PyTorch
PyTorch is an open-source deep learning framework developed by Meta that provides flexible tensor computation with GPU acceleration. It is the most popular framework for AI research due to its intuitive design and dynamic computation graphs.
Understanding PyTorch
PyTorch is an open-source deep learning framework developed by Meta AI that has become the dominant platform for AI research and increasingly for production deployment. Its defining feature is dynamic computational graphs (eager execution), which allow developers to build and modify neural network architectures using standard Python control flow, making debugging intuitive and experimentation flexible. PyTorch provides comprehensive libraries for computer vision (torchvision), natural language processing (torchtext), and audio processing (torchaudio), along with robust support for distributed training across multiple GPUs and nodes. The framework powers research at leading AI labs and universities, and most major large language models including LLaMA and GPT variants have been developed using PyTorch. Its ecosystem includes Hugging Face Transformers, PyTorch Lightning for streamlined training loops, and TorchServe for model deployment, making it a comprehensive platform from research prototyping through production optimization.
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AI Infrastructure
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AI Chip
An AI chip is a specialized processor designed specifically for artificial intelligence workloads like neural network training and inference. Examples include NVIDIA's GPUs, Google's TPUs, and custom ASICs.
API
An API (Application Programming Interface) is a set of protocols and tools that allows different software systems to communicate. AI APIs enable developers to integrate machine learning capabilities like text generation, image recognition, and speech processing into applications.
CUDA
CUDA (Compute Unified Device Architecture) is NVIDIA's parallel computing platform that allows developers to use GPUs for general-purpose processing. CUDA is the foundation of GPU-accelerated deep learning training.
Data Lake
A data lake is a centralized storage repository that holds vast amounts of raw data in its native format. AI systems often draw training data from data lakes that store structured, semi-structured, and unstructured information.
Data Pipeline
A data pipeline is an automated series of data processing steps that moves and transforms data from source systems to a destination. ML data pipelines handle ingestion, cleaning, feature engineering, and model training workflows.
Data Warehouse
A data warehouse is a centralized repository for structured, processed data optimized for analysis and reporting. AI and ML systems often source their training data from enterprise data warehouses.
Distributed Training
Distributed training is the practice of splitting model training across multiple GPUs or machines to handle large models and datasets. It uses data parallelism or model parallelism to accelerate training.
Edge AI
Edge AI refers to running artificial intelligence algorithms locally on hardware devices rather than in the cloud. Edge AI enables real-time inference with lower latency, better privacy, and reduced bandwidth requirements.