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PyTorch vs TensorFlow: An In-Depth Comparison for Deep Learning

Deep learning is driving advances across industries from healthcare to autonomous vehicles. At the heart of many deep learning applications are frameworks like PyTorch and TensorFlow. With its dynamic graphs and Pythonic approach, PyTorch makes AI development intuitive and debugging easy. TensorFlow pioneered the field and offers optimizations that make it ideal for large-scale production deployment. So which one should you use?

This comprehensive guide will compare every aspect of PyTorch and TensorFlow to help you decide. I‘ll share my experience using both frameworks for computer vision and NLP projects over the past few years. You‘ll see code examples and benchmarks demonstrating their key differences. Let‘s dive in!

A Brief History

To understand the design and philosophy of PyTorch and TensorFlow, it helps to know where they came from.

TensorFlow was created by the Google Brain team to conduct research and build production AI systems. It was engineered from the start for advanced features like distributed training, heterogeneous hardware support, and production deployment optimizations.

PyTorch was created by Facebook‘s AI research group. It was designed to mimic Numpy and leverage dynamic Python execution. The goal was to create a framework tailored for research and rapid experimentation.

Both projects were open-sourced in 2015-2016 and have large communities on GitHub:

  • TensorFlow: 139k stars, 50k forks
  • PyTorch: 57k stars, 20k forks

TensorFlow gained popularity initially but PyTorch adoption has accelerated, especially in research. The frameworks have different philosophies butborrow the best ideas from each other. Let‘s see how they compare across various criteria.

Ease of Use

For getting started with deep learning, PyTorch is generally regarded as the simpler framework. It uses dynamic computational graphs and feels more like idiomatic Python code:

x = torch.rand(5, 3)
y = torch.rand(5, 3)
z = x + y

TensorFlow code looks more complex in comparison:

x = tf.random.normal([5, 3])
y = tf.random.normal([5, 3])
z = tf.add(x, y)

The static graph paradigm of TensorFlow requires you to first build the entire graph, then execute it later. Debugging issues with the predefined graph is also challenging.

PyTorch constructs graphs on the fly which is more intuitive coming from an imperative programming mindset. You can easily inspect and modify internal model variables. Here is a graph comparing developer productivity in one survey:

PyTorch vs TensorFlow - Productivity


For quick experiments and iterating on models, PyTorch fits most developers‘ mental models better. But TensorFlow 2.0 has narrowed the gap substantially by adopting eager execution.

Performance Benchmarks

TensorFlow has a reputation for being faster – but how much faster? And under what conditions? Here are some benchmarks from the past year:

Training ResNet50 on Cloud TPUs (Source)

FrameworkImages/secSpeedup vs PyTorch

Training BERT Base on 1x V100 GPU (Source)

FrameworkSeq/secSpeedup vs PyTorch

Transformer Training on 64x V100 GPUs (Source)

FrameworkSeq/secSpeedup vs PyTorch

TensorFlow shows substantial speedups on Google‘s TPU hardware. On GPUs the gap is smaller – TensorFlow is up to ~2x faster for some models. On CPUs, performance is now generally on par between the frameworks.

So while TensorFlow has more optimizations, especially for production use cases, for many research workloads PyTorch is competitive. And TensorFlow‘s integration of features like just-in-time compilation continue to close the performance gap with PyTorch.

Hardware Support

TensorFlow supports a diverse range of hardware accelerators:

  • TPUs – Google‘s custom ML chips that come in v2, v3, and v4 variants.
  • GPUs – Nvidia, AMD GPUs via CUDA, ROCm.
  • CPUs – x86, ARM CPUs. Optimized implementations.

PyTorch is focused primarily on GPUs currently:

  • GPUs – Nvidia, AMD GPUs through CUDA, OpenCL drivers.
  • CPUs – x86, ARM CPUs.

Here is a comparison of hardware support:


So if you need access to Google‘s cutting-edge TPUs, TensorFlow is the best choice today. But PyTorch offers excellent GPU support and optimizations.

Distributed Training

Large deep learning models now require parallel training across multiple GPUs or TPU chips to reduce time and scale to huge datasets.

Both frameworks offer distributed training modules. TensorFlow has more mature capabilities for now like synchronized multi-node training.

TensorFlow‘s MultiWorkerMirroredStrategy handles splitting data across workers and aggregating gradients efficiently.

PyTorch originally focused on single-node distributed training. But recent releases added DistributedDataParallel for multi-node training. There are also emerging tools like Horovod for PyTorch distributed training.

Here are some key differences in distributed training support:

Multi-nodeYesYes (recently added)
Synchronous SGDYesVia 3rd party
All-reduceYesVia 3rd party

So TensorFlow still has a lead in built-in synchronous training features. But PyTorch is catching up as its distribution capabilities mature.

Debugging Experience

Debugging and troubleshooting model issues is critical during development. Let‘s see how TensorFlow and PyTorch compare.

PyTorch‘s dynamic graphs and eager execution enable the use of standard Python debugging tools. You can pause code in a debugger or insert print statements while building PyTorch models.

Debugging TensorFlow requires special APIs to track graph execution. The static graph paradigm makes it more difficult to inspect intermediate values. Some describe debugging TensorFlow as more of an "art" than science.

Here‘s an example of setting a breakpoint and inspecting variables in PyTorch:

# PyTorch

model = ResNet50()
x = torch.rand(10, 3, 224, 224)

import pdb; pdb.set_trace()

y = model(x) 

# TensorFlow 

model = ResNet50()
x = tf.random.normal([10, 224, 224, 3])

y = model(x)

Debugging PyTorch with Python breakpoints and pdb is far more straightforward than TensorFlow‘s specialized tracing and dumping approaches.

Cloud Integration

If you want to build models on public cloud infrastructure, TensorFlow integrates more tightly with Google Cloud Platform whereas PyTorch deployment is cloud-neutral.

TensorFlow support on GCP includes:

  • AI Platform – Managed service for training & deploying models
  • Keras Tuner – Cloud integration for hyperparameter tuning
  • TPU access – Using Cloud TPUs for training
  • Integrated notebooks – Cloud-hosted Jupyter and Colab

PyTorch itself does not have special GCP features, but can run using cloud services like:

  • Compute Engine – IaaS VMs for PyTorch clusters
  • Deep Learning Containers – Prebuilt Docker images
  • AI Platform Training – Generic ML training service

The story is similar on AWS and Azure. TensorFlow works well leveraging services like SageMaker and Azure ML. But PyTorch and TensorFlow have rough parity in cloud-agnostic training and deployment options.

High-Level APIs

For quickly iterating on models, both TensorFlow and PyTorch offer high-level APIs that abstract low-level details:

  • TensorFlow – Keras
  • PyTorch – PyTorch Lightning

Keras provides things like saving/loading models, managing checkpoints, and A/B testing different models. It has many pretrained models available via TensorFlow Hub.

PyTorch Lightning offers a lightweight Trainer module that accelerates research. It interoperates seamlessly with core PyTorch and other Python tools.

Keras has a more batteries-included philosophy, while PyTorch Lightning aims to stay lean. Both enable fast model exploration. Keras makes sense if you specifically want TensorFlow plus high-level conveniences, but comes with more dependencies.

I‘d suggest trying PyTorch Lightning if you want a simpler, more Pythonic experience that feels decoupled from PyTorch itself.

Deployment Environments

For production deployment, TensorFlow provides optimization tools like TensorRT, TensorFlow Lite, and TensorFlow JS for serving, mobile, and web deployment targets respectively.

PyTorch relies on TorchScript for serializable, optimized models and the TorchServe library is coming soon also. ONNX provides an interchange format for portable deployment.

TensorFlow just has more deployment tools battle-tested for large-scale production. But PyTorch covers the basics like ONNX export and TorchScript for creating production-ready models. The gap here continues to narrow as PyTorch matures.

Should You Use TensorFlow or PyTorch?

There is no single best framework for every scenario. Based on their respective strengths, here are some guidelines on when to use each:

Use PyTorch for:

  • Research and prototyping new models
  • Often changing model architecture and hyperparameters
  • Need for easy debugging during development
  • Latest deep learning innovations, like transformers

Use TensorFlow for:

  • Large-scale production deployment
  • Applications requiring TPU acceleration
  • Multi-node distributed training
  • Deployment on web, mobile, or specialized hardware

For many applications and use cases they offer similar capabilities. Starting out with either framework and occasionally switching between them is not difficult. Given their popularity, it‘s valuable to gain experience with both TensorFlow and PyTorch.

The Best of Both Worlds

Having to pick between TensorFlow and PyTorch is not necessary. The Keras team is working to integrate both frameworks as backends in Keras. The goal is to support any Keras model running on either TensorFlow or PyTorch.

This Keras integration and abstraction layer will provide the simplicity of Keras and interoperability with your choice of backend. It gives you the best of both worlds – easy model building with Keras and then access to both TensorFlow‘s and PyTorch‘s capabilities.


TensorFlow pioneered production-oriented features like multi-GPU training, model deployment tools, and optimized hardware support. But PyTorch is becoming competitive while retaining its strengths like eager execution, Pythonic design, and strong GPU performance.

There is healthy competition and idea-sharing between the projects that benefits the entire deep learning community. Both frameworks now support the must-have capabilities needed for research and production. Picking the right tool depends on your specific needs and use case rather than a single winner.

I hope walking through their key differences and tradeoffs helps you decide on the best framework for your deep learning initiatives. Let me know if you have any other questions!

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