Package: azuremlsdk 1.10.0

Diondra Peck

azuremlsdk: Interface to the 'Azure Machine Learning' 'SDK'

Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.

Authors:Diondra Peck [cre, aut], Minna Xiao [aut], AzureML R SDK Team [ctb], Microsoft [cph, fnd], Google Inc. [cph], The TensorFlow Authors [cph], RStudio Inc. [cph]

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manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
azuremlsdk/json (API)

# Install 'azuremlsdk' in R:
install.packages('azuremlsdk', repos = c('https://azure.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/azure/azureml-sdk-for-r/issues

On CRAN:

Conda:

amlcomputeazureazure-machine-learningazuremldsimachine-learningrstudiosdk-r

8.93 score 104 stars 273 scripts 334 downloads 163 exports 63 dependencies

Last updated from:19301e106c. Checks:7 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
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source / vignettesOK218
linux-release-x86_64NOTE154
macos-release-arm64NOTE116
macos-oldrel-arm64NOTE120
windows-develNOTE116
windows-releaseNOTE115
windows-oldrelNOTE101
wasm-releaseOK116

Exports:aci_webservice_deployment_configaks_webservice_deployment_configattach_aks_computeazuremlbandit_policybayesian_parameter_samplingcancel_runchoicecomplete_runcontainer_registryconvert_to_dataset_with_csv_filesconvert_to_dataset_with_parquet_filescran_packagecreate_aks_computecreate_aml_computecreate_child_runcreate_child_runscreate_file_dataset_from_filescreate_tabular_dataset_from_delimited_filescreate_tabular_dataset_from_json_lines_filescreate_tabular_dataset_from_parquet_filescreate_tabular_dataset_from_sql_querycreate_workspacedata_pathdata_type_booldata_type_datetimedata_type_doubledata_type_longdata_type_stringdataset_consumption_configdefine_timestamp_columns_for_datasetdelete_computedelete_local_webservicedelete_modeldelete_secretsdelete_webservicedelete_workspacedeploy_modeldetach_aks_computedownload_file_from_rundownload_files_from_rundownload_from_datastoredownload_from_file_datasetdownload_modeldrop_columns_from_datasetestimatorexperimentfilter_dataset_after_timefilter_dataset_before_timefilter_dataset_between_timefilter_dataset_from_recent_timegenerate_new_webservice_keyget_aks_compute_credentialsget_best_run_by_primary_metricget_child_run_hyperparametersget_child_run_metricsget_child_runsget_child_runs_sorted_by_primary_metricget_computeget_current_runget_dataset_by_idget_dataset_by_nameget_datastoreget_default_datastoreget_default_keyvaultget_environmentget_file_dataset_pathsget_input_dataset_from_runget_modelget_model_package_container_registryget_model_package_creation_logsget_runget_run_detailsget_run_details_with_logsget_run_file_namesget_run_metricsget_runs_in_experimentget_secretsget_secrets_from_runget_webserviceget_webservice_keysget_webservice_logsget_webservice_tokenget_workspaceget_workspace_detailsgithub_packagegrid_parameter_samplinghyperdrive_configinference_configinstall_azuremlinteractive_login_authenticationinvoke_webservicekeep_columns_from_datasetlist_nodes_in_aml_computelist_secretslist_supported_vm_sizeslist_workspacesload_dataset_into_data_frameload_workspace_from_configlocal_webservice_deployment_configlog_accuracy_table_to_runlog_confusion_matrix_to_runlog_image_to_runlog_list_to_runlog_metric_to_runlog_predictions_to_runlog_residuals_to_runlog_row_to_runlog_table_to_runlognormalloguniformmedian_stopping_policymount_file_datasetnormalpackage_modelplot_run_detailsprimary_metric_goalpromote_headers_behaviorpull_model_package_imageqlognormalqloguniformqnormalquniformr_environmentrandintrandom_parameter_samplingrandom_split_datasetregister_azure_blob_container_datastoreregister_azure_file_share_datastoreregister_azure_postgre_sql_datastoreregister_azure_sql_database_datastoreregister_datasetregister_environmentregister_modelregister_model_from_runreload_local_webservice_assetsresource_configurationsave_model_package_filesservice_principal_authenticationset_default_datastoreset_secretsskip_from_datasetstart_logging_runsubmit_experimenttake_from_datasettake_sample_from_datasettruncation_selection_policyuniformunregister_all_dataset_versionsunregister_datastoreupdate_aci_webserviceupdate_aks_webserviceupdate_aml_computeupdate_local_webserviceupload_files_to_datastoreupload_files_to_runupload_folder_to_runupload_to_datastorewait_for_deploymentwait_for_model_package_creationwait_for_provisioning_completionwait_for_run_completionwrite_workspace_config

Dependencies:base64encbslibcachemclicommonmarkcpp11crosstalkdigestDTevaluatefarverfastmapfontawesomefsggplot2gluegtableherehighrhtmltoolshtmlwidgetshttpuvisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclemagrittrMatrixmemoisemimeotelplyrpngpromisesR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrmarkdownrprojrootrstudioapiS7sassscalesservrshinyshinycssloaderssourcetoolstinytexvctrsviridisLitewithrxfunxtableyaml

Known issues and troubleshooting
Installation | Compute instance | Older version of azuremlsdk | Error: "Unable to install packages" | Training | "ModuleNotFoundError": "No module named 'ruamel'" | "PermissionError" when submitting an experiment in RStudio | "Error": "C stack usage ______________ is too close to the limit" | Deployment | Resource quotas
Rendered fromtroubleshooting.Rmdusingknitr::rmarkdown

Last update: 2021-01-19
Started: 2020-05-06

A Deeper Dive into Experiments
Load the data | Try out several models | Select a model for deployment | Deploy a model to Azure Container Instances | Use the prediction service in a Shiny application
Rendered fromexperiments-deep-dive.Rmdusingknitr::rmarkdown

Last update: 2020-09-30
Started: 2020-05-07

Deploying models
Register your model | Register a model from an Azure ML run | Register a model from local file(s) | Choose a compute target | Prepare deloyment artifacts | 1. Define your entry script and dependencies | Locate model files in your entry script | Example entry script | 2. Define your inference environment | 3. Define your deployment configuration | Deploy to target | 1. Local deployment | 2. ACI deployment | 3. AKS deployment | Troubleshooting deployment | Web service authentication | Key-based authentication | Token-based authentication | Consume web service | Update web service | Clean up resources | Additional references
Rendered fromdeploying-models.Rmdusingknitr::rmarkdown

Last update: 2020-09-30
Started: 2020-05-06

Hyperparameter tune a Keras model
What are hyperparameters? | Prerequisites | Set up development environment | Import azuremlsdk package | Load your workspace | Create an experiment | Create a compute target | Prepare the training script | Create an estimator | Configure the HyperDrive run | Define search space | Define termination policy | Finalize configuration | Submit the HyperDrive run | Analyse runs by performance | Clean up resources
Rendered fromhyperparameter-tune-with-keras.Rmdusingknitr::rmarkdown

Last update: 2020-09-30
Started: 2020-05-06

Train a TensorFlow model
Prerequisites | Set up development environment | Import azuremlsdk package | Load your workspace | Create an experiment | Create a compute target | Prepare the training script | Create an estimator | Submit the job | View run metrics | Clean up resources
Rendered fromtrain-with-tensorflow.Rmdusingknitr::rmarkdown

Last update: 2020-09-30
Started: 2020-05-06

Building custom Docker images for training and deployment
Prerequisites | Writing the Dockerfile | Choose the base image | Specify conda dependencies | Specify R packages | Build and publish the image | Publish on Docker Hub | Publish on Azure Container Registry | Create an Azure ML Environment | Use the custom image for training | Use the custom image for deployment | Additional references
Rendered frombuilding-custom-docker-images.Rmdusingknitr::rmarkdown

Last update: 2020-08-25
Started: 2020-08-25

Install the Azure ML SDK for R
Install Conda | Install the azuremlsdk R package | Install the Azure ML Python SDK | Test installation | Troubleshooting
Rendered frominstallation.Rmdusingknitr::rmarkdown

Last update: 2020-07-09
Started: 2019-10-09

Deploy a web service to Azure Kubernetes Service
Prerequisites | Set up your testing environment | Import package | Load your workspace | Register the model | Provision an AKS cluster | Deploy as a web service | Define the inference dependencies | Deploy to AKS | Test the deployed service | Web service authentication | Key-based authentication | Token-based authentication | Clean up resources
Rendered fromdeploy-to-aks.Rmdusingknitr::rmarkdown

Last update: 2020-05-07
Started: 2020-05-06

Train and deploy your first model with Azure ML
Prerequisites | Set up your development environment | Install required packages | Load your workspace | Create an experiment | Create a compute target | Prepare data for training | Upload data to the datastore | Train a model | Prepare the training script | Create an estimator | Submit the job on the remote cluster | Retrieve training results | Get the logged metrics | Get the trained model | Deploy as a web service | Register the model | Define the inference dependencies | Deploy to ACI | Test the deployed service | Clean up resources
Rendered fromtrain-and-deploy-first-model.Rmdusingknitr::rmarkdown

Last update: 2020-05-07
Started: 2020-05-06

Set up an Azure ML workspace
What is an Azure ML workspace? | Setup | Azure subscription | Azure ML SDK installation | Configure your workspace | Workspace parameters | Create a new workspace | Access an existing workspace | Authenticate a workspace
Rendered fromconfiguration.Rmdusingknitr::rmarkdown

Last update: 2020-03-03
Started: 2019-10-25

Readme and manuals

Help Manual

Help pageTopics
Create a deployment config for deploying an ACI web serviceaci_webservice_deployment_config
Create a deployment config for deploying an AKS web serviceaks_webservice_deployment_config
Attach an existing AKS cluster to a workspaceattach_aks_compute
azureml module User can access functions/modules in azureml that are not exposed through the exported R functions.azureml
Define a Bandit policy for early termination of HyperDrive runsbandit_policy
Define Bayesian sampling over a hyperparameter search spacebayesian_parameter_sampling
Cancel a runcancel_run
Specify a discrete set of options to sample fromchoice
Mark a run as completed.complete_run
Specify Azure Container Registry detailscontainer_registry
Convert the current dataset into a FileDataset containing CSV files.convert_to_dataset_with_csv_files
Convert the current dataset into a FileDataset containing Parquet files.convert_to_dataset_with_parquet_files
Specifies a CRAN package to install in environmentcran_package
Create an AksCompute clustercreate_aks_compute
Create an AmlCompute clustercreate_aml_compute
Create a child runcreate_child_run
Create one or many child runscreate_child_runs
Create a FileDataset to represent file streams.create_file_dataset_from_files
Create an unregistered, in-memory Dataset from delimited files.create_tabular_dataset_from_delimited_files
Create a TabularDataset to represent tabular data in JSON Lines files (http://jsonlines.org/).create_tabular_dataset_from_json_lines_files
Create an unregistered, in-memory Dataset from parquet files.create_tabular_dataset_from_parquet_files
Create a TabularDataset to represent tabular data in SQL databases.create_tabular_dataset_from_sql_query
Create a new Azure Machine Learning workspacecreate_workspace
Represents a path to data in a datastore.data_path
Configure conversion to bool.data_type_bool
Configure conversion to datetime.data_type_datetime
Configure conversion to 53-bit double.data_type_double
Configure conversion to 64-bit integer.data_type_long
Configure conversion to string.data_type_string
Represent how to deliver the dataset to a compute target.dataset_consumption_config
Define timestamp columns for the dataset.define_timestamp_columns_for_dataset
Delete a clusterdelete_compute
Delete a local web service from the local machinedelete_local_webservice
Delete a model from its associated workspacedelete_model
Delete secrets from a keyvaultdelete_secrets
Delete a web service from a given workspacedelete_webservice
Delete a workspacedelete_workspace
Deploy a web service from registered model(s)deploy_model
Detach an AksCompute cluster from its associated workspacedetach_aks_compute
Download a file from a rundownload_file_from_run
Download files from a rundownload_files_from_run
Download data from a datastore to the local file systemdownload_from_datastore
Download file streams defined by the dataset as local files.download_from_file_dataset
Download a model to the local file systemdownload_model
Drop the specified columns from the dataset.drop_columns_from_dataset
Create an estimatorestimator
Create an Azure Machine Learning experimentexperiment
Filter Tabular Dataset with time stamp columns after a specified start time.filter_dataset_after_time
Filter Tabular Dataset with time stamp columns before a specified end time.filter_dataset_before_time
Filter Tabular Dataset between a specified start and end time.filter_dataset_between_time
Filter Tabular Dataset to contain only the specified duration (amount) of recent data.filter_dataset_from_recent_time
Generates the control script for the experiment.generate_entry_script
Regenerate one of a web service's keysgenerate_new_webservice_key
Get the credentials for an AksCompute clusterget_aks_compute_credentials
Return the best performing run amongst all completed runsget_best_run_by_primary_metric
Get the hyperparameters for all child runsget_child_run_hyperparameters
Get the metrics from all child runsget_child_run_metrics
Get all children for the current run selected by specified filtersget_child_runs
Get the child runs sorted in descending order by best primary metricget_child_runs_sorted_by_primary_metric
Get an existing compute clusterget_compute
Get the context object for a runget_current_run
Get Dataset by ID.get_dataset_by_id
Get a registered Dataset from the workspace by its registration name.get_dataset_by_name
Get an existing datastoreget_datastore
Get the default datastore for a workspaceget_default_datastore
Get the default keyvault for a workspaceget_default_keyvault
Get an existing environmentget_environment
Get a list of file paths for each file stream defined by the dataset.get_file_dataset_paths
Return the named list for input datasets.get_input_dataset_from_run
Get a registered modelget_model
Get the Azure container registry that a packaged model usesget_model_package_container_registry
Get the model package creation logsget_model_package_creation_logs
Get an experiment runget_run
Get the details of a runget_run_details
Get the details of a run along with the log files' contentsget_run_details_with_logs
List the files that are stored in association with a runget_run_file_names
Get the metrics logged to a runget_run_metrics
Return a generator of the runs for an experimentget_runs_in_experiment
Get secrets from a keyvaultget_secrets
Get secrets from the keyvault associated with a run's workspaceget_secrets_from_run
Get a deployed web serviceget_webservice
Retrieve auth keys for a web serviceget_webservice_keys
Retrieve the logs for a web serviceget_webservice_logs
Retrieve the auth token for a web serviceget_webservice_token
Get an existing workspaceget_workspace
Get the details of a workspaceget_workspace_details
Specifies a Github package to install in environmentgithub_package
Define grid sampling over a hyperparameter search spacegrid_parameter_sampling
Create a configuration for a HyperDrive runhyperdrive_config
Create an inference configuration for model deploymentsinference_config
Install azureml sdk packageinstall_azureml
Manages authentication and acquires an authorization token in interactive login workflows.interactive_login_authentication
Call a web service with the provided inputinvoke_webservice
Keep the specified columns and drops all others from the dataset.keep_columns_from_dataset
Get the details (e.g IP address, port etc) of all the compute nodes in the compute targetlist_nodes_in_aml_compute
List the secrets in a keyvaultlist_secrets
List the supported VM sizes in a regionlist_supported_vm_sizes
List all workspaces that the user has access to in a subscription IDlist_workspaces
Load all records from the dataset into a dataframe.load_dataset_into_data_frame
Load workspace configuration details from a config fileload_workspace_from_config
Create a deployment config for deploying a local web servicelocal_webservice_deployment_config
Log an accuracy table metric to a runlog_accuracy_table_to_run
Log a confusion matrix metric to a runlog_confusion_matrix_to_run
Log an image metric to a runlog_image_to_run
Log a vector metric value to a runlog_list_to_run
Log a metric to a runlog_metric_to_run
Log a predictions metric to a runlog_predictions_to_run
Log a residuals metric to a runlog_residuals_to_run
Log a row metric to a runlog_row_to_run
Log a table metric to a runlog_table_to_run
Specify a normal distribution of the form 'exp(normal(mu, sigma))'lognormal
Specify a log uniform distributionloguniform
Define a median stopping policy for early termination of HyperDrive runsmedian_stopping_policy
Combine the results from the parallel training.merge_results
Create a context manager for mounting file streams defined by the dataset as local files.mount_file_dataset
Specify a real value that is normally-distributed with mean 'mu' and standard deviation 'sigma'normal
Create a model package that packages all the assets needed to host a model as a web servicepackage_model
Generate table of run detailsplot_run_details
Define supported metric goals for hyperparameter tuningprimary_metric_goal
Defines options for how column headers are processed when reading data from files to create a dataset.promote_headers_behavior
Pull the Docker image from a 'ModelPackage' to your local Docker environmentpull_model_package_image
Specify a normal distribution of the form 'round(exp(normal(mu, sigma)) / q) * q'qlognormal
Specify a uniform distribution of the form round(exp(uniform(min_value, max_value) / q) * qqloguniform
Specify a normal distribution of the form round(normal(mu, sigma) / q) * qqnormal
Specify a uniform distribution of the form 'round(uniform(min_value, max_value) / q) * q'quniform
Create an environmentr_environment
Specify a set of random integers in the range [0, upper)randint
Define random sampling over a hyperparameter search spacerandom_parameter_sampling
Split file streams in the dataset into two parts randomly and approximately by the percentage specified.random_split_dataset
Register an Azure blob container as a datastoreregister_azure_blob_container_datastore
Initialize a new Azure Data Lake Gen2 Datastore.register_azure_data_lake_gen2_datastore
Register an Azure file share as a datastoreregister_azure_file_share_datastore
Initialize a new Azure PostgreSQL Datastore.register_azure_postgre_sql_datastore
Initialize a new Azure SQL database Datastore.register_azure_sql_database_datastore
Register a Dataset in the workspaceregister_dataset
Registers AMLCompute as a parallel backend with the foreach package.register_do_azureml_parallel
Register an environment in the workspaceregister_environment
Register a model to a given workspaceregister_model
Register a model for operationalization.register_model_from_run
Reload a local web service's entry script and dependenciesreload_local_webservice_assets
Initialize the ResourceConfiguration.resource_configuration
Save a Dockerfile and dependencies from a 'ModelPackage' to your local file systemsave_model_package_files
Manages authentication using a service principle instead of a user identity.service_principal_authentication
Set the default datastore for a workspaceset_default_datastore
Add secrets to a keyvaultset_secrets
Skip file streams from the top of the dataset by the specified count.skip_from_dataset
Splits the job into parallel tasks.split_tasks
Create an interactive logging runstart_logging_run
Submit an experiment and return the active child runsubmit_child_run
Submit an experiment and return the active created runsubmit_experiment
Take a sample of file streams from top of the dataset by the specified count.take_from_dataset
Take a random sample of file streams in the dataset approximately by the probability specified.take_sample_from_dataset
Define a truncation selection policy for early termination of HyperDrive runstruncation_selection_policy
Specify a uniform distribution of options to sample fromuniform
Unregister all versions under the registration name of this dataset from the workspace.unregister_all_dataset_versions
Unregister a datastore from its associated workspaceunregister_datastore
Update a deployed ACI web serviceupdate_aci_webservice
Update a deployed AKS web serviceupdate_aks_webservice
Update scale settings for an AmlCompute clusterupdate_aml_compute
Update a local web serviceupdate_local_webservice
Upload files to the Azure storage a datastore points toupload_files_to_datastore
Upload files to a runupload_files_to_run
Upload a folder to a runupload_folder_to_run
Upload a local directory to the Azure storage a datastore points toupload_to_datastore
Wait for a web service to finish deployingwait_for_deployment
Wait for a model package to finish creatingwait_for_model_package_creation
Wait for a cluster to finish provisioningwait_for_provisioning_completion
Wait for the completion of a runwait_for_run_completion
Write out the workspace configuration details to a config filewrite_workspace_config