Introduction
PyTorch and TensorFlow have emerged as the most popular open-source frameworks for deep learning in recent years. Both frameworks offer rich feature sets for tasks like computer vision, natural language processing and reinforcement learning.
But which one should you choose for your next project? In this comprehensive guide, we dive into the key similarities and differences between PyTorch and TensorFlow to help you decide.
Brief History
PyTorch
PyTorch was created in 2016 by the AI research group at Facebook. It is based on the older Torch framework written in Lua. The goal was to create a Python-first framework that allowed researchers to quickly iterate and debug models.
Some key facts about PyTorch:
- Released in 2016
- Created by Facebook AI research team
- Python-first API, built on Torch backend
- Primary focus on research and ease of use
TensorFlow
TensorFlow was released by Google in 2015. It originated from Google‘s internal neural network research projects going back many years.
Some key facts:
- Released in 2015
- Created by Google Brain team
- Initial focus on productionization and performance
- Supported by wide range of Google cloud infrastructure
Popularity and Adoption
According to the State of AI report for 2022, PyTorch continues to be more popular with researchers, while TensorFlow leads in production usage. Specifically among data scientists and machine learning developers, 65% reported using PyTorch vs 45% using TensorFlow.
However when it comes to running models in production, TensorFlow remains dominant at 67% adoption vs PyTorch at 33%. This aligns with how the two frameworks have evolved – PyTorch focusing more research and experimentation usage, while TensorFlow has invested heavily in deployment and MLOps capabilities.
Over time, both frameworks have become more similar in capabilities though. TensorFlow has added a high level Keras API for easier model building. And PyTorch has improved in performance and productionisation abilities.
So the gap is closing as both strive to support the full machine learning lifecycle. But their separate origins and focus areas still result in subtle differences in approach.
Ease of Use
For developers and researchers who are comfortable with Python and eager execution, PyTorch generally provides a more intuitive and easier to use experience.
The underlying design principles for PyTorch emphasize Python idioms, dynamical graphs and ease of debugging. This gives PyTorch a more direct, imperative style API resembling native Python code.
In contrast, TensorFlow uses static graphs and sessions which indirectly represent the computation. And while the Keras API does provide a simpler way to construct models, it is still more removed compared to native Python.
So for tasks like quickly iterating on novel model architectures, PyTorch hits the sweet spot of leveraging the full expressiveness of Python without any constraints. Debugging model code is also straightforward as everything runs eagerly like regular Python code.
However, TensorFlow starts to reveal benefits once models start getting really large. Explicitly managing the construction and execution phases separately allows for much better performance. So for very complex research models as well, TensorFlow offers more control to optimize and troubleshoot training.
Performance
Out of the box, TensorFlow delivers better performance across metrics like speed and memory usage since it was designed ground up for productionization. The static graphs and sessions provide optimization opportunities that eager execution misses out.
However in recent years, PyTorch has added features like eager mode performance profiling, quantization aware training and optimization passes that help close the performance gap significantly. There are even projects bringing static graph semantics similar to TensorFlow into the PyTorch ecosystem for better large scale research models.
So nowadays, performance should not be a blocker in choosing PyTorch over TensorFlow, unless pushing the absolute limits. And the ease of use advantages will generally outweigh small performance differences for most real world usage. For simpler models, PyTorch will actually be faster since it avoids the session and graph overheads.
In terms of hardware support, both frameworks exploit GPU acceleration effectively, with TensorFlow holding a slight edge for more specialized hardware like TPUs.
Cutting Edge Research
PyTorch has consolidated its lead as the framework of choice for most research teams pushing state of the art neural networks across domains like Vision, NLP and Speech. The Pythonic nature directly maps research ideas into code for quick experiments. So most new techniques like attention, transformers, GANs etc are available in PyTorch first.
TensorFlow supports the latest ideas as well, but usually after a small lag. So people researching brand new models tend to default to PyTorch.
Over time though, TensorFlow incorporates any validated research into the core library or ecosystem like TensorFlow Addons. So gap between cutting edge capabilities has reduced. But PyTorch retains the advantage for experimentation velocity.
Pretrained Models and Datasets
Both frameworks provide access to a wide selection of pretrained models and standard datasets across modalities like Vision, Text, Time Series etc.
For PyTorch, the TorchVision, TorchText, TorchAudio submodules offer popular architectures like ResNet, BERT as well as datasets like ImageNet and more. There are over 100+ pretrained models across domains available to download and use.
TensorFlow offers the Model Garden collection and TensorFlow Hub with equivalent capabilities. The TensorFlow Datasets library provides direct access to over 500 datasets for common ML tasks. And integration with Keras makes it easy to load these for transfer learning and fine tuning experiments.
So there is plenty of choice available no matter which framework you use. The Hugging Face model hub also makes it easy to retrieve and load models into either framework.
Visualization and Debugging
The eager execution model and dynamic graphs make debugging PyTorch models far easier. You can use regular Python debugging tools like pdb or just insert print statements while iterating models.
For visualization, PyTorch ecosystem offers options like:
- PyTorch Ignite has model logging and visualization tools
- Netron network viewer supports PyTorch formats
- Tensorboard integration available
TensorFlow relies more heavily on TensorBoard for inspection and debugging. The graph network viewer gives powerful visualization capabilities for models and training runs. There is also integration with profilers and debuggers.
So while TensorFlow provides great tooling for model diagnosis, it does require more setup and instrumentation compared to straightforward PyTorch debugging.
Deployment Options
Deployment is where TensorFlow shows most of its heritage from serving Google scale models over the years. TensorFlow Serving provides a production grade server for models, supporting versioning, monitoring and high availability. There are optimized SavedModel formats for transporting models from training into production.
TensorFlow Lite and TensorFlow JS allow packaging models for inference on mobile, edge and web platforms. And TensorFlow Extended (TFX) provides orchestration services around pipelines and MLOps.
PyTorch has been working hard to expand deployment options under the PyTorch Enterprise ecosystem. TorchServe is the model server equivalent to TensorFlow Serving. And throughput performance is impressive, on par with TensorFlow thanks to batching. There is also ONNX support now for exporting models.
So PyTorch covers all the basics for enterprise deployment and serving. But TensorFlow still offers richer production oriented capabilities overall, with tighter cloud integration. This means your team will likely need to put in more custom effort when serving PyTorch.
Model Interpretation Tools
Understanding how neural network models arrive at predictions is an important challenge. Both frameworks provide libraries to help explain model decisions and interpret internal state.
Captum is a model interpretability library for PyTorch focused on attribution techniques like salience maps, input gradients and layer analytics. It also includes quantitative metrics to score explanation quality.
The TensorFlow ecosystem has packages like TensorForest for local explanation models like LIME and TreeShap. There are also visualization packages like TensorWatch that generate useful inspection panels programmatically.
So again, TensorFlow has an edge currently in structured model analysis tools. But Captum covers popular techniques like GradCAM as well, just with less complementary tooling. And visual debuggers for PyTorch like Netron also allow model architecture introspection.
Privacy Preserving Machine Learning
As machine learning penetrates sensitive domains, interest has increased in techniques that preserve data privacy and manage access securely.
PyTorch supports differential privacy natively via the Opacus library. It allows researchers to quickly experiment with DP model training using a high level API. This enables techniques like private federated learning for practical applications.
For TensorFlow, integration with TensorFlow Federated (TFF) and TensorFlow Privacy allows similar experimentation with federated and differential privacy enhanced models. The libraries also accelerate research using prebuilt utilities for common techniques.
So both PyTorch with Opacus and TensorFlow with TFF provide foundational support for cutting edge privacy ML research. Adoption momentum has been stronger so far on the PyTorch side within the research community. But over time TFF usage should spread as well.
Community Support and Resources
As popular open source projects, both TensorFlow and PyTorch enjoy rich community support across channels like Stack Overflow, GitHub issues, blogs etc. There is no shortage of tutorials and guides for most common applications.
PyTorch leads slightly in terms of engagement among researchers, students and enthusiasts. The eager execution model allows sharing more self contained code snippets and ideas on channels like Twitter for feedback.
But TensorFlow usage within Google and massive production deployments means answers for specific architecture and performance questions can be deeper. The contributor community is very well organized to curate these insights on blogs and special interest groups.
Between countless courses, workshops, blogs and papers though – there really is outstanding material available freely for both frameworks.
Conclusion
TensorFlow and PyTorch now offer broadly similar capabilities for most developer and researcher needs. TensorFlow holds optimization advantages for specialized hardware and true industrial scale models. But PyTorch delivers a more intuitive experience thanks to Pythonic idioms and eager execution.
So PyTorch continues as the preferred choice for most researchers and data science teams at the prototype and experimentation stage. While TensorFlow extends further into downstream productionization once models mature. But there is significant overlap now – PyTorch is deployed to production at scale as well in many places.
Given both frameworks are free and open source, it makes sense to become comfortably familiar with each. TensorFlow skills continue to be in high demand within industry – if your goal is to maximize employability. PyTorch fluency demonstrates strong modern machine learning chops to research labs and innovative teams.
With multi-framework projects like Keras also bridging the gap, developers and researchers have ample choice. As always, real world relevance for your domain and team should drive technology decisions more than any intrinsic framework advantages. But now you have the comprehensive view to make an informed call between PyTorch and TensorFlow!
