MLflow mlflow.exceptions.MlflowException: Model '...' not found
MLflow mlflow.exceptions.MlflowException: Model '...' not found
Understanding MLflow
MLflow is an open-source platform designed to manage the machine learning lifecycle, including experimentation, reproducibility, and deployment. It provides tools to track experiments, package code into reproducible runs, and share and deploy models. One of its key components is the MLflow Model Registry, which is a centralized repository to manage the full lifecycle of an ML model.
Identifying the Symptom
When using MLflow, you might encounter the error: mlflow.exceptions.MlflowException: Model '...' not found. This error typically occurs when attempting to access a model that is not available in the MLflow Model Registry.
Exploring the Issue
The error message indicates that the specified model cannot be found in the registry. This could happen if the model name or version is incorrect, or if the model has not been registered yet. The MLflow Model Registry is crucial for managing model versions and ensuring that the correct model is used in production.
Common Causes
- The model name is misspelled or incorrect.
- The specified version of the model does not exist.
- The model has not been registered in the MLflow Model Registry.
Steps to Fix the Issue
To resolve this issue, follow these steps:
1. Verify Model Name and Version
Ensure that the model name and version you are trying to access are correct. You can list all registered models and their versions using the following command:
mlflow models list
This command will display all models in the registry, helping you verify the correct name and version.
2. Register the Model
If the model is not registered, you need to register it. Use the following command to register a model:
mlflow models register -m <model-path> -n <model-name>
Replace <model-path> with the path to your model and <model-name> with the desired name for your model.
3. Check Model Registry
Visit the MLflow UI to manually check the Model Registry. You can access it by navigating to your MLflow tracking server's UI and selecting the 'Models' tab. This will provide a visual overview of all registered models and their versions.
Additional Resources
For more detailed information on managing models with MLflow, refer to the official MLflow Model Registry documentation. Additionally, the MLflow Tracking documentation provides insights into tracking experiments and managing model lifecycle.
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