Metadata-Version: 2.4
Name: zendag
Version: 0.1.5
Summary: A framework for integrating Hydra/DVC/MLflow for reproducible ML experiments.
Project-URL: Homepage, https://github.com/quentinf00/zendag
Project-URL: Repository, https://github.com/quentinf00/zendag
Project-URL: Issues, https://github.com/quentinf00/zendag/issues
Project-URL: Documentation, https://quentinf00.github.io/zendag
Author-email: Your Name <your.email@example.com>
License: 
                                         Apache License
                                   Version 2.0, January 2004
                                http://www.apache.org/licenses/
        
           TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
        
           1. Definitions.
        
              "License" shall mean the terms and conditions for use, reproduction,
              and distribution as defined by Sections 1 through 9 of this document.
        
              "Licensor" shall mean the copyright owner or entity authorized by
              the copyright owner that is granting the License.
        
              "Legal Entity" shall mean the union of the acting entity and all
              other entities that control, are controlled by, or are under common
              control with that entity. For the purposes of this definition,
              "control" means (i) the power, direct or indirect, to cause the
              direction or management of such entity, whether by contract or
              otherwise, or (ii) ownership of fifty percent (50%) or more of the
              outstanding shares, or (iii) beneficial ownership of such entity.
        
              "You" (or "Your") shall mean an individual or Legal Entity
              exercising permissions granted by this License.
        
              "Source" form shall mean the preferred form for making modifications,
              including but not limited to software source code, documentation
              source, and configuration files.
        
              "Object" form shall mean any form resulting from mechanical
              transformation or translation of a Source form, including but
              not limited to compiled object code, generated documentation,
              and conversions to other media types.
        
              "Work" shall mean the work of authorship, whether in Source or
              Object form, made available under the License, as indicated by a
              copyright notice that is included in or attached to the work
              (an example is provided in the Appendix below).
        
              "Derivative Works" shall mean any work, whether in Source or Object
              form, that is based on (or derived from) the Work and for which the
              editorial revisions, annotations, elaborations, or other modifications
              represent, as a whole, an original work of authorship. For the purposes
              of this License, Derivative Works shall not include works that remain
              separable from, or merely link (or bind by name) to the interfaces of,
              the Work and Derivative Works thereof.
        
              "Contribution" shall mean any work of authorship, including
              the original version of the Work and any modifications or additions
              to that Work or Derivative Works thereof, that is intentionally
              submitted to Licensor for inclusion in the Work by the copyright owner
              or by an individual or Legal Entity authorized to submit on behalf of
              the copyright owner. For the purposes of this definition, "submitted"
              means any form of electronic, verbal, or written communication sent
              to the Licensor or its representatives, including but not limited to
              communication on electronic mailing lists, source code control systems,
              and issue tracking systems that are managed by, or on behalf of, the
              Licensor for the purpose of discussing and improving the Work, but
              excluding communication that is conspicuously marked or otherwise
              designated in writing by the copyright owner as "Not a Contribution."
        
              "Contributor" shall mean Licensor and any individual or Legal Entity
              on behalf of whom a Contribution has been received by Licensor and
              subsequently incorporated within the Work.
        
           2. Grant of Copyright License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              copyright license to reproduce, prepare Derivative Works of,
              publicly display, publicly perform, sublicense, and distribute the
              Work and such Derivative Works in Source or Object form.
        
           3. Grant of Patent License. Subject to the terms and conditions of
              this License, each Contributor hereby grants to You a perpetual,
              worldwide, non-exclusive, no-charge, royalty-free, irrevocable
              (except as stated in this section) patent license to make, have made,
              use, offer to sell, sell, import, and otherwise transfer the Work,
              where such license applies only to those patent claims licensable
              by such Contributor that are necessarily infringed by their
              Contribution(s) alone or by combination of their Contribution(s)
              with the Work to which such Contribution(s) was submitted. If You
              institute patent litigation against any entity (including a
              cross-claim or counterclaim in a lawsuit) alleging that the Work
              or a Contribution incorporated within the Work constitutes direct
              or contributory patent infringement, then any patent licenses
              granted to You under this License for that Work shall terminate
              as of the date such litigation is filed.
        
           4. Redistribution. You may reproduce and distribute copies of the
              Work or Derivative Works thereof in any medium, with or without
              modifications, and in Source or Object form, provided that You
              meet the following conditions:
        
              (a) You must give any other recipients of the Work or
                  Derivative Works a copy of this License; and
        
              (b) You must cause any modified files to carry prominent notices
                  stating that You changed the files; and
        
              (c) You must retain, in the Source form of any Derivative Works
                  that You distribute, all copyright, patent, trademark, and
                  attribution notices from the Source form of the Work,
                  excluding those notices that do not pertain to any part of
                  the Derivative Works; and
        
              (d) If the Work includes a "NOTICE" text file as part of its
                  distribution, then any Derivative Works that You distribute must
                  include a readable copy of the attribution notices contained
                  within such NOTICE file, excluding those notices that do not
                  pertain to any part of the Derivative Works, in at least one
                  of the following places: within a NOTICE text file distributed
                  as part of the Derivative Works; within the Source form or
                  documentation, if provided along with the Derivative Works; or,
                  within a display generated by the Derivative Works, if and
                  wherever such third-party notices normally appear. The contents
                  of the NOTICE file are for informational purposes only and
                  do not modify the License. You may add Your own attribution
                  notices within Derivative Works that You distribute, alongside
                  or as an addendum to the NOTICE text from the Work, provided
                  that such additional attribution notices cannot be construed
                  as modifying the License.
        
              You may add Your own copyright statement to Your modifications and
              may provide additional or different license terms and conditions
              for use, reproduction, or distribution of Your modifications, or
              for any such Derivative Works as a whole, provided Your use,
              reproduction, and distribution of the Work otherwise complies with
              the conditions stated in this License.
        
           5. Submission of Contributions. Unless You explicitly state otherwise,
              any Contribution intentionally submitted for inclusion in the Work
              by You to the Licensor shall be under the terms and conditions of
              this License, without any additional terms or conditions.
              Notwithstanding the above, nothing herein shall supersede or modify
              the terms of any separate license agreement you may have executed
              with Licensor regarding such Contributions.
        
           6. Trademarks. This License does not grant permission to use the trade
              names, trademarks, service marks, or product names of the Licensor,
              except as required for reasonable and customary use in describing the
              origin of the Work and reproducing the content of the NOTICE file.
        
           7. Disclaimer of Warranty. Unless required by applicable law or
              agreed to in writing, Licensor provides the Work (and each
              Contributor provides its Contributions) on an "AS IS" BASIS,
              WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
              implied, including, without limitation, any warranties or conditions
              of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
              PARTICULAR PURPOSE. You are solely responsible for determining the
              appropriateness of using or redistributing the Work and assume any
              risks associated with Your exercise of permissions under this License.
        
           8. Limitation of Liability. In no event and under no legal theory,
              whether in tort (including negligence), contract, or otherwise,
              unless required by applicable law (such as deliberate and grossly
              negligent acts) or agreed to in writing, shall any Contributor be
              liable to You for damages, including any direct, indirect, special,
              incidental, or consequential damages of any character arising as a
              result of this License or out of the use or inability to use the
              Work (including but not limited to damages for loss of goodwill,
              work stoppage, computer failure or malfunction, or any and all
              other commercial damages or losses), even if such Contributor
              has been advised of the possibility of such damages.
        
           9. Accepting Warranty or Additional Liability. While redistributing
              the Work or Derivative Works thereof, You may choose to offer,
              and charge a fee for, acceptance of support, warranty, indemnity,
              or other liability obligations and/or rights consistent with this
              License. However, in accepting such obligations, You may act only
              on Your own behalf and on Your sole responsibility, not on behalf
              of any other Contributor, and only if You agree to indemnify,
              defend, and hold each Contributor harmless for any liability
              incurred by, or claims asserted against, such Contributor by reason
              of your accepting any such warranty or additional liability.
        
           END OF TERMS AND CONDITIONS
        
           APPENDIX: How to apply the Apache License to your work.
        
              To apply the Apache License to your work, attach the following
              boilerplate notice, with the fields enclosed by brackets "[]"
              replaced with your own identifying information. (Don't include
              the brackets!)  The text should be enclosed in the appropriate
              comment syntax for the file format. We also recommend that a
              file or class name and description of purpose be included on the
              same "printed page" as the copyright notice for easier
              identification within third-party archives.
        
           Copyright [yyyy] [name of copyright owner]
        
           Licensed under the Apache License, Version 2.0 (the "License");
           you may not use this file except in compliance with the License.
           You may obtain a copy of the License at
        
               http://www.apache.org/licenses/LICENSE-2.0
        
           Unless required by applicable law or agreed to in writing, software
           distributed under the License is distributed on an "AS IS" BASIS,
           WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
           See the License for the specific language governing permissions and
           limitations under the License.
License-File: LICENSE
Keywords: dvc,hydra,machine-learning,mlflow,mlops,reproducibility,workflow
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: >=3.9
Requires-Dist: hydra-zen<1.0,>=0.14.0
Requires-Dist: mlflow<4.0,>=2.0.0
Requires-Dist: pandas<3.0,>=1.3.0
Requires-Dist: toolz<1.0,>=0.11.0
Provides-Extra: dev
Requires-Dist: mypy>=1.0; extra == 'dev'
Requires-Dist: pre-commit>=3.0; extra == 'dev'
Requires-Dist: pytest-cov>=3.0; extra == 'dev'
Requires-Dist: pytest>=7.0; extra == 'dev'
Requires-Dist: ruff>=0.1.0; extra == 'dev'
Provides-Extra: docs
Requires-Dist: ipykernel; extra == 'docs'
Requires-Dist: jupyter-client; extra == 'docs'
Requires-Dist: myst-nb<1.1,>=0.17; extra == 'docs'
Requires-Dist: sphinx-autodoc-typehints<2.0,>=1.20; extra == 'docs'
Requires-Dist: sphinx-copybutton<0.6,>=0.5; extra == 'docs'
Requires-Dist: sphinx-rtd-theme<2.0,>=1.0; extra == 'docs'
Requires-Dist: sphinx<8.0,>=5.0; extra == 'docs'
Description-Content-Type: text/markdown

**Experimental:** This project is still in fast development phase, as I use it personnally and internally for different usecases, I will consolidate the interface. 
      
# ZenDag

**ZenDag** is a Python framework designed to streamline Machine Learning experimentation workflows by integrating:

*   **Configuration Management:** [Hydra](https://hydra.cc/) and [Hydra-Zen](https://mit-ll-responsible-ai.github.io/hydra-zen/) for modular, reusable, and composable configuration-as-code.
*   **Pipeline Orchestration & Versioning:** [DVC](https://dvc.org/) for defining experiment pipelines (DAGs) and versioning data, artifacts, and models.
*   **Experiment Tracking:** [MLflow](https://mlflow.org/) for logging parameters, metrics, artifacts, and comparing runs.

The core idea is to **drive the DVC pipeline definition directly from your Hydra configurations**, minimizing redundancy and ensuring consistency between your code, configuration, and the execution pipeline.

## Core Concepts

1.  **Configuration as Code:** Define all aspects of your experiment (data sources, preprocessing steps, model architecture, training parameters, evaluation metrics, logger settings) using Python code via Hydra-Zen and store them in a structured way (e.g., using `hydra_zen.ZenStore`).
2.  **Stage-Based Pipelines:** Structure your ML workflow into logical stages (e.g., `data_prep`, `feature_eng`, `train`, `evaluate`, `deploy`). Each stage corresponds to a node in the DVC pipeline graph.
3.  **Automatic DAG Generation:** ZenDag automatically generates the `dvc.yaml` file. It discovers dependencies (`deps`) and outputs (`outs`) by inspecting your Hydra configurations during a resolution step. You declare these using `${deps:...}` and `${outs:...}` interpolations directly within your configuration values (e.g., file paths).
4.  **Integrated Experiment Tracking:** A simple decorator (`@zendag.mlflow_run`) wraps your stage execution functions to automatically handle MLflow setup, log parameters from the Hydra config, capture artifacts (including logs and the config itself), and manage nested runs within a parent pipeline run.

## Installation

```bash
pip install zendag # Or install from source/git if needed
```
    

## API Reference

**zendag.core.configure_pipeline(...)**

      
```python
def configure_pipeline(
    store: hydra_zen.ZenStore,
    stage_groups: List[str],
    stage_dir_fn: Callable[[str, str], str] = default_stage_dir_fn,
    configs_dir_fn: Callable[[str], str] = default_configs_dir_fn,
    dvc_filename: str = "dvc.yaml",
    run_script: str = "zendag.run",
    config_root: Optional[str] = None,
) -> None:
    # ... (Full signature in docstring above) ...
```
    


* Purpose: The main function to generate the dvc.yaml file.

* How it works:

    * Iterates through specified stage_groups in the hydra_zen.ZenStore.

    * For each configuration (name) within a stage group (stage):

        *Composes the full Hydra config (e.g., hydra.compose(overrides=[f"+{stage}={name}"])).

        *Writes the composed config to <configs_dir_fn(stage)>/<name>.yaml. This file is tracked as a param by DVC.

        *Registers temporary Hydra resolvers for ${deps:...} and ${outs:...}.

        *Calls OmegaConf.resolve(cfg). During resolution, any ${deps:path} or ${outs:path} encountered trigger the resolvers, which append the path to internal lists (side-effect).

        *Collects the unique dependencies and outputs discovered during resolution.

        *Defines a DVC stage entry in a dictionary (e.g., stages['stage/name'] = {...}). The cmd calls the specified run_script using the composed config. deps, outs, and params are populated.

    * Writes the complete stage dictionary to the dvc_filename.

* Logging: Provides INFO and DEBUG level logs about the process, including discovered deps/outs. Configure Python's logging to see these.


**zendag.config_utils.deps_path(...) & zendag.config_utils.outs_path(...)**

```python   
def deps_path(s: str, input_stage: Optional[str] = None, input_name: Optional[str] = None, stage_dir_fn=None) -> str:
    # ...

def outs_path(s: str) -> str:
    # ...
```
    


* Purpose: These functions format strings suitable for Hydra interpolation to declare DVC dependencies and outputs within your configuration values.

* Mechanism: They return strings like "${deps:path/to/dependency}" or "${outs:path/to/output}". When configure_pipeline calls OmegaConf.resolve, the registered resolvers detect these prefixes and capture the path (path/to/dependency or path/to/output) for the dvc.yaml generation. The resolver also returns the path part (k in the lambda lambda k: current_list.append(k) or k) so that the config value itself resolves to the intended path after interpolation (relative to the stage's output directory for outs).

* Usage: Use these inside your Hydra-Zen configurations where file paths are defined:

```python      
    from zendag.config_utils import deps_path, outs_path
    from hydra_zen import builds

    DataConfig = builds(
        MyDataset,
        data_file=deps_path("raw_data.csv", input_stage="data_fetch", input_name="fetch_europe"),
        processed_file=outs_path("processed_data.parquet"),
        # Need stage_dir_fn for deps_path resolution if using input_stage/name
        zen_meta=dict(stage_dir_fn=my_stage_dir_function) # Or rely on default/global
    )
```

        


**@zendag.mlflow_utils.mlflow_run(...)**

```python
@mlflow_run(project_name: str = os.environ.get("MLFLOW_PROJECT_NAME", "DefaultProject"))
def my_training_stage(cfg: DictConfig):
    # ... stage logic ...
```
    

* Purpose: Decorator for your main stage functions.

* Functionality:

    * Sets the MLflow experiment.

    * Handles parent/nested MLflow runs using .pipeline_id and DVC_STAGE env var.

    * If run via DVC (DVC_STAGE is set), loads the corresponding composed Hydra config (artifacts/<stage>/<name>.yaml).

    * Logs parameters from the resolved Hydra config to the nested MLflow run.

    * Logs the composed config .yaml file as an artifact.

    * Executes the decorated function.

    * Logs the run.log file from the Hydra output directory as an artifact on success or failure.

    * Manages exceptions and MLflow run states.

## Recommended Project Structure 

```
my_project/
├── artifacts/             # DVC-managed outputs (configs, logs, models...)
│   ├── data_prep/
│   │   ├── config_a.yaml
│   │   └── config_a/      # Stage output dir
│   │       └── run.log
│   └── training/
│       ├── config_b.yaml
│       └── config_b/
│           ├── checkpoints/
│           ├── model.onnx
│           └── run.log
├── configs/               # Hydra-Zen config definitions (structured)
│   ├── __init__.py
│   ├── common.py
│   ├── data.py
│   ├── model.py
│   └── training.py
├── data/                  # Raw data (potentially DVC-managed)
├── src/                   # Project source code
│   └── my_project_pkg/
│       ├── __init__.py
│       ├── stages/        # Stage logic functions (decorated)
│       │   ├── __init__.py
│       │   ├── data_prep.py
│       │   └── train.py
│       └── utils.py       # Utility functions
├── tests/                 # Unit/integration tests
├── .dvc/                  # DVC internal files
├── .dvcignore
├── .gitignore
├── .pipeline_id           # Stores current parent MLflow run ID (auto-managed)
├── configure.py           # Script to run zendag.configure_pipeline
├── dvc.yaml               # Generated by configure.py (defines pipeline)
└── README.md
```
    

* configs/: Organize your Hydra-Zen builds calls here, grouped by functionality (data, model, trainer, logger, etc.). Use hydra_zen.make_custom_builds_fn for brevity. Import these into configure.py.

* src/my_project_pkg/stages/: Implement the core logic for each pipeline stage here. Decorate the main function for each stage with @zendag.mlflow_run. These functions typically accept the Hydra DictConfig as an argument.

* configure.py: The script that:

    * Imports configs from configs/.

    * Populates a hydra_zen.ZenStore.

    * Defines the list of stage_groups to process.

    * Calls zendag.core.configure_pipeline(store, stage_groups, ...).


## How Automatic DAG Generation Works Internally

The key is the interaction between configure_pipeline, OmegaConf.resolve, and the custom deps/outs resolvers:

1. configure_pipeline registers temporary resolvers for deps and outs just before calling OmegaConf.resolve(cfg) for a specific stage config.

2. resolvers are simple lambdas, e.g., lambda k: my_list.append(k) or k.

3. OmegaConf.resolve encounters ${deps:some/path} within the config structure:

    * It calls the deps resolver with k = "some/path".

    * The resolver appends "some/path" to the current_deps list (side-effect).

    * The resolver returns k ("some/path").

    * OmegaConf uses this returned value to replace the ${deps:some/path} interpolation.

4. The same happens for ${outs:other/path}.

5. After OmegaConf.resolve(cfg) finishes, the current_deps and current_outs lists contain all paths discovered via these interpolations for that specific stage configuration.

6. These lists are then used to populate the deps and outs fields in the generated dvc.yaml.

This avoids manual duplication of paths between the config where they are used and the DVC pipeline definition.


## License
Apache 2.0