examples: update some examples to use the new task execution engine

The Python scripts for the following 3 examples have been converted into the new declarative YAML
file format:

- tensorflow-mnist-classifier
- tensorflow-mnist-model-inversion
- tensorflow-mnist-pixel-threshold

The original demos that used Python scripts have been moved into "legacy" folders in the example
directory.

Closes #103

examples/task-plugins/dioptra_custom/evaluation/mlflow.py

@@ -0,0 +1,94 @@
+# This Software (Dioptra) is being made available as a public service by the
+# National Institute of Standards and Technology (NIST), an Agency of the United
+# States Department of Commerce. This software was developed in part by employees of
+# NIST and in part by NIST contractors. Copyright in portions of this software that
+# were developed by NIST contractors has been licensed or assigned to NIST. Pursuant
+# to Title 17 United States Code Section 105, works of NIST employees are not
+# subject to copyright protection in the United States. However, NIST may hold
+# international copyright in software created by its employees and domestic
+# copyright (or licensing rights) in portions of software that were assigned or
+# licensed to NIST. To the extent that NIST holds copyright in this software, it is
+# being made available under the Creative Commons Attribution 4.0 International
+# license (CC BY 4.0). The disclaimers of the CC BY 4.0 license apply to all parts
+# of the software developed or licensed by NIST.
+#
+# ACCESS THE FULL CC BY 4.0 LICENSE HERE:
+# https://creativecommons.org/licenses/by/4.0/legalcode
+"""A task plugin module for using the MLFlow model registry."""
+
+from __future__ import annotations
+
+from typing import Optional
+
+import mlflow
+import structlog
+from mlflow.entities.model_registry import ModelVersion
+from mlflow.tracking import MlflowClient
+from structlog.stdlib import BoundLogger
+
+from dioptra import pyplugs
+
+LOGGER: BoundLogger = structlog.stdlib.get_logger()
+
+
+@pyplugs.register
+def add_model_to_registry(
+    name: str, model_dir: str
+) -> Optional[ModelVersion]:
+    """Registers a trained model logged during the current run to the MLFlow registry.
+
+    Args:
+        active_run: The :py:class:`mlflow.ActiveRun` object managing the current run's
+            state.
+        name: The registration name to use for the model.
+        model_dir: The relative artifact directory where MLFlow logged the model trained
+            during the current run.
+
+    Returns:
+        A :py:class:`~mlflow.entities.model_registry.ModelVersion` object created by the
+        backend.
+    """
+    if not name.strip():
+        return None
+
+    active_run = mlflow.active_run()
+
+    run_id: str = active_run.info.run_id
+    artifact_uri: str = active_run.info.artifact_uri
+    source: str = f"{artifact_uri}/{model_dir}"
+
+    registered_models = [x.name for x in MlflowClient().list_registered_models()]
+
+    if name not in registered_models:
+        LOGGER.info("create registered model", name=name)
+        MlflowClient().create_registered_model(name=name)
+
+    LOGGER.info("create model version", name=name, source=source, run_id=run_id)
+    model_version: ModelVersion = MlflowClient().create_model_version(
+        name=name, source=source, run_id=run_id
+    )
+
+    return model_version
+
+
+@pyplugs.register
+def get_experiment_name() -> str:
+    """Gets the name of the experiment for the current run.
+
+    Args:
+        active_run: The :py:class:`mlflow.ActiveRun` object managing the current run's
+            state.
+
+    Returns:
+        The name of the experiment.
+    """
+    active_run = mlflow.active_run()
+
+    experiment_name: str = (
+        MlflowClient().get_experiment(active_run.info.experiment_id).name
+    )
+    LOGGER.info(
+        "Obtained experiment name of active run", experiment_name=experiment_name
+    )
+
+    return experiment_name

examples/tensorflow-mnist-classifier-legacy/README.md

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+# Tensorflow MNIST Classifier demo (legacy)
+
+This example demonstrates how to run a simple experiment on the transferability of the fast gradient method (FGM) evasion attack between two neural network architectures.
+The demo can be found in the Jupyter notebook file [demo.ipynb](demo.ipynb).
+
+## Running the example
+
+To prepare your environment for running this example, follow the linked instructions below:
+
+1.  [Create and activate a Python virtual environment and install the necessary dependencies](../README.md#creating-a-virtual-environment)
+2.  [Download the MNIST dataset using the download_data.py script.](../README.md#downloading-datasets)
+3.  [Follow the links in these User Setup instructions](../../README.md#user-setup) to do the following:
+    -   Build the containers
+    -   Use the cookiecutter template to generate the scripts, configuration files, and Docker Compose files you will need to run Dioptra
+4.  [Edit the docker-compose.yml file to mount the data folder in the worker containers](../README.md#mounting-the-data-folder-in-the-worker-containers)
+5.  [Initialize and start Dioptra](https://pages.nist.gov/dioptra/getting-started/running-dioptra.html#initializing-the-deployment)
+6.  [Register the custom task plugins for Dioptra's examples and demos](../README.md#registering-custom-task-plugins)
+7.  [Register the queues for Dioptra's examples and demos](../README.md#registering-queues)
+8.  [Start JupyterLab and open `demo.ipynb`](../README.md#starting-jupyter-lab)
+
+Steps 1–4 and 6–7 only need to be run once.
+**Returning users only need to repeat Steps 5 (if you stopped Dioptra using `docker compose down`) and 8 (if you stopped the `jupyter lab` process)**.