Skip to main content

An IA training system, based on domain driven design and an event driven architecture, created on top of the pybondi library.

Project description

torch-system

An IA training system, created using domain driven design and an event driven architecture.

Installation

Make sure you have a pytorch distribution installed. If you don't, go to the official website and follow the instructions.

Then, you can install the package using pip:

pip install torchsystem

Soon I will be adding the package to conda-forge when the package is more stable.

Introduction

Machine learning systems are getting more and more complex, and the need for a more organized and structured way to build and maintain them is becoming more evident. Training a neural network requires to define a cluster of related objects that should be treated as a single unit, this defines an aggregate. The training process mutates the state of the aggregate producing data that should be stored alongside the state of the aggregate in a transactional way. This establishes a clear bounded context that should be modeled using Domain Driven Design (DDD) principles.

The torch-system is a framework based on DDD and Event Driven Architecture (EDA) principles, using the pybondi library. It aims to provide a way to model complex machine models using aggregates and training flows using commands and events, and persist states and results using the repositories, the unit of work pattern and pub/sub.

It also provides out of the box tools for managing the training process, model compilation, centralized settings with enviroments variables using pydantic-settings, automatic parameter tracking using mlregistry.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchsystem-0.1.7.tar.gz (8.3 kB view details)

Uploaded Source

Built Distribution

torchsystem-0.1.7-py3-none-any.whl (11.6 kB view details)

Uploaded Python 3

File details

Details for the file torchsystem-0.1.7.tar.gz.

File metadata

  • Download URL: torchsystem-0.1.7.tar.gz
  • Upload date:
  • Size: 8.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.4 CPython/3.12.1 Linux/6.5.0-1025-azure

File hashes

Hashes for torchsystem-0.1.7.tar.gz
Algorithm Hash digest
SHA256 419eded2b335a63fa28fe8dfec8e226992bc1d15987285ac85e0227ae014a61e
MD5 d23c5b4af69a659cb7e3a1840aaf70ef
BLAKE2b-256 07492e47c73aedbfffc08d6e56586e568892837ba884547992988e0fc725f99c

See more details on using hashes here.

File details

Details for the file torchsystem-0.1.7-py3-none-any.whl.

File metadata

  • Download URL: torchsystem-0.1.7-py3-none-any.whl
  • Upload date:
  • Size: 11.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: poetry/1.8.4 CPython/3.12.1 Linux/6.5.0-1025-azure

File hashes

Hashes for torchsystem-0.1.7-py3-none-any.whl
Algorithm Hash digest
SHA256 942e330fa549294e416a1e897c998b7ea3aaea776e43e0b0801fd407a99cdeb9
MD5 512ca2b27f70e86af7b9a283893a8b11
BLAKE2b-256 66ac2469c24b6986727e1150fd8aa91d0eab5c7cda68501f8d2fa29ee162fc46

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page