Deep Learning is one of the hottest industry skill right now. Deep learning is being now being used in many sectors to solve problems that were previously thought unsolvable for eg: self-driving cars, face recognition/augmentation etc.
Coding a deep learning model from scratch is a tedious and mind-bending task. It requires a lot of expertise and time. So we use certain frameworks that provide us with the baseline for creating the models.
Python Frameworks for Deep Learning
Standing in 2021, they’re a lot of frameworks for Deep Learning in Python. These frameworks are different in terms of abstraction, usage, portability. Choosing your required framework from this list can be a bit difficult. So here is a list of the top 5 frameworks/libraries that you can consider learning in 2021.
1. TensorFlow
TensorFlow is the most popular deep learning framework in 2021. TensorFlow was developed by the Google Brain team before open-sourcing it in 2015. The current version of TensorFlow features Keras as a high-level API which abstracts away a lot of underlying code making it easier and faster to create and train our models.
TensorFlow works with a wide range of computational devices – CPU, GPU(both NVIDIA and AMD) and even TPUs. For low compute edge devices TensorFlow Lite can save your day.
TensorFlow has a wide range of support for other languages like C++, JavaScript, Swift etc. This feature makes TensorFlow the go-to option if you are thinking of production. Once you train and save a model, the model can be used in the language of your choice alleviating the problem of multi-language dependencies.
2. PyTorch
PyTorch, developed by Facebook is the 2nd leading framework in terms of popularity. As the name suggests is the Python version of Torch(C++ library). PyTorch integrates seamlessly with Python and Numpy. PyTorch works on multi-dimensional arrays called Tensors which has an API quite similar to Numpy.
PyTorch provides a strong and flexible API to work with CPU and GPUs. PyTorch’s excellent support for GPUs makes distributed training more optimized and feasible. PyTorch is very extensible. Because of this extensibility, a lot of other frameworks and tools are built on top of PyTorch, one of them being HuggingFace TransFormers.
In PyTorch, you have to define your own training loops, weight update manually. This helps you attain more control over your model. This is the main reason why researchers tend to favor PyTorch. But this approach often results in boilerplate code, which is bad in terms of software deployment.
3. FastAI
FastAi is another deep learning library created by Jeremy Howard and Rachel Thomas. It aims to DL practitioners with high-level components that can quickly and easily provide state-of-the-art results in standard deep learning domains and provides researchers with low-level components that can be mixed and matched to build new approaches.
It aims to do both things without substantial compromises in ease of use, flexibility, or performance. FastAI takes the good from both worlds – PyTorch and Keras and puts it into one. FastAI has well-defined layers of abstraction- the higher, middle, and lower layer. The lower layer based on PyTorch API.
FastAI makes production easy avoiding boilerplate code and easy syntax for the sake of development.
4. MxNet
Apache MxNet might be one of the most surprising headings on this list. MxNet is backed up by a very small community is not as popular as most other frameworks listed here but it does what it promises to do.
MxNet tries to solve the issue of learning a different language to do machine learning. MxNet supports a range of languages like Scala, Python, R, Clojure, C++ etc.
MxNet API is very similar to PyTorch. So it won’t be very difficult to transition between the two. Along with the benefits of PyTorch API, it comes with benefits in terms of deployment. It is fast, scalable, and uses less memory than other frameworks.
5. PyTorch Lightning
PyTorch lighting is a relatively new framework when compared with the others in this list. Lighting is just a wrapper around the original PyTorch library. It makes adds a thin layer of abstraction reducing the amount of boilerplate code without taking away the power and beauty of PyTorch.
Lightning makes profiling, metric logging and visualisation and distributed training easier. Moreover, the transition from GPUs to TPUs does not require extra lines of code. So it brings PyTorch closer to what we call deployable.
Conclusion
This brings us to the end of this article. There is no such as thing called “best” framework. Every framework has some features which are better than others. So if you are looking for a framework, to begin with, you can go with any one of these. As you proceed further, you will understand what framework is best suited for you or your work and change it accordingly.