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修改--修改模型管理方法的返回参数

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haotian 2025-02-19 17:42:14 +08:00
parent d867a3004d
commit 44b6b665b5
480 changed files with 1606 additions and 12 deletions
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26
doc/接口文档code.md
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@ -395,6 +395,32 @@ Response:
}
```
### 2.5 获取MLFlow中保存的实验
```http
GET /api/experiments
Response:
{
"status": "success",
"experiments": [
{
"experiment_id": "656341556838275234",
"name": "breast_cancer_classification_2",
"artifact_location": "mlruns/656341556838275234",
"lifecycle_stage": "active",
"creation_time": "2025-02-19T08:43:02",
"last_update_time": "2025-02-19T14:30:00",
"tags": {
"mlflow.note.content": "乳腺癌分类实验",
"mlflow.user": "admin"
},
"runs_count": 5
}
],
"total_count": 1,
"page": 1,
"page_size": 10
}
```
### 2.6 获取已经训练好的模型列表
```http

3
example_model_manager.py
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@ -31,6 +31,9 @@ if result['status'] == 'success':
print(f"训练开始时间: {model['training_start_time']}")
print(f"训练结束时间: {model['training_end_time']}")
print("模型参数:")
for k, v in model['parameters'].items():
print(f" {k}: {v}")
print("评估指标:")
for metric_name, metric_value in model['metrics'].items():
print(f" {metric_name}: {metric_value:.4f}")

25
example_model_trainer.py
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@ -30,18 +30,19 @@ model_config = {
# 训练模型, 删除训练实验时要删除 mlruns/.trash/ 回收站里的文件
# 模型文件 直接在 mlruns/文件夹下
result = trainer.train_model(
{
'features': X_train,
'labels': y_train
},
{
'features': X_val,
'labels': y_val
},
model_config,
'breast_cancer_classification_2'
)
for i in range(8, 20):
result = trainer.train_model(
{
'features': X_train,
'labels': y_train
},
{
'features': X_val,
'labels': y_val
},
model_config,
f'breast_cancer_classification_{i}'
)
# 打印结果
print("\n训练结果:")

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20
mlartifacts/111298548125054288/569fccd21c0e4c429752a87e0b6d725b/artifacts/model/MLmodel Normal file
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@ -0,0 +1,20 @@
artifact_path: model
flavors:
python_function:
env:
conda: conda.yaml
virtualenv: python_env.yaml
loader_module: mlflow.sklearn
model_path: model.pkl
predict_fn: predict
python_version: 3.9.19
sklearn:
code: null
pickled_model: model.pkl
serialization_format: cloudpickle
sklearn_version: 1.5.2
mlflow_version: 2.20.1
model_size_bytes: 96534
model_uuid: 3145221114ff4b51abdaf0ae7c28dd36
run_id: 569fccd21c0e4c429752a87e0b6d725b
utc_time_created: '2025-02-19 09:08:51.667593'

15
mlartifacts/111298548125054288/569fccd21c0e4c429752a87e0b6d725b/artifacts/model/conda.yaml Normal file
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@ -0,0 +1,15 @@
channels:
- conda-forge
dependencies:
- python=3.9.19
- pip<=24.0
- pip:
- mlflow==2.20.1
- cloudpickle==3.1.0
- numpy==1.26.4
- pandas==2.2.2
- psutil==6.0.0
- scikit-learn==1.5.2
- scipy==1.13.1
- xgboost==2.1.4
name: mlflow-env

BIN
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7
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@ -0,0 +1,7 @@
python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools==60.2.0
- wheel==0.43.0
dependencies:
- -r requirements.txt

8
mlartifacts/111298548125054288/569fccd21c0e4c429752a87e0b6d725b/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,8 @@
mlflow==2.20.1
cloudpickle==3.1.0
numpy==1.26.4
pandas==2.2.2
psutil==6.0.0
scikit-learn==1.5.2
scipy==1.13.1
xgboost==2.1.4

20
mlartifacts/120983226253681124/435ab004649d42ef8fa67b6f9b2a1263/artifacts/model/MLmodel Normal file
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@ -0,0 +1,20 @@
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sklearn:
code: null
pickled_model: model.pkl
serialization_format: cloudpickle
sklearn_version: 1.5.2
mlflow_version: 2.20.1
model_size_bytes: 96534
model_uuid: ddfeb220cad04f56bc973e63c1681050
run_id: 435ab004649d42ef8fa67b6f9b2a1263
utc_time_created: '2025-02-19 09:10:38.183644'

15
mlartifacts/120983226253681124/435ab004649d42ef8fa67b6f9b2a1263/artifacts/model/conda.yaml Normal file
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@ -0,0 +1,15 @@
channels:
- conda-forge
dependencies:
- python=3.9.19
- pip<=24.0
- pip:
- mlflow==2.20.1
- cloudpickle==3.1.0
- numpy==1.26.4
- pandas==2.2.2
- psutil==6.0.0
- scikit-learn==1.5.2
- scipy==1.13.1
- xgboost==2.1.4
name: mlflow-env

BIN
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mlartifacts/120983226253681124/435ab004649d42ef8fa67b6f9b2a1263/artifacts/model/python_env.yaml Normal file
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@ -0,0 +1,7 @@
python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools==60.2.0
- wheel==0.43.0
dependencies:
- -r requirements.txt

8
mlartifacts/120983226253681124/435ab004649d42ef8fa67b6f9b2a1263/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,8 @@
mlflow==2.20.1
cloudpickle==3.1.0
numpy==1.26.4
pandas==2.2.2
psutil==6.0.0
scikit-learn==1.5.2
scipy==1.13.1
xgboost==2.1.4

20
mlartifacts/134915692878522294/7283286bf5464a298c33888867e2d576/artifacts/model/MLmodel Normal file
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predict_fn: predict
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code: null
pickled_model: model.pkl
serialization_format: cloudpickle
sklearn_version: 1.5.2
mlflow_version: 2.20.1
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run_id: 7283286bf5464a298c33888867e2d576
utc_time_created: '2025-02-19 09:10:27.607253'

15
mlartifacts/134915692878522294/7283286bf5464a298c33888867e2d576/artifacts/model/conda.yaml Normal file
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@ -0,0 +1,15 @@
channels:
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dependencies:
- python=3.9.19
- pip<=24.0
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- mlflow==2.20.1
- cloudpickle==3.1.0
- numpy==1.26.4
- pandas==2.2.2
- psutil==6.0.0
- scikit-learn==1.5.2
- scipy==1.13.1
- xgboost==2.1.4
name: mlflow-env

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python: 3.9.19
build_dependencies:
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dependencies:
- -r requirements.txt

8
mlartifacts/134915692878522294/7283286bf5464a298c33888867e2d576/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,8 @@
mlflow==2.20.1
cloudpickle==3.1.0
numpy==1.26.4
pandas==2.2.2
psutil==6.0.0
scikit-learn==1.5.2
scipy==1.13.1
xgboost==2.1.4

20
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serialization_format: cloudpickle
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mlflow_version: 2.20.1
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run_id: bd3697dc238c4d1587e0f4f319d04448
utc_time_created: '2025-02-19 09:10:22.302072'

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mlartifacts/212002307524450906/bd3697dc238c4d1587e0f4f319d04448/artifacts/model/conda.yaml Normal file
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@ -0,0 +1,15 @@
channels:
- conda-forge
dependencies:
- python=3.9.19
- pip<=24.0
- pip:
- mlflow==2.20.1
- cloudpickle==3.1.0
- numpy==1.26.4
- pandas==2.2.2
- psutil==6.0.0
- scikit-learn==1.5.2
- scipy==1.13.1
- xgboost==2.1.4
name: mlflow-env

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python: 3.9.19
build_dependencies:
- pip==24.0
- setuptools==60.2.0
- wheel==0.43.0
dependencies:
- -r requirements.txt

8
mlartifacts/212002307524450906/bd3697dc238c4d1587e0f4f319d04448/artifacts/model/requirements.txt Normal file
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@ -0,0 +1,8 @@
mlflow==2.20.1
cloudpickle==3.1.0
numpy==1.26.4
pandas==2.2.2
psutil==6.0.0
scikit-learn==1.5.2
scipy==1.13.1
xgboost==2.1.4

20
mlartifacts/231104280109669409/27ca4b5f64ab4809b82724be5e020e0e/artifacts/model/MLmodel Normal file
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channels:
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mlflow==2.20.1
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scikit-learn==1.5.2
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channels:
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- xgboost==2.1.4
name: mlflow-env

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mlflow==2.20.1
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pandas==2.2.2
psutil==6.0.0
scikit-learn==1.5.2
scipy==1.13.1
xgboost==2.1.4

20
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name: mlflow-env

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python: 3.9.19
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mlflow==2.20.1
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psutil==6.0.0
scikit-learn==1.5.2
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- xgboost==2.1.4
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python: 3.9.19
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mlflow==2.20.1
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numpy==1.26.4
pandas==2.2.2
psutil==6.0.0
scikit-learn==1.5.2
scipy==1.13.1
xgboost==2.1.4

20
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0.1

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