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pytorch_example.py
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pytorch_example.py
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# Copyright 2023 Neal Lathia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from libraries.util.datasets import load_regression_dataset
from libraries.util.domains import DIABETES_DOMAIN
from sklearn.metrics import mean_squared_error
from torch import nn
from modelstore.model_store import ModelStore
# pylint: disable=missing-class-docstring
class ExampleNet(nn.Module):
def __init__(self):
super(ExampleNet, self).__init__()
self.linear = nn.Linear(10, 1)
def forward(self, x):
return self.linear(x)
def _train_example_model() -> ExampleNet:
# Load the data
X_train, X_test, y_train, y_test = load_regression_dataset(as_numpy=True)
# Train the model
model = ExampleNet()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters())
for epoch in range(5):
print(f"🤖 Training epoch: {epoch}...")
optimizer.zero_grad()
outputs = model(X_train)
loss = criterion(outputs, y_train)
loss.backward()
optimizer.step()
results = mean_squared_error(y_test, model(X_test).detach().numpy())
print(f"🔍 Fit model MSE={results}.")
return model, optimizer
def train_and_upload(modelstore: ModelStore) -> dict:
# Train a PyTorch model
model, optimizer = _train_example_model()
# Upload the model to the model store
print(f'⤴️ Uploading the pytorch model to the "{DIABETES_DOMAIN}" domain.')
meta_data = modelstore.upload(DIABETES_DOMAIN, model=model, optimizer=optimizer)
return meta_data
def load_and_test(modelstore: ModelStore, model_domain: str, model_id: str):
# Load the model back into memory!
print(f'⤵️ Loading the pytorch "{model_domain}" domain model={model_id}')
model = modelstore.load(model_domain, model_id)
model.eval()
_, X_test, _, y_test = load_regression_dataset(as_numpy=True)
results = mean_squared_error(y_test, model(X_test).detach().numpy())
print(f"🔍 Loaded model MSE={results}.")