MLflow 是一个功能强大的ML生命周期管理平台，主要用于 ML/DL/LLM 实验管理、模型跟踪、模型部署等。
# 1.1 创建虚拟环境 conda create -n mlflow-env python=3.10 conda activate mlflow-env# 1.2 Install mlflow pip install mlflow# 2.1 Run a local Tracking Server mlflow server --host 127.0.0.1 --port 8080 # 默认5000# 3. 构建demo训练 mkdir /your/path/mlflow-demo cd /your/path/mlflow-demo# 3.1 Train a model and prepare metadata for logging vi train.py======================================================== # train.py import mlflow from mlflow.models import infer_signatureimport pandas as pd from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score# Load the Iris dataset X, y = datasets.load_iris(return_X_y=True)# Split the data into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42 )# Define the model hyperparameters params = {"solver": "lbfgs","max_iter": 1000,"multi_class": "auto","random_state": 8888, }# Train the model lr = LogisticRegression(**params) lr.fit(X_train, y_train)# Predict on the test set y_pred = lr.predict(X_test)# Calculate metrics accuracy = accuracy_score(y_test, y_pred) ========================================================# 3.2 Log the model and its metadata to MLflow ======================================================== # Set our tracking server uri for logging mlflow.**set_tracking_uri**(uri="http://127.0.0.1:8080")# Create a new MLflow Experiment mlflow.set_experiment("MLflow Quickstart")# Start an MLflow run with mlflow.start_run():# Log the hyperparametersmlflow.log_params(params)# Log the loss metricmlflow.log_metric("accuracy", accuracy)# Infer the model signaturesignature = infer_signature(X_train, lr.predict(X_train))# Log the model, which inherits the parameters and metricmodel_info = mlflow.sklearn.log_model(sk_model=lr,name="iris_model",signature=signature,input_example=X_train,registered_model_name="tracking-quickstart",)# Set a tag that we can use to remind ourselves what this model was formlflow.set_logged_model_tags(model_info.model_id, {"Training Info": "Basic LR model for iris data"}) ========================================================# 4. Load the model as a Python Function (pyfunc) and use it for inference ======================================================== # Load the model back for predictions as a generic Python Function model loaded_model = mlflow.pyfunc.**load_model**(model_info.model_uri)predictions = loaded_model.predict(X_test)iris_feature_names = datasets.load_iris().feature_namesresult = pd.DataFrame(X_test, columns=iris_feature_names) result["actual_class"] = y_test result["predicted_class"] = predictionsresult[:4] ========================================================# 注意在启动训练前，需要保证mlflow server已经启动 # 5.1 View the Run and Model in the MLflow UI python train.py# 5.1 启动ui，然后打开浏览器访问：http://127.0.0.1:8080 查看运行结果。 mlflow ui