PyTorch server

Model Information

Basic

PropertyDescription

Model image

infuseai/pytorch-prepackaged:v0.2.0

Input

ndarray

Output

ndarray

Repository

Link

Model URI Structure

1. PyTorch model

<model uri>
├── ModelClass.py
└── model.pt

There should be two files in the model URI.

  1. model.pt: The model file should be saved by state_dict() format.

  2. ModelClass.py: A python file defines the PyTorchModel class that inherited from torch.nn.Module class.

For more information, please see PyTorch Saving & Loading documents.

2. MLflow autologging model (PyTorch_Lightning)

<model uri>
├── MLmodel
└── <model files>

There should be two files in the model URI.

  1. model.pth: The model file is automatically saved by mlflow.pytorch.autolog().

  2. ModelClass.py: A python file defines the PyTorchModel class that inherited from pytorch_lightning.LightningModule class, this file needs to be manually saved to artifact.

You can check the training example in Github, here are some required actions.

  1. Call mlflow.pytorch.autolog() before training started.

  2. Call pytorch_lightning.Trainer().fit(...) within mlflow.start_run() statement.

  3. Call mlflow.log_artifact("ModelClass.py", artifact_path="model/data") within mlflow.start_run() statement.

For more information, please see PyTorch Lightning Autologging.

How It Works

You can check the detailed code in Github. Here, we demonstrate by the pseudo-code.

Load the model

def __init__(self, model_uri):
    model_file, model_class_file = load_from_model_uri(model_uri)
    model_class = load_model_class(model_class_file)
    self.model = model_class()
    self.model.load_state_dict(torch.load(model_file))
    self.model.eval()

Predict

def predict(self, X):
    with torch.no_grad():
        inputs = torch.from_numpy(X)
        return self.model(inputs).numpy()

Example

In this example, we use the CIFAR-10 dataset, which is used in the PyTorch tutorial.

PropertyDescription

Model Image

infuseai/pytorch-prepackaged:v0.2.0

Model URI

gs://primehub-models/pytorch/CIFAR10

Test Request

curl -X POST http://localhost:9000/api/v1.0/predictions \
    -H 'Content-Type: application/json' \
    -d '{ "data": {"ndarray": [[[[0.23921573162078857, 0.24705886840820312, 0.29411768913269043, 0.301960825920105, 0.2549020051956177, 0.22352945804595947, 0.2705882787704468, 0.24705886840820312, 0.23921573162078857, 0.24705886840820312, 0.2627451419830322, 0.2549020051956177, 0.2627451419830322, 0.301960825920105, 0.32549023628234863, 0.3333333730697632, 0.30980396270751953, 0.2705882787704468, 0.2549020051956177, 0.2549020051956177, 0.22352945804595947, 0.16862750053405762, 0.17647063732147217, 0.16078436374664307, 0.16862750053405762, 0.12156867980957031, 0.09803926944732666, 0.10588240623474121, 0.12156867980957031, 0.07450985908508301, -0.011764705181121826, -0.09019607305526733], [0.19215691089630127, 0.18431377410888672, 0.24705886840820312, 0.301960825920105, 0.2705882787704468, 0.2549020051956177, 0.2862745523452759, 0.2705882787704468, 0.27843141555786133, 0.22352945804595947, 0.21568632125854492, 0.24705886840820312, 0.27843141555786133, 0.3333333730697632, 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Test Result

{"data":{"names":[],"ndarray":[[-1.3545894622802734,-2.094174385070801,0.3954501748085022,3.433469295501709,-0.08178478479385376,1.7413527965545654,0.9827516674995422,-1.3434531688690186,0.6330254673957825,-1.5354188680648804]]},"meta":{"requestPath":{"model":"infuseai/pytorch-prepackaged:v0.2.0"}}}
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