Breaking Changes in MLflow 3.0
MLflow 3.0 introduces several breaking changes as part of our commitment to improving the framework's consistency, performance, and maintainability. This guide will help you understand what's changing and how to update your code accordingly.
Core Framework Changes
MLflow Recipes Removal
What's changing: MLflow Recipes (previously known as MLflow Pipelines) has been completely removed from MLflow (#15250).
Why: MLflow Recipes was deprecated in previous versions as the team refocused on core MLflow functionality and more modern machine learning workflows.
How to migrate: If you're using MLflow Recipes, you'll need to migrate to alternative workflow management solutions. Consider using standard MLflow tracking and model registry functionality directly in your workflows, and see if MLflow Projects will work better for your use cases.
AI Gateway Configuration Changes
What's changing: The 'routes' and 'route_type' config keys in the gateway server configuration have been removed (#15331).
Why: The AI Gateway configuration has been simplified and modernized to better support current deployment patterns.
How to migrate: Update your AI Gateway configuration to use the new configuration format. Check the MLflow 3.0 documentation for the updated gateway configuration syntax.
MLflow Deployment Server Removal
What's changing: The MLflow deployment server application and the start-server CLI command have been removed (#15327).
Why: MLflow has evolved its deployment strategy to better support modern serving architectures.
How to migrate: Use MLflow's built-in model serving capabilities with mlflow models serve or containerized deployment options. For more complex deployments, consider using integration with cloud providers or platforms like Kubernetes.
Model Flavor Changes
fastai Flavor Removal
What's changing: The fastai model flavor has been completely removed (#15255).
Why: The fastai library has evolved significantly since this flavor was first added, and usage of this specific flavor has declined over time.
How to migrate: If you're using the fastai flavor, consider using the more general Python Function flavor (mlflow.pyfunc) to log your fastai models. Wrap your fastai model logic in a custom Python class that implements the predict method.
mleap Flavor Removal
What's changing: The mleap flavor has been removed from MLflow (#15259).
Why: Usage of this specialized flavor has decreased as more flexible alternatives became available.
How to migrate: Use the ONNX flavor (mlflow.onnx), the MLflow pyfunc (mlflow.pyfunc) to handle serialization of JVM-based models that need to be deployed in a container or edge deployed.
API Changes
Tracking API Changes
run_uuid Attribute Removal
What's changing: The run_uuid attribute has been removed from the RunInfo object (#15342).
Why: To simplify the API and reduce duplication, as run_id provides the same information.
How to migrate: Replace all occurrences of run_uuid with run_id in your code.
Git Tag Changes
What's changing: The run tags mlflow.gitBranchName and mlflow.gitRepoURL have been removed (#15366).
Why: MLflow is standardizing how git information is tracked.
How to migrate: Use the remaining git-related tags for tracking source version information.
TensorFlow Autologging Change
What's changing: The every_n_iter parameter has been removed from TensorFlow autologging (#15412).
Why: To simplify the API and standardize behavior across autologging implementations.
How to migrate: If you relied on fine-tuning logging frequency, you may need to implement custom logging callbacks.
Model API Changes
Parameter Removals
What's changing: Several parameters have been removed from model logging and saving APIs:
example_no_conversionparameter from model logging APIs (#15322)code_pathparameter from model logging and model saving APIs (#15368)requirements_fileparameter frompytorchflavor model logging and saving APIs (#15369)inference_configparameter fromtransformersflavor model logging and saving APIs (#15415)
Why: These parameters were deprecated in earlier releases and have now been fully removed to simplify the API.
How to migrate: Update your model logging and saving calls to remove these parameters. Use the recommended alternatives:
- For
code_path, use the code directory structure MLflow expects by default - For
requirements_file, specify dependencies with thepip_requirementsorextra_pip_requirementsarguments when logging your torch models - For
inference_configin transformers models, set your configuration before logging the model
Other Model Changes
What's changing: The signature_dict property has been removed from the ModelInfo object (#15367).
Why: To standardize how model signatures are represented in MLflow.
How to migrate: Use the signature property on ModelInfo objects instead, which provides the same information in a more consistent format.
Evaluation API Changes
Baseline Model Comparison Removal
What's changing: The baseline_model parameter and related parameters have been removed from the evaluation API (#15362).
Why: This functionality has been replaced with the more flexible mlflow.validate_evaluation_results API.
How to migrate: Instead of using the baseline_model parameter, first evaluate your models separately, then use the mlflow.validate_evaluation_results API to compare them.
MetricThreshold Constructor Change
What's changing: The higher_is_better parameter has been removed from the constructor of class MetricThreshold (#15343).
Why: It was deprecated in MLflow 2.3.0 in favor of greater_is_better. This was done to make a less confusing argument name.
How to migrate: Use the greater_is_better parameter instead of higher_is_better when creating MetricThreshold objects.
Custom Metrics Parameter Removal
What's changing: The custom_metrics parameter has been removed from the evaluation API (#15361).
Why: A newer, more flexible approach for custom metrics has been implemented.
How to migrate: Use the newer custom metrics approach documented in the MLflow evaluation API.
Environment Variable Changes
What's changing: The environment variable MLFLOW_GCS_DEFAULT_TIMEOUT configuration has been removed (#15365).
Why: To standardize how timeouts are configured across different storage backends.
How to migrate: Update your code to handle GCS timeouts using the standard approach for your GCS client library.
mlflow.evaluate no longer logs an explainer as a model by default.
What's changing: mlflow.evaluate no longer logs an explainer as a model by default.
How to migrate: Set the log_explainer config to True in the evaluator_config parameter to log the explainer as a model.
mlflow.evaluate(
...,
evaluator_config={
"log_model_explainability": True,
"log_explainer": True,
},
)
Summary
These breaking changes are part of MLflow's evolution to provide a more consistent, maintainable, and future-proof machine learning framework. While updating your code to accommodate these changes may require some effort, the resulting improvements in clarity, consistency, and performance should make the migration worthwhile.
For detailed guidance on migrating specific code, please consult the MLflow 3.0 documentation or join the MLflow community forum for assistance.