Description
Hello,
in the Documentation MLflow is used for logging only one model in a run. Now there is the functionality to train several models like in the example provided below.
models = [
KAN(input_size=2*horizon,
h=horizon,
scaler_type='robust',
max_steps=2,
early_stop_patience_steps=3),
LSTM(input_size=2*horizon,
h=horizon,
scaler_type='robust',
max_steps=2,
early_stop_patience_steps=3)
]
mlflow.pytorch.autolog(checkpoint=False)
with mlflow.start_run() as run:
nf = NeuralForecast(models=models, freq='M')
nf.fit(full_train, val_size=horizon)
mlflow.pytorch.get_default_conda_env()
`mlflow.pytorch.get_default_pip_requirements()
When logging with MLflow, one run is created for all models and the logged metrics cannot be differentiated between the individual models.
Is there a possibility to log each model individually when fitting nf objects which contains multiple models? Or is the only way to train each model individually?
Use case
No response
Description
Hello,
in the Documentation MLflow is used for logging only one model in a run. Now there is the functionality to train several models like in the example provided below.
When logging with MLflow, one run is created for all models and the logged metrics cannot be differentiated between the individual models.
Is there a possibility to log each model individually when fitting nf objects which contains multiple models? Or is the only way to train each model individually?
Use case
No response