Documentation Index
Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
Use this file to discover all available pages before exploring further.
Log your metrics and models
Libraries
import copy
import subprocess
import time
import lightgbm as lgb
import mlflow
import pandas as pd
import requests
from sklearn.linear_model import LinearRegression
from utilsforecast.data import generate_series
from utilsforecast.losses import rmse, smape
from utilsforecast.evaluation import evaluate
from utilsforecast.feature_engineering import fourier
import mlforecast.flavor
from mlforecast import MLForecast
from mlforecast.lag_transforms import ExponentiallyWeightedMean
from mlforecast.utils import PredictionIntervals
Data setup
freq = 'h'
h = 10
series = generate_series(5, freq=freq)
valid = series.groupby('unique_id', observed=True).tail(h)
train = series.drop(valid.index)
train, X_df = fourier(train, freq=freq, season_length=24, k=2, h=h)
Parameters
params = {
'init': {
'models': {
'lgb': lgb.LGBMRegressor(
n_estimators=50, num_leaves=16, verbosity=-1
),
'lr': LinearRegression(),
},
'freq': freq,
'lags': [24],
'lag_transforms': {
1: [ExponentiallyWeightedMean(0.9)],
},
'num_threads': 2,
},
'fit': {
'static_features': ['unique_id'],
'prediction_intervals': PredictionIntervals(n_windows=2, h=h),
}
}
Logging
If you have a tracking server, you can run
mlflow.set_tracking_uri(your_server_uri) to connect to it.
mlflow.set_experiment("mlforecast")
with mlflow.start_run() as run:
train_ds = mlflow.data.from_pandas(train)
valid_ds = mlflow.data.from_pandas(valid)
mlflow.log_input(train_ds, context="training")
mlflow.log_input(valid_ds, context="validation")
logged_params = copy.deepcopy(params)
logged_params['init']['models'] = {
k: (v.__class__.__name__, v.get_params())
for k, v in params['init']['models'].items()
}
mlflow.log_params(logged_params)
mlf = MLForecast(**params['init'])
mlf.fit(train, **params['fit'])
preds = mlf.predict(h, X_df=X_df)
eval_result = evaluate(
valid.merge(preds, on=['unique_id', 'ds']),
metrics=[rmse, smape],
agg_fn='mean',
)
models = mlf.models_.keys()
logged_metrics = {}
for _, row in eval_result.iterrows():
metric = row['metric']
for model in models:
logged_metrics[f'{metric}_{model}'] = row[model]
mlflow.log_metrics(logged_metrics)
mlforecast.flavor.log_model(model=mlf, artifact_path="model", registered_model_name=None)
model_uri = mlflow.get_artifact_uri("model")
run_id = run.info.run_id
Load model
mlflow.get_artifact_uri()
'file:///Users/deven367/projects/public/mlforecast/nbs/docs/how-to-guides/mlruns/135880152043890861/2a94793bbbe244f09dd68961d5883f5b/artifacts'
fallback_uri = f"runs:/{run_id}/model"
loaded_model = mlforecast.flavor.load_model(model_uri=fallback_uri)
print("Model loaded successfully from run artifacts!")
results = loaded_model.predict(h=h, X_df=X_df, ids=[3])
results.head(2)
Downloading artifacts: 0%| | 0/1 [00:00<?, ?it/s]
Downloading artifacts: 0%| | 0/7 [00:00<?, ?it/s]
Model loaded successfully from run artifacts!
| unique_id | ds | lgb | lr |
|---|
| 0 | 3 | 2000-01-10 16:00:00 | 22.187576 | 22.168456 |
| 1 | 3 | 2000-01-10 17:00:00 | 23.204075 | 23.413620 |
# Load model using the model URI from the registry
fallback_uri = f"runs:/{run_id}/model"
try:
loaded_model = mlforecast.flavor.load_model(model_uri=model_uri)
print("Model loaded successfully from registry!")
except Exception as e:
print(f"Failed to load from registry URI: {e}")
# Fallback: try loading from run artifacts
print(f"Trying fallback URI: {fallback_uri}")
loaded_model = mlforecast.flavor.load_model(model_uri=fallback_uri)
print("Model loaded successfully from run artifacts!")
results = loaded_model.predict(h=h, X_df=X_df, ids=[3])
results.head(2)
# Load model using the model URI from the registry
fallback_uri = f"runs:/{run_id}/model"
try:
loaded_model = mlforecast.flavor.load_model(model_uri=model_uri)
print("Model loaded successfully from registry!")
except Exception as e:
print(f"Failed to load from registry URI: {e}")
# Fallback: try loading from run artifacts
print(f"Trying fallback URI: {fallback_uri}")
loaded_model = mlforecast.flavor.load_model(model_uri=fallback_uri)
print("Model loaded successfully from run artifacts!")
results = loaded_model.predict(h=h, X_df=X_df, ids=[3])
results.head(2)
Failed to load from registry URI: No such file or directory: '/Users/deven367/projects/public/mlforecast/nbs/docs/how-to-guides/mlruns/135880152043890861/6ddb20bc3ccf4c048ede20b2c331f9c0/artifacts/model'
Trying fallback URI: runs:/6ddb20bc3ccf4c048ede20b2c331f9c0/model
Model loaded successfully from run artifacts!
Downloading artifacts: 0%| | 0/1 [00:00<?, ?it/s]
Downloading artifacts: 0%| | 0/7 [00:00<?, ?it/s]
| unique_id | ds | lgb | lr |
|---|
| 0 | 3 | 2000-01-10 16:00:00 | 22.187576 | 22.168456 |
| 1 | 3 | 2000-01-10 17:00:00 | 23.204075 | 23.413620 |
PyFunc
fallback_uri = f"runs:/{run_id}/model"
try:
loaded_pyfunc = mlforecast.flavor.pyfunc.load_model(model_uri=model_uri)
except Exception as e:
print(f"Failed to load from registry URI: {e}")
loaded_pyfunc = mlforecast.flavor.pyfunc.load_model(model_uri=fallback_uri)
# single row dataframe
predict_conf = pd.DataFrame(
[
{
"h": h,
"ids": [0, 2],
"X_df": X_df,
"level": [80]
}
]
)
pyfunc_result = loaded_pyfunc.predict(predict_conf)
pyfunc_result.head(2)
Failed to load from registry URI: No such file or directory: '/Users/deven367/projects/public/mlforecast/nbs/docs/how-to-guides/mlruns/135880152043890861/6ddb20bc3ccf4c048ede20b2c331f9c0/artifacts/model'
Downloading artifacts: 0%| | 0/1 [00:00<?, ?it/s]
Downloading artifacts: 0%| | 0/7 [00:00<?, ?it/s]
| unique_id | ds | lgb | lr | lgb-lo-80 | lgb-hi-80 | lr-lo-80 | lr-hi-80 |
|---|
| 0 | 0 | 2000-01-09 20:00:00 | 20.214914 | 20.095987 | 20.074128 | 20.355699 | 19.951723 | 20.240250 |
| 1 | 0 | 2000-01-09 21:00:00 | 21.180581 | 21.099920 | 20.943417 | 21.417746 | 20.755529 | 21.444311 |
Model serving
host = 'localhost'
port = '5001'
fallback_uri = f"runs:/{run_id}/model"
cmd = f'mlflow models serve -m {fallback_uri} -h {host} -p {port} --env-manager local'
print(f"Serving model from runs: {fallback_uri}")
# initialize server
process = subprocess.Popen(cmd.split())
time.sleep(5)
# single row dataframe. must be JSON serializable
predict_conf = pd.DataFrame(
[
{
"h": h,
"ids": [3, 4],
"X_df": X_df.astype({'ds': 'str'}).to_dict(orient='list'),
"level": [95]
}
]
)
payload = {'dataframe_split': predict_conf.to_dict(orient='split', index=False)}
try:
resp = requests.post(f'http://{host}:{port}/invocations', json=payload)
print(pd.DataFrame(resp.json()['predictions']).head(2))
except Exception as e:
print(f"Error making prediction request: {e}")
print(f"Response status: {resp.status_code if 'resp' in locals() else 'No response'}")
finally:
process.terminate()
process.wait(timeout=10)
Serving model from runs: runs:/6ddb20bc3ccf4c048ede20b2c331f9c0/model
INFO: ::1:52011 - "POST /invocations HTTP/1.1" 200 OK
unique_id ds lgb lr lgb-lo-95 lgb-hi-95 \
0 3 2000-01-10T16:00:00 22.187576 22.168456 21.973679 22.401473
1 3 2000-01-10T17:00:00 23.204075 23.413620 23.012538 23.395612
lr-lo-95 lr-hi-95
0 21.884265 22.452647
1 22.994313 23.832926
Downloading artifacts: 0%| | 0/1 [00:00<?, ?it/s]
Downloading artifacts: 100%|██████████| 7/7 [00:00<00:00, 5180.89it/s]
2025/08/04 09:39:36 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function'
Downloading artifacts: 0%| | 0/1 [00:00<?, ?it/s]
Downloading artifacts: 100%|██████████| 7/7 [00:00<00:00, 6882.36it/s]
2025/08/04 09:39:36 INFO mlflow.pyfunc.backend: === Running command 'exec uvicorn --host localhost --port 5001 --workers 1 mlflow.pyfunc.scoring_server.app:app'
INFO: Started server process [10158]
INFO: Waiting for application startup.
INFO: Application startup complete.
INFO: Uvicorn running on http://localhost:5001 (Press CTRL+C to quit)
/Users/deven367/miniforge3/envs/nixtla/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be %d '
