mlflow
Use this command to to monitor the lifecycle of the machine learning models from training progress to artifact deployment.
For more information about the Machine Learning Anomaly Detection feature, see the FortiAnalyzer Administration Guide.
Syntax
diagnose mlflow config
diagnose mlflow infer-result <artifact_name>
diagnose mlflow list-artifacts <arg0>
diagnose mlflow show-assets <artifact_name>
diagnose mlflow show-details <artifact_name>
diagnose mlflow test-result <artifact_name>
diagnose mlflow training-status
|
Variable |
Description |
|---|---|
|
config |
Show per-model config and artifact stats (count, deployed, last trained). |
|
infer-result <artifact_name> |
Display latest inference results for an artifact. Enter the name of the artifact (for example, |
|
list-artifacts <arg0> |
List all trained artifacts. Optional filters: |
|
show-assets <artifact_name> |
List assets trained and excluded for the artifact. Enter the name of the artifact (for example, |
|
show-details <artifact_name> |
Show detailed information about a specific artifact. Enter the name of the artifact (for example, |
|
test-result <artifact_name> |
Display latest test results for an artifact. Enter the name of the artifact (for example, |
|
training-status |
Show current training progress for models in training. |