Building a Gin Config Controlled PyTorch Pipeline with Configurable MLP Variants, Cosine Scheduling, and Runtime Parameter Overrides
In this tutorial, we implement a Gin Config–controlled PyTorch experiment pipeline in which the executable training code remains stable. At the same time, the experimental degrees of freedom are moved into declarative configuration files. We construct a nonlinear spiral binary classification task, define a configurable MLP with scoped architectural variants, and expose parameters for the optimizer, scheduler, loss, batching, seeding, and training loop via @gin.configurable bindings. We use Gin’
This tutorial demonstrates how to build a machine learning training pipeline using Gin Config, a tool that separates experimental settings from source code by storing them in configuration files. The approach keeps the executable training code stable while moving architectural choices, optimizer settings, scheduling parameters, and other training variables into declarative configuration files that can be modified without editing the code itself. The tutorial constructs a practical example using PyTorch that includes a spiral dataset classification task, a configurable neural network with adjustable layers and activation functions, and support for runtime parameter overrides. This separation of configuration from code is significant because it enables researchers and engineers to run multiple experiments with different hyperparameters and model variants without changing the underlying implementation.
One short email a day with the stories that matter. Free. Unsubscribe with one click.
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on Maven
OpenAI’s quarterly revenue reportedly crosses $10B for the first time
Sales of $10B represents an almost 50% jump from revenue of $6.7 billion the company recorded in the second quarter.
Agents that build their own databases are forcing a data foundation rethink
AI agents are changing how enterprises create, move and govern information, and data observability is turning into a prerequisite rather than a nice-to-have. Models get most of the attention, but the data beneath them decides whether they hold up in production. That pressure is now reaching the cloud-native community, where KubeCon organizers added a dedicated […] The post Agents that build their own databases are forcing a data foundation rethink appeared first on SiliconANGLE.
Hands-on with Ring’s new AI drone camera for your home
Ring’s new Drone Cam is a flying indoor security camera. Ring is finally shipping a flying indoor drone camera. For real this time. The Ring Drone Cam will arrive on January 27th, 2027, for $249.99, Ring founder Jamie Siminoff said at a launch event in New York City this week. If it does actually ship, it will arrive almost six years after the company announced its first flying camera, the Ring Always Home Cam. Designed to patrol inside your home when you're not there, the Drone
Also reported by TechCrunch and Ars Technica
OpenAI rolls out new business profiles, making ChatGPT just a little bit more like Google
OpenAI is looking to make ChatGPT a more robust consumer discovery engine, and a host of new features seek to empower businesses to help it do that.
Also reported by The Verge
AI agents carry memory and storage like people, so their data needs guarding while in use
As AI agents move into production on Kubernetes, cloud engineering teams at financial companies face a security problem that encryption alone can’t solve. Container orchestration has become the default foundation for AI workloads, and the data those agents use is now the main target. Payment networks feel that pressure more than most because every transaction […] The post AI agents carry memory and storage like people, so their data needs guarding while in use appeared first on Silic
Serve Robotics opens robot delivery network for others to build on
The AV Delivery Developer Portal enables anyone to call robots from Serve’s delivery fleet. Source: Serve Robotics Serve Robotics Inc. today launched the AV Delivery Developer Portal, which it said gives developers, businesses, hobbyists, and AI assistants the opportunity to programmatically summon its sidewalk delivery robots. The company said its public application programming interface, or API, allows others to work with its autonomous robot fleet. It also released a sandbox environment