before you get it. When it goes to higher-level frameworks such as Keras or TFLearn get ready to lose at least some of the flexibility TensorFlow has to offer.

TensorFlow is very powerful and mature deep learning library with strong visualization capabilities and several options to use for high-level model development. It has production-ready deployment options and support for mobile platforms. TensorFlow is a good option if you:

  • Develop models for production
  • Develop models which need to be deployed on mobile platforms
  • Want good community support and comprehensive documentation
  • Want rich learning resources in various forms (TensorFlow has an an entire MOOC)
  • Want or need to use Tensorboard
  • Need to use large-scale distributed model training

PyTorch is still a young framework which is getting momentum fast. You may find it a good fit if you:

  • Do research or your production non-functional requirements are not very demanding
  • Want better development and debugging experience
  • Love all things Pythonic

If you have the time the best advice would be to try both and see what fits your needs best.

If you liked this article, please leave a few 👏. It lets me know that I am helping.

PyTorch 1.0 will be released soon. It introduces lots of amazing features, including native C++ API, JIT compilation and ONNX integration. This means that you will be able to write production-ready services and do what TensorFlow Serving does. This is a big step to PyTorch and surely will empower its position as a fully featured framework for both research and production purposes.

Need help with TensorFlow or PyTorch? Contact us at [email protected]

Want to get regular stream of interesting resources on Data Science and Entrepreneurship?

Follow me on 🐦 Twitter and 📝Medium.

Sours: https://towardsdatascience.com/pytorch-vs-tensorflow-spotting-the-difference-25c75777377b

And pytorch tensorflow

PyTorch vs. TensorFlow: Which Framework Is Best for Your Deep Learning Project?

If you are reading this you've probably already started your journey into deep learning. If you are new to this field, in simple terms deep learning is an add-on to develop human-like computers to solve real-world problems with its special brain-like architectures called artificial neural networks. To help develop these architectures, tech giants like Google, Facebook and Uber have released various frameworks for the Python deep learning environment, making it easier for to learn, build and train diversified neural networks. In this article, we’ll take a look at two popular frameworks and compare them: PyTorch vs. TensorFlow. be comparing, in brief, the most used and relied Python frameworks TensorFlow and PyTorch.

Pytorch vs. Tensorflow: At a Glance

TensorFlow is a very powerful and mature deep learning library with strong visualization capabilities and several options to use for high-level model development. It has production-ready deployment options and support for mobile platforms. PyTorch, on the other hand, is still a young framework with stronger community movement and it's more Python friendly.

Table of Contents

  1. Google’s TensorFlow
  2. Facebook’s PyTorch
  3. What Can We Build With TensorFlow and PyTorch?
  4. Comparing PyTorch and TensorFlow
  5. Pros and Cons of PyTorch and TensorFlow
  6. PyTorch and TF Installation, Versions, Updates
  7. TensorFlow vs. PyTorch: My Recommendation

Google’s TensorFlow

TensorFlow is open source deep learning framework created by developers at Google and released in 2015. The official research is published in the paper “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.”

TensorFlow is now widely used by companies, startups, and business firms to automate things and develop new systems. It draws its reputation from its distributed training support, scalable production and deployment options, and support for various devices like Android.

Facebook’s PyTorch

PyTorch is one of the latest deep learning frameworks and was developed by the team at Facebook and open sourced on GitHub in 2017. You can read more about its development in the research paper "Automatic Differentiation in PyTorch."

PyTorch is gaining popularity for its simplicity, ease of use, dynamic computational graph and efficient memory usage, which we'll discuss in more detail later.

What can we build with TensorFlow and PyTorch?

Initially, neural networks were used to solve simple classification problems like handwritten digit recognition or identifying a car’s registration number using cameras. But thanks to the latest frameworks and NVIDIA’s high computational graphics processing units (GPU’s), we can train neural networks on terra bytes of data and solve far more complex problems. A few notable achievements include reaching state of the art performance on the IMAGENET dataset using convolutional neural networks implemented in both TensorFlow and PyTorch. The trained model can be used in different applications, such as object detection, image semantic segmentation and more.

Although the architecture of a neural network can be implemented on any of these frameworks, the result will not be the same. The training process has a lot of parameters that are framework dependent. For example, if you are training a dataset on PyTorch you can enhance the training process using GPU’s as they run on CUDA (a C++ backend). In TensorFlow you can access GPU’s but it uses its own inbuilt GPU acceleration, so the time to train these models will always vary based on the framework you choose.

Top TensorFlow Projects

Magenta: An open source research project exploring the role of machine learning as a tool in the creative process. (https://magenta.tensorflow.org/)

Sonnet: Sonnet is a library built on top of TensorFlow for building complex neural networks. (https://sonnet.dev/)

Ludwig: Ludwig is a toolbox to train and test deep learning models without the need to write code. (https://uber.github.io/ludwig/)

Top PyTorch Projects

CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. (https://stanfordmlgroup.github.io/projects/chexnet/)

PYRO: Pyro is a universal probabilistic programming language (PPL) written in Python and supported byPyTorch on the backend. (https://pyro.ai/)

Horizon:A platform for applied reinforcement learning (Applied RL) (https://horizonrl.com)

These are a few frameworks and projects that are built on top of TensorFlow and PyTorch. You can find more on Github and the official websites of TF and PyTorch.

Comparing PyTorch and TensorFlow

The key difference between PyTorch and TensorFlow is the way they execute code. Both frameworks work on the fundamental datatype tensor. You can imagine a tensor as a multi-dimensional array shown in the below picture.

tensor as multi-dimensional array

1. Mechanism: Dynamic vs Static graph definition

TensorFlow is a framework composed of two core building blocks:

  1. A library for defining computational graphs and runtime for executing such graphs on a variety of different hardware.
  2. A computational graph which has many advantages (but more on that in just a moment).

A computational graph is an abstract way of describing computations as a directed graph. A graph is a data structure consisting of nodes (vertices) and edges. It’s a set of vertices connected pairwise by directed edges.

When you run code in TensorFlow, the computation graphs are defined statically. All communication with the outer world is performed via object and , which are tensors that will be substituted by external data at runtime. For example, consider the following code snippet. 

pytorch code snippet

This is how a computational graph is generated in a static way before the code is run in TensorFlow. The core advantage of having a computational graph is allowing parallelism or dependency driving scheduling which makes training faster and more efficient.

tensorflow vs pytorch parallelism

Similar to TensorFlow, PyTorch has two core building  blocks:

  • Imperative and dynamic building of computational graphs.
  • Autograds: Performs automatic differentiation of the dynamic graphs.

As you can see in the animation below, the graphs change and execute nodes as you go with no special session interfaces or placeholders. Overall, the framework is more tightly integrated with the Python language and feels more native most of the time. Hence, PyTorch is more of a pythonic framework and TensorFlow feels like a completely new language.

pytorch graph

These differ a lot in the software fields based on the framework you use. TensorFlow provides a way of implementing dynamic graph using a library called TensorFlow Fold, but PyTorch has it inbuilt.

Stay Up Date on the Latest Data Science Trends

Sign up for free to get more Data Science stories like this.

2. Distributed Training

One main feature that distinguishes PyTorch from TensorFlow is data parallelism. PyTorch optimizes performance by taking advantage of native support for asynchronous execution from Python. In TensorFlow, you'll have to manually code and fine tune every operation to be run on a specific device to allow distributed training. However, you can replicate everything in TensorFlow from PyTorch but you need to put in more effort. Below is the code snippet explaining how simple it is to implement distributed training for a model in PyTorch.

distributed training pytorch

3. Visualization

When it comes to visualization of the training process, TensorFlow takes the lead. Visualization helps the developer track the training process and debug in a more convenient way. TenforFlow’s visualization library is called TensorBoard. PyTorch developers use Visdom, however, the features provided by Visdom are very minimalistic and limited, so TensorBoard scores a point in visualizing the training process.

Features of TensorBoard

  • Tracking and visualizing metrics such as loss and accuracy.
  • Visualizing the computational graph (ops and layers).
  • Viewing histograms of weights, biases or other tensors as they change over time.
  • Displaying images, text and audio data.
  • Profiling TensorFlow programs.
Visualising Training in TensorBoard

Features of Visdom

  • Handling callbacks.
  • Plotting graphs and details.
  • Managing environments.
Visualising Training in Visdom

4. Production Deployment

When it comes to deploying trained models to production, TensorFlow is the clear winner. We can directly deploy models in TensorFlow using TensorFlow serving which is a framework that usesREST Client API.

In PyTorch, these production deployments became easier to handle than in it’s latest 1.0 stable version, but it doesn't provide any framework to deploy models directly on to the web. You'll have to use either Flask or Django as the backend server. So, TensorFlow serving may be a better option if performance is a concern.

5. Defining a simple Neural Network in PyTorch and TensorFlow

Let's compare how we declare the neural network in PyTorch and TensorFlow.

In PyTorch, your neural network will be a class and using torch.nn package we import the necessary layers that are needed to build your architecture. All the layers are first declared in the method, and then in the method we define how input x is traversed to all the layers in the network. Lastly, we declare a variable model and assign it to the defined architecture ().

Defining a simple Neural Network in PyTorch

Recently Keras, a neural network framework which uses TensorFlow as the backend was merged into TF Repository. From then on the syntax of declaring layers in TensorFlow was similar to the syntax of Keras. First, we declare the variable and assign it to the type of architecture we will be declaring, in this case a “” architecture. Next, we directly add layers in a sequential manner using method. The type of layer can be imported from as shown in the code snippet below.

Defining a simple Neural Network in Tensorflow

Pros and Cons of PyTorch and TensorFlow

TEnsorFlow Pros:

  • Simple built-in high-level API.
  • Visualizing trainingwith Tensorboard.
  • Production-ready thanks to TensorFlow serving.
  • Easy mobile support.
  • Open source.
  • Good documentation and community support.

TensorFlow Cons:

  • Static graph.
  • Debugging method.
  • Hard to make quick changes.

PyTorch Pros:

  • Python-like coding.
  • Dynamic graph.
  • Easy & quick editing.
  • Good documentation and community support.
  • Open source.
  • Plenty of projects out there using PyTorch.

Pytorch Cons:

  • Third-party needed for visualization.
  • API server needed for production.

PyTorch and TF Installation, Versions, Updates

Recently PyTorch and TensorFlow released new versions, PyTorch 1.0 (the first stable version) and TensorFlow 2.0 (running on beta). Both these versions have major updates and new features that make the training process more efficient, smooth and powerful.

To install the latest version of these frameworks on your machine you can either build from source or install from pip

PyTorch Installation

macOS and Linux

Windows

TensorFlow Installation

macOS, Linux, and Windows

To check if you’re installation was successful, go to your command prompt or terminal and follow the below steps.

tensorflow-install

TensorFlow vs PyTorch: My REcommendation

TensorFlow is a very powerful and mature deep learning library with strong visualization capabilities and several options to use for high-level model development. It has production-ready deployment options and support for mobile platforms. PyTorch, on the other hand, is still a young framework with stronger community movement and it's more Python friendly.

What I would recommend is if you want to make things faster and build AI-related products, TensorFlow is a good choice. PyTorch is mostly recommended for research-oriented developers as it supports fast and dynamic training.

RelatedRead More About Data Science

Sours: https://builtin.com/data-science/pytorch-vs-tensorflow
Jeremy Howard: Deep Learning Frameworks - TensorFlow, PyTorch, fast.ai - AI Podcast Clips

.

You will also be interested:

.



477 478 479 480 481