The MLflow UI and API support searching runs within a single experiment or a group of experiments
using a search filter API. This API is a simplified version of the SQL
Table of Contents
A search filter can be one or more expressions joined by the
The syntax does not support
OR. Each expression has three parts: an identifier on
the left-hand side (LHS), a comparator, and constant on the right-hand side (RHS).
Search for the subset of runs with logged accuracy metric greater than 0.92.
metrics.accuracy > 0.92
Search for runs created using a Logistic Regression model, a learning rate (lambda) of 0.001, and recorded error metric under 0.05.
params.model = "LogisticRegression" and params.lambda = "0.001" and metrics.error <= 0.05
Required in the LHS of a search expression. Signifies an entity to compare against. An identifier has two
parts separated by a period: the type of the entity and the name of the entity.
The type of the entity is
tags. The entity name can contain alphanumeric characters and special characters.
When a metric, parameter, or tag name contains a special character like hyphen, space, period, and so on, enclose the entity name in double quotes.
Unlike SQL syntax for column names, MLflow allows logging metrics, parameters, and tags with names that have a leading number. If an entity name contains a leading number, enclose the entity name in double quotes. For example:
metrics."2019-04-02 error rate"
There are two classes of comparators: numeric and string.
- Numeric comparators (
- String comparators (
The search syntax requires the RHS of the expression to be a constant. The type of the constant depends on LHS.
- If LHS is a metric, the RHS must be an integer or float number.
- If LHS is a parameter or tag, the RHS must be a string constant enclosed in single or double quotes.
The MLflow UI supports searching runs contained within the current experiment. To search runs across multiple experiments, use one of the client APIs.
Get all active runs from experiments with IDs 3, 4, and 17 that used a CNN model with 10 layers and had a prediction accuracy of 94.5% or higher.
from mlflow.tracking.client import MlflowClient() query = "params.model = 'CNN' and params.layers = '10' and metrics.'prediction accuracy' >= 0.945" runs = MlflowClient().search_runs([3, 4, 17], query, ViewTypes.ACTIVE_ONLY)
Search all known experiments for any MLflow runs created using the Inception model architecture.
from mlflow.tracking.client import MlflowClient() runs = MlflowClient().search_runs(MlflowClient().list_experiments(), "params.model = 'Inception'", ViewType.ALL)
The Java API is similar to Python API.
List<Long> experimentIds = Arrays.asList(1, 2, 4, 8); List<RunInfo> searchResult = client.searchRuns(experimentIds, "metrics.accuracy_score < 99.90");