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Do not remove cpu-cluster compute #1934

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Nov 30, 2022
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Do not remove cpu-cluster compute #1934

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2 changes: 1 addition & 1 deletion ...orecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced-mlflow.ipynb
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Expand Up @@ -244,7 +244,7 @@
"|**target_column_name**|The name of the label column.|\n",
"|**primary_metric**|This is the metric that you want to optimize.<br> Forecasting supports the following primary metrics <br><i>spearman_correlation</i><br><i>normalized_root_mean_squared_error</i><br><i>r2_score</i><br><i>normalized_mean_absolute_error</i>|\n",
"|**training_data**|The training data to be used for this experiment. You can use a registered MLTable in the workspace using the format `<mltable_name>:<version>` OR you can use a local file or folder as a MLTable. For e.g `Input(mltable='my_mltable:1')` OR `Input(mltable=MLTable(local_path=\"./data\"))` The parameter 'training_data' must always be provided.|\n",
"|**compute**|The compute on which the AutoML job will run. In this example we are using a compute called 'cpu-cluster' present in the workspace. You can replace it with any other compute in the workspace.|\n",
"|**compute**|The compute on which the AutoML job will run. In this example we are using a compute called 'energy-cluster-v2' present in the workspace. You can replace it with any other compute in the workspace.|\n",
"|**n_cross_validations**|Number of cross-validation folds to use for model/pipeline selection. The default value is \"auto\", in which case AutoMl determines the number of cross-validations automatically, if a validation set is not provided. Or, users could specify an integer value.|\n",
"|**name**|The name of the Job/Run. This is an optional property. If not specified, a random name will be generated.\n",
"|**experiment_name**|The name of the Experiment. An Experiment is like a folder with multiple runs in Azure ML Workspace that should be related to the same logical machine learning experiment. For example, if a user runs this notebook multiple times, there will be multiple runs associated with the same Experiment name.|\n",
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4 changes: 2 additions & 2 deletions ...utoml-forecasting-task-energy-demand/automl-forecasting-task-energy-demand-advanced.ipynb
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Expand Up @@ -172,7 +172,7 @@
"- `training_data` - The data to be used for training. It should contain both training feature columns and a target column. Optionally, this data can be split for segregating a validation or test dataset. \n",
"You can use a registered MLTable in the workspace using the format '<mltable_name>:<version>' OR you can use a local file or folder as a MLTable. For e.g Input(mltable='my_mltable:1') OR Input(mltable=MLTable(local_path=\"./data\"))\n",
"The parameter 'training_data' must always be provided.\n",
"- `compute` - The compute on which the AutoML job will run. In this example we are using a compute called 'cpu-cluster' present in the workspace. You can replace it any other compute in the workspace. \n",
"- `compute` - The compute on which the AutoML job will run. In this example we are using a compute called 'adv-energy-cluster-v2' present in the workspace. You can replace it any other compute in the workspace. \n",
"- `name` - The name of the Job/Run. This is an optional property. If not specified, a random name will be generated.\n",
"- `experiment_name` - The name of the Experiment. An Experiment is like a folder with multiple runs in Azure ML Workspace that should be related to the same logical machine learning experiment.\n",
"\n",
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"outputs": [],
"source": [
"# general job parameters\n",
"compute_name = \"cpu-cluster\"\n",
"compute_name = \"adv-energy-cluster-v2\"\n",
"max_trials = 5\n",
"exp_name = \"dpv2-forecasting-experiment\""
]
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2 changes: 1 addition & 1 deletion sdk/python/notebooks_config.ini
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Expand Up @@ -16,7 +16,7 @@ COMPUTE_NAMES = bike-share-v2

[automl-forecasting-task-energy-demand-advanced]
USE_FORECAST_REQUIREMENTS = 0
COMPUTE_NAMES = cpu-cluster
COMPUTE_NAMES = adv-energy-cluster-v2

[automl-forecasting-task-energy-demand-advanced-mlflow]
USE_FORECAST_REQUIREMENTS = 1
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