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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>
51 lines
1.6 KiB
Markdown
51 lines
1.6 KiB
Markdown
FLAML can be used together with AzureML and mlflow.
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### Prerequisites
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Install the [azureml] option.
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```bash
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pip install "flaml[azureml]"
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```
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Setup a AzureML workspace:
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```python
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from azureml.core import Workspace
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ws = Workspace.create(name='myworkspace', subscription_id='<azure-subscription-id>',resource_group='myresourcegroup')
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```
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### Enable mlflow in AzureML workspace
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```python
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import mlflow
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from azureml.core import Workspace
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ws = Workspace.from_config()
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mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())
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```
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### Start an AutoML run
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```python
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from flaml.data import load_openml_dataset
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# 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.
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X_train, X_test, y_train, y_test = load_openml_dataset(dataset_id=1169, data_dir="./")
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from flaml import AutoML
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automl = AutoML()
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settings = {
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"time_budget": 60, # total running time in seconds
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"metric": "accuracy", # metric to optimize
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"task": "classification", # task type
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"log_file_name": "airlines_experiment.log", # flaml log file
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}
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mlflow.set_experiment("flaml") # the experiment name in AzureML workspace
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with mlflow.start_run() as run: # create a mlflow run
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automl.fit(X_train=X_train, y_train=y_train, **settings)
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```
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The metrics in the run will be automatically logged in an experiment named "flaml" in your AzureML workspace.
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[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/integrate_azureml.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/integrate_azureml.ipynb) |