jprdonnelly/MachineLearningNotebooks
1
0
Fork 0
mirror of https://github.com/Azure/MachineLearningNotebooks.git synced 2025-12-23 20:00:06 -05:00
Code Issues Projects Releases Wiki Activity

update samples from Release-6 as a part of 1.3.0 SDK stable release

Browse Source
This commit is contained in:
vizhur
2020-04-13 16:22:23 +00:00
parent c520bd1d41
commit 057e22b253
83 changed files with 3024 additions and 1249 deletions
Download Patch File Download Diff File Expand all files Collapse all files

22
tutorials/create-first-ml-experiment/tutorial-1st-experiment-sdk-train.ipynb
View File

@@ -31,7 +31,7 @@
"\n",
"> * Connect your workspace and create an experiment \n",
"> * Load data and train a scikit-learn model\n",
"> * View training results in the portal\n",
"> * View training results in the studio\n",
"> * Retrieve the best model"
]
},
@@ -74,7 +74,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create an experiment in your workspace. An experiment is another foundational cloud resource that represents a collection of trials (individual model runs). In this tutorial you use the experiment to create runs and track your model training in the Azure Portal. Parameters include your workspace reference, and a string name for the experiment."
"Now create an experiment in your workspace. An experiment is another foundational cloud resource that represents a collection of trials (individual model runs). In this tutorial you use the experiment to create runs and track your model training in the Azure Machine Learning studio. Parameters include your workspace reference, and a string name for the experiment."
]
},
{
@@ -171,7 +171,7 @@
"\n",
"1. For each alpha hyperparameter value in the `alphas` array, a new run is created within the experiment. The alpha value is logged to differentiate between each run.\n",
"1. In each run, a Ridge model is instantiated, trained, and used to run predictions. The root-mean-squared-error is calculated for the actual versus predicted values, and then logged to the run. At this point the run has metadata attached for both the alpha value and the rmse accuracy.\n",
"1. Next, the model for each run is serialized and uploaded to the run. This allows you to download the model file from the run in the portal.\n",
"1. Next, the model for each run is serialized and uploaded to the run. This allows you to download the model file from the run in the studio.\n",
"1. At the end of each iteration the run is completed by calling `run.complete()`.\n",
"\n"
]
@@ -180,7 +180,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"After the training has completed, call the `experiment` variable to fetch a link to the experiment in the portal."
"After the training has completed, call the `experiment` variable to fetch a link to the experiment in the studio."
]
},
{
@@ -196,14 +196,14 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## View training results in portal"
"## View training results in studio"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Following the **Link to Azure Portal** takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (`alpha_value` and `rmse`, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.\n",
"Following the **Link to Azure Machine Learning studio** takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (`alpha_value` and `rmse`, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.\n",
"\n",
"When training models at scale over hundreds and thousands of runs, this page makes it easy to see every model you trained, specifically how they were trained, and how your unique metrics have changed over time."
]
@@ -212,21 +212,21 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"![Main Experiment page in Portal](imgs/experiment_main.png)"
"![Main Experiment page in the studio](../imgs/experiment_main.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clicking on a run number link in the `RUN NUMBER` column takes you to the page for each individual run. The default tab **Details** shows you more-detailed information on each run. Navigate to the **Outputs** tab, and you see the `.pkl` file for the model that was uploaded to the run during each training iteration. Here you can download the model file, rather than having to retrain it manually."
"Select a run number link in the `RUN NUMBER` column to see the page for an individual run. The default tab **Details** shows you more-detailed information on each run. Navigate to the **Outputs + logs** tab, and you see the `.pkl` file for the model that was uploaded to the run during each training iteration. Here you can download the model file, rather than having to retrain it manually."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Run details page in Portal](imgs/model_download.png)"
"![Run details page in the studio](../imgs/model_download.png)"
]
},
{
@@ -240,7 +240,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition to being able to download model files from the experiment in the portal, you can also download them programmatically. The following code iterates through each run in the experiment, and accesses both the logged run metrics and the run details (which contains the run_id). This keeps track of the best run, in this case the run with the lowest root-mean-squared-error."
"In addition to being able to download model files from the experiment in the studio, you can also download them programmatically. The following code iterates through each run in the experiment, and accesses both the logged run metrics and the run details (which contains the run_id). This keeps track of the best run, in this case the run with the lowest root-mean-squared-error."
]
},
{
@@ -352,7 +352,7 @@
"\n",
"> * Connected your workspace and created an experiment\n",
"> * Loaded data and trained scikit-learn models\n",
"> * Viewed training results in the portal and retrieved models\n",
"> * Viewed training results in the studio and retrieved models\n",
"\n",
"[Deploy your model](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml) with Azure Machine Learning.\n",
"Learn how to develop [automated machine learning](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models) experiments."