mlflow 1.30 -> 2.1

artifacts/0/24469fc083d6470a9cad7f17a6eeeea0/artifacts/model/MLmodel

@@ -1,16 +0,0 @@
-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: 2382b7a39c064e7b9b1465cfd84140a3
-run_id: 24469fc083d6470a9cad7f17a6eeeea0
-utc_time_created: '2023-02-21 05:57:41.973454'

artifacts/0/24469fc083d6470a9cad7f17a6eeeea0/artifacts/model/conda.yaml

@@ -1,11 +0,0 @@
-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/0/24469fc083d6470a9cad7f17a6eeeea0/artifacts/model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/24469fc083d6470a9cad7f17a6eeeea0/artifacts/model/python_env.yaml

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

artifacts/0/24469fc083d6470a9cad7f17a6eeeea0/artifacts/model/requirements.txt

@@ -1,4 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1
-tqdm==4.64.1

artifacts/0/2820b379bfc945358bfd516e5577846c/artifacts/cls_model/MLmodel

@@ -1,16 +0,0 @@
-artifact_path: cls_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: e40643f3e1b9481896e1ae6ed30e8654
-run_id: 2820b379bfc945358bfd516e5577846c
-utc_time_created: '2023-02-21 05:33:10.779919'

artifacts/0/2820b379bfc945358bfd516e5577846c/artifacts/cls_model/conda.yaml

@@ -1,10 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.10.9
-- pip<=23.0.1
-- pip:
-  - mlflow
-  - cloudpickle==2.2.1
-  - torch==1.13.1
-name: mlflow-env

artifacts/0/2820b379bfc945358bfd516e5577846c/artifacts/cls_model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/2820b379bfc945358bfd516e5577846c/artifacts/cls_model/python_env.yaml

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

artifacts/0/2820b379bfc945358bfd516e5577846c/artifacts/cls_model/requirements.txt

@@ -1,3 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1

artifacts/0/3ef01a1e3e3d4ba2be705da789bbb8e1/artifacts/models/MLmodel

@@ -1,16 +0,0 @@
-artifact_path: models
-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: faf1bec9ecb64581b22a0b8e09a9cca8
-run_id: 3ef01a1e3e3d4ba2be705da789bbb8e1
-utc_time_created: '2023-02-21 05:07:17.344052'

artifacts/0/3ef01a1e3e3d4ba2be705da789bbb8e1/artifacts/models/conda.yaml

@@ -1,10 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.10.9
-- pip<=23.0.1
-- pip:
-  - mlflow
-  - cloudpickle==2.2.1
-  - torch==1.13.1
-name: mlflow-env

artifacts/0/3ef01a1e3e3d4ba2be705da789bbb8e1/artifacts/models/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/3ef01a1e3e3d4ba2be705da789bbb8e1/artifacts/models/python_env.yaml

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

artifacts/0/3ef01a1e3e3d4ba2be705da789bbb8e1/artifacts/models/requirements.txt

@@ -1,3 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1

artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/code/train.py

@@ -1,82 +0,0 @@
-# train.py
-#
-# author: deng
-# date  : 20230221
-
-import torch
-import torch.nn as nn
-from torch.optim import SGD
-from tqdm import tqdm
-import mlflow
-
-
-class Net(nn.Module):
-    """ define a simple neural network model """
-    def __init__(self):
-        super(Net, self).__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 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 i, (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=i)
-
-    return loss
-
-
-if __name__ == '__main__':
-
-    # set hyper parameters
-    learning_rate = 1e-2
-    epochs = 20
-
-    # create a dataloader with fake data
-    dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
-    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
-
-    # 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')
-
-    # start the MLflow run
-    with mlflow.start_run():
-
-        # train the model and log the loss
-        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)
-
-        # log trained  model
-        mlflow.pytorch.log_model(model, 'model')
-
-        # log training code
-        mlflow.log_artifact('./train.py', 'code')
-
-    print('Completed.')

artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/MLmodel

@@ -1,16 +0,0 @@
-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: 18b69aa38c064c579c9b465d7a826081
-run_id: 410d85525e5f4cfe9839a432d35f9ad2
-utc_time_created: '2023-02-22 00:42:48.668457'

artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/conda.yaml

@@ -1,11 +0,0 @@
-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/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/python_env.yaml

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

artifacts/0/410d85525e5f4cfe9839a432d35f9ad2/artifacts/model/requirements.txt

@@ -1,4 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1
-tqdm==4.64.1

artifacts/0/63c7363339e042f4848d9041ba8deb82/artifacts/cls_model/MLmodel

@@ -1,16 +0,0 @@
-artifact_path: cls_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: a0ecc970cadb47a9b839283e9514732d
-run_id: 63c7363339e042f4848d9041ba8deb82
-utc_time_created: '2023-02-21 05:37:55.904472'

artifacts/0/63c7363339e042f4848d9041ba8deb82/artifacts/cls_model/conda.yaml

@@ -1,10 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.10.9
-- pip<=23.0.1
-- pip:
-  - mlflow
-  - cloudpickle==2.2.1
-  - torch==1.13.1
-name: mlflow-env

artifacts/0/63c7363339e042f4848d9041ba8deb82/artifacts/cls_model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/63c7363339e042f4848d9041ba8deb82/artifacts/cls_model/python_env.yaml

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

artifacts/0/63c7363339e042f4848d9041ba8deb82/artifacts/cls_model/requirements.txt

@@ -1,3 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1

artifacts/0/6845ef0d54024cb3bdb32050f6a46fea/artifacts/models/MLmodel

@@ -1,16 +0,0 @@
-artifact_path: models
-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: 4ebe94bd0249452a90b3497d3b00a1c3
-run_id: 6845ef0d54024cb3bdb32050f6a46fea
-utc_time_created: '2023-02-21 05:25:14.020335'

artifacts/0/6845ef0d54024cb3bdb32050f6a46fea/artifacts/models/conda.yaml

@@ -1,10 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.10.9
-- pip<=23.0.1
-- pip:
-  - mlflow
-  - cloudpickle==2.2.1
-  - torch==1.13.1
-name: mlflow-env

artifacts/0/6845ef0d54024cb3bdb32050f6a46fea/artifacts/models/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/6845ef0d54024cb3bdb32050f6a46fea/artifacts/models/python_env.yaml

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

artifacts/0/6845ef0d54024cb3bdb32050f6a46fea/artifacts/models/requirements.txt

@@ -1,3 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1

artifacts/0/68e8a3cbbafa46538ebb8a60d80f185d/artifacts/models/MLmodel

@@ -1,16 +0,0 @@
-artifact_path: models
-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: 8cd1e70114e548ea8d9bfb1bf468e285
-run_id: 68e8a3cbbafa46538ebb8a60d80f185d
-utc_time_created: '2023-02-21 05:05:46.624814'

artifacts/0/68e8a3cbbafa46538ebb8a60d80f185d/artifacts/models/conda.yaml

@@ -1,10 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.10.9
-- pip<=23.0.1
-- pip:
-  - mlflow
-  - cloudpickle==2.2.1
-  - torch==1.13.1
-name: mlflow-env

artifacts/0/68e8a3cbbafa46538ebb8a60d80f185d/artifacts/models/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/68e8a3cbbafa46538ebb8a60d80f185d/artifacts/models/python_env.yaml

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

artifacts/0/68e8a3cbbafa46538ebb8a60d80f185d/artifacts/models/requirements.txt

@@ -1,3 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1

artifacts/0/8ba27f225a7442be8816977c2077c510/artifacts/models/MLmodel

@@ -1,16 +0,0 @@
-artifact_path: models
-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: dd1b1e3a6b5f4274a5690a8843751ff3
-run_id: 8ba27f225a7442be8816977c2077c510
-utc_time_created: '2023-02-21 05:05:04.660670'

artifacts/0/8ba27f225a7442be8816977c2077c510/artifacts/models/conda.yaml

@@ -1,10 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.10.9
-- pip<=23.0.1
-- pip:
-  - mlflow
-  - cloudpickle==2.2.1
-  - torch==1.13.1
-name: mlflow-env

artifacts/0/8ba27f225a7442be8816977c2077c510/artifacts/models/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/8ba27f225a7442be8816977c2077c510/artifacts/models/python_env.yaml

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

artifacts/0/8ba27f225a7442be8816977c2077c510/artifacts/models/requirements.txt

@@ -1,3 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1

artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/MLmodel

@@ -1,16 +0,0 @@
-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: 957e2f6e4fd048c99aee3150c73c4078
-run_id: c4fd84a025e1474d87cdc2919874b88c
-utc_time_created: '2023-02-22 00:41:33.282088'

artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/conda.yaml

@@ -1,11 +0,0 @@
-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/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/python_env.yaml

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

artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/model/requirements.txt

@@ -1,4 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1
-tqdm==4.64.1

artifacts/0/c4fd84a025e1474d87cdc2919874b88c/artifacts/train.py

@@ -1,82 +0,0 @@
-# train.py
-#
-# author: deng
-# date  : 20230221
-
-import torch
-import torch.nn as nn
-from torch.optim import SGD
-from tqdm import tqdm
-import mlflow
-
-
-class Net(nn.Module):
-    """ define a simple neural network model """
-    def __init__(self):
-        super(Net, self).__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 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 i, (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=i)
-
-    return loss
-
-
-if __name__ == '__main__':
-
-    # set hyper parameters
-    learning_rate = 1e-2
-    epochs = 20
-
-    # create a dataloader with fake data
-    dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
-    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
-
-    # 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')
-
-    # start the MLflow run
-    with mlflow.start_run():
-
-        # train the model and log the loss
-        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)
-
-        # log trained  model
-        mlflow.pytorch.log_model(model, 'model')
-
-        # log training code
-        mlflow.log_artifact('./train.py')
-
-    print('Completed.')

artifacts/0/f1320882f24c4f489cbf85159627eaf8/artifacts/cls_model/MLmodel

@@ -1,16 +0,0 @@
-artifact_path: cls_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: aaa800b217da4dd0b8f17e8dbfdc5c45
-run_id: f1320882f24c4f489cbf85159627eaf8
-utc_time_created: '2023-02-21 05:34:08.242864'

artifacts/0/f1320882f24c4f489cbf85159627eaf8/artifacts/cls_model/conda.yaml

@@ -1,10 +0,0 @@
-channels:
-- conda-forge
-dependencies:
-- python=3.10.9
-- pip<=23.0.1
-- pip:
-  - mlflow
-  - cloudpickle==2.2.1
-  - torch==1.13.1
-name: mlflow-env

artifacts/0/f1320882f24c4f489cbf85159627eaf8/artifacts/cls_model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/0/f1320882f24c4f489cbf85159627eaf8/artifacts/cls_model/python_env.yaml

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

artifacts/0/f1320882f24c4f489cbf85159627eaf8/artifacts/cls_model/requirements.txt

@@ -1,3 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1

artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/code/train.py

@@ -1,83 +0,0 @@
-# train.py
-#
-# author: deng
-# date  : 20230221
-
-import torch
-import torch.nn as nn
-from torch.optim import SGD
-from tqdm import tqdm
-import mlflow
-
-
-class Net(nn.Module):
-    """ define a simple neural network model """
-    def __init__(self):
-        super(Net, self).__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 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 i, (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=i)
-
-    return loss
-
-
-if __name__ == '__main__':
-
-    # set hyper parameters
-    learning_rate = 1e-2
-    epochs = 20
-
-    # create a dataloader with fake data
-    dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
-    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
-
-    # 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('/mlflow_testing')
-
-    # start the 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)
-
-        # log trained model
-        mlflow.pytorch.log_model(model, 'model')
-
-        # log training code
-        mlflow.log_artifact('./train.py', 'code')
-
-    print('Completed.')

artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/MLmodel

@@ -1,16 +0,0 @@
-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: ff8b845d6a174ffabfc49a18673c6c04
-run_id: c248a4299f97423987a9496a2241ab1a
-utc_time_created: '2023-02-22 01:10:55.971443'

artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/conda.yaml

@@ -1,11 +0,0 @@
-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/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/python_env.yaml

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

artifacts/1/c248a4299f97423987a9496a2241ab1a/artifacts/model/requirements.txt

@@ -1,4 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1
-tqdm==4.64.1

artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/code/train.py

@@ -1,83 +0,0 @@
-# train.py
-#
-# author: deng
-# date  : 20230221
-
-import torch
-import torch.nn as nn
-from torch.optim import SGD
-from tqdm import tqdm
-import mlflow
-
-
-class Net(nn.Module):
-    """ define a simple neural network model """
-    def __init__(self):
-        super(Net, self).__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 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 i, (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=i)
-
-    return loss
-
-
-if __name__ == '__main__':
-
-    # set hyper parameters
-    learning_rate = 1e-2
-    epochs = 20
-
-    # create a dataloader with fake data
-    dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
-    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
-
-    # 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('mlflow_testing')
-
-    # start the 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)
-
-        # log trained model
-        mlflow.pytorch.log_model(model, 'model')
-
-        # log training code
-        mlflow.log_artifact('./train.py', 'code')
-
-    print('Completed.')

artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/MLmodel

@@ -1,16 +0,0 @@
-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: 27a96ad04f5a4578a3e1302500ad9a11
-run_id: b7d7395b6b53404497f7656b07b71bf8
-utc_time_created: '2023-02-22 01:11:36.809812'

artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/conda.yaml

@@ -1,11 +0,0 @@
-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/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/python_env.yaml

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

artifacts/2/b7d7395b6b53404497f7656b07b71bf8/artifacts/model/requirements.txt

@@ -1,4 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1
-tqdm==4.64.1

artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/code/train.py

@@ -1,83 +0,0 @@
-# train.py
-#
-# author: deng
-# date  : 20230221
-
-import torch
-import torch.nn as nn
-from torch.optim import SGD
-from tqdm import tqdm
-import mlflow
-
-
-class Net(nn.Module):
-    """ define a simple neural network model """
-    def __init__(self):
-        super(Net, self).__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 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 i, (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=i)
-
-    return loss
-
-
-if __name__ == '__main__':
-
-    # set hyper parameters
-    learning_rate = 1e-2
-    epochs = 20
-
-    # create a dataloader with fake data
-    dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
-    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
-
-    # 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('mlflow_testing')
-
-    # start the 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)
-
-        # log trained model
-        mlflow.pytorch.log_model(model, 'model')
-
-        # log training code
-        mlflow.log_artifact('./train.py', 'code')
-
-    print('Completed.')

artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/MLmodel

@@ -1,16 +0,0 @@
-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: 2625ea164ff248c194686ed5afb9a510
-run_id: c293e8294f4f46adacd21465be08c608
-utc_time_created: '2023-02-22 01:11:28.646127'

artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/conda.yaml

@@ -1,11 +0,0 @@
-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/2/c293e8294f4f46adacd21465be08c608/artifacts/model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/python_env.yaml

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

artifacts/2/c293e8294f4f46adacd21465be08c608/artifacts/model/requirements.txt

@@ -1,4 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1
-tqdm==4.64.1

artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/code/train.py

@@ -1,83 +0,0 @@
-# train.py
-#
-# author: deng
-# date  : 20230221
-
-import torch
-import torch.nn as nn
-from torch.optim import SGD
-from tqdm import tqdm
-import mlflow
-
-
-class Net(nn.Module):
-    """ define a simple neural network model """
-    def __init__(self):
-        super(Net, self).__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 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 i, (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=i)
-
-    return loss
-
-
-if __name__ == '__main__':
-
-    # set hyper parameters
-    learning_rate = 1e-2
-    epochs = 20
-
-    # create a dataloader with fake data
-    dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
-    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
-
-    # 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('mlflow_testing')
-
-    # start the 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)
-
-        # log trained model
-        mlflow.pytorch.log_model(model, 'model')
-
-        # log training code
-        mlflow.log_artifact('./train.py', 'code')
-
-    print('Completed.')

artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/MLmodel

@@ -1,16 +0,0 @@
-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: 2ee49fdb3ec647a58b1235498b186722
-run_id: d548729629634031a93a46d6dab8b7da
-utc_time_created: '2023-02-22 01:11:33.149151'

artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/conda.yaml

@@ -1,11 +0,0 @@
-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/2/d548729629634031a93a46d6dab8b7da/artifacts/model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/python_env.yaml

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

artifacts/2/d548729629634031a93a46d6dab8b7da/artifacts/model/requirements.txt

@@ -1,4 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1
-tqdm==4.64.1

artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/code/train.py

@@ -1,83 +0,0 @@
-# train.py
-#
-# author: deng
-# date  : 20230221
-
-import torch
-import torch.nn as nn
-from torch.optim import SGD
-from tqdm import tqdm
-import mlflow
-
-
-class Net(nn.Module):
-    """ define a simple neural network model """
-    def __init__(self):
-        super(Net, self).__init__()
-        self.fc1 = nn.Linear(10, 5)
-        self.fc2 = nn.Linear(5, 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 i, (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=i)
-
-    return loss
-
-
-if __name__ == '__main__':
-
-    # set hyper parameters
-    learning_rate = 1e-2
-    epochs = 20
-
-    # create a dataloader with fake data
-    dataloader = [(torch.randn(10), torch.randn(1)) for _ in range(100)]
-    dataloader = torch.utils.data.DataLoader(dataloader, batch_size=10)
-
-    # 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 the 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)
-
-        # log trained model
-        mlflow.pytorch.log_model(model, 'model')
-
-        # log training code
-        mlflow.log_artifact('./train.py', 'code')
-
-    print('Completed.')

artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/MLmodel

@@ -1,16 +0,0 @@
-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: 75aa23c5bb33452c978feeeffcdcb393
-run_id: 0be79b1f3f7d480a9c7f497312887a37
-utc_time_created: '2023-02-22 01:12:26.682417'

artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/conda.yaml

@@ -1,11 +0,0 @@
-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/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/data/pickle_module_info.txt

@@ -1 +0,0 @@
-mlflow.pytorch.pickle_module

artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/python_env.yaml

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

artifacts/3/0be79b1f3f7d480a9c7f497312887a37/artifacts/model/requirements.txt

@@ -1,4 +0,0 @@
-mlflow
-cloudpickle==2.2.1
-torch==1.13.1
-tqdm==4.64.1

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

@@ -1,98 +0,0 @@
-# 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

@@ -1,21 +0,0 @@
-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

@@ -1,11 +0,0 @@
-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