PyTorch Model Type

Upload a PyTorch Model to a Machine Learning Repository

Use the following function to upload a PyTorch model to a Machine Learning repository in Artifactory:

import frogml
import torch.nn as nn

repository = "repository-name"
name = "model-name"
namespace = "namespace"
version = "version-1"
properties = {"key1": "value1"}
dependencies = ["pandas==1.2.3"]
code_dir = "full/path/to/code/dir"

class Classifier(nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden1 = nn.Linear(8, 12)
        self.act1 = nn.ReLU()
        self.hidden2 = nn.Linear(12, 8)
        self.act2 = nn.ReLU()
        self.output = nn.Linear(8, 1)
        self.act_output = nn.Sigmoid()

    def forward(self, x):
        x = self.act1(self.hidden1(x))
        x = self.act2(self.hidden2(x))
        x = self.act_output(self.output(x))
        return x

frogml.pytorch.log_model(
    model=Classifier(),
    repository=repository,
    model_name=name,
    namespace=namespace,
    version=version,
    properties=properties,
    dependencies=dependencies,
    code_dir=code_dir,
)

Download a PyTorch Model from a Machine Learning Repository

Use the following function to download a PyTorch model from a Machine Learning repository in Artifactory:

import frogml

repository = "repository-name"
name = "model-name"
namespace = "namespace"
version = "version-1"

model = frogml.pytorch.load_model(
    repository=repository,
    namespace=namespace,
    model_name=name,
    version=version,
)

Get Information on a PyTorch Model in a Machine Learning Repository

Use the following function to retrieve information on a specific PyTorch model stored in a Machine Learning repository in Artifactory.

import frogml

repository = "repository-name"
name = "model-name"
namespace = "namespace"
version = "version-1"

frogml.pytorch.get_model_info(
    repository=repository,
    name=name,
    namespace=namespace,
    version=version,
)