- Published at
lightning
In computer science, ‘lightning’ often refers to a fast and sudden surge of activity or data processing. It’s used metaphorically to describe situations where systems experience a brief but intense spike in load, requests, or network traffic. This can be due to various factors like viral content going online, unexpected events triggering automated processes, or large-scale deployments. Unlike sustained high loads which might indicate a systemic problem, ‘lightning’ is typically short-lived and may not necessarily cause long-term issues if the system is designed to handle such bursts. Monitoring systems often look for these lightning strikes to understand usage patterns and identify potential vulnerabilities that could be exploited during more prolonged periods of stress. For example, a sudden spike in database queries might be described as ‘lightning’ – it’s a quick burst rather than a constant drain.
- aiohttp (1)
- api-development (1)
- api-testing (1)
- api (2)
- app (1)
- archlinux (1)
- asgi (1)
- async-tasks (1)
- async-views (1)
- asynchronous-tasks (1)
- asyncio (1)
- authentication (1)
- automation (1)
- azure-blob-storage (1)
- azure (1)
- bert (1)
- blob-storage (1)
- casaos (1)
- celery (1)
- channels (1)
- cifar10 (1)
- cnn (1)
- colab (1)
- cors (2)
- data labeling (1)
- decorators (1)
- Default (0)
- deployment (1)
- development (1)
- django-ninja (3)
- django-q (1)
- django (13)
- docker (2)
- elasticsearch (1)
- email (1)
- firebase (1)
- flask (1)
- flet (1)
- frontend integration (1)
- full-text search (1)
- gcloud (1)
- graphql (1)
- homebrew (1)
- installation (3)
- ipython (1)
- jwt (2)
- label studio (1)
- lightning (1)
- llm (1)
- lmstudio (1)
- m2m100 (1)
- management-command (1)
- markdown (1)
- moviepy (1)
- ninja (1)
- notebook (1)
- nunchaku (1)
- openai (1)
- optimization (1)
- orm (1)
- package-manager (1)
- performance (1)
- portainer (1)
- postgres (1)
- pydantic (1)
- pytest (1)
- python (9)
- pytorch-lightning (1)
- pytorch (1)
- queryset (1)
- rest-framework (1)
- sendgrid (1)
- sentiment-analysis (1)
- settings (1)
- smart-home (1)
- storage (2)
- task group (1)
- template (1)
- timing (1)
- token (1)
- traefik (1)
- transformers (2)
- translation (1)
- uv (1)
- video-processing (1)
- vite (1)
- websockets (1)
- This tutorial demonstrates building and training a Convolutional Neural Network (CNN) for the CIFAR-10 dataset using PyTorch Lightning. It covers data loading, model definition, training loop setup, TensorBoard integration, and early stopping.