An Azure Machine Learning Environment allows you to create, manage, and reuse the software dependencies required for training and deployment.
Represent how to deliver the dataset to a compute target.įunctions for managing environments. Represents a path to data in a datastore. Load all records from the dataset into a dataframe.Ĭonvert the current dataset into a FileDataset containing CSV files.Ĭonvert the current dataset into a FileDataset containing Parquet files.ĭefines options for how column headers are processed when reading data from files to create a dataset.
Keep the specified columns and drops all others from the dataset.įilter Tabular Dataset with time stamp columns after a specified start time.įilter Tabular Dataset with time stamp columns before a specified end time.įilter Tabular Dataset between a specified start and end time.įilter Tabular Dataset to contain only the specified duration (amount) of recent data.ĭefine timestamp columns for the dataset. Split file streams in the dataset into two parts randomly and approximately by the percentage specified.Ĭreate_tabular_dataset_from_parquet_files()Ĭreate an unregistered, in-memory Dataset from parquet files.Ĭreate_tabular_dataset_from_delimited_files()Ĭreate an unregistered, in-memory Dataset from delimited files.Ĭreate_tabular_dataset_from_json_lines_files()Ĭreate a TabularDataset to represent tabular data in JSON Lines files ().Ĭreate a TabularDataset to represent tabular data in SQL databases.ĭrop the specified columns from the dataset. Take a random sample of file streams in the dataset approximately by the probability specified. Take a sample of file streams from top of the dataset by the specified count. Skip file streams from the top of the dataset by the specified count. Get a list of file paths for each file stream defined by the dataset.ĭownload file streams defined by the dataset as local files.Ĭreate a context manager for mounting file streams defined by the dataset as local files. Return the named list for input datasets.Ĭreate a FileDataset to represent file streams.
Get a registered Dataset from the workspace by its registration name. Unregister all versions under the registration name of this dataset from the workspace. Azure ML supports Dataset types of FileDataset and TabularDataset. Datasets can be created from local files, public urls, or specific file(s) in your datastores. An Azure Machine Learning Dataset allows you to interact with data in your datastores and package your data into a consumable object for machine learning tasks. Unregister a datastore from its associated workspaceįunctions for managing datasets. Initialize a new Azure Data Lake Gen2 Datastore. Register_azure_data_lake_gen2_datastore() Initialize a new Azure PostgreSQL Datastore. Initialize a new Azure SQL database Datastore. Register an Azure file share as a datastore Register an Azure blob container as a datastore Register_azure_blob_container_datastore() Upload a local directory to the Azure storage a datastore points toĭownload data from a datastore to the local file system Upload files to the Azure storage a datastore points to A Datastore is attached to a workspace and is used to store connection information to an Azure storage service. Wait for a cluster to finish provisioningįunctions for accessing your data in Azure Storage services. Get the credentials for an AksCompute clusterĪttach an existing AKS cluster to a workspaceĭetach an AksCompute cluster from its associated workspace
Update scale settings for an AmlCompute cluster Get the details (e.g IP address, port etc) of all the compute nodes in the Supported compute target types in the R SDK include AmlCompute and AksCompute. Compute targets make it easy to change your compute environment without changing your code. A Compute Target is a designated compute resource where you run your scripts or host your service deployments. Manages authentication and acquires an authorization token in interactive login workflows.įunctions for managing compute resources. List all workspaces that the user has access to in a subscription ID Set the default datastore for a workspace Get the default datastore for a workspace
Write out the workspace configuration details to a config file Load workspace configuration details from a config file Manages authentication using a service principle instead of a user identity. It provides a centralized place to work with all the artifacts you create when you use Azure ML.Ĭreate a new Azure Machine Learning workspace A Workspace is the top-level resource for Azure Machine Learning. Functions for managing workspace resources.