waylay-ml-adapter-torch 0.0.4

ML_adapter for torch.

waylay-ml-adapter-torch

Provides the ml_adapter.sklearn module as Waylay ML Adapter for pytorch.

Installation

pip install waylay-ml-adapter-torch

You might want to install additional libraries such as torchaudio or torchvision.

Usage

This ML Adapter uses the standard torch mechanisms to save and load models within a waylay plugin or webscript. The model_path argument defines the file name of the serialized model in the function archive:

• A model_path ending in weights.pt or weights.pth save/loads the model weights using its state_dict. It is a recommended, more robust method, but requires you to also specifiy a model_class.
• Any other model_path with .pt or .pth suffix save/loads the entire model. It implicitly saves (references to) the used model class. You'll have to make sure that all dependencies used are also included or declared in the archive.
• You can also pass an instantiated the model directly to the adapter.

Creating a model for a _webscript

from ml_adapter.torch import V1TorchAdapter

# assuming we save a AutoEncoder torch.nn.Module class in a `autoencoder.py` file
from autoencoder import AutoEncoder
model = AutoEncoder()
# ... train the model ...

# a local directory to prepare the webscript archive
ARCHIVE_LOC='~/webscripts/autoencoder-pytorch'
# use a `weights` model path to use _weights_ serialization
MODEL_PATH='autoencoder.weights.pt'

adapter = V1TorchAdapter(
    model=model,
    model_path='model-weights.pt',
    location=ARCHIVE_LOC,
)

# add our model script to the webscript archive
await adapter.add_script('autoencoder.py')
# write the archive
await adapter.save()
# inspect the archive:
list(adapter.assets)
#> [requirements.txt <ml_adapter.base.assets.python.PythonRequirementsAsset>,
#> main.py <ml_adapter.base.assets.python.PythonScriptAsset>,
#> model-weights.pt <ml_adapter.torch.adapter.TorchModelWeightsAsset>,
#> autoencoder.py <ml_adapter.base.assets.python.PythonScriptAsset>]

Upload the adapter archive as webscript using the ml_tool SDK plugin

from waylay.sdk import WaylayClient
client = WaylayClient.from_profile('staging')
ref = await client.ml_tool.create_webscript(adapter, name='MyAutoEncoder', version='0.0.1')
ref = await client.ml_tool.wait_until_ready(ref)
await client.ml_tool.test_webscript(ref, [2,3,4])

The generated code in main.py uses the following to load your model:

MODEL_PATH = os.environ.get('MODEL_PATH', 'model-weights.pt')
MODEL_CLASS = os.environ.get('MODEL_CLASS', 'autoencoder.AutoEncoder')
adapter = V1TorchAdapter(model_path=MODEL_PATH, model_class=MODEL_CLASS)

You can modify that loading mechanism, e.g. by creating the model your self, and providing it as

adapter = V1TorchAdapter(model=model)

Exported classes

This module exports the following classes:

ml_adapter.torch.V1TorchAdapter

Adapts a callable with torch arrays as input and output.

ml_adapter.torch.V1TorchMarshaller

Convert v1 payload from and to torch tensors.

See also

• waylay-ml-adapter-sklearn ML adapter for scikit-learn models.
• waylay-ml-adapter-sdk provides the ml_tool extension to the waylay-sdk
• waylay-ml-adapter-base provides the basic ML adapter infrastructure.
• waylay-ml-adapter-api defines the remote data interfaces.