feat: add pipeline packaging utils

.github/workflows/tests.yml

@@ -45,6 +45,7 @@ jobs:
       - name: Install dependencies
         run: |
           pip install -e '.[dev]'
+          pip install poetry build
 
       - name: Test with Pytest on Python ${{ matrix.python-version }}
         run: python -m pytest --cov edspdf --cov-report xml

changelog.md

@@ -6,6 +6,7 @@
 
 - Add multi-modal transformers (`huggingface-embedding`) with windowing options
 - Add `render_page` option to `pdfminer` extractor, for multi-modal PDF features
+- Packaging utils (`pipeline.package(...)`) to make a pip installable package from a pipeline
 
 ### Changed
 

docs/pipeline.md

@@ -11,6 +11,8 @@ Processing PDFs usually involves many steps such as extracting lines, running OC
     can use any technology in static components, we do not provide tools to train
     components built with other deep learning frameworks.
 
+## Creating a pipeline
+
 A pipe is a processing block (like a function) that applies a transformation on its input and returns a modified object.
 
 At the moment, four types of pipes are implemented in the library:
@@ -57,7 +59,33 @@ model(pdf_bytes)
 model.pipe([pdf_bytes, ...])
 ```
 
-## Hybrid models
+### Hybrid models
 
 EDS-PDF was designed to facilitate the training and inference of hybrid models that
 arbitrarily chain static components or trained deep learning components. Static components are callable objects that take a PDFDoc object as input, perform arbitrary transformations over the input, and return the modified object. [Trainable pipes][edspdf.trainable_pipe.TrainablePipe], on the other hand, allow for deep learning operations to be performed on the [PDFDoc][edspdf.structures.PDFDoc] object and must be trained to be used.
+
+## Saving and loading a pipeline
+
+Pipelines can be saved and loaded using the `save` and `load` methods. The saved pipeline is not a pickled objet but a folder containing the config file, the weights and extra resources for each pipeline. This allows for easy inspection and modification of the pipeline, and avoids the execution of arbitrary code when loading a pipeline.
+
+```python
+model.save("path/to/your/model")
+model = edspdf.load("path/to/your/model")
+```
+
+To share the pipeline and turn it into a pip installable package, you can use the `package` method, which will use or create a pyproject.toml file, fill it accordingly, and create a wheel file. At the moment, we only support the poetry package manager.
+
+```python
+model.package(
+    name="path/to/your/package",
+    version="0.0.1",
+    root_dir="path/to/project/root",  # optional, to retrieve an existing pyproject.toml file
+    # if you don't have a pyproject.toml, you can provide the metadata here instead
+    metadata=dict(
+        authors="Firstname Lastname <[email protected]>",
+        description="A short description of your package",
+    ),
+)
+```
+
+This will create a wheel file in the root_dir/dist folder, which you can share and install with pip

edspdf/pipeline.py

@@ -28,8 +28,10 @@
 from confit.utils.collections import join_path, split_path
 from confit.utils.xjson import Reference
 from tqdm import tqdm
+from typing_extensions import Literal
 
 import edspdf
+from build import ConfigSettingsType
 
 from .registry import CurriedFactory, registry
 from .structures import PDFDoc
@@ -40,6 +42,7 @@
     decompress_dict,
     multi_tee,
 )
+from .utils.package import ModuleName, package
 
 EMPTY_LIST = FrozenList()
 
@@ -944,6 +947,35 @@ def select_pipes(
         yield self
         self._disabled = disabled_before
 
+    def package(
+        self,
+        name: ModuleName,
+        root_dir: Union[str, Path] = ".",
+        artifacts_name: ModuleName = "artifacts",
+        check_dependencies: bool = False,
+        project_type: Optional[Literal["poetry", "setuptools"]] = None,
+        version: str = "0.1.0",
+        metadata: Optional[Dict[str, Any]] = {},
+        distributions: Optional[Sequence[Literal["wheel", "sdist"]]] = ["wheel"],
+        config_settings: Optional[ConfigSettingsType] = None,
+        isolation: bool = True,
+        skip_build_dependency_check: bool = False,
+    ):
+        return package(
+            pipeline=self,
+            name=name,
+            root_dir=root_dir,
+            artifacts_name=artifacts_name,
+            check_dependencies=check_dependencies,
+            project_type=project_type,
+            version=version,
+            metadata=metadata,
+            distributions=distributions,
+            config_settings=config_settings,
+            isolation=isolation,
+            skip_build_dependency_check=skip_build_dependency_check,
+        )
+
 
 def load(
     config: Union[Path, str, Config],