PyTorch is a popular open-source machine learning library built on top of the Torch library. It is prominently used for deep learning. PyTorch serves primarily two purposes as a machine learning framework. First, it provides an interface to perform NumPy-like tensor operations using GPUs and CPUs, and second, the ability to set up machine learning algorithms that require iterative optimization (including deep learning).
This competency area includes the implementation of different neural network architectures, distributed training, quantization, debugging issues in the machine learning workflow, and deployment.
- Neural Network Architectures - Ability to use PyTorch to solve more complex problems on unstructured data such as: image classification, segmentation, object detection, text summarization, video/audio classification., etc using different neural network architectures that can be built with basic blocks available in torch.nn such as convolution layers, recurrent layers, etc.
- Using Pre-trained models - Ability to load and fine tune pre-trained models for different tasks.
- Distributed Training - Usage of multiple GPUs for training models using Model Parallelism or Data Parallelism.
- Quantization - Theoretical understanding and practical usage of quantization in PyTorch which increases speed and reduces memory footprint of models.
- Debugging - Ability to debug a model pipeline built in PyTorch and narrowing down on root cause of any failure such as parameters exploding during training, etc.
- Deployment - Ability to serve models as services and building REST APIs for inference.