Skip to content

Project Structure

Initialize and structure a Python workflow project so releases can be built reproducibly and release runners can discover and execute its tasks.

A Python workflow project contains task classes, a Runner definition, and a tilebox.workflow.toml file. The Tilebox command-line tool uses these files to build a workflow release, discover the tasks it can execute, and make the release available to release runners after deployment.

Keep the project small and importable from its root. Release builds import the configured runner object, check that the Python runtime starts, and package the selected files into an immutable artifact.

For a new Python workflow project, use the CLI to create the Tilebox workflow and scaffold the local files.

Terminal window
tilebox workflow init --name "Scene QA"

The --name flag is optional. When omitted, the command derives the local project slug from the current directory name. The command converts the name to a slug, truncates it to at most 40 characters, creates the remote workflow, and writes the API-returned workflow slug to tilebox.workflow.toml.

tilebox workflow init creates tilebox.workflow.toml, pyproject.toml, and runner.py, adds the tilebox Python dependency, and runs uv sync to create the local environment and uv.lock file. It requires uv on PATH and aborts without changing the directory if any of tilebox.workflow.toml, pyproject.toml, runner.py, or uv.lock already exists.

The generated project is intentionally small. Edit runner.py directly for prototypes, or move task code into a package as the workflow grows.

Use a layout where task code and the runner definition are importable from the project root.

  • Directorymy-workflow/
    • pyproject.toml
    • uv.lock
    • tilebox.workflow.toml
    • Directorymy_workflow/
      • __init__.py
      • tasks.py
      • runner.py

Use a minimal pyproject.toml with the dependencies your workflow needs. For this example, only the Tilebox Python package is required.

pyproject.toml
[project]
name = "my-workflow"
version = "0.1.0"
requires-python = ">=3.11"
dependencies = [
"tilebox",
]

Create the package directory and an empty __init__.py file so Python can import my_workflow.runner from the project root.

Terminal window
mkdir -p my_workflow
touch my_workflow/__init__.py
uv lock

Put task classes in a module that can be imported during release validation.

my_workflow/tasks.py
# my_workflow/tasks.py
from tilebox.workflows import ExecutionContext, Task
class ProcessScene(Task):
scene_id: str
@staticmethod
def identifier() -> tuple[str, str]:
return "tilebox.com/example/ProcessScene", "v1.0"
def execute(self, context: ExecutionContext) -> None:
context.current_task.display = f"ProcessScene({self.scene_id})"
context.logger.info("Processing scene", scene_id=self.scene_id)

Use explicit identifiers for workflow code that will be published. A stable identifier lets existing jobs continue to run after refactors and compatible bug fixes.

Create a module that exports a Runner object. This object defines the task registrations for the workflow, and release builds import it during validation.

my_workflow/runner.py
# my_workflow/runner.py
from tilebox.workflows import Runner
from tilebox.workflows.cache import LocalFileSystemCache
from my_workflow.tasks import ProcessScene
runner = Runner(
tasks=[ProcessScene],
cache=LocalFileSystemCache(),
)

Point tilebox.workflow.toml at the exported Runner object and include the files required by the release runner.

[workflow]
slug = "my-workflow"
root = "."
runner = "my_workflow.runner:runner"
[build]
include = [
"pyproject.toml",
"uv.lock",
"my_workflow/**",
]
exclude = [
".venv/**",
"**/__pycache__/**",
"**/*.pyc",
".pytest_cache/**",
]
use_gitignore = true

The Tilebox command-line tool imports the runner object during build-release and publish-release, discovers its task identifiers, and records them in the workflow release. A release runner later loads the release artifact and invokes the Python runtime through the command-line tool.

Include source code, lock files, and small configuration. Exclude local virtual environments, test caches, downloaded provider data, model checkpoints, generated outputs, and other large runtime artifacts.

If a task needs a large model or reference file, fetch it lazily at runtime and cache it in a deterministic runner-local path such as ~/.cache/tilebox/.... The workflow should still work when a release runner starts with an empty cache.