Let's print out the resulting torch.Tensor object as a numpy.array object using the .numpy() function:
The code should return the following output:
You've successfully created two arrays and converted them into tensors.
TensorFlow vs. PyTorch
Now that you've grasped the basics of how the two frameworks work, it is time to know the differences between the two.
This will help you arrive at a decision on which framework to choose for your deep learning projects.
If you're an experienced Python programmer, PyTorch will feel easy for you.
PyTorch is more native to Python, making it easy for developers to develop deep learning models.
On the other hand, TensorFlow has many entities like placeholders, sessions and more, which may make it a bit complex to learn.
In deep learning and artificial neural networks, computation graphs provide a way to represent the evaluation of mathematical expressions using the graph data structure.
The two libraries differ in terms of how the computational graphs are defined and used.
TensorFlow uses a static graph for computation, which means the entire computation graph should be defined before execution is done.
However, PyTorch is different. It allows you to define and manipulate computational graphs dynamically, making it the best option when dealing with inputs that change in real-time when working with artificial neural networks.
As we stated above, computational graphs are very dynamic when working with PyTorch.
Due to this, any Python debugging tool like pdb or ipdb can be used to debug code effectively.
For TensorFlow, you've to install a separate tool called "tfdbg" to be able to evaluate TensorFlow expressions at runtime.
Note that you cannot debug the code natively with Python, hence, you must install this tool.
Most people are curious to learn deep learning, and that's why the deep learning community is highly revered.
A larger community means the ability to find solutions and help easily and quickly.
TensorFlow has a larger community compared to PyTorch.
The reason is that PyTorch is newer to the market than TensorFlow, hence, there has been more content online about TensorFlow than PyTorch.
However, since people have realized how easy it is to use PyTorch, its user community may grow and surpass that of TensorFlow.
Next, we'll be exploring how these two frameworks differ in terms of visualization tools.
TensorFlow comes with a great visualization tool named "TensorBoard".
TensorBoard allows you to visualize your machine learning models on the web browser.
TensorBoard provides numerous visualizations and appealing graphs that can be understood easily.
PyTorch doesn't have such a tool, hence, to visualize your data, you must use a tool like Matplotlib.
Although you can still use TensorBoard in PyTorch, the process of integrating the two tools is complex.
TensorFlow wins when it comes to deployment because it comes with a framework named "TensorFlow Serving" that helps us to rapidly deploy models to gRPC servers with much ease.
In PyTorch, you can achieve a similar result when you use it with Flask or other REST APIs that have been built on top of the model.
You can also use TensorFlow with the REST APIs, hence, TensorFlow wins when it comes to deployment.
In this case, parallelism has to do with supporting a pipeline for the distribution of data instead of leaving one entity to process the data.
PyTorch provides its users with better parallelism capabilities.
PyTorch users use Torch.nn.Parallel to wrap modules that are to be used parallely over other batch data.
This makes it easy to harness the power of multiple GPUs simulateneously without much effort.
TensorFlow also offers parallelism. However, it requires several manual implementations that make it complex to use in both learning and production environments.
Prototyping and Production
TensorFlow has an advantage as far as building scalable production models is concerned.
However, the feature should be easy to learn and implement, and PyTorch excels in this.
It makes it easy to create prototypes and work on projects that have a less production implementation.
With the above information, you can know which framework, whether TensorFlow or PyTorch, suits your needs.
This is what you've learnt in this article:
TensorFlow and PyTorch are the two most popular frameworks for performing deep learning tasks.
Deep learning involves solving real-world problems using human-like computers.
TensorFlow was developed by Google, while PyTorch was developed by Facebook.
PyTorch is a new deep learning framework, hence, not much information about it is available online compared to TensorFlow.
PyTorch is easier to use that TensorFlow, and it is more native to Python.