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A new documentation website. And: * add actions for doc * update docstr * installation instructions for doc dev * unify README and Getting Started * rename notebook * doc about best_model_for_estimator #340 * docstr for keep_search_state #340 * DNN Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu> Co-authored-by: Z.sk <shaokunzhang@psu.edu>
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1.6 KiB
FLAML can be used together with AzureML and mlflow.
Prerequisites
Install the [azureml] option.
pip install "flaml[azureml]"
Setup a AzureML workspace:
from azureml.core import Workspace
ws = Workspace.create(name='myworkspace', subscription_id='<azure-subscription-id>',resource_group='myresourcegroup')
Enable mlflow in AzureML workspace
import mlflow
from azureml.core import Workspace
ws = Workspace.from_config()
mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())
Start an AutoML run
from flaml.data import load_openml_dataset
# Download [Airlines dataset](https://www.openml.org/d/1169) from OpenML. The task is to predict whether a given flight will be delayed, given the information of the scheduled departure.
X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir="./")
from flaml import AutoML
automl = AutoML()
settings = {
"time_budget": 60, # total running time in seconds
"metric": "accuracy", # metric to optimize
"task": "classification", # task type
"log_file_name": "airlines_experiment.log", # flaml log file
}
mlflow.set_experiment("flaml") # the experiment name in AzureML workspace
with mlflow.start_run() as run: # create a mlflow run
automl.fit(X_train=X_train, y_train=y_train, **settings)
The metrics in the run will be automatically logged in an experiment named "flaml" in your AzureML workspace.