TensorFlow vs. Pytorch: Which Should You Use?

TensorFlow vs. Pytorch: Which Should You Use?

TensorFlow vs. Pytorch—which deep learning framework is right for you? In this article, we’ll walk you through the thought process of selecting a deep learning framework by comparing the two most popular AI frameworks: PyTorch and TensorFlow.

How to Choose a Deep Learning Framework

What Is TensorFlow?

What Is Pytorch?

Tensorflow vs. Pytorch: Key Differences

Static vs Dynamic Computational Graphs

Learning Curve


Data Parallelism




Should I Use Tensorflow or Pytorch?

Work as a Deep Learning Developer on Upwork

How to choose a deep learning framework

Picking a deep learning framework really boils down to a few key selection criteria:

  • Machine-learning models. A huge part of AI development revolves around the quality of the models that are available. As the industry has evolved, so too have the complexity and number of models available for use. Depending on your needs you may want to look at which models are supported by a given framework.
  • Deployment infrastructure. It’s great if your deep learning project works on your individual machine, but will that AI-powered app work as well when deployed at scale to the cloud? You’ll want to pick frameworks that reduce time to deploy and play well with common deployment infrastructure.
  • Community ecosystem. Nothing speeds up the learning process like an active developer community—GitHub stars, Stack Overflow questions, and plenty of documentation and tutorials. Beyond that, industry adoption of certain deep learning frameworks can greatly simplify your development process.
  • Big-name backers. The developer landscape is constantly evolving. The level of investment from a major tech company with a good developer reputation can tell you how long a framework is likely to stick around.
  • Technology. Does a framework give an edge in performance, deployment, rapid prototyping, or developer friendliness? Which languages do you prefer to work with?

Tip: For the mathematically curious, this post on backpropagation can get you up to speed on the philosophy behind graphs and their relevance to machine learning.

What is TensorFlow?

Are you a Python developer looking to write production-ready deep learning models that perform efficiently at scale? TensorFlow is for you. TensorFlow benefits from the largest, most active community among machine learning frameworks. Among its valuable features:

  • Supported by Google Brain
  • Supports static computational graphs
  • Provides access, via TensorFlow Serving, to Google’s battle-tested server infrastructure for your projects
  • With its TensorBoard, gives you a great visualization tool for picturing the graphs, nodes, and edges that represent your algorithms
  • Via TensorFlow Fold, lets you use dynamic graphs with TensorFlow.
  • Provides a Python control flow with easier debugging via eager execution

In the static-graph approach to machine learning, you specify the sequence of computations you want to use and then flow data through the application. The advantage to this approach is it makes distributed training of models easier.

What is Pytorch?

Are you an academic who enjoys using Python to crunch numbers? PyTorch is for you. It’s a low-level framework that makes o working directly with array expressions easy. PyTorch gives you fine-grain control over your models, supports NumPy, and integrates seamlessly into a Python developer’s workflow.

Here’s a quick roundup of PyTorch’s highlights:

  • The framework of choice for academic researchers
  • Supported by Meta’s artificial intelligence research group
  • Supports dynamic computational graphs by default
  • Easy to debug models line by line
  • Exports models via pickle (a Python-based file type)

Because the dynamic approach lets you define models that can change during runtime, PyTorch is well suited to the development of recurrent neural networks (RNN). If you want low-level control, customizability, ease of use, and dynamic graphs, PyTorch is the hot pick.

Tensorflow vs. Pytorch: key differences

In this section we’ll take a closer look at the key differences between Tensorflow and PyTorch. We’ve summarized these differences in a table below:

Tensorflow vs Pytorch

Static vs dynamic computational graphs

Computational graphs are directed graphs from graph theory in which operations are represented by nodes and the data flowing between operations are represented by edges.

Frameworks that favor static graphs encourage you to build fixed, reusable structures that you can repeatedly run data through. This can give you a performance boost, especially for larger data sets, because you don’t have to worry about mutations at runtime. TensorFlow uses static graphs because they’re easier to deploy at scale and optimize for higher performance in production.

Frameworks that favor dynamic graphs place programmer flexibility front and center. They are well suited for situations where the graph must deal with inputs of varying size (e.g., processing a few words vs. a paragraph in natural language processing). A dynamic graph is implicitly defined by the logic you write and can change at runtime, following the define-by-run convention that is more natural to programmers. PyTorch uses dynamic graphs for their flexibility and ease of use.

Learning curve

TensorFlow is generally considered to have a more difficult learning curve than PyTorch, particularly for users who are new to deep learning. This is because TensorFlow has a more complex API and requires more explicit programming, which can make it harder to learn and use.

PyTorch, on the other hand, has a more intuitive API and is easier to learn and use, especially for users who are familiar with Python. Additionally, PyTorch includes more debugging information in its error messages, which can make troubleshooting problems easier.

Overall, PyTorch may be a better choice for users who are new to deep learning and want something that’s easy to learn and use, while TensorFlow may be more suitable for users who are more experienced and have specific performance or production requirements.


As the deep learning ecosystem continues to mature, we’re starting to see a plethora of large state-of-the-art (SOTA) models made publicly available for machine learning projects. We’re also starting to see a noteworthy divergence in model availability between the two most popular deep learning frameworks: TensorFlow and Pytorch.

PyTorch is the clear winner here. From PapersWithCode to HuggingFace, most of the SOTA models available on the web support PyTorch. Models either support both TensorFlow and PyTorch or are exclusive to PyTorch. Because a lot of machine learning research is driven by academics, it’s no surprise that PyTorch has cornered the market for available SOTA models.

Data parallelism

Data parallelism is a technique for distributing the training of a deep learning model across multiple devices, such as GPUs. Both TensorFlow and PyTorch support data parallelism, but they have different approaches to implementing it.

In TensorFlow, data parallelism is implemented using the tf.distribute.Strategy API, which allows users to specify how the model should be distributed across devices. TensorFlow supports a variety of strategies for distributing the model, including tf.distribute.MirroredStrategy, which supports model parallelism by creating copies of the model on different devices, and tf.distribute.experimental.MultiWorkerMirroredStrategy, which supports model parallelism across multiple machines.

In PyTorch, data parallelism is implemented using the torch.nn.DataParallel class, which wraps a model and parallelizes the forward pass using multiple GPUs. The DataParallel class is designed to be used with models that have a single input and a single output, and it uses the device parallelism provided by the CUDA (Compute Unified Device Architecture) library to parallelize the forward pass.

PyTorch also supports model parallelism using the torch.nn.parallel.DistributedDataParallel class, which is designed to be used with models that have multiple inputs and outputs and can be distributed across multiple devices and machines.

Overall, both TensorFlow and PyTorch provide tools for implementing data parallelism, but they have different approaches and capabilities. It’s much easier to implement data parallelism in PyTorch, because you can take advantage of native support for asynchronous execution in Python. TensorFlow requires manual fine-tuning of every operation for each device you wish to distribute your workload.

Visualization: TensorBoard vs. Visdom

Both TensorFlow and PyTorch have tools that allow users to visualize the computation graph, track training progress, and monitor various metrics.


Tensorboard Example

TensorBoard provides a suite of visualization tools for exploring and understanding TensorFlow runs. Its suite of web apps include visualizations for graphs, scalars, audio, histograms, and images. To use TensorBoard with TensorFlow, users need to write code to log relevant data and then run TensorBoard in a separate process.


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PyTorch’s native visualization tool Visdom is less a specific tool for visualizing your training progress and more a lightweight Python library for plotting data visualizations that can be viewed in your browser. Similar to Matplotlib.

Overall, TensorFlow and PyTorch provide ways to visualize your machine learning projects. TensorFlow provides a stand-alone tool called TensorBoard for visualization, while PyTorch has the lighter-weight minimalist Visdom. But since you aren’t limited to out-of-the-box features, it’s important to note that a variety of visualization tools are available for both frameworks. In fact you can even use TensorBoard with PyTorch.


TensorFlow and PyTorch have different approaches to debugging; each has its own set of tools for debugging deep learning models.

In TensorFlow, debugging can be challenging, especially for users who are new to the framework. TensorFlow has a number of tools for debugging, including the tfdbg Python debugger, which allows users to pause the execution of the model and examine intermediate results. TensorFlow also provides a high-level API called tf.debugging, which includes a number of functions for debugging TensorFlow programs. However, TensorFlow's debugging tools can be difficult to use and may not provide as much information as some users would like.

In PyTorch, debugging is generally easier, thanks to the framework's more intuitive API and more descriptive error messages. PyTorch integrates with the pdb Python debugger, which allows users to pause the execution of the model and examine intermediate results. PyTorch also has the torch.autograd module that can be used for debugging, including .gradcheck() and .debug_anomaly() functions. Additionally, PyTorch's dynamic computation graph and ability to execute models incrementally make it easier to understand and debug model behavior.

Overall, PyTorch may be easier to debug than TensorFlow, due to its ability to use native Python debugging tools, its more intuitive API, and its more descriptive error messages. However, TensorFlow might have an edge in debugging larger-scale applications due to its use of static graphs. Of course debugging ease for large projects is still largely dependent on how well you design your projects.


TensorFlow and PyTorch are both popular deep learning frameworks. Each has its own community of users and developers. Consider a few key differences between the TensorFlow and PyTorch communities:

  • Origins: TensorFlow was developed by Google as an open-source deep learning framework and was released in 2015. PyTorch was developed by Facebook and eventually released as an open-source project on GitHub in 2017.
  • Focus: The TensorFlow community tends to focus more on production and deployment, while the PyTorch community tends to focus more on research and development. TensorFlow has a number of tools and features specifically designed for deploying models to production and monitoring their performance, while PyTorch has a more flexible and modular design that is well suited to rapid prototyping and experimentation.
  • Language: The TensorFlow community is more diverse, with users and developers working in a variety of languages, including Python, C++, and Java. The PyTorch community is primarily centered on the Python language.

Overall, the TensorFlow and PyTorch communities are both active and supportive, but they have different areas of focus and cater to different audiences.

Should I use Tensorflow or Pytorch?

In this article, we took a quick look at two popular deep learning platforms: PyTorch and TensorFlow.

Your choice of tool really boils down to which computational graph you prefer and if there’s a high-level front-end API available to make your job easier. The other major deciding factor is the level of community support and the specific SOTA models you wish to work with.

Google has heavily invested in Keras/TensorFlow while Facebook has backed PyTorch. Both frameworks have thriving open-source communities. It’s the confluence of these factors that can give you confidence that a given framework is worth the long-term investment. Choose the best deep learning framework that meets your needs.

Work as a deep learning developer on Upwork

There are a few steps you can follow to find work as a deep learning developer on Upwork:

  • Create a strong Upwork profile: Your Upwork profile is essentially your online resume, so make sure it’s well written and showcases your skills and experience as a deep learning developer.
  • Complete relevant skills tests: Upwork offers a variety of skills tests and certifications that allow you to demonstrate your expertise in different areas. Taking relevant skills tests can help you stand out to potential clients and show them that you have the skills and knowledge they’re looking for.
  • Find relevant job opportunities: Use the Upwork job search function to find job opportunities that match your skills and experience as a deep learning developer. Be sure to carefully review the job descriptions and requirements to ensure that you’re qualified for the positions you’re applying for.
  • Customize your proposals: When applying for jobs on Upwork, be sure to tailor your proposals to the specific needs and requirements of each client. This will help you stand out from other candidates and increase your chances of being selected for the job.
  • Participate in the Upwork community: Engaging with the Upwork community can help you build your reputation on the platform and make it easier to find work. Consider joining relevant forums, answering questions, and sharing your expertise with others.

Overall, the key to finding work as a deep learning developer on Upwork is to have a strong profile, demonstrate your skills and expertise, and be proactive in your job search.

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Author Spotlight

TensorFlow vs. Pytorch: Which Should You Use?
Yoshitaka Shiotsu
Technical Copywriter & SEO Consultant

Yoshitaka Shiotsu is a project engineer turned technical copywriter and SEO consultant who regularly contributes to the Upwork Resource Center. He specializes in helping tech companies, startups, and entrepreneurs set themselves up as voices of authority within their target industries.

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