What Are Deep Learning Frameworks?

Deep Learning (DL) frameworks are libraries, tools, and interfaces that are designed to help you build deep learning models more quickly and easily. DL frameworks use a collection of pre-built components that are optimized to provide you with concise ways to define models, so you do not have to concern yourself with underlying algorithms.

The Advantages of Using Deep Learning Frameworks

DL frameworks provide you with tools that are suitable for building DL models more quickly so you can save the time and work it would have taken you to write hundreds of lines of codes.

  • Makes coding easier—DL frameworks offer tools and features to reduce the amount of work it takes to build DL models.
  • Community support—DL frameworks have active communities that share guides, tips, insights and can also help less-experienced coders with their
  • Support for parallel computations—DL frameworks support parallel processing, so you can do more tasks simultaneously.

Intro To PyTorch - The Python-Native Deep Learning Framework

PyTorch is an open source, Python-based, deep learning framework introduced in 2017 by Facebook’s Artificial Intelligence (AI) research team. PyTorch is designed as a flexible DL development platform that uses features such as dynamic computation graphs to provide more relevant data. PyTorch offers a workflow that is similar to numpy, the platform it was designed to replace, which is Python’s scientific computing library.

The developers of PyTorch designed the framework to be imperative, so you can immediately run your computations and check if your code runs instead of waiting until it is finished to do so.

Why You Should Choose PyTorch as Your Deep Learning Framework

PyTorch uses a simplified API which makes it a more beginner-friendly platform compared to other DL frameworks such as TensorFlow which requires a more steep learning curve. PyTorch makes it easy to write your own code without sacrificing versatile and powerful features.

Reasons To Choose PyTorch over other DL platforms:

  • Improves performance—PyTorch uses Graphics Processing Unit (GPU) accelerated libraries such as cuDNN to deliver high-performance multi-GPU model training.
  • Makes coding easier—PyTorch uses an API that is easy to use as Python can be. PyTorch is also easier to learn because it uses a library similar to traditional program practices.
  • Python support—PyTorch is based on Python, which is the most popular coding language among data scientists, DL engineers, and academics. PyTorch also benefits analysts by smoothly integrating the Python data science stack.
  • Dynamically updated graphs—PyTorch offers a flexible framework that allows you to build your own computational graphs and change them on the fly instead of having to use pre-defined ones. This feature allows you to view the data that is most relevant to you.
  • Increases productivity—PyTorch is designed to be simple to code on and allows developers to automate many processes so they potentially make fewer errors and become more productive.

6 Great Things You Can Do with PyTorch

We have compiled a list of six beginner-friendly tips and tricks that you can try to gain a better understanding of the advantages of using PyTorch.

#1: Build DL applications on top of dynamic graphs

Build your own computational graphs and change them on the fly instead of having to use pre-defined ones. You can use PyTorch to display any metric that is relevant to you. For example, if you worry about your equipment overheating, you can set a graph that shows you the processing and thermals of your Central Processing Unit (CPU) and your GPU.

#2: Easier debugging

You can set the PyTorch environment variable to CUDA_LAUNCH_BLOCKING=1 before you run your script to make PyTorch synchronously execute the CUDA code so it reports on errors on the fly. While it makes the process slower, it allows you to fix the reported errors faster and more accurately. In other DL frameworks, you would have to have to wait until the code is complete before receiving an error message that might not direct you to the original source of error.

Additionally, the dynamic computational graph that PyTorch defines at runtime makes it easier to use other Python tools. Thus, you can use any Python debugging tool such as pdb and PyCharm debugger to debug your PyTorch code.

#3: Increase your multi-GPU setup efficiency with data parallelism

PyTorch has a feature called declarative data parallelism. The torch.nn.DataParallel library allows you to wrap modules and run them in batches, in parallel, on a multi-GPU setup. You can leverage deep learning platforms like MissingLink and FloydHub to help schedule and automate PyTorch jobs on multiple machines.

#4: Use numpy testing tools to enhance your unit testing

If you are writing code that contains a sequence of complex tensor operations, it is a good idea to break it up into smaller portions that are in charge of simpler functions and test these units individually. You can use test functions in numpy.testing to compare the results of these functions to your expectations.

#5: Transfer computations between the GPU and the CPU

With PyTorch, you can easily move computations between processing units with a simple command. Use model.cuda() to deliver data to the GPU and model.cpu() to deliver data to the CPU. However, this step can be hardware-demanding so a better way to run your machine is telling PyTorch to use the GPU when it is available and use the CPU when it isn’t. To do so, you need to use

# command to use
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
py

And then use model.to(device) to pass the data around.

#6: Shrink your images to increase performance on a CPU

You can set your code to look for a GPU within the running machine and automatically make optimizations in case no GPU is available. This will ensure that your code will also run well on a machine that relies solely on a CPU. For example, image size affects performance, GPUs can compute images like in Artificial Neural Network (ANN) training, much faster compared to a CPU. In this scenario, you can use:

# command to use
imsize = 512 if torch.cuda.is_available() else 128  # use small size if no GPU

to ensure that performance persists across machines with different hardware.

Conclusion

PyTorch is a flexible, Python-native, deep learning framework, which uses a simple API that makes it beginner-friendly and easy to write code with. You can use PyTorch to leverage from tools and features, like data parallelism, that other DL framework, such as TensorFlow offer, without the steep learning curve.