PyTorch or TensorFlow?

PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.

Awni Hannun, Stanford.

Pytorch Vs Tensorflow
This is a guide to the main differences I’ve found between PyTorch and TensorFlow. This post is intended to be useful for anyone considering starting a new project or making the switch from one deep learning framework to another. The focus is on programmability and flexibility when setting up the components of the training and deployment deep learning stack. I won’t go into performance (speed / memory usage) trade-offs.


PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.

Ramp-up Time

Winner: PyTorch

PyTorch is essentially a GPU enabled drop-in replacement for NumPy equipped with higher-level functionality for building and training deep neural networks. This makes PyTorch especially easy to learn if you are familiar with NumPy, Python and the usual deep learning abstractions (convolutional layers, recurrent layers, SGD, etc.).

On the other hand, a good mental model for TensorFlow is a programming language embedded within Python. When you write TensorFlow code it gets “compiled” into a graph by Python and then run by the TensorFlow execution engine. I’ve seen newcomers to TensorFlow struggle to wrap their head around this added layer of indirection. Also because of this, TensorFlow has a few extra concepts to learn such as the session, the graph, variable scoping and placeholders. Also more boilerplate code is needed to get a basic model running. The ramp-up time to get going with TensorFlow is definitely longer than PyTorch.

Graph Creation and Debugging

Winner: PyTorch

Creating and running the computation graph is perhaps where the two frameworks differ the most. In PyTorch the graph construction is dynamic, meaning the graph is built at run-time. In TensorFlow the graph construction is static, meaning the graph is “compiled” and then run. As a simple example, in PyTorch you can write a for loop construction using standard Python syntax

for _ in range(T):
    h = torch.matmul(W, h) + b


and T can change between executions of this code. In TensorFlow this requires the use of control flow operations in constructing the graph such as the tf.while_loop. TensorFlow does have thedynamic_rnn for the more common constructs but creating custom dynamic computations is more difficult.

The simple graph construction in PyTorch is easier to reason about, but perhaps even more importantly, it’s easier to debug. Debugging PyTorch code is just like debugging Python code. You can use pdb and set a break point anywhere. Debugging TensorFlow code is not so easy. The two options are to request the variables you want to inspect from the session or to learn and use the TensorFlow debugger (tfdbg).


Winner: TensorFlow

As PyTorch ages, I expect the gap here will converge to zero. However, there is still some functionality which TensorFlow supports that PyTorch doesn’t. A few features that PyTorch doesn’t have (at the time of writing) are:

  • Flipping a tensor along a dimension (np.flipnp.flipudnp.fliplr)
  • Checking a tensor for NaN and infinity (np.is_nannp.is_inf)
  • Fast Fourier transforms (np.fft)

These are all supported in TensorFlow. Also the TensorFlow contrib package has many more higher level functions and models than PyTorch.


Winner: TensorFlow

Saving and loading models is simple in both frameworks. PyTorch has an especially simple API which can either save all the weights of a model or pickle the entire class. The TensorFlow Saver object is also easy to use and exposes a few more options for check-pointing.

The main advantage TensorFlow has in serialization is that the entire graph can be saved as a protocol buffer. This includes parameters as well as operations. The graph can then be loaded in other supported languages (C++, Java). This is critical for deployment stacks where Python is not an option. Also this can, in theory, be useful when you change the model source code but want to be able to run old models.


Winner: TensorFlow

For small scale server-side deployments both frameworks are easy to wrap in e.g. a Flask web server.

For mobile and embedded deployments TensorFlow works. This is more than can be said of most other deep learning frame-works including PyTorch. Deploying to Android or iOS does require a non-trivial amount of work in TensorFlow but at least you don’t have to rewrite the entire inference portion of your model in Java or C++.

For high-performance server-side deployments there is TensorFlow Serving. I don’t have experience with TensorFlow Serving, so I can’t write confidently about the pros and cons. For heavily used machine learning services, I suspect TensorFlow Serving could be a sufficient reason to stay with TensorFlow. Other than performance, one of the noticeable features of TensorFlow Serving is that models can be hot-swapped easily without bringing the service down. Checkout this blog post from Zendesk for an example deployment of a QA bot with TensorFlow serving.


Winner: Tie

I’ve found everything I need in the docs for both frameworks. The Python APIs are well documented and there are enough examples and tutorials to learn either framework.

One edge case gripe is that the PyTorch C library is mostly undocumented. However, this really only matters when writing a custom C extension and perhaps if contributing to the software.

Data Loading

Winner: PyTorch

The APIs for data loading are well designed in PyTorch. The interfaces are specified in a dataset, a sampler, and a data loader. A data loader takes a dataset and a sampler and produces an iterator over the dataset according to the sampler’s schedule. Parallelizing data loading is as simple as passing a num_workers argument to the data loader.

I haven’t found the tools for data loading in TensorFlow (readers, queues, queue runners, etc.) especially useful. In part this is because adding all the preprocessing code you want to run in parallel into the TensorFlow graph is not always straight-forward (e.g. computing a spectrogram). Also, the API itself is more verbose and harder to learn.

Device Management

Winner: TensorFlow

Device management in TensorFlow is about as seamless as it gets. Usually you don’t need to specify anything since the defaults are set well. For example, TensorFlow assumes you want to run on the GPU if one is available. In PyTorch you have to explicitly move everything onto the device even if CUDA is enabled.

The only downside with TensorFlow device management is that by default it consumes all the memory on all available GPUs even if only one is being used. The simple workaround is to specify CUDA_VISIBLE_DEVICES. Sometimes people forget this, and GPUs can appear to be busy when they are in fact idle.

In PyTorch, I’ve found my code needs more frequent checks for CUDA availability and more explicit device management. This is especially the case when writing code that should be able to run on both the CPU and GPU. Also converting say a PyTorch Variable on the GPU into a NumPy array is somewhat verbose.

 numpy_var = variable.cpu().data.numpy()

Custom Extensions

Winner: PyTorch

Building or binding custom extensions written in C, C++ or CUDA is doable with both frameworks. TensorFlow again requires more boiler plate code though is arguably cleaner for supporting multiple types and devices. In PyTorch you simply write an interface and corresponding implementation for each of the CPU and GPU versions. Compiling the extension is also straight-forward with both frameworks and doesn’t require downloading any headers or source code outside of what’s included with the pip installation.

A note on TensorBoard

TensorBoard is a tool for visualizing various aspects of training machine learning models. It’s one of the most useful features to come out of the TensorFlow project. With a few code snippets in a training script you can view training curves and validation results of any model. TensorBoard runs as a web service which is especially convenient for visualizing results stored on headless nodes.

This was one feature that I made sure I could keep (or find an alternative to) before using PyTorch. Thankfully there are, at least, two open-source projects which allow for this. The first istensorboard_logger and the second is crayon. The tensorboard_logger library is even easier to use than TensorBoard “summaries” in TensorFlow, though you need TensorBoard installed to use it. The crayon project is a complete replacement for TensorBoard but requires more setup (docker is a prerequisite).

A note on Keras

Keras is a higher-level API with a configurable back-end. At the moment TensorFlow, Theano and CNTK are supported, though perhaps in the not too distant future PyTorch will be included as well. Keras is also distributed with TensorFlow as a part of tf.contrib.

Though I didn’t discuss Keras above, the API is especially easy to use. It’s one of the fastest ways to get running with many of the more commonly used deep neural network architectures. That said, the API is not as flexible as PyTorch or core TensorFlow.

A note on TensorFlow Fold

Google announced TensorFlow Fold in February of 2017. The library is built on top of TensorFlow and allows for more dynamic graph construction. The main advantage of the library appears to be the dynamic batching. Dynamic batching automatically batches computations on inputs of varying size (think recursive networks on parse trees). In terms of programmability, the syntax is not as straightforward as PyTorch, though in a some cases the performance improvements from batching may be worth the cost.

Original. Reposted with permission.

Bio: Awni Hannun is a PhD student in CS at Stanford, and previously was at Baidu Silicon Valley AI Lab.