PyTorch server
Last updated
Last updated
Property | Description |
---|---|
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 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.
model.pth: The model file is automatically saved by mlflow.pytorch.autolog()
.
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.
Call mlflow.pytorch.autolog()
before training started.
Call pytorch_lightning.Trainer().fit(...)
within mlflow.start_run()
statement.
Call mlflow.log_artifact("ModelClass.py", artifact_path="model/data")
within mlflow.start_run()
statement.
For more information, please see PyTorch Lightning Autologging.
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()
In this example, we use the CIFAR-10 dataset, which is used in the PyTorch tutorial.
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"}}}
Property | Description |
---|---|
Model image
infuseai/pytorch-prepackaged:v0.2.0
Input
ndarray
Output
ndarray
Repository
Model Image
infuseai/pytorch-prepackaged:v0.2.0
Model URI
gs://primehub-models/pytorch/CIFAR10