Training First Model (with Configs)
===================================

Prerequisites
--------------

- You have cloned the **Noether Framework**
- You have a ``tutorial/`` folder in the repo root

In this example we look at ``ShapeNet-Car`` dataset.

The fetching and preprocessing instructions are in the ``README.md`` located in the
``src/noether/data/datasets/cfd/shapenet_car/`` folder. Review them first and proceed with the next step when ready.

Execution
---------

In your ``tutorial/configs/`` folder create a file called ``static_config.yaml`` (a template for it can be found under
``noether/yaml_templates/`` ("noether" is the repo root name). In that file, define the output folder - a location
where the artifacts will be stored, for example:

.. code-block:: yaml

    output_path: /Users/user/data/shapenet_car/outputs

This file is loaded by the training config and overrides default paths. Note that this path is valid for macOS,
adapt it to your system.

Without changing anything else we can use the following command to start the training:

.. code-block:: bash

    uv run noether-train --hp tutorial/configs/train_shapenet.yaml \
        +experiment/shapenet=upt \
        dataset_root=/Users/user/data/shapenet_car

We expose ``noether-train`` command via `uv` for your convenience to run the training. Here we specify the location
of the training configuration ``train_shapenet.yaml``, experiment type ``shapenet=upt`` (it means that we run  ``UPT``
model from the ``experiment/shapenet/`` folder, and the dataset root.

Additionally, we can alter the hyperparameters right from the CLI:

.. code-block:: bash

    uv run noether-train --hp tutorial/configs/train_shapenet.yaml \
        +experiment/shapenet=upt \
        dataset_root=/Users/user/data/shapenet_car \
        trainer.precision=fp32 \
        +accelerator=mps \
        tracker=disabled \
        +seed=1

Here we enforce ``fp32`` precision for training, ``mps`` (Apple Silicon) as our accelerator, ``tracker=disabled`` means
that there will be no experiment tracking used, for example Weights & Biases, and last but not least we set random seed
to 1.

Note the specific syntax that comes from `Hydra <https://hydra.cc/>`_:

- ``key=value`` → override an existing config key.
- ``+key=value`` → add a new key that isn’t in the config schema yet (``Hydra`` calls this “force-add”).

And for config groups:

- ``+experiment/shapenet=upt`` → select a config from a config group (compose ``experiment/shapenet/upt.yaml``).
The ``+`` here commonly means “this group wasn’t already set in the base config, so add it”.

Once it's running, you should observe the logs appearing in your terminal with the status updates from your first
training using the **Noether Framework**!