Differentiating PyTorch from all other Deep Learning frameworks
“PyTorch are dynamic Neural Networks which are like tensors on steroids running in Python with strong GPU acceleration”
How does PyTorch fare among all other Deep Learning frameworks? Is it a cut above the rest right on its early staging or is it still flowing its way towards the top of the chart in terms of features and performance?
Deep Learning systems and applications are gaining popularity in numerous fields, such as image classifications, object detection, translations, and Natural Language Processing. We witness emerging trends of a growing number of DL software frameworks in both open source and commercial markets which are represented by those top of the line such as Tensor-Flow, Caffe, Torch, Keras, Theano, and the one described as the “youngest” but would most likely be the fastest framework called PyTorch.
Most DL frameworks utilize a similar layered software architecture to provide APIs to enable users to configure the Deep Neural Network model and the training methods which includes the optimizer.
Before comparing further, let’s explore the special features of PyTorch:
PyTorch has seen a high level of adoption within the deep learning framework community. It shines for a rapid prototyping with Dynamic Neural Networks, auto-differentiation, deep Python integration, and strong support for GPUs. It also enables fast, flexible experimentation and efficient production through a hybrid front-end, distributed training, and ecosystem of tools and libraries.
PyTorch runs on Python which means that anyone with a basic understanding of Python can immediately start to build their own first deep learning models. Given PyTorch framework’s architectural style, an entire Deep Learning model process is far simpler and transparent as compared to all other Deep Learning frameworks. It was developed by Facebook and is used by Twitter, Salesforce, the University of Oxford, and many others.
PyTorch is mainly used to train deep learning models quickly and effectively, so it is the framework of choice for a large number of researchers, engineers, and developers
Advantages of PyTorch:
1.)It is Based On Python (Python is the simplest programming language)
2.)Faster Deep Learning training (More parameters, faster training time)
3.)Dynamic approach via GPU (Each level of computation can be viewed)
4.)Increase in Developer productivity (It is user-friendly and simple to use)
5.)Easier to learn (The PyTorch documentation is helpful for beginners)
6.)A community of focused developers (Small group who solve big issues)
7.)Transparent to the user(Dynamic Graph outputs viewed faster)
8.)Easy to debug (Uses PyCharm for define-by-run mode during runtime)
9.)Data parallelism (Allows “torch.nn.DataParallel” to wrap any module)
Disadvantages of PyTorch:
1.)It lacks model serving in production (Although it will change in the future)
2.)It’s new and not yet widely known (Has less costumers/users at this point)
3.) It lacks interfaces for monitoring and visualization such as Tensorboard (As a workaround, you can connect externally to Tensorboard)
Comparative Analysis of DL Frameworks speed:
The Diagram represents grouping of the frameworks by models to see which models were fastest using which framework. In case of inception models, only Tensor Flow can be compared to Keras and in both cases Tensorflow is faster. The ResNet50 achieves lowest training time when Tensorflow is used. VGG models stand in opposition to that, because both are trained quickest through PyTorch.
PyTorch has various advantages and counting. It may have disadvantages but will eventually be addressed in the near future. It is definitely one of the top Deep Learning frameworks nowadays due to its high performance, fast training of different models, and outstanding features which trends it as researchers’ favorite and engineers’ choice recently.
Related reference links for more reads:
I never want to end explaining this interesting topic but I’ve realized the need to allow you to explore furthermore on the important facts in Deep Learning through PyTorch. I also highly encourage you to read more about PyTorch and its pros and cons updates in comparison to other Deep Learning Frameworks. Regularly practice and test to see it yourself as to why we would love to utilize it onwards. Also, I wish all of you the best on your deep learning ventures in the years to come!
Up Next — The further emergence of PyTorch in 2019 and onwards
