Search Results for "recipes"

...Deployer is a cli tool for deployment of any PHP applications, including frameworks such as Laravel, Symfony, Zend Framework and many more. Main concept of Deployer is recipe, a php file containing tasks definitions. Recipe can require other recipes and extend/ override functionality. Also Deployer comes with bunch of ready to use recipes from community for Slack, etc. Deployer can be easily installed via composer or as phar archive. By default deployer keeps the last 5 releases, but you can increase this number by modifying the associated parameter. You may want to run some task before/after other tasks. ...

The Symfony ORM Pack is a meta-package that installs Doctrine ORM and related tools into a Symfony project. It’s an essential bundle for database-driven applications, offering object-relational mapping and advanced query features out of the box.

...Start with some quick examples, then read the manual and the Sphinx docs, watch videos, and join a user group for discussion. Take advantage of the layouts, plugins, appliances, and recipes.

...Experiments are expressed as dependent steps that execute in topological order, enabling reproducible training workflows. Marin also provides model checkpoints, training recipes, scaling research, documentation, and reusable infrastructure for large-scale experiments.

Component/integration test is a hybrid between E2E and unit tets. It's gaining a lot of popularity and going by the testing diamond model it is considered as the default technique for the modern backend. Its main idea is testing an entire component (e.g., Microservice) as-is, through the API, with all the layers including the database but fake anything extraneous. This brings both high confidence and great developer experience. However, doing it right, fast, exhaustive and maximizing the...

...It focuses on the full reasoning stack—data preparation, supervised fine-tuning, preference or outcome-based optimization, and lightweight evaluation—so you can iterate quickly on chain-of-thought–heavy tasks. The project’s philosophy is practicality: sensible defaults, one-command recipes, and compatibility with popular base models let you stand up experiments without wrestling infrastructure. It emphasizes memory-efficient training strategies so you can train long-context or reasoning-dense models on commodity GPUs. The framework is also organized to help you compare training strategies (e.g., pure SFT vs. preference optimization) so you can see what actually moves metrics in math, code, and multi-step reasoning. ...