|image1|

.. |image1| image:: https://github.com/mansiagr4/gifs/raw/main/new_small_logo.svg

Image Classification Model Recommendation
=========================================

This example notebook uses the ``modlee`` package to train a recommended
model. We will perform image classification on CIFAR10 from
``torchvision``.

Here is a video explanation of this
`exercise <https://www.youtube.com/watch?v=3m5pNudQ1TA>`__.

.. raw:: html

   <iframe width="560" height="315" src="https://www.youtube.com/embed/3m5pNudQ1TA" frameborder="0" allowfullscreen>

.. raw:: html

   </iframe>

|Open in Kaggle|

.. |Open in Kaggle| image:: https://kaggle.com/static/images/open-in-kaggle.svg
   :target: https://www.kaggle.com/code/modlee/modlee-image-recommender

First, import ``torch``- and ``modlee``-related packages.

.. code:: python

   import os
   import lightning.pytorch as pl

   # Set your API key

   os.environ['MODLEE_API_KEY'] = "replace-with-your-api-key"

   import torch, torchvision
   import torchvision.transforms as transforms

Next, initialize the package.

.. code:: python

   import modlee

   # Initialize the Modlee package
   modlee.init(api_key=os.environ.get('MODLEE_API_KEY'))

Now, we can create a dataloader from CIFAR10.

.. code:: python

   transforms = transforms.Compose([
       transforms.ToTensor(), #converts images to PyTorch tensors
       transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) #adjusts the color values 
       ])

   train_dataset = torchvision.datasets.CIFAR10( #this command gets the CIFAR-10 images
       root='./data',
       train=True, #loading the training split of the dataset
       download=True,
       transform=transforms) #applies transformations defined earlier

   val_dataset = torchvision.datasets.CIFAR10(
       root='./data',
       train=False, #loading the validation split of the dataset
       download=True,
       transform=transforms)

   train_dataloader = torch.utils.data.DataLoader( #this tool loads the data
       train_dataset,
       batch_size=16, #we will load the images in groups of 16
      )

   val_dataloader = torch.utils.data.DataLoader(
       val_dataset,
       batch_size=16
   )

Create a ``modlee`` recommender object and fit to the dataset. This
process will calculate the dataset metafeatures to send to the server.
The server will return a recommended model for the dataset assigned to
``recommender.model``.

.. code:: python

   # create a Modlee recommender object
   recommender = modlee.recommender.ImageClassificationRecommender(
       num_classes=10
   )

   # recommender analyzes training data to suggest best model
   recommender.fit(train_dataloader)

   #retrieves the recommended model
   modlee_model = recommender.model
   print(f"\nRecommended model: \n{modlee_model}")

::

   INFO:Analyzing dataset based on data metafeatures...
   INFO:Finished analyzing dataset.
   INFO:The model is available at the recommender object's `model` attribute.

   Recommended model: 
   RecommendedModel(
     (model): GraphModule(
       (Conv): Conv2d(3, 3, kernel_size=(1, 1), stride=(1, 1))
       (Conv_1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
       (Relu): ReLU()
       (MaxPool): MaxPool2d(kernel_size=[3, 3], stride=[2, 2], padding=[1, 1], dilation=[1, 1], ceil_mode=False)
       (Conv_2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (Relu_1): ReLU()
       (Conv_3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (Add): OnnxBinaryMathOperation()
       (Relu_2): ReLU()
       (Conv_4): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       (Relu_3): ReLU()
       (Conv_5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
       ...

We can train the model, with automatic documentation of metafeatures.

.. code:: python

   with modlee.start_run() as run:
       trainer = pl.Trainer(max_epochs=1)
       trainer.fit( 
           model=modlee_model,
           train_dataloaders=train_dataloader,
           val_dataloaders=val_dataloader
       )

::

     | Name  | Type        | Params
   --------------------------------------
   0 | model | GraphModule | 11.7 M
   --------------------------------------
   11.7 M    Trainable params
   0         Non-trainable params
   11.7 M    Total params
   46.779    Total estimated model params size (MB)
   Epoch 0: 100%|██████████| 3125/3125 [01:14<00:00, 41.86it/s, v_num=0]

Finally, we can view the saved assets from training. With Modlee, your
training assets are automatically saved, preserving valuable insights
for future reference and collaboration.

.. code:: python

   last_run_path = modlee.last_run_path()
   print(f"Run path: {last_run_path}")
   artifacts_path = os.path.join(last_run_path, 'artifacts')
   artifacts = sorted(os.listdir(artifacts_path))
   print(f"Saved artifacts: {artifacts}")

::

   Run path: /home/ubuntu/projects/modlee_pypi/examples/mlruns/0/ff1e754d6401438fba506a0d98ca1f91
   Saved artifacts: ['cached_vars', 'checkpoints', 'model', 'model.py', 'model_graph.py', 'model_graph.txt', 'model_size', 'model_summary.txt', 'stats_rep', 'transforms.txt']