TechTrash-OstrisTraining 0.0.4

A package to use the OstrisTraining

Libs-Ostris (TechTrash_OstrisTraining)

This package provides a small Python wrapper around an Ostris training workflow.

The main entrypoint is OstrisTraining.train().

What train() does

• Download + prepare dataset from a public ZIP URL
  • ZIP root must contain image files + matching caption files (.txt)
  • Example pair: photobelle.png + photobelle.txt
  • Output: files are normalized to image_0.<ext>, image_0.txt, image_1.<ext>, image_1.txt, ...
• Write config YAML from a string into a deterministic file:
  • {absolute_path_racine}/config-{user_name}.yaml
• Run training
  • Currently _train_model() is a stub in main.py (replace with real training call)

Install (local dev)

From this folder:

pip install -e .

Usage (recommended)

The best reference is src/ostristraining/example.py.

You can run it directly after installing:

python -m ostristraining.example

Or copy/paste this minimal usage:

from ostristraining.main import OstrisTraining

trainer = OstrisTraining(
    user_name="demo_user",
    absolute_path_ostris="/tmp/ostris_project",
    absolute_path_racine="/tmp/ostris_runs",
)

# Public ZIP URL containing images + captions at the ZIP root.
url_zip_dataset = "https://example.com/dataset.zip"

# YAML config must be a STRING. Newlines + indentation matter in YAML.
config_yaml_content = \"\"\"\
model:
  name: my_model
training:
  steps: 1000
\"\"\"

trained_model_path = trainer.train(
    url_zip_dataset=url_zip_dataset,
    config_yaml_content=config_yaml_content,
)

print(trained_model_path)

Dataset ZIP format (important)

At the root of the ZIP, you must have:

• images: .png, .jpg, .jpeg, .webp, .bmp, .gif
• captions: .txt

And each image must have a caption with the same base name:

• photo001.png + photo001.txt
• photo002.jpg + photo002.txt

If an image has no matching .txt, it is skipped.