MLflow autologging integration for Meridian.
This module enables MLflow tracking for Meridian. When enabled via autolog()
,
parameters, metrics, and other metadata will be automatically logged to MLflow,
allowing for improved experiment tracking and analysis.
To enable MLflow autologging for your Meridian workflows, simply call autolog.autolog()
once before your model run.
Example usage:
import
mlflow
from
meridian.data
import
load
from
meridian.mlflow
import
autolog
from
meridian.model
import
model
# Enable autologging (call this once per session)
autolog
.
autolog
(
log_metrics
=
True
)
# Start an MLflow run (optionally name it for better grouping)
with
mlflow
.
start_run
(
run_name
=
"my_run"
):
# Load data
data
=
load
.
CsvDataLoader
(
...
)
.
load
()
# Initialize Meridian model
mmm
=
model
.
Meridian
(
input_data
=
data
)
# Run Meridian sampling processes
mmm
.
sample_prior
(
n_draws
=
100
,
seed
=
123
)
mmm
.
sample_posterior
(
n_chains
=
7
,
n_adapt
=
500
,
n_burnin
=
500
,
n_keep
=
1000
,
seed
=
1
)
# After the run completes, you can retrieve run results using the MLflow client.
client
=
mlflow
.
tracking
.
MlflowClient
()
# Get the experiment ID for the run you just launched
experiment_id
=
"0"
# Search for runs matching the run name
runs
=
client
.
search_runs
(
experiment_id
,
max_results
=
1000
,
filter_string
=
f
"attributes.run_name = 'my_run'"
)
# Print details of the run
if
runs
:
print
(
runs
[
0
])
else
:
print
(
"No runs found."
)
Functions
autolog(...)
: Enables MLflow tracking for Meridian.
