PyTorch¶

The Snowflake ML Model Registry supports models created using PyTorch (models derived from torch.nn.Module).

The following additional options can be used in the options dictionary when you call log_model:

Option	Description
`target_methods`	A list of the names of the methods available on the model object. PyTorch models have the following target method by default: `forward`.
`cuda_version`	The version of the CUDA runtime to be used when deploying to a platform with GPU; defaults to 11.8. If manually set to `None`, the model cannot be deployed to a platform having a GPU.
`multiple_inputs`	Whether the model expects multiple tensor inputs. Defaults to `False`. When `True`, the model will accept a list of tensors as input instead of a single tensor.

You must specify either the sample_input_data or signatures parameter when logging a PyTorch model so that the registry knows the signatures of the target methods.

Note

When using pandas DataFrames (which use float64 by default), ensure your PyTorch model layers are created with dtype=torch.float64 to avoid dtype mismatch errors.

Example¶

This example assumes reg is an instance of snowflake.ml.registry.Registry.

import torch
import torch.nn as nn
from sklearn import datasets, model_selection

# Define a simple neural network for classification
class IrisClassifier(nn.Module):
    def __init__(self, input_dim: int, hidden_dim: int, output_dim: int):
        super().__init__()
        # Use float64 to match pandas DataFrame default dtype
        self.model = nn.Sequential(
            nn.Linear(input_dim, hidden_dim, dtype=torch.float64),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim, dtype=torch.float64),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim, dtype=torch.float64),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.model(x)

# Load dataset
iris = datasets.load_iris(as_frame=True)
X = iris.data
y = iris.target

# Rename columns for valid Snowflake identifiers
X.columns = [col.replace(' ', '_').replace('(', '').replace(')', '') for col in X.columns]

X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)

# Create model
model = IrisClassifier(input_dim=4, hidden_dim=32, output_dim=3)

# Train the model
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()

X_train_tensor = torch.tensor(X_train.values)
y_train_tensor = torch.tensor(y_train.values, dtype=torch.long)

model.train()
for epoch in range(100):
    optimizer.zero_grad()
    outputs = model(X_train_tensor)
    loss = criterion(outputs, y_train_tensor)
    loss.backward()
    optimizer.step()

# Log the model
model_ref = reg.log_model(
    model=model,
    model_name="my_iris_classifier",
    version_name="v1",
    sample_input_data=X_test,
)

# Make predictions
result_df = model_ref.run(X_test[-10:])

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