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Prepare your environment for Surface Hub 2S
https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-prepare-environment Learn what you need to do to prepare your environment for Surface Hub 2S.Published Date : 2019年10月21日
Manage Surface Hub 2S with Intune
https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-manage-intune Learn how to update and manage Surface Hub 2S using Intune.Published Date : 2019年10月21日
First time Setup for Surface Hub 2S
https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-setup Learn how to complete first time Setup for Surface Hub 2S.Published Date : 2019年10月21日
Surface Hub 2S deployment checklists
https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-deploy-checklist Verify your deployment of Surface Hub 2S using pre- and post-deployment checklists.Published Date : 2019年10月21日
Create provisioning packages for Surface Hub 2S
https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-deploy This page describes how to deploy Surface Hub 2S using provisioning packages and other tools.Published Date : 2019年10月21日
Controlling access to Common Data Service - Power Platform Admin center
https://docs.microsoft.com/en-us/power-platform/admin/wp-controlling-access Provides information about how you can control access to Common Data Service using Azure AD.Published Date : 2019年10月21日
Test recorder and Regression suite automation tool for Retail Cloud POS - Retail | Dynamics 365
https://docs.microsoft.com/en-us/dynamics365/retail/dev-itpro/pos-rsat This topic explains how to automate user acceptance testing (UAT) by using the POS test recorder and the Regression suite automation tool (RSAT).Published Date : 2019年10月21日
Knowledge Mining Solution Accelerator Guide - Code Samples
https://docs.microsoft.com/en-us/samples/azure-samples/azure-search-knowledge-mining/azure-search-knowledge-mining/ Welcome to the Knowledge Mining Solution Accelerator!Published Date : 2019年10月21日
Anomaly Detector API Samples - Code Samples
https://docs.microsoft.com/en-us/samples/azure-samples/anomalydetector/anomalydetector/ This repository contains samples for Anomaly Detector API. The Anomaly Detector API enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume.Published Date : 2019年10月21日
azure.batch.BatchServiceClient class
https://docs.microsoft.com/en-us/python/api/azure.batch.batchserviceclient A client for issuing REST requests to the Azure Batch service.Published Date : 2019年10月15日
azureml.dataprep.FileFormatArguments class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.fileformatarguments Defines and stores the arguments which can affect learning on a ɿileFormatBuilder'.Published Date : 2019年10月15日
azureml.dataprep.api.builders.MinMaxScalerBuilder class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.builders.minmaxscalerbuilder Interactive object that can be used to min-max scale a column.Published Date : 2019年10月15日
API Reference - Azure Machine Learning Data Prep Python
https://docs.microsoft.com/zh-cn/python/api/overview/azure/dataprep/Published Date : 2019年10月21日
azureml.dataprep.ParseParquetProperties class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.parseparquetproperties Describes and stores the properties required to read a Parquet File.Published Date : 2019年10月15日
azureml.dataprep.JoinType enum - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.jointype Describes different possible types of join.Published Date : 2019年9月30日
azureml.dataprep.ValueCountEntry class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.valuecountentryPublished Date : 2019年10月15日
azureml.dataprep.ParseLinesProperties class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.parselinesproperties Describes and stores the properties required to parse a Text-file containing raw lines.Published Date : 2019年10月15日
azureml.dataprep.api.functions module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.functions Module containing higher level function expressions.Published Date : 2019年10月15日
azureml.dataprep.api.builders.PivotBuilder class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.builders.pivotbuilder Interactive object that can be used to generate pivoted columns from the selected pivot columns.Published Date : 2019年10月15日
azureml.dataprep.MismatchAsOption enum - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.mismatchasoption An enumeration.Published Date : 2019年9月30日
azureml.dataprep.DecimalMark enum - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.decimalmark An enumeration.Published Date : 2019年9月30日
azureml.dataprep.TypeCountEntry class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.typecountentryPublished Date : 2019年10月15日
azureml.dataprep.AssertPolicy enum - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.assertpolicy An enumeration.Published Date : 2019年9月30日
azureml.dataprep.ReadExcelProperties class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.readexcelproperties Describes and stores the properties required to read an Excel file.Published Date : 2019年10月15日
azureml.dataprep.TrimType enum - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.trimtype An enumeration.Published Date : 2019年9月30日
用于 Python 的 Azure 监视库
https://docs.microsoft.com/zh-cn/python/api/overview/azure/monitoring/ 用于 Python 的 Azure 监视库参考Published Date : 2019年10月21日
azureml.pipeline.steps.adla_step.AdlaStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep Creates an Azure ML Pipeline step to run a U-SQL script with Azure Data Lake Analytics. For an example of using this AdlaStep, see the notebook https:/˺ka.ms/pl-adla.Published Date : 2019年10月15日
azureml.pipeline.core.module.Module class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.module.module Represents a computation unit used in a Azure Machine Learning pipeline. A module is a collection of files which will run on a compute target and a description of an interface. The collection of files can be script, binaries, or any other files required to execute on the compute target. The module interface describes inputs, outputs, and parameter definitions. It doesn't bind them to specific values or data. A module has a snapshot associated with it, which captures the collection of files defined for the module.Published Date : 2019年10月15日
azureml.train.hyperdrive.BanditPolicy class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.banditpolicy Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.Published Date : 2019年9月23日
azureml.train.automl.exceptions.SystemException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.systemexception Exception for internal errors that happen within the SDK.Published Date : 2019年10月17日
azureml.pipeline.core.run.PipelineRun class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.run.pipelinerun Represents a run of a . This class can be used to manage, check status, and retrieve run details once a pipeline run is submitted. Use to retrieve the objects which are created by the pipeline run. Other uses include retrieving the object associated with the pipeline run, fetching the status of the pipeline run, and waiting for run completion.Published Date : 2019年10月15日
azureml.train.hyperdrive.PrimaryMetricGoal enum - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.primarymetricgoal Defines supported metric goals for hyperparameter tuning. A metric goal is used to determine whether a higher value for a metric is better or worse. Metric goals are used when comparing runs based on the primary metric. For example, you may want to maximize accuracy or minimize error. The primary metric name and goal are specified in the class when you configure a HyperDrive run.Published Date : 2019年9月30日
azureml.train.estimator.Mpi class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.estimator.mpi Manages MPI settings for distributed training jobs.Published Date : 2019年9月23日
azureml.train.automl.run module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.run The automated machine learning Run class. Provides methods for starting/stopping runs, monitoring run status, and retrieving model output.Published Date : 2019年10月15日
azureml.train.automl.exceptions.DataException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.dataexception Exception related to data validations.Published Date : 2019年9月30日
azureml.train.automl.exceptions.OnnxException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.onnxexception Exception for failures while trying to run ONNX operations.Published Date : 2019年10月17日
azureml.train.hyperdrive.GridParameterSampling class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.gridparametersampling Defines grid sampling over a hyperparameter search space.Published Date : 2019年9月23日
azureml.train.hyperdrive.HyperParameterSampling class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperparametersampling Abstract base class for all hyperparameter sampling algorithms. This class encapsulates the hyperparameter space, the sampling method, and additional properties for derived sampling classes: , , and .Published Date : 2019年9月23日
azureml.train.automl.exceptions.ProblemInfoException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.probleminfoexception Exception for calculating data characteristics.Published Date : 2019年10月17日
azureml.train.hyperdrive.NoTerminationPolicy class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.noterminationpolicy Specifies that no early termination policy is applied. Each run will execute until completion.Published Date : 2019年9月23日
azureml.train.sklearn.SKLearn class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.sklearn.sklearn Creates an estimator for training in Scikit-learn experiments. This estimator only supports single-node CPU training. Supported versions: 0.20.3Published Date : 2019年9月30日
azureml.pipeline.steps.data_transfer_step module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step Contains functionality to create an Azure ML Pipeline step that transfers data between storage options.Published Date : 2019年10月15日
azureml.pipeline.steps.estimator_step.EstimatorStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep Creates an Azure ML Pipeline step to run Estimator for Machine Learning model training. For an example of using EstimatorStep, see the notebook https:/˺ka.ms/pl-estimator.Published Date : 2019年10月15日
azureml.train.estimator package - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.estimator Generic Estimator and FrameworkBaseEstimator-related files.Published Date : 2019年10月17日
azureml.train.estimator.MMLBaseEstimator class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.estimator.mmlbaseestimator Abstract base class for all estimators.Published Date : 2019年10月17日
azureml.train.automl.automl_explain_utilities.AutoMLExplainerSetupClass class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.automl_explain_utilities.automlexplainersetupclass Placeholder class for all objects needed for interface with AzureML explain package.Published Date : 2019年9月23日
API Reference - Azure Machine Learning Python
https://docs.microsoft.com/zh-cn/python/api/overview/azure/ml/Published Date : 2019年10月21日
azureml.pipeline.steps.DataTransferStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.datatransferstep Creates an Azure ML Pipeline step that transfers data between storage options. This step supports the following storage types as sources and sinks except where noted: Azure Blob Storage Azure Data Lake Storage Gen1 and Gen2 Azure SQL Database Azure Database for PostgreSQL (source only) For an example of using DataTransferStep, see the notebook https:/˺ka.ms/pl-data-trans.Published Date : 2019年10月15日
azureml.telemetry.activity.ActivityType class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.activity.activitytype The type of activity (code) monitored. The default type is "PublicAPI".Published Date : 2019年10月15日
azureml.train.automl.exceptions.ServiceException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.serviceexception Exception related to JOS.Published Date : 2019年10月17日
azureml.train.automl.AutoMLConfig class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.automlconfig Configuration for submitting an Automated Machine Learning experiment in Azure Machine Learning service. This configuration object contains and persists the parameters for configuring the experiment run parameters, as well as the training data to be used at run time. For guidance on selecting your settings, you may refer to https:/˽ocs.microsoft.com˾n-us˺zure/machine-learning/service/how-to-configure-auto-train. The following code shows a basic example of creating an AutoMLConfig object, and submitting an experiment with the defined configuration: from azureml.core.experiment import Experiment from azureml.core.workspace import Workspace from azureml.train.automl import AutoMLConfig automated_ml_config = AutoMLConfig(task = 'regression', X = your_training_features, y = your_training_labels, iterations=30, iteration_timeout_minutes=5, primary_metric="spearman_correlation") ws = Workspace.from_config() experiment = Experiment(ws, "your-experiment-name") run = experiment.submit(automated_ml_config, show_output=True)Published Date : 2019年10月15日
azureml.train.automl.exceptions.ConfigException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.configexception Exception related to invalid user config.Published Date : 2019年10月17日
azureml.telemetry.logger.AbstractEventLogger class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.logger.abstracteventlogger Abstract event logger class.Published Date : 2019年10月17日
azureml.pipeline.steps.mpi_step.MpiStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.mpi_step.mpistep Creates an Azure ML pipeline step to run an MPI job. For an example of using MpiStep, see the notebook https:/˺ka.ms/pl-style-trans.Published Date : 2019年10月15日
azureml.train.automl.exceptions.OnnxConvertException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.onnxconvertexception Exception related to ONNX convert.Published Date : 2019年10月17日
azureml.telemetry package - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry Provides functionality for logging, capturing events and metrics, and monitoring code activity. This package enables you to collect different types of telemetry using free text or structured logging. For example, for unstructured text in high volumes you can use one of the logs from the module. For collecting and aggregating metrics or capturing low volume events or user activities with a defined schema use the structured schema defined in the module. You can also, monitor blocks of code with the the module. Log messages, metrics, events, and activity messages can written to Application Insights. For example, you can the function to get a handle to an Application Insights instance.Published Date : 2019年9月30日
azureml.pipeline.steps.hyper_drive_step.HyperDriveStepRun class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyper_drive_step.hyperdrivesteprun Manage, check status, and retrieve run details for a pipeline step. HyperDriveStepRun provides the functionality of with the additional support of . The HyperDriveStepRun class enables you to manage, check status, and retrieve run details for the HyperDrive run and each of its generated child runs. The StepRun class enables you to do this once the parent pipeline run is submitted and the pipeline has submitted the step run.Published Date : 2019年9月30日
azureml.pipeline.core.module_step_base module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.module_step_base Contains functionality to add a step to a pipeline using a version of a .Published Date : 2019年10月17日
azureml.pipeline.steps.AdlaStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adlastep Creates an Azure ML Pipeline step to run a U-SQL script with Azure Data Lake Analytics. For an example of using this AdlaStep, see the notebook https:/˺ka.ms/pl-adla.Published Date : 2019年10月15日
azureml.telemetry.loggers.Loggers class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.loggers.loggers Factory of loggers.Published Date : 2019年10月17日
azureml.train.automl.exceptions.ClientException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.clientexception Exception related to client.Published Date : 2019年10月17日
azureml.train.automl.exceptions.InvalidRunState class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.invalidrunstate Exception for trying to use a run that is not in a valid state for the given operation.Published Date : 2019年9月30日
azureml.train.automl.constants.ExperimentObserver class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.constants.experimentobserver Constants used by the Experiment Observer to report progress during preprocessing.Published Date : 2019年10月15日
azureml.telemetry.contracts.Metric class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.contracts.metric Metric object for telemetry usage. Use metrics for collecting and aggregating data that can be best aggregated into buckets for analysis.Published Date : 2019年10月15日
azureml.pipeline.steps.data_transfer_step.DataTransferStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step.datatransferstep Creates an Azure ML Pipeline step that transfers data between storage options. This step supports the following storage types as sources and sinks except where noted: Azure Blob Storage Azure Data Lake Storage Gen1 and Gen2 Azure SQL Database Azure Database for PostgreSQL (source only) For an example of using DataTransferStep, see the notebook https:/˺ka.ms/pl-data-trans.Published Date : 2019年10月15日
azureml.pipeline.core.graph.Param class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.param Instance of an parameter on a node.Published Date : 2019年10月15日
azureml.pipeline.core.module_step_base.ModuleStepBase class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.module_step_base.modulestepbase Adds a step to a pipeline that uses a specific module. A derives from ModuleStepBase and is a node in a pipeline that uses an existing , and specifically, one of its versions. In order to define which ModuleVersion would eventually be used in the submitted pipeline, you can define one of the following when creating the ModuleStep: object object and a version value Only Module without a version value; in this case, the version resolution used may vary across submissions. You also need to define the mapping between the step's inputs and outputs to the object's inputs and outputs.Published Date : 2019年10月17日
azureml.train.automl.constants.SupportedModels class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.constants.supportedmodels Customer Facing Names for all algorithms supported by AutoML.Published Date : 2019年10月17日
azureml.telemetry.logging_handler module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.logging_handler Contains functionality for sending telemetry to Application Insights.Published Date : 2019年10月17日
azureml.telemetry.activity.ActivityCompletionStatus class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.activity.activitycompletionstatus The activity (code) completion status, success, or failure.Published Date : 2019年10月15日
azureml.train.automl.exceptions.MetricCalculationException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.metriccalculationexception Exception for metric not being able to be calculated.Published Date : 2019年10月17日
azureml.telemetry.loggers module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.loggers Factory for creating loggers.Published Date : 2019年10月15日
azureml.pipeline.steps.databricks_step module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step Contains functionality to create an Azure ML pipeline step to run a Databricks notebook or Python script on DBFS.Published Date : 2019年10月15日
azureml.pipeline.core.pipeline_draft module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline_draft Defines classes for managing mutable pipelines.Published Date : 2019年10月17日
azureml.pipeline.steps.mpi_step module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.mpi_step Contains functionality to add a Azure ML Pipeline step to run an MPI job for Machine Learning model training.Published Date : 2019年10月15日
azureml.dataprep.Dataflow class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.dataflow A Dataflow represents a series of lazily-evaluated, immutable operations on data. It is only an execution plan. No data is loaded from the source until you get data from the Dataflow using one of head, to_pandas_dataframe, get_profile or the write methods.Published Date : 2019年10月15日
azureml.train.automl.ensemble.Ensemble class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.ensemble.ensemble Class for ensembling previous AutoML iterations. The ensemble pipeline is initialized from a collection of already fitted pipelines. :param automl_settings -- The settings for this current experiment :param ensemble_run_id -- The id of the current ensembling run :param experiment_name -- The name of the current Azure ML experiment :param workspace_name -- The name of the current Azure ML workspace where the experiment is run :param subscription_id -- The id of the current Azure ML subscription where the experiment is run :param resource_group_name -- The name of the current Azure resource groupPublished Date : 2019年10月17日
azureml.pipeline.steps.estimator_step module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step Contains functionality to create a pipeline step that runs an Estimator for Machine Learning model training.Published Date : 2019年10月15日
azureml.pipeline.core.pipeline module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline Defines the class for creating reusable Azure Machine Learning workflows.Published Date : 2019年10月15日
azureml.train.automl.exceptions.DataPrepValidationException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.dataprepvalidationexception Exception related to dataprep validation service.Published Date : 2019年9月30日
azureml.pipeline.core.StepRun class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun A run of a step in a . This class can be used to manage, check status, and retrieve run details once the parent pipeline run is submitted and the pipeline has submitted the step run.Published Date : 2019年10月15日
azureml.opendatasets.dataaccess.pandas_data_load_limit.PandasDataLoadLimitToDay class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.dataaccess.pandas_data_load_limit.pandasdataloadlimittoday PandasDataLoadLimitToDay controls how many parquets of days will be loaded.Published Date : 2019年10月17日
azureml.opendatasets.accessories.us_population_zip_accessory module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.us_population_zip_accessory US population by zip accessory.Published Date : 2019年9月23日
azureml.pipeline.core.builder.PipelineStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep Represents an execution step in an Azure Machine Learning pipeline. Pipelines are constructed from multiple pipeline steps, which are distinct computational units in the pipeline. Each step can run independently and use isolated compute resources. Each step typically has its own named inputs, outputs, and parameters. The PipelineStep class is the base class from which other built-in step classes designed for common scenarios inherit, such as , , and . For an overview of how Pipelines and PipelineSteps relate, see What are ML Pipelines.Published Date : 2019年10月15日
azureml.pipeline.core.PipelineRun class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun Represents a run of a . This class can be used to manage, check status, and retrieve run details once a pipeline run is submitted. Use to retrieve the objects which are created by the pipeline run. Other uses include retrieving the object associated with the pipeline run, fetching the status of the pipeline run, and waiting for run completion.Published Date : 2019年10月15日
azureml.pipeline.core.builder.PipelineData class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinedata Represents intermediate data in an Azure Machine Learning pipeline. Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.Published Date : 2019年10月15日
azureml.pipeline.core.PipelineData class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata Represents intermediate data in an Azure Machine Learning pipeline. Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.Published Date : 2019年10月15日
azureml.pipeline.core.graph.OutputPort class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.outputport Instance of an output port on a node, which can be connected to an input port.Published Date : 2019年10月15日
azureml.pipeline.core.graph.OutputPortBinding class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.outputportbinding OutputPortBinding specifies a named output of a step. OutputPortBinding can be used to specify the type of data which will be produced by a step and how the data will be produced. It can be used with to specify that the step output is a required input of another step.Published Date : 2019年10月15日
azureml.pipeline.core.PipelineStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinestep Represents an execution step in an Azure Machine Learning pipeline. Pipelines are constructed from multiple pipeline steps, which are distinct computational units in the pipeline. Each step can run independently and use isolated compute resources. Each step typically has its own named inputs, outputs, and parameters. The PipelineStep class is the base class from which other built-in step classes designed for common scenarios inherit, such as , , and . For an overview of how Pipelines and PipelineSteps relate, see What are ML Pipelines.Published Date : 2019年10月15日
azureml.pipeline.core.graph.Node class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.node Represents a basic unit in a graph, e.g it could be any datasource or step.Published Date : 2019年10月17日
azureml.opendatasets.selectors.time_nearest_selector.TimeNearestSelector class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.selectors.time_nearest_selector.timenearestselector Time nearest selector with rounding to different time granularities.Published Date : 2019年10月17日
azureml.pipeline.core.graph.Graph class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.graph A class to define a pipeline run graph.Published Date : 2019年10月17日
azureml.opendatasets package - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets Enable consuming Azure open datasets into dataframes and enrich customer data.Published Date : 2019年10月15日
azureml.pipeline.core.graph.OutputPortDef class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.outputportdef Definition of an output port.Published Date : 2019年10月17日
azureml.opendatasets.selectors.location_closest_selector module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.selectors.location_closest_selector Location closest selector class.Published Date : 2019年10月17日
azureml.opendatasets.accessories.us_population_zip_accessory.UsPopulationZipAccessory class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.us_population_zip_accessory.uspopulationzipaccessory US population by zip accessory class.Published Date : 2019年9月23日
azureml.pipeline.core.graph.DataSource class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.datasource A datasource that can be used in a graph.Published Date : 2019年10月17日
azureml.pipeline.core.graph.InputPortBinding class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.inputportbinding Defines a binding from a source to an input of a step. An InputPortBinding can be used as an input to a step. The source can be a , , , , or . InputPortBinding is useful to specify the name of the step input, if it should be different than the name of the bind object (i.e. to avoid duplicate input/output names or because the step script needs an input to have a certain name). It can also be used to specify the bind_mode for inputs.Published Date : 2019年10月15日
azureml.opendatasets.dataaccess.blob_parquet_descriptor.BlobParquetDescriptor class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.dataaccess.blob_parquet_descriptor.blobparquetdescriptor Public data accessor.Published Date : 2019年10月17日