Search Results for "input-output model"

...Build models with scripts that are as close as possible to how you’d describe the object to a human, using a standard, already established programming language. Create parametric models that can be very easily customized by end users. Output high-quality CAD formats like STEP and AMF in addition to traditional STL. Provide a non-proprietary, plain text model format that can be edited and executed with only a web browser. The scripts use a standard programming language, Python, and thus can benefit from the associated infrastructure. This includes many standard libraries and IDEs. ...

...Provides a GeometryField, which is a subclass of Django Rest Framework (from now on DRF) WritableField. This field handles GeoDjango geometry fields, providing custom to_native and from_native methods for GeoJSON input/output. While precision and remove_duplicates are designed to reduce the byte size of the API response, they will also increase the processing time required to render the response. This will likely be negligible for small GeoJSON responses but may become an issue for large responses. The primary key of the model (usually the "id" attribute) is automatically used as the id field of each GeoJSON Feature Object. ...

The bu-agent-sdk from the Browser Use project is a minimalistic Python framework that defines an AI agent as a simple loop of tool calls, aiming to keep abstractions low so developers can build autonomous agents without unnecessary complexity. At its core, the agent loop repeatedly queries a large language model, interprets its output, and executes defined “tools” — functions annotated with task names — to perform actions, allowing the agent to complete tasks like arithmetic, decision-making, or domain-specific work. The SDK emphasizes simplicity and control, avoiding heavy orchestration frameworks and instead letting developers specify exactly what tools an agent can employ and how it should signal task completion. ...

Model Search is an AutoML research system for discovering neural network architectures with minimal human intervention. Instead of hand-crafting models, you define a search space and objectives, then the system explores candidate architectures using controllers and population-based strategies. It supports multiple tasks (such as vision or text) by letting you express reusable building blocks—layers, cells, and topologies—that the search can recombine. Training, evaluation, and promotion of...

This framework allows the definition of attributes, entities and relations as used in entity-relationship (ER) models. It will then handle input and representation of data appropriately.