A Promenade of PyTorch 23 minute read

For the past two years, I’ve been quite heavily invested in TensorFlow, either writing papers about it, giving talks on how to extend its backend or using it for my own deep learning research. As part of this journey, I’ve gotten quite a good sense of both TensorFlow’s strong points as well as weaknesses – or simply architectural decisions – that leave room for competition. That said, I have recently joined the PyTorch team at Facebook AI Research (FAIR), arguably TensorFlow’s biggest competitor to date, and currently much favored in the research community for reasons that will become apparent in subsequent paragraphs.

In this article, I want to provide a sweeping promenade of PyTorch (having given a tour of TensorFlow in another blog post), shedding some light on its raîson d’être and giving an overview of its API.

Overview and Philosophy

Let’s begin by reviewing what PyTorch is fundamentally, what programming model it imposes on its users and how it fits into the existing deep learning framework ecosystem:

PyTorch is, at its core, a Python library enabling GPU-accelerated tensor computation, similar to NumPy. On top of this, PyTorch provides a rich API for neural network applications.

PyTorch differentiates itself from other machine learning frameworks in that it does not use static computational graphs – defined once, ahead of time – like TensorFlow, Caffe2 or MXNet. Instead, PyTorch computation graphs are dynamic and defined by run. This means that each invocation of a PyTorch model’s layers defines a new computation graph, on the fly. The creation of this graph is implicit, in the sense that the library takes care of recording the flow of data through the program and linking function calls (nodes) together (via edges) into a computation graph.

Dynamic vs. Static Graphs

Let’s go into more detail about what I mean with static versus dynamic. Generally, in the majority of programming environments, adding two variables x and y representing numbers produces a value containing the result of that addition. For example, in Python:

In [ 1 ]: x = 4 In [ 2 ]: y = 2 In [ 3 ]: x + y Out [ 3 ]: 6

In TensorFlow, however, this is not the case. In TensorFlow, x and y would not be numbers directly, but would instead be handles to graph nodes representing those values, rather than explicitly containing them. Furthermore, and more importantly, adding x and y would not produce the value of the sum of these numbers, but would instead be a handle to a computation graph, which, only when executed, produces that value:

In [ 1 ]: import tensorflow as tf In [ 2 ]: x = tf . constant ( 4 ) In [ 3 ]: y = tf . constant ( 2 ) In [ 4 ]: x + y Out [ 4 ]: < tf . Tensor 'add:0' shape = () dtype = int32 >

As such, when we write TensorFlow code, we are in fact not programming, but metaprogramming – we write a program (our code) that creates a program (the TensorFlow computation graph). Naturally, the first programming model is much simpler than the second. It is much simpler to speak and think in terms of things that are than speak and think in terms of things that represent things that are.

PyTorch’s major advantage is that its execution model is much closer to the former than the latter. At its core, PyTorch is simply regular Python, with support for Tensor computation like NumPy, but with added GPU acceleration of Tensor operations and, most importantly, built-in automatic differentiation (AD). Since the majority of contemporary machine learning algorithms rely heavily on linear algebra datatypes (matrices and vectors) and use gradient information to improve their estimates, these two pillars of PyTorch are sufficient to enable arbitrary machine learning workloads.

Going back to the simple showcase above, we can see that programming in PyTorch resembles the natural “feeling” of Python:

In [ 1 ]: import torch In [ 2 ]: x = torch . ones ( 1 ) * 4 In [ 3 ]: y = torch . ones ( 1 ) * 2 In [ 4 ]: x + y Out [ 4 ]: 6 [ torch . FloatTensor of size 1 ]

PyTorch deviates from the basic intuition of programming in Python in one particular way: it records the execution of the running program. That is, PyTorch will silently “spy” on the operations you perform on its datatypes and, behind the scenes, construct – again – a computation graph. This computation graph is required for automatic differentiation, as it must walk the chain of operations that produced a value backwards in order to compute derivatives (for reverse mode AD). The way this computation graph, or rather the process of assembling this computation graph, differs notably from TensorFlow or MXNet, is that a new graph is constructed eagerly, on the fly, each time a fragment of code is evaluated. Conversely, in Tensorflow, a computation graph is constructed only once, by the metaprogram that is your code. Furthermore, while PyTorch will actually walk the graph backwards dynamically each time you ask for the derivative of a value, TensorFlow will simply inject additional nodes into the graph that (implicitly) calculate this derivative and are evaluated like all other nodes. This is where the distinction between dynamic and static graphs is most apparent.

The choice of using static or dynamic computation graphs severely impacts the ease of programming in one of these environments. The aspect it influences most severely is control flow. In a static graph environment, control flow must be represented as specialized nodes in the graph. For example, to enable branching, Tensorflow has a tf.cond() operation, which takes three subgraphs as input: a condition subgraph and two subgraphs for the if and else branches of the conditional. Similarly, loops must be represented in TensorFlow graphs as tf.while() operations, taking a condition and body subgraph as input. In a dynamic graph setting, all this is simplified. Since graphs are traced from Python code as it appears during each evaluation, control flow can be implemented natively in the language, using if clauses and while loops as you would for any other program. This turns awkward and unintuitive Tensorflow code:

import tensorflow as tf x = tf . constant ( 2 , shape = [ 2 , 2 ]) w = tf . while_loop ( lambda x : tf . reduce_sum ( x ) < 100 , lambda x : tf . nn . relu ( tf . square ( x )), [ x ])

into natural and intuitive PyTorch code:

import torch.nn from torch.autograd import Variable x = Variable ( torch . ones ([ 2 , 2 ]) * 2 ) while x . sum () < 100 : x = torch . nn . ReLU ()( x ** 2 )

The benefits of dynamic graphs from an ease-of-programming perspective reach far beyond this, of course. Simply being able to inspect intermediate values with print statements (as opposed to tf.Print() nodes) or a debugger is already a big plus. Of course, as much as dynamism can aid programmability, it can also harm performance and makes it more difficult to optimize graphs. The differences and tradeoffs between PyTorch and TensorFlow are thus much the same as the differences and tradeoffs between a dynamic, interpreted language like Python and a static, compiled language like C or C++. The former is easier and faster to work with, while the latter can be transformed into more optimized artifacts. The former is easier to use, while the latter is easier to analyze and (therefore) optimize. It is a tradeoff between flexibility and performance.

A Remark on PyTorch’s API

A general remark I want to make about PyTorch’s API, especially for neural network computation, compared to other libraries like TensorFlow or MXNet, is that it is quite batteries-included. As someone once remarked to me, TensorFlow’s API never really went beyond the “assembly level”, in the sense that it only ever provided the basic “assembly” instructions required to construct computational graphs (addition, multiplication, pointwise functions etc.), with a basically non-existent “standard library” for the most common kinds of program fragments people would eventually go on to repeat thousands of times. Instead, it relied on the community to build higher level APIs on top of TensorFlow.

And indeed, the community did build higher level APIs. Unfortunately, however, not just one such API, but about a dozen – concurrently. This means that on a bad day you could read five papers for your research and find the source code of each of these papers to use a different “frontend” to TensorFlow. These APIs typically have quite little in common, such that you would essentially have to learn 5 different frameworks, not just TensorFlow. A few of the most popular such APIs are:

PyTorch, on the other hand, already comes with the most common building blocks required for every-day deep learning research. It essentially has a “native” Keras-like API in its torch.nn package, allowing chaining of high-level neural network modules.

PyTorch’s Place in the Ecosystem

Having explained how PyTorch differs from static graph frameworks like MXNet, TensorFlow or Theano, let me say that PyTorch is not, in fact, unique in its approach to neural network computation. Before PyTorch, there were already libraries like Chainer or DyNet that provided a similar dynamic graph API. Today, PyTorch is more popular than these alternatives, though.

At Facebook, PyTorch is also not the only framework in use. The majority of our production workloads currently run on Caffe2, which is a static graph framework born out of Caffe. To marry the flexibility PyTorch provides to researchers with the benefits of static graphs for optimized production purposes, Facebook is also developing ONNX, which is intended to be an interchange format between PyTorch, Caffe2 and other libraries like MXNet or CNTK.

Lastly, a word on history: Before PyTorch, there was Torch – a fairly old (early 2000s) scientific computing library programmed via the Lua language. Torch wraps a C codebase, making it fast and efficient. Fundamentally, PyTorch wraps this same C codebase (albeit with a layer of abstraction in between) while providing a Python API to its users. Let’s talk about this Python API next.

Using PyTorch

In the following paragraphs I will discuss the basic concepts and core components of the PyTorch library, covering its fundamental datatypes, its automatic differentiation machinery, its neural network specific functionality as well as utilities for loading and processing data.

Tensors

The most fundamental datatype in PyTorch is a tensor . The tensor datatype is very similar, both in importance and function, to NumPy’s ndarray . Furthermore, since PyTorch aims to interoperate reasonably well with NumPy, the API of tensor also resembles (but not equals) that of ndarray . PyTorch tensors can be created with the torch.Tensor constructor, which takes the tensor’s dimensions as input and returns a tensor occupying an uninitialized region of memory:

import torch x = torch . Tensor ( 4 , 4 )

In practice, one will most often want to use one of PyTorch’s functions that return tensors initialized in a certain manner, such as:

torch.rand : values initialized from a random uniform distribution,

: values initialized from a random uniform distribution, torch.randn : values initialized from a random normal distribution,

: values initialized from a random normal distribution, torch.eye(n) : an $n \times n$ identity matrix,

: an $n \times n$ identity matrix, torch.from_numpy(ndarray) : a PyTorch tensor from a NumPy ndarray ,

: a PyTorch tensor from a NumPy , torch.linspace(start, end, steps) : a 1-D tensor with steps values spaced linearly between start and end ,

: a 1-D tensor with values spaced linearly between and , torch.ones : a tensor with ones everywhere,

: a tensor with ones everywhere, torch.zeros_like(other) : a tensor with the same shape as other and zeros everywhere,

: a tensor with the same shape as and zeros everywhere, torch.arange(start, end, step) : a 1-D tensor with values filled from a range.

Similar to NumPy’s ndarray , PyTorch tensors provide a very rich API for combination with other tensors as well as in-place mutation. Also like NumPy, unary and binary operations can usually be performed via functions in the torch module, like torch.add(x, y) , or directly via methods on the tensor objects, like x.add(y) . For the usual suspects, operator overloads like x + y exist. Furthermore, many functions have in-place alternatives that will mutate the receiver instance rather than creating a new tensor. These functions have the same name as the out-of-place variants, but are suffixed with an underscore, e.g. x.add_(y) .

A selection of operations includes:

torch.add(x, y) : elementwise addition,

: elementwise addition, torch.mm(x, y) : matrix multiplication (not matmul or dot ),

: matrix multiplication (not or ), torch.mul(x, y) : elementwise multiplication,

: elementwise multiplication, torch.exp(x) : elementwise exponential,

: elementwise exponential, torch.pow(x, power) : elementwise exponentiation,

: elementwise exponentiation, torch.sqrt(x) : elementwise squaring,

: elementwise squaring, torch.sqrt_(x) : in-place elementwise squaring,

: in-place elementwise squaring, torch.sigmoid(x) : elementwise sigmoid.

: elementwise sigmoid. torch.cumprod(x) : product of all values,

: product of all values, torch.sum(x) : sum of all values,

: sum of all values, torch.std(x) : standard deviation of all values,

: standard deviation of all values, torch.mean(x) : mean of all values.

Tensors support many of the familiar semantics of NumPy ndarray ’s, such as broadcasting, advanced (fancy) indexing ( x[x > 5] ) and elementwise relational operators ( x > y ). PyTorch tensors can also be converted to NumPy ndarray ’s directly via the torch.Tensor.numpy() function. Finally, since the primary improvement of PyTorch tensors over NumPy ndarray s is supposed to be GPU acceleration, there is also a torch.Tensor.cuda() function, which will copy the tensor memory onto a CUDA-capable GPU device, if one is available.

Autograd

At the core of most modern machine learning techniques is the calculation of gradients. This is especially true for neural networks, which use the backpropagation algorithm to update weights. For this reason, Pytorch has strong and native support for gradient computation of functions and variables defined within the framework. The technique with which gradients are computed automatically for arbitrary computations is called automatic (sometimes algorithmic) differentiation.

Frameworks that employ the static computation graph model implement automatic differentiation by analyzing the graph and adding additional computation nodes to it that compute the gradient of one value with respect to another step by step, piecing together the chain rule by linking these additional gradient nodes with edges.

PyTorch, however, does not have static computation graphs and thus does not have the luxury of adding gradient nodes after the rest of the computations have already been defined. Instead, PyTorch must record or trace the flow of values through the program as they occur, thus creating a computation graph dynamically. Once such a graph is recorded, PyTorch has the information required to walk this computation flow backwards and calculate gradients of outputs from inputs.

The PyTorch Tensor currently does not have sufficient machinery to participate in automatic differentiation. For a tensor to be “recordable”, it must be wrapped with torch.autograd.Variable . The Variable class provides almost the same API as Tensor , but augments it with the ability to interplay with torch.autograd.Function in order to be differentiated automatically. More precisely, a Variable records the history of operations on a Tensor .

Usage of torch.autograd.Variable is very simple. One needs only to pass it a Tensor and inform torch whether or not this variable requires recording of gradients:

x = torch . autograd . Variable ( torch . ones ( 4 , 4 ), requires_grad = True )

The requires_grad function may need to be False in the case of data inputs or labels, for example, since those are usually not differentiated. However, they still need to be Variable s to be usable in automatic differentiation. Note that requires_grad defaults to False , thus must be set to True for learnable parameters.

To compute gradients and perform automatic differentiation, one calls the backward() function on a Variable . This will compute the gradient of that tensor with respect to the leaves of the computation graph (all inputs that influenced that value). These gradients are then collected in the Variable class’ grad member:

In [ 1 ]: import torch In [ 2 ]: from torch.autograd import Variable In [ 3 ]: x = Variable ( torch . ones ( 1 , 5 )) In [ 4 ]: w = Variable ( torch . randn ( 5 , 1 ), requires_grad = True ) In [ 5 ]: b = Variable ( torch . randn ( 1 ), requires_grad = True ) In [ 6 ]: y = x . mm ( w ) + b # mm = matrix multiply In [ 7 ]: y . backward () # perform automatic differentiation In [ 8 ]: w . grad Out [ 8 ]: Variable containing : 1 1 1 1 1 [ torch . FloatTensor of size ( 5 , 1 )] In [ 9 ]: b . grad Out [ 9 ]: Variable containing : 1 [ torch . FloatTensor of size ( 1 ,)] In [ 10 ]: x . grad None

Since every Variable except for inputs is the result of an operation, each Variable has an associated grad_fn , which is the torch.autograd.Function that is used to compute the backward step. For inputs it is None :

In [ 11 ]: y . grad_fn Out [ 11 ]: < AddBackward1 at 0x1077cef60 > In [ 12 ]: x . grad_fn None

torch.nn

The torch.nn module exposes neural-network specific functionality to PyTorch users. One of its most important members is torch.nn.Module , which represents a reusable block of operations and associated (trainable) parameters, most commonly used for neural network layers. Modules may contain other modules and implicitly get a backward() function for backpropagation. An example of a module is torch.nn.Linear() , which represents a linear (dense/fully-connected) layer (i.e. an affine transformation $Wx + b$):

In [ 1 ]: import torch In [ 2 ]: from torch import nn In [ 3 ]: from torch.autograd import Variable In [ 4 ]: x = Variable ( torch . ones ( 5 , 5 )) In [ 5 ]: x Out [ 5 ]: Variable containing : 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [ torch . FloatTensor of size ( 5 , 5 )] In [ 6 ]: linear = nn . Linear ( 5 , 1 ) In [ 7 ]: linear ( x ) Out [ 7 ]: Variable containing : 0.3324 0.3324 0.3324 0.3324 0.3324 [ torch . FloatTensor of size ( 5 , 1 )]

During training, one will often call backward() on a module to compute gradients for its variables. Since calling backward() sets the grad member of Variable s, there is also a nn.Module.zero_grad() method that will reset the grad member of all Variable s to zero. Your training loop will commonly call zero_grad() at the start, or just before calling backward() , to reset the gradients for the next optimization step.

When writing your own neural network models, you will often end up having to write your own module subclasses to encapsulate common functionality that you want to integrate with PyTorch. You can do this very easily, by deriving a class from torch.nn.Module and giving it a forward method. For example, here is a module I wrote for one of my models that adds gaussian noise to its input:

class AddNoise ( torch . nn . Module ): def __init__ ( self , mean = 0.0 , stddev = 0.1 ): super ( AddNoise , self ). __init__ () self . mean = mean self . stddev = stddev def forward ( self , input ): noise = input . clone (). normal_ ( self . mean , self . stddev ) return input + noise

To connect or chain modules into full-fledged models, you can use the torch.nn.Sequential() container, to which you pass a sequence of modules and which will in turn act as a module of its own, evaluating the modules you passed to it sequentially on each invocation. For example:

In [ 1 ]: import torch In [ 2 ]: from torch import nn In [ 3 ]: from torch.autograd import Variable In [ 4 ]: model = nn . Sequential ( ...: nn . Conv2d ( 1 , 20 , 5 ), ...: nn . ReLU (), ...: nn . Conv2d ( 20 , 64 , 5 ), ...: nn . ReLU ()) ...: In [ 5 ]: image = Variable ( torch . rand ( 1 , 1 , 32 , 32 )) In [ 6 ]: model ( image ) Out [ 6 ]: Variable containing : ( 0 , 0 ,.,.) = 0.0026 0.0685 0.0000 ... 0.0000 0.1864 0.0413 0.0000 0.0979 0.0119 ... 0.1637 0.0618 0.0000 0.0000 0.0000 0.0000 ... 0.1289 0.1293 0.0000 ... ⋱ ... 0.1006 0.1270 0.0723 ... 0.0000 0.1026 0.0000 0.0000 0.0000 0.0574 ... 0.1491 0.0000 0.0191 0.0150 0.0321 0.0000 ... 0.0204 0.0146 0.1724

Losses

torch.nn also provides a number of loss functions that are naturally important to machine learning applications. Examples of loss functions include:

torch.nn.MSELoss : a mean squared error loss,

: a mean squared error loss, torch.nn.BCELoss : a binary cross entropy loss,

: a binary cross entropy loss, torch.nn.KLDivLoss : a Kullback-Leibler divergence loss.

In PyTorch jargon, loss functions are often called criterions. Criterions are really just simple modules that you can parameterize upon construction and then use as plain functions from there on:

In [ 1 ]: import torch In [ 2 ]: import torch.nn In [ 3 ]: from torch.autograd import Variable In [ 4 ]: x = Variable ( torch . randn ( 10 , 3 )) In [ 5 ]: y = Variable ( torch . ones ( 10 ). type ( torch . LongTensor )) In [ 6 ]: weights = Variable ( torch . Tensor ([ 0.2 , 0.2 , 0.6 ])) In [ 7 ]: loss_function = torch . nn . CrossEntropyLoss ( weight = weights ) In [ 8 ]: loss_value = loss_function ( x , y ) Out [ 8 ]: Variable containing : 1.2380 [ torch . FloatTensor of size ( 1 ,)]

Optimizers

After neural network building blocks ( nn.Module ) and loss functions, the last piece of the puzzle is an optimizer to run (a variant of) stochastic gradient descent. For this, PyTorch provides the torch.optim package, which defines a number of common optimization algorithms, such as:

Each of these optimizers are constructed with a list of parameter objects, usually retrieved via the parameters() method of a nn.Module subclass, that determine which values are updated by the optimizer. Besides this parameter list, the optimizers each take a certain number of additional arguments to configure their optimization strategy. For example:

In [ 1 ]: import torch In [ 2 ]: import torch.optim In [ 3 ]: from torch.autograd import Variable In [ 4 ]: x = Variable ( torch . randn ( 5 , 5 )) In [ 5 ]: y = Variable ( torch . randn ( 5 , 5 ), requires_grad = True ) In [ 6 ]: z = x . mm ( y ). mean () # Perform an operation In [ 7 ]: opt = torch . optim . Adam ([ y ], lr = 2e-4 , betas = ( 0.5 , 0.999 )) In [ 8 ]: z . backward () # Calculate gradients In [ 9 ]: y . data Out [ 9 ]: - 0.4109 - 0.0521 0.1481 1.9327 1.5276 - 1.2396 0.0819 - 1.3986 - 0.0576 1.9694 0.6252 0.7571 - 2.2882 - 0.1773 1.4825 0.2634 - 2.1945 - 2.0998 0.7056 1.6744 1.5266 1.7088 0.7706 - 0.7874 - 0.0161 [ torch . FloatTensor of size 5 x5 ] In [ 10 ]: opt . step () # Update y according to Adam's gradient update rules In [ 11 ]: y . data Out [ 11 ]: - 0.4107 - 0.0519 0.1483 1.9329 1.5278 - 1.2398 0.0817 - 1.3988 - 0.0578 1.9692 0.6250 0.7569 - 2.2884 - 0.1775 1.4823 0.2636 - 2.1943 - 2.0996 0.7058 1.6746 1.5264 1.7086 0.7704 - 0.7876 - 0.0163 [ torch . FloatTensor of size 5 x5 ]

Data Loading

For convenience, PyTorch provides a number of utilities to load, preprocess and interact with datasets. These helper classes and functions are found in the torch.utils.data module. The two major concepts here are:

A Dataset , which encapsulates a source of data, A DataLoader , which is responsible for loading a dataset, possibly in parallel.

New datasets are created by subclassing the torch.utils.data.Dataset class and overriding the __len__ method to return the number of samples in the dataset and the __getitem__ method to access a single value at a certain index. For example, this would be a simple dataset encapsulating a range of integers:

import math class RangeDataset ( torch . utils . data . Dataset ): def __init__ ( self , start , end , step = 1 ): self . start = start self . end = end self . step = step def __len__ ( self , length ): return math . ceil (( self . end - self . start ) / self . step ) def __getitem__ ( self , index ): value = self . start + index * self . step assert value < self . end return value

Inside __init__ we would usually configure some paths or change the set of samples ultimately returned. In __len__ , we specify the upper bound for the index with which __getitem__ may be called, and in __getitem__ we return the actual sample, which could be an image or an audio snippet.

To iterate over the dataset we could, in theory, simply have a for i in range loop and access samples via __getitem__ . However, it would be much more convenient if the dataset implemented the iterator protocol itself, so we could simply loop over samples with for sample in dataset . Fortunately, this functionality is provided by the DataLoader class. A DataLoader object takes a dataset and a number of options that configure the way samples are retrieved. For example, it is possible to load samples in parallel, using multiple processes. For this, the DataLoader constructor takes a num_workers argument. Note that DataLoader s always return batches, whose size is set with the batch_size parameter. Here is a simple example:

dataset = RangeDataset ( 0 , 10 ) data_loader = torch . utils . data . DataLoader ( dataset , batch_size = 4 , shuffle = True , num_workers = 2 , drop_last = True ) for i , batch in enumerate ( data_loader ): print ( i , batch )

Here, we set batch_size to 4 , so returned tensors will contain exactly four values. By passing shuffle=True , the index sequence with which data is accessed is permuted, such that individual samples will be returned in random order. We also passed drop_last=True , so that if the number of samples left for the final batch of the dataset is less than the specified batch_size , that batch is not returned. This ensures that all batches have the same number of elements, which may be an invariant that we need. Finally, we specified num_workers to be two, meaning data will be fetched in parallel by two processes. Once the DataLoader has been created, iterating over the dataset and thereby retrieving batches is simple and natural.

A final interesting observation I want to share is that the DataLoader actually has some reasonably sophisticated logic to determine how to collate individual samples returned from your dataset’s __getitem__ method into a batch, as returned by the DataLoader during iteration. For example, if __getitem__ returns a dictionary, the DataLoader will aggregate the values of that dictionary into a single mapping for the entire batch, using the same keys. This means that if the Dataset ’s __getitem__ returns a dict(example=example, label=label) , then the batch returned by the DataLoader will return something like dict(example=[example1, example2, ...], label=[label1, label2, ...]) , i.e. unpacking the values of indidvidual samples and re-packing them into a single key for the batch’s dictionary. To override this behavior, you can pass a function argument for the collate_fn parameter to the DataLoader object.

Note that the torchvision package already provides a number of datasets, such as torchvision.datasets.CIFAR10 , ready to use. The same is true for torchaudio and torchtext packages.

Outro

At this point, you should be equipped with an understanding of both PyTorch’s philosophy as well as its basic API, and are thus ready to go forth and conquer (PyTorch models). If this is your first exposure to PyTorch but you have experience with other deep learning frameworks, I would recommend taking your favorite neural network model and re-implementing it in PyTorch. For example, I re-wrote a TensorFlow implementation of the LSGAN (least-squares GAN) architecture I had lying around in PyTorch, and thus learnt the crux of using it. Further articles that may be of interest can be found here and here.

Summing up, PyTorch is a very exciting player in the field of deep learning frameworks, exploiting its unique niche of being a research-first library, while still providing the performance necessary to get the job done. Its dynamic graph computation model is an exciting contrast to static graph frameworks like TensorFlow or MXNet, that many will find more suitable for performing their experiments. I sure look forward to working on it.