.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples_apps/lightning_classy_vision/model.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_examples_apps_lightning_classy_vision_model.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_apps_lightning_classy_vision_model.py:


Tiny ImageNet Model
====================

This is a toy model for doing regression on the tiny imagenet dataset. It's used
by the apps in the same folder.

.. GENERATED FROM PYTHON SOURCE LINES 14-92

.. code-block:: default


    import os.path
    import subprocess
    from typing import Tuple

    import fsspec
    import pytorch_lightning as pl
    import torch.jit
    from torch.nn import functional as F


    class TinyImageNetModel(pl.LightningModule):
        """
        An very simple linear model for the tiny image net dataset.
        """

        def __init__(self) -> None:
            super().__init__()
            self.l1 = torch.nn.Linear(64 * 64, 4096)

        # pyre-fixme[14]
        def forward(self, x: torch.Tensor) -> torch.Tensor:
            return torch.relu(self.l1(x.view(x.size(0), -1)))

        # pyre-fixme[14]
        def training_step(
            self, batch: Tuple[torch.Tensor, torch.Tensor], batch_nb: int
        ) -> torch.Tensor:
            x, y = batch
            loss = F.cross_entropy(self(x), y)
            return loss

        def configure_optimizers(self) -> torch.optim.Adam:
            return torch.optim.Adam(self.parameters(), lr=0.02)


    def export_inference_model(
        model: TinyImageNetModel, out_path: str, tmpdir: str
    ) -> None:
        """
        export_inference_model uses TorchScript JIT to serialize the
        TinyImageNetModel into a standalone file that can be used during inference.
        TorchServe can also handle interpreted models with just the model.py file if
        your model can't be JITed.
        """

        print("exporting inference model")
        jit_path = os.path.join(tmpdir, "model_jit.pt")
        jitted = torch.jit.script(model)
        print(f"saving JIT model to {jit_path}")
        torch.jit.save(jitted, jit_path)

        model_name = "tiny_image_net"

        mar_path = os.path.join(tmpdir, f"{model_name}.mar")
        print(f"creating model archive at {mar_path}")
        subprocess.run(
            [
                "torch-model-archiver",
                "--model-name",
                "tiny_image_net",
                "--handler",
                "lightning_classy_vision/handler/handler.py",
                "--version",
                "1",
                "--serialized-file",
                jit_path,
                "--export-path",
                tmpdir,
            ],
            check=True,
        )

        remote_path = os.path.join(out_path, "model.mar")
        print(f"uploading to {remote_path}")
        fs, _, rpaths = fsspec.get_fs_token_paths(remote_path)
        assert len(rpaths) == 1, "must have single path"
        fs.put(mar_path, rpaths[0])


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_examples_apps_lightning_classy_vision_model.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: model.py <model.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: model.ipynb <model.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_