PyTorch server
Model Information
Basic
Model URI Structure
1. PyTorch model
<model uri>
├── ModelClass.py
└── model.pt
There should be two files in the model URI.
model.pt: The model file should be saved by
state_dict()
format.ModelClass.py: A python file defines the
PyTorchModel
class that inherited fromtorch.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.
model.pth: The model file is automatically saved by
mlflow.pytorch.autolog()
.ModelClass.py: A python file defines the
PyTorchModel
class that inherited frompytorch_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.
Call
mlflow.pytorch.autolog()
before training started.Call
pytorch_lightning.Trainer().fit(...)
withinmlflow.start_run()
statement.Call
mlflow.log_artifact("ModelClass.py", artifact_path="model/data")
withinmlflow.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.
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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