mlflow

import mlflow
from sklearn.ensemble import RandomForestClassifier
 
# Enable autologging
mlflow.autolog()
 
# Train (automatically logged)
model = RandomForestClassifier(n_estimators=100, max_depth=5)
model.fit(X_train, y_train)
 
# Metrics, parameters, and model logged automatically!

import mlflow
 
# Create/set experiment
mlflow.set_experiment("my-experiment")
 
# Start a run
with mlflow.start_run(run_name="baseline-model"):
    # Log params
    mlflow.log_param("model", "ResNet50")
    mlflow.log_param("epochs", 10)
 
    # Train
    model = train()
 
    # Log metrics
    mlflow.log_metric("accuracy", 0.95)
 
    # Log model
    mlflow.pytorch.log_model(model, "model")
 
# Run ID is automatically generated
print(f"Run ID: {mlflow.active_run().info.run_id}")

with mlflow.start_run():
    # Single parameter
    mlflow.log_param("learning_rate", 0.001)
 
    # Multiple parameters
    mlflow.log_params({
        "batch_size": 32,
        "epochs": 50,
        "optimizer": "Adam",
        "dropout": 0.2
    })
 
    # Nested parameters (as dict)
    config = {
        "model": {
            "architecture": "ResNet50",
            "pretrained": True
        },
        "training": {
            "lr": 0.001,
            "weight_decay": 1e-4
        }
    }
 
    # Log as JSON string or individual params
    for key, value in config.items():
        mlflow.log_param(key, str(value))

with mlflow.start_run():
    # Training loop
    for epoch in range(NUM_EPOCHS):
        train_loss = train_epoch()
        val_loss = validate()
 
        # Log metrics at each step
        mlflow.log_metric("train_loss", train_loss, step=epoch)
        mlflow.log_metric("val_loss", val_loss, step=epoch)
 
        # Log multiple metrics
        mlflow.log_metrics({
            "train_accuracy": train_acc,
            "val_accuracy": val_acc
        }, step=epoch)
 
    # Log final metrics (no step)
    mlflow.log_metric("final_accuracy", final_acc)

with mlflow.start_run():
    # Log file
    model.save('model.pkl')
    mlflow.log_artifact('model.pkl')
 
    # Log directory
    os.makedirs('plots', exist_ok=True)
    plt.savefig('plots/loss_curve.png')
    mlflow.log_artifacts('plots')
 
    # Log text
    with open('config.txt', 'w') as f:
        f.write(str(config))
    mlflow.log_artifact('config.txt')
 
    # Log dict as JSON
    mlflow.log_dict({'config': config}, 'config.json')

# PyTorch
import mlflow.pytorch
 
with mlflow.start_run():
    model = train_pytorch_model()
    mlflow.pytorch.log_model(model, "model")
 
# Scikit-learn
import mlflow.sklearn
 
with mlflow.start_run():
    model = train_sklearn_model()
    mlflow.sklearn.log_model(model, "model")
 
# Keras/TensorFlow
import mlflow.keras
 
with mlflow.start_run():
    model = train_keras_model()
    mlflow.keras.log_model(model, "model")
 
# HuggingFace Transformers
import mlflow.transformers
 
with mlflow.start_run():
    mlflow.transformers.log_model(
        transformers_model={
            "model": model,
            "tokenizer": tokenizer
        },
        artifact_path="model"
    )

import mlflow
 
# Enable for all supported frameworks
mlflow.autolog()
 
# Or enable for specific framework
mlflow.sklearn.autolog()
mlflow.pytorch.autolog()
mlflow.keras.autolog()
mlflow.xgboost.autolog()

import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
 
# Enable autologging
mlflow.sklearn.autolog()
 
# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
 
# Train (automatically logs params, metrics, model)
with mlflow.start_run():
    model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=42)
    model.fit(X_train, y_train)
 
    # Metrics like accuracy, f1_score logged automatically
    # Model logged automatically
    # Training duration logged

import mlflow
import pytorch_lightning as pl
 
# Enable autologging
mlflow.pytorch.autolog()
 
# Train
with mlflow.start_run():
    trainer = pl.Trainer(max_epochs=10)
    trainer.fit(model, datamodule=dm)
 
    # Hyperparameters logged
    # Training metrics logged
    # Best model checkpoint logged

import mlflow
 
# Log and register model
with mlflow.start_run():
    model = train_model()
 
    # Log model
    mlflow.sklearn.log_model(
        model,
        "model",
        registered_model_name="my-classifier"  # Register immediately
    )
 
# Or register later
run_id = "abc123"
model_uri = f"runs:/{run_id}/model"
mlflow.register_model(model_uri, "my-classifier")

from mlflow.tracking import MlflowClient
 
client = MlflowClient()
 
# Promote to staging
client.transition_model_version_stage(
    name="my-classifier",
    version=3,
    stage="Staging"
)
 
# Promote to production
client.transition_model_version_stage(
    name="my-classifier",
    version=3,
    stage="Production",
    archive_existing_versions=True  # Archive old production versions
)
 
# Archive model
client.transition_model_version_stage(
    name="my-classifier",
    version=2,
    stage="Archived"
)

import mlflow.pyfunc
 
# Load latest production model
model = mlflow.pyfunc.load_model("models:/my-classifier/Production")
 
# Load specific version
model = mlflow.pyfunc.load_model("models:/my-classifier/3")
 
# Load from staging
model = mlflow.pyfunc.load_model("models:/my-classifier/Staging")
 
# Use model
predictions = model.predict(X_test)

client = MlflowClient()
 
# List all versions
versions = client.search_model_versions("name='my-classifier'")
 
for v in versions:
    print(f"Version {v.version}: {v.current_stage}")
 
# Get latest version by stage
latest_prod = client.get_latest_versions("my-classifier", stages=["Production"])
latest_staging = client.get_latest_versions("my-classifier", stages=["Staging"])
 
# Get model version details
version_info = client.get_model_version(name="my-classifier", version="3")
print(f"Run ID: {version_info.run_id}")
print(f"Stage: {version_info.current_stage}")
print(f"Tags: {version_info.tags}")

client = MlflowClient()
 
# Add description
client.update_model_version(
    name="my-classifier",
    version="3",
    description="ResNet50 classifier trained on 1M images with 95% accuracy"
)
 
# Add tags
client.set_model_version_tag(
    name="my-classifier",
    version="3",
    key="validation_status",
    value="approved"
)
 
client.set_model_version_tag(
    name="my-classifier",
    version="3",
    key="deployed_date",
    value="2025-01-15"
)

from mlflow.tracking import MlflowClient
 
client = MlflowClient()
 
# Search all runs in experiment
experiment_id = client.get_experiment_by_name("my-experiment").experiment_id
runs = client.search_runs(
    experiment_ids=[experiment_id],
    filter_string="metrics.accuracy > 0.9",
    order_by=["metrics.accuracy DESC"],
    max_results=10
)
 
for run in runs:
    print(f"Run ID: {run.info.run_id}")
    print(f"Accuracy: {run.data.metrics['accuracy']}")
    print(f"Params: {run.data.params}")
 
# Search with complex filters
runs = client.search_runs(
    experiment_ids=[experiment_id],
    filter_string="""
        metrics.accuracy > 0.9 AND
        params.model = 'ResNet50' AND
        tags.dataset = 'ImageNet'
    """,
    order_by=["metrics.f1_score DESC"]
)

import mlflow
import torch
import torch.nn as nn
 
# Enable autologging
mlflow.pytorch.autolog()
 
with mlflow.start_run():
    # Log config
    config = {
        "lr": 0.001,
        "epochs": 10,
        "batch_size": 32
    }
    mlflow.log_params(config)
 
    # Train
    model = create_model()
    optimizer = torch.optim.Adam(model.parameters(), lr=config["lr"])
 
    for epoch in range(config["epochs"]):
        train_loss = train_epoch(model, optimizer, train_loader)
        val_loss, val_acc = validate(model, val_loader)
 
        # Log metrics
        mlflow.log_metrics({
            "train_loss": train_loss,
            "val_loss": val_loss,
            "val_accuracy": val_acc
        }, step=epoch)
 
    # Log model
    mlflow.pytorch.log_model(model, "model")

import mlflow
from transformers import Trainer, TrainingArguments
 
# Enable autologging
mlflow.transformers.autolog()
 
training_args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=16,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    load_best_model_at_end=True
)
 
# Start MLflow run
with mlflow.start_run():
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset
    )
 
    # Train (automatically logged)
    trainer.train()
 
    # Log final model to registry
    mlflow.transformers.log_model(
        transformers_model={
            "model": trainer.model,
            "tokenizer": tokenizer
        },
        artifact_path="model",
        registered_model_name="hf-classifier"
    )

import mlflow
import xgboost as xgb
 
# Enable autologging
mlflow.xgboost.autolog()
 
with mlflow.start_run():
    dtrain = xgb.DMatrix(X_train, label=y_train)
    dval = xgb.DMatrix(X_val, label=y_val)
 
    params = {
        'max_depth': 6,
        'learning_rate': 0.1,
        'objective': 'binary:logistic',
        'eval_metric': ['logloss', 'auc']
    }
 
    # Train (automatically logged)
    model = xgb.train(
        params,
        dtrain,
        num_boost_round=100,
        evals=[(dtrain, 'train'), (dval, 'val')],
        early_stopping_rounds=10
    )
 
    # Model and metrics logged automatically

# ✅ Good: Separate experiments for different tasks
mlflow.set_experiment("sentiment-analysis")
mlflow.set_experiment("image-classification")
mlflow.set_experiment("recommendation-system")
 
# ❌ Bad: Everything in one experiment
mlflow.set_experiment("all-models")

# ✅ Good: Descriptive names
with mlflow.start_run(run_name="resnet50-imagenet-lr0.001-bs32"):
    train()
 
# ❌ Bad: No name (auto-generated UUID)
with mlflow.start_run():
    train()

with mlflow.start_run():
    # Log hyperparameters
    mlflow.log_params({
        "learning_rate": 0.001,
        "batch_size": 32,
        "epochs": 50
    })
 
    # Log system info
    mlflow.set_tags({
        "dataset": "ImageNet",
        "framework": "PyTorch 2.0",
        "gpu": "A100",
        "git_commit": get_git_commit()
    })
 
    # Log data info
    mlflow.log_param("train_samples", len(train_dataset))
    mlflow.log_param("val_samples", len(val_dataset))

# Link runs to understand lineage
with mlflow.start_run(run_name="preprocessing"):
    data = preprocess()
    mlflow.log_artifact("data.csv")
    preprocessing_run_id = mlflow.active_run().info.run_id
 
with mlflow.start_run(run_name="training"):
    # Reference parent run
    mlflow.set_tag("preprocessing_run_id", preprocessing_run_id)
    model = train(data)

# ✅ Good: Use registry for production
model_uri = "models:/my-classifier/Production"
model = mlflow.pyfunc.load_model(model_uri)
 
# ❌ Bad: Hard-code run IDs
model_uri = "runs:/abc123/model"
model = mlflow.pyfunc.load_model(model_uri)

# Serve registered model
mlflow models serve -m "models:/my-classifier/Production" -p 5001
 
# Serve from run
mlflow models serve -m "runs:/<RUN_ID>/model" -p 5001
 
# Test endpoint
curl http://127.0.0.1:5001/invocations -H 'Content-Type: application/json' -d '{
  "inputs": [[1.0, 2.0, 3.0, 4.0]]
}'

# Deploy to AWS SageMaker
mlflow sagemaker deploy -m "models:/my-classifier/Production" --region-name us-west-2
 
# Deploy to Azure ML
mlflow azureml deploy -m "models:/my-classifier/Production"

# Start tracking server with backend store
mlflow server \
  --backend-store-uri postgresql://user:password@localhost/mlflow \
  --default-artifact-root s3://my-bucket/mlflow \
  --host 0.0.0.0 \
  --port 5000

import mlflow
 
# Set tracking URI
mlflow.set_tracking_uri("http://localhost:5000")
 
# Or use environment variable
# export MLFLOW_TRACKING_URI=http://localhost:5000