update data storage

artifacts/3/128f833fc0a2426db86e5073db557a3e/artifacts/code/train.py

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+# train.py
+#
+# author: deng
+# date  : 20230221
+
+import torch
+import torch.nn as nn
+from torch.optim import SGD
+import mlflow
+from mlflow.models.signature import ModelSignature
+from mlflow.types.schema import Schema, ColSpec
+from tqdm import tqdm
+
+
+class Net(nn.Module):
+    """ define a simple neural network model """
+    def __init__(self):
+        super(Net, self).__init__()
+        self.fc1 = nn.Linear(5, 3)
+        self.fc2 = nn.Linear(3, 1)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        return x
+
+
+def train(model, dataloader, criterion, optimizer, epochs):
+    """ define the training function """
+    for epoch in tqdm(range(epochs), 'Epochs'):
+
+        for batch, (inputs, labels) in enumerate(dataloader):
+
+            # forwarding
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
+
+            # update gradient
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        # log loss
+        mlflow.log_metric('train_loss', loss.item(), step=epoch)
+
+    return loss
+
+
+if __name__ == '__main__':
+
+    # set hyper parameters
+    learning_rate = 1e-2
+    batch_size = 10
+    epochs = 20
+
+    # create a dataloader with fake data
+    dataloader = [(torch.randn(5), torch.randn(1)) for _ in range(100)]
+    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=batch_size)
+
+    # create the model, criterion, and optimizer
+    model = Net()
+    criterion = nn.MSELoss()
+    optimizer = SGD(model.parameters(), lr=learning_rate)
+
+    # set the tracking URI to the model registry
+    mlflow.set_tracking_uri('http://127.0.0.1:5000')
+    mlflow.set_experiment('train_fortune_predict_model')
+
+    # start a new MLflow run
+    with mlflow.start_run():
+
+        # train the model
+        loss = train(model, dataloader, criterion, optimizer, epochs)
+
+        # log some additional metrics
+        mlflow.log_metric('final_loss', loss.item())
+        mlflow.log_param('learning_rate', learning_rate)
+        mlflow.log_param('batch_size', batch_size)
+
+        # create a signature to record model input and output info
+        input_schema = Schema([
+            ColSpec('float', 'age'),
+            ColSpec('float', 'mood level'),
+            ColSpec('float', 'health level'),
+            ColSpec('float', 'hungry level'),
+            ColSpec('float', 'sexy level')
+        ])
+        output_schema = Schema([ColSpec('float', 'fortune')])
+        signature = ModelSignature(inputs=input_schema, outputs=output_schema)
+
+        # log trained model
+        mlflow.pytorch.log_model(model, 'model', signature=signature)
+
+        # log training code
+        mlflow.log_artifact('./train.py', 'code')
+
+    print('Completed.')

artifacts/3/128f833fc0a2426db86e5073db557a3e/artifacts/model/MLmodel

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+artifact_path: model
+flavors:
+  python_function:
+    data: data
+    env: conda.yaml
+    loader_module: mlflow.pytorch
+    pickle_module_name: mlflow.pytorch.pickle_module
+    python_version: 3.10.9
+  pytorch:
+    code: null
+    model_data: data
+    pytorch_version: 1.13.1
+mlflow_version: 1.30.0
+model_uuid: 1e929c95d90347419e3e0a49d5d783fd
+run_id: 128f833fc0a2426db86e5073db557a3e
+signature:
+  inputs: '[{"name": "age", "type": "float"}, {"name": "mood level", "type": "float"},
+    {"name": "health level", "type": "float"}, {"name": "hungry level", "type": "float"},
+    {"name": "sexy level", "type": "float"}]'
+  outputs: '[{"name": "fortune", "type": "float"}]'
+utc_time_created: '2023-02-23 01:38:39.421914'

artifacts/3/128f833fc0a2426db86e5073db557a3e/artifacts/model/conda.yaml

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+channels:
+- conda-forge
+dependencies:
+- python=3.10.9
+- pip<=23.0.1
+- pip:
+  - mlflow
+  - cloudpickle==2.2.1
+  - torch==1.13.1
+  - tqdm==4.64.1
+name: mlflow-env

artifacts/3/128f833fc0a2426db86e5073db557a3e/artifacts/model/data/pickle_module_info.txt

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+mlflow.pytorch.pickle_module

artifacts/3/128f833fc0a2426db86e5073db557a3e/artifacts/model/python_env.yaml

@@ -0,0 +1,7 @@
+python: 3.10.9
+build_dependencies:
+- pip==23.0.1
+- setuptools==67.3.2
+- wheel==0.38.4
+dependencies:
+- -r requirements.txt

artifacts/3/128f833fc0a2426db86e5073db557a3e/artifacts/model/requirements.txt

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+mlflow
+cloudpickle==2.2.1
+torch==1.13.1
+tqdm==4.64.1

get_registered_model_via_rest_api.py

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+# get_registered_model_via_rest_api.py
+#
+# author: deng
+# date  : 20230224
+
+import json
+import requests
+
+def main():
+
+    registered_model_name = 'fortune_predict_model'
+    production_model_version = None
+
+    query = {'name': registered_model_name}
+    res = requests.get('http://127.0.0.1:5000/api/2.0/mlflow/registered-models/get', params=query)
+    content = json.loads(res.text)
+    print(content)
+
+    for model in content['registered_model']['latest_versions']:
+
+        if model['current_stage'] == 'Production':
+            production_model_version = model['version']
+
+    if production_model_version is not None:
+        query = {'name': registered_model_name, 'version': production_model_version}
+    res = requests.get('http://127.0.0.1:5000/api/2.0/mlflow/model-versions/get-download-uri', params=query)
+    print(res.text)
+
+if __name__ == '__main__':
+    main()