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Roope Astala
2018-10-12 14:39:33 -04:00
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15
automl/03.auto-ml-remote-execution.ipynb
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@@ -15,7 +15,7 @@
"source": [
"# AutoML 03: Remote Execution using DSVM (Ubuntu)\n",
"\n",
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) to showcase how you can use AutoML for a simple classification problem.\n",
"In this example we use the scikit-learn's [digit dataset](http://scikit-learn.org/stable/datasets/index.html#optical-recognition-of-handwritten-digits-dataset) to showcase how you can use AutoML for a simple classification problem.\n",
"\n",
"Make sure you have executed the [00.configuration](00.configuration.ipynb) before running this notebook.\n",
"\n",
@@ -145,7 +145,7 @@
"source": [
"## Create Get Data File\n",
"For remote executions you should author a `get_data.py` file containing a `get_data()` function. This file should be in the root directory of the project. You can encapsulate code to read data either from a blob storage or local disk in this file.\n",
"In this example, the `get_data()` function returns data from scikit-learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html)."
"In this example, the `get_data()` function returns data using scikit-learn's [load_digits](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html) method."
]
},
{
@@ -234,7 +234,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train the Model\n",
"## Train the Models\n",
"\n",
"Call the `submit` method on the experiment object and pass the run configuration. For remote runs the execution is asynchronous, so you will see the iterations get populated as they complete. You can interact with the widgets and models even when the experiment is running to retrieve the best model up to that point. Once you are satisfied with the model, you can cancel a particular iteration or the whole run.\n",
"\n",
@@ -354,7 +354,7 @@
"source": [
"### Retrieve the Best Model\n",
"\n",
"Below we select the best pipeline from our iterations. The `get_output` method on `automl_classifier` returns the best run and the fitted model for the last invocation. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
"Below we select the best pipeline from our iterations. The `get_output` method returns the best run and the fitted model. The Model includes the pipeline and any pre-processing. Overloads on `get_output` allow you to retrieve the best run and fitted model for *any* logged metric or for a particular *iteration*."
]
},
{
@@ -433,7 +433,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Test Our Best Pipeline"
"#### Test Our Best Fitted Model"
]
},
{
@@ -457,6 +457,11 @@
}
],
"metadata": {
"authors": [
{
"name": "savitam"
}
],
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",