PyTorch server
Enterprise
Applicable to Enterprise tier only
Model Information
基本資訊
Property | Description |
---|---|
Model image | infuseai/pytorch-prepackaged_rest:v0.1.0 |
Input | ndarray |
Output | ndarray |
Repository | Link |
Model URI 檔案結構
<model uri>
├── ModelClass.py
└── model.pt
Model URI 結構中需有二個檔案
- model.pt: 透過
state_dict()
輸出模型檔案。 - ModelClass.py: A python
PyTorchModel
class 定義檔。
詳細資訊,請見 PyTorch Saving & Loading documents。
How It Works
完整範例代碼,請見 Github。
摘要代碼
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
範例採用 PyTorch tutorial 中的 CIFAR-10 dataset。
Property | Description |
---|---|
Model Image | infuseai/pytorch-prepackaged_rest:v0.1.0 |
Model URI | gs://primehub-models/pytorch/CIFAR10 |
請求範例
curl -X POST http://localhost:5000/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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回應範例
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