PyTorch vs TensorFlow: A Data Scientist‘s Perspective

Hey there! As a data scientist and machine learning practitioner, I‘ve used both PyTorch and TensorFlow extensively. And I often get asked – which one is better for deep learning?

It‘s not an easy question to answer! Both PyTorch and TensorFlow have evolved a lot recently. They have different philosophies but can achieve similar results.

In this comprehensive guide, we‘ll dive deep into how PyTorch and TensorFlow compare across many criteria. I‘ll share my experiences using both and offer data-driven insights on their strengths and weaknesses.

My goal is to provide an unbiased, technical view of PyTorch vs TensorFlow. This will help you decide which framework may be a better fit for your deep learning projects and use cases.

Let‘s get started!

A Brief Overview of PyTorch and TensorFlow

First, a quick introduction to both frameworks for context:

PyTorch is a Python-based deep learning library built for flexibility and speed. It was open sourced by Facebook‘s AI Research Lab in 2016.

Some key facts about PyTorch:

Built for intuitive research and experimentation
Uses dynamic computational graphs
Native Python support with eager execution
Strong GPU acceleration via CUDA
Active development by Facebook AI researchers

TensorFlow was originally developed by researchers in Google Brain and released in 2015. It quickly became one of the most popular deep learning frameworks.

Here are some key highlights about TensorFlow:

Created for large-scale production deployment
Uses static computational graphs
Supports multiple languages (Python, C++, etc)
Strong focus on distributed training
Backed by Google with extensive resources

So at a high-level, PyTorch was designed for flexibility and research while TensorFlow was optimized for scalability and production readiness.

But they‘ve adopted features from each other over time. Let‘s see how they compare in depth across six major criteria:

Criterion 1: Ease of Use For Rapid Experimentation

For many data scientists and researchers, PyTorch has a lower learning curve and allows faster experimentation:

PyTorch uses imperative programming while TensorFlow uses declarative. With imperative models, you write code to execute each step sequentially. This maps closely to how we logically think about solving problems. Declarative models require you to first define an entire computational graph before executing it later.
PyTorch has dynamic computational graphs while TensorFlow uses static graphs. Dynamic graphs can evolve during runtime as your code changes. With static graphs, once a graph is built it‘s fixed – you must build a new one to make changes.
PyTorch adopted eager execution from the start, while this was only added later in TensorFlow 2.0. Eager execution evaluates operations immediately without requiring a session or graph run. This allows for easy debugging and a workflow similar to NumPy.

According to a recent Kaggle survey of data scientists, 75% found PyTorch easier to use than TensorFlow. And 70% preferred PyTorch for rapid prototyping compared to 25% for TensorFlow.

This aligns with my experience as well. I have an easier time getting started with PyTorch, iterating quickly, and debugging models. The dynamic graphs and eager execution provide a very "Pythonic" experience.

However, TensorFlow 2.0 has made strides, introducing eager execution and a more intuitive Keras-based API. So the gap in usability has narrowed recently.

Criterion 2: Execution Speed and Performance

When it comes to model training and deployment speed, TensorFlow has some advantages – but performance is generally comparable:

For most tasks, PyTorch and TensorFlow have similar execution times during training. But PyTorch seems to be faster for smaller models.
On benchmarks like DAWNBench, PyTorch edged out TensorFlow initially. But TensorFlow performance improved significantly after adjustments.
For large distributed training, TensorFlow‘s architecture can utilize parallelism better. This benchmark found TensorFlow trained BERT 73% faster on 2048 TPU cores than PyTorch.
Once models are trained, TensorFlow‘s optimizations often result in faster inferencing performance in production. The table below shows sample speedups:

Model	TensorFlow Speedup
ResNet-50 v1.5	33% faster
ResNet-50 v2	55% faster
BERT-Large	21% faster

So while training speeds are fairly equivalent, TensorFlow has some optimizations that provide faster inference after deployment. But PyTorch is improving here as well.

Criterion 3: Deployment and Production Readiness

One of the biggest differences between PyTorch and TensorFlow is the focus on deployment:

Out-of-the-box deployment experience – TensorFlow Serving provides a robust server and API to easily deploy trained models. While convenient, this encourages cloud vendor lock-in on Google Cloud. PyTorch tooling for deployment and serving is usable but not as mature yet.
Optimized execution – TensorFlow has proprietary optimization tools like XLA, TensorRT, and TF-Rank that optimize models for faster inference. For mobile, TensorFlow Lite leverages optimized C++ kernels. These improvements are harder to achieve with stock PyTorch.
Cross-platform – For production use, TensorFlow supports stable APIs across platforms like C++, Java, Go. PyTorch is mainly focused on Python with some C++/Java support.
Go-to-market support – Google Cloud provides extensive documentation, tooling, and managed services to deploy TensorFlow models at scale. Going from research to production is streamlined. PyTorch relies more on open source community contributions.

When I led an NLP model from research to production for a prior company, TensorFlow made the deployment process very smooth. The ability to export models, leverage optimizations, and serve predictions was simple and performant.

While TensorFlow has more momentum, PyTorch is expanding production use through TorchScript, ONNX support, model serving, and other initiatives like TorchServe. The gap is narrowing as PyTorch matures.

Criterion 4: Community Adoption and Support

Considering the breadth of community support is important when choosing a framework:

User base – Given TensorFlow‘s multi-year head start, it has a larger user base. However, PyTorch adoption has skyrocketed recently. On GitHub, TensorFlow has ~151k stars while PyTorch has ~57k. The gap is rapidly shrinking.
Corporate sponsorship – Google obviously uses TensorFlow extensively, but many other large companies do as well – Airbnb, eBay, Uber, NVIDIA, and more. Facebook utilizes PyTorch across its business. Usage is also growing at Apple, Amazon, Microsoft, Salesforce, and other major companies.
Learning resources – With TensorFlow‘s first mover advantage, there are more tutorials and courses available. But PyTorch resources have proliferated, including the fantastic fast.ai courses. In general, finding help and documentation for either framework is easy online.
Research publications – A recent analysis looked at deep learning research paper implementations on Papers with Code. PyTorch was used in 47% of papers while TensorFlow usage has dropped to just 15%. So PyTorch seems to have more momentum in academic research.

From my standpoint, both frameworks now have ample resources and reached critical mass in terms of community size. PyTorch does seem to be gaining momentum though, especially among researchers and data scientists.

Criterion 5: Access to Pretrained Models

Leveraging pretrained models can accelerate deep learning projects. Both frameworks provide access to many popular pretrained models:

For computer vision, PyTorch has torchvision while TensorFlow has TensorFlow Hub. Both contain models like ResNet, VGG, Inception pre-trained on ImageNet and other datasets.
For NLP, PyTorch has transformers and TensorFlow has TensorFlow Hub again. These provide access to BERT, GPT-2, T5, and other state-of-the-art models for natural language.
There are even third party model hubs like Hugging Face Transformers. These contain PyTorch and TensorFlow versions for all major NLP models.
TensorFlow likely has a few more niche pretrained models available, such as Universal Sentence Encoder. But PyTorch provides all the major computer vision and NLP models you‘d need.

In my work, having easy access to high-quality pretrained models has been hugely beneficial. And both PyTorch and TensorFlow now make this very feasible.

Criterion 6: Debugging and Model Interpretability

Understanding and debugging complex deep learning models can be challenging. Some key differences in capabilities:

Interactivity – PyTorch has adopted eager execution since the beginning. This allows for more interactive debugging as models train, with easy breakpoints and sequential stepping through code. TensorFlow eager execution provides similar benefits but was adopted later on.
Model graphs – Visualizing model graphs is helpful for understanding data flow. TensorFlow‘s TensorBoard integrates models graphs nicely. For PyTorch, model graphs are harder to export and visualize in external tools.
Visualizations – PyTorch has built-in plotting of metrics like losses and accuracies as models train. This helps surface issues with convergence. TensorFlow can achieve similar functionality via callbacks but requires more customized code.
Interpretability – TensorFlow has tools like TensorBoard and What-If Tool to understand model inferences after the fact. PyTorch relies more on open source interpretation libraries like Captum.

Having trained hundreds of deep learning models, these transparency and debugging capabilities are crucial. I prefer PyTorch‘s real-time plotting and debugging support during active development. But TensorFlow provides better capabilities for understanding models post-training.

Summary of PyTorch vs TensorFlow Comparison

Let‘s recap the key findings from our in-depth PyTorch vs TensorFlow comparison:

	PyTorch	TensorFlow
Ease of Use	More intuitive and Pythonic. Lower learning curve.	Steeper initial learning curve but improving.
Performance	Faster for small models. Adds optimizations.	Scales better for distributed training. Optimized serving.
Deployment	Improving for production via TorchServe, TorchScript.	More built-in support through TensorFlow Serving.
Community	Rapidly growing user base. Gaining research mindshare.	Larger user base. Broad corporate sponsorship.
Pretrained Models	Access to all major computer vision and NLP models.	Extensive model availability, including niche models.
Debugging	More interactivity and built-in visualizations.	Advanced model analysis tools.

So in summary, PyTorch feels more intuitive and streamlined for hands-on experimentation. But TensorFlow provides optimizations, tooling, and support that simplify production deployment.

Next, we‘ll cover guidelines on when to use each framework.

Recommendations: When to Use PyTorch vs TensorFlow

Based on our analysis, here are my recommendations on when to use PyTorch vs TensorFlow:

When to Use PyTorch:

Your priority is rapid prototyping of models and iterative development speed.
You want to do interactive debugging and real-time visualization during training.
Your team is more proficient in Python and prefers a dynamic coding style.
You are doing academic research or exploring novel model architectures.

When to Use TensorFlow:

Your priority is large-scale production deployment and serving performance.
You need multi-platform support beyond Python or cross-language APIs.
You want to leverage Google Cloud services and tooling on the backend.
Your team has more experience with static data flow graphs.

Of course, you can build excellent models with either framework. But I hope these recommendations help point you in the right direction!

Hands-On Resources to Learn PyTorch and TensorFlow

To wrap up, here are some great free resources I recommend to get hands-on experience with PyTorch and TensorFlow:

Learn PyTorch:

PyTorch 60 Minute Blitz – Official PyTorch tutorial covering basics in 60 minutes
PyTorch Zero to GANs – Free 7 hour course to go from basics to building a GAN

Learn TensorFlow:

TensorFlow Developer Certificate – Official certification from Google and Coursera (some paid content)
Intro to TensorFlow for Deep Learning – Free intro course from Udacity

I highly recommend getting hands-on experience with code to understand the strengths of each framework. Let me know if you have any other questions!

Conclusion

I hope this thorough, unbiased guide gave you a good understanding of PyTorch vs TensorFlow.

Here are my key takeaways:

PyTorch is more intuitive and delivers a streamlined experience for research and prototyping.
TensorFlow powers incredibly optimized production deployment pipelines and tooling.
Both frameworks have fantastic communities, resources, and pretrained models available.

Ultimately, you should choose a framework based on your team‘s skills, project priorities, and where you are in the product development lifecycle.

I‘m passionate about helping more developers and companies successfully leverage deep learning. So please reach out if you have any other questions! I‘m happy to help point you in the right direction.