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text: Learn how to use Ansible to create and manage Azure compute, network, and storage infrastructure.
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- html: <a href="/azure/virtual-machines/linux/ansible-create-complete-vm?toc=%2Fen-us%2Fazure%2Fansible%2Ftoc.json&bc=%2Fen-us%2Fazure%2Fbread%2Ftoc.json">Create a Linux virtual machine, including virtual network, public IP address, and virtual network interface card.</a>
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- html: <a href="./ansible-run-playbook-in-cloudshell">Run Ansible with Bash in Azure Cloud Shell.</a>
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- html: <a href="./ansible-manage-azure-dynamic-inventories">Create and manage a dynamic inventory of your Azure resources.</a>
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- html: <a href="./ansible-create-configure-vmss">Create and manage virtual machine scale sets in Azure.</a>
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- html: <a href="./ansible-deploy-app-vmss">Deploy applications to virtual machine scale sets in Azure.</a>
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- html: <a href="./ansible-create-configure-aks">Create and manage Azure Kubernetes Service clusters.</a>
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- html: <a href="./ansible-create-configure-application-gateway">Manage web traffic with an Azure application gateway.</a>
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- html: <a href="./ansible-auto-scale-vmss">Automatically scale virtual machine scale sets in Azure using Ansible.</a>
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- html: <a href="./ansible-scale-azure-web-apps">Scale Azure App Service web apps by using Ansible.</a>
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- html: <a href="./ansible-create-configure-route-table">Create, change, or delete an Azure route table using Ansible.</a>
Copy file name to clipboardExpand all lines: articles/machine-learning/service/how-to-create-register-datasets.md
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@@ -41,8 +41,10 @@ To create and work with datasets, you need:
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## Dataset Types
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Datasets are categorized into various types based on how users consume them in training. List of Dataset types:
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*[TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas DataFrame. A `TabularDataset` object can be created from csv, tsv, parquet files, SQL query results etc. For a complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference). A timestamp can be specified from a column in the data or the path pattern data is stored in to enable a timeseries trait, which allows for easy and efficient filtering by time.
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Datasets are categorized into two types based on how users consume them in training.
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*[TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas DataFrame. A `TabularDataset` object can be created from csv, tsv, parquet files, SQL query results etc. For a complete list, please visit our [documentation](https://aka.ms/tabulardataset-api-reference).
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*[FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.file_dataset.filedataset?view=azure-ml-py) references single or multiple files in your datastores or public urls. This provides you with the ability to download or mount the files to your compute. The files can be of any format, which enables a wider range of machine learning scenarios including deep learning.
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To find out more about upcoming API changes, see [here](https://aka.ms/tabular-dataset).
TabularDatasets can be created via the SDK or by using the workspace landing page (preview).
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TabularDatasets can be created via the SDK or by using the workspace landing page (preview). A timestamp can be specified from a column in the data or the path pattern data is stored in to enable a timeseries trait, which allows for easy and efficient filtering by time.
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#### SDK
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#### Using the SDK
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Use the `from_delimited_files()` method on `TabularDatasetFactory` class to read files in csv or tsv format, and create an unregistered TabularDataset. If you are reading from multiple files, results will be aggregated into one tabular representation.
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Use the [`from_delimited_files()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-delimited-files-path--validate-true--include-path-false--infer-column-types-true--set-column-types-none--separator------header--promoteheadersbehavior-all-files-have-same-headers--3---partition-format-none-) method on `TabularDatasetFactory` class to read files in csv or tsv format, and create an unregistered TabularDataset. If you are reading from multiple files, results will be aggregated into one tabular representation.
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```Python
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# create a TabularDataset from multiple paths in datastore
Use the `with_timestamp_columns()` method on `TabularDataset` class to enable easy and efficient filtering by time. More examples and details can be found [here](http://aka.ms/azureml-tsd-notebook).
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Use the [`with_timestamp_columns()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py#with-timestamp-columns-fine-grain-timestamp--coarse-grain-timestamp-none--validate-false-) method on `TabularDataset` class to enable easy and efficient filtering by time. More examples and details can be found [here](http://aka.ms/azureml-tsd-notebook).
Sign in to the [workspace landing page](https://ml.azure.com) to create a dataset via the web experience. Currently, the workspace landing page only supports the creation of TabularDatasets.
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### Create FileDatasets
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Use the `from_files()` method on `FileDatasetFactory` class to load files in any format, and create an unregistered FileDataset.
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Use the [`from_files()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.filedatasetfactory?view=azure-ml-py#from-files-path--validate-true-) method on `FileDatasetFactory` class to load files in any format, and create an unregistered FileDataset.
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```Python
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# create a FileDataset from multiple paths in datastore
To complete the creation process, register your datasets with workspace:
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Use the `register()` method to register datasets to your workspace so they can be shared with others and reused across various experiments.
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Use the [`register()`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.dataset.dataset?view=azure-ml-py#register-workspace--name--description-none--tags-none--visible-true--exist-ok-false--update-if-exist-false-) method to register datasets to your workspace so they can be shared with others and reused across various experiments.
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