Membuat datastore

BERLAKU UNTUK:Ekstensi ML Azure CLI v2 (saat ini)Python SDK Azure-AI-ML v2 (saat ini)

Dalam artikel ini, Anda mempelajari cara menyambungkan ke layanan penyimpanan data Azure dengan menggunakan penyimpanan data Azure Machine Learning.

Prasyarat

Langganan Azure. Jika Anda tidak memiliki langganan Azure, buat akun gratis sebelum Anda memulai. Coba versi gratis atau berbayar Azure Machine Learning.
Python 3.10 atau yang lebih baru.
SDK Azure Machine Learning untuk Python.
Ruang kerja Pembelajaran Mesin.

Petunjuk / Saran

Sampel kode Python SDK dalam artikel ini menggunakan MLClient.from_config(), yang memerlukan config.json file di direktori Anda saat ini atau direktori induk. Unduh file ini dari portal Microsoft Azure: buka ruang kerja Anda, lalu pilih Gambaran Umum>Unduh config.json. Atau, Anda dapat membuat MLClient secara manual:

ml_client = MLClient(
    credential=DefaultAzureCredential(),
    subscription_id="<your-subscription-id>",
    resource_group_name="<your-resource-group>",
    workspace_name="<your-workspace-name>",
)

Catatan

Datastore Pembelajaran Mesin tidak membuat sumber daya akun penyimpanan dasar. Sebagai gantinya, mereka menautkan akun penyimpanan yang ada untuk penggunaan Pembelajaran Mesin. Setiap jenis datastore (Azure Blob, ADLS Gen2, Azure Files, OneLake) dibuat secara independen. Urutan bagian dalam artikel ini tidak mewakili urutan langkah yang diperlukan.

Membuat datastore Azure Blob

from azure.ai.ml.entities import AzureBlobDatastore
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureBlobDatastore(
    name="",
    description="",
    account_name="",
    container_name=""
)

ml_client.create_or_update(store)

from azure.ai.ml.entities import AzureBlobDatastore
from azure.ai.ml.entities import AccountKeyConfiguration
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureBlobDatastore(
    name="blob_protocol_example",
    description="Datastore pointing to a blob container using https protocol.",
    account_name="mytestblobstore",
    container_name="data-container",
    protocol="https",
    credentials=AccountKeyConfiguration(
        account_key="aaaaaaaa-0b0b-1c1c-2d2d-333333333333"
    ),
)

ml_client.create_or_update(store)

from azure.ai.ml.entities import AzureBlobDatastore
from azure.ai.ml.entities import SasTokenConfiguration
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureBlobDatastore(
    name="blob_sas_example",
    description="Datastore pointing to a blob container using SAS token.",
    account_name="mytestblobstore",
    container_name="data-container",
    credentials=SasTokenConfiguration(
        sas_token= "?xx=A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u&xx=C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1wx&xx=Ff6Gg~7Hh8.-Ii9Jj0Kk1Ll2Mm3Nn4_Oo5Pp6Qq7&xx=N7oP8qR9sT0uV1wX2yZ3aB4cD5eF6g&xxx=Ee5Ff~6Gg7.-Hh8Ii9Jj0Kk1Ll2Mm3_Nn4Oo5Pp6&xxx=C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1w"
    ),
)

ml_client.create_or_update(store)

Buat file YAML berikut (perbarui nilai yang sesuai):

# my_blob_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureBlob.schema.json
name: my_blob_ds # add your datastore name here
type: azure_blob
description: here is a description # add a datastore description here
account_name: my_account_name # add the storage account name here
container_name: my_container_name # add the storage container name here

Buat datastore Pembelajaran Mesin di Azure CLI:

az ml datastore create --file my_blob_datastore.yml

Buat file YAML ini (perbarui nilai yang sesuai):

# my_blob_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureBlob.schema.json
name: blob_example
type: azure_blob
description: Datastore pointing to a blob container.
account_name: mytestblobstore
container_name: data-container
credentials:
  account_key: aaaaaaaa-0b0b-1c1c-2d2d-333333333333

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_blob_datastore.yml

Buat file YAML ini (perbarui nilai yang sesuai):

# my_blob_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureBlob.schema.json
name: blob_sas_example
type: azure_blob
description: Datastore pointing to a blob container using SAS token.
account_name: mytestblobstore
container_name: data-container
credentials:
  sas_token: "?xx=A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u&xx=C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1wx&xx=Ff6Gg~7Hh8.-Ii9Jj0Kk1Ll2Mm3Nn4_Oo5Pp6Qq7&xx=N7oP8qR9sT0uV1wX2yZ3aB4cD5eF6g&xxx=Ee5Ff~6Gg7.-Hh8Ii9Jj0Kk1Ll2Mm3_Nn4Oo5Pp6&xxx=C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1w"

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_blob_datastore.yml

Membuat datastore Azure Data Lake Storage Gen2

from azure.ai.ml.entities import AzureDataLakeGen2Datastore
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureDataLakeGen2Datastore(
    name="",
    description="",
    account_name="",
    filesystem=""
)

ml_client.create_or_update(store)

from azure.ai.ml.entities import AzureDataLakeGen2Datastore
from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials

from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureDataLakeGen2Datastore(
    name="adls_gen2_example",
    description="Datastore pointing to an Azure Data Lake Storage Gen2.",
    account_name="mytestdatalakegen2",
    filesystem="my-gen2-container",
     credentials=ServicePrincipalCredentials(
        tenant_id= "bbbbcccc-1111-dddd-2222-eeee3333ffff",
        client_id= "44445555-eeee-6666-ffff-7777aaaa8888",
        client_secret= "Cc3Dd~4Ee5.-Ff6Gg7Hh8Ii9Jj0Kk1_Ll2Mm3Nn4",
    ),
)

ml_client.create_or_update(store)

Buat file YAML ini (perbarui nilai):

# my_adls_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureDataLakeGen2.schema.json
name: adls_gen2_credless_example
type: azure_data_lake_gen2
description: Credential-less datastore pointing to an Azure Data Lake Storage Gen2 instance.
account_name: mytestdatalakegen2
filesystem: my-gen2-container

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_adls_datastore.yml

Buat file YAML ini (perbarui nilai):

# my_adls_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureDataLakeGen2.schema.json
name: adls_gen2_example
type: azure_data_lake_gen2
description: Datastore pointing to an Azure Data Lake Storage Gen2 instance.
account_name: mytestdatalakegen2
filesystem: my-gen2-container
credentials:
  tenant_id: bbbbcccc-1111-dddd-2222-eeee3333ffff
  client_id: 44445555-eeee-6666-ffff-7777aaaa8888
  client_secret: Cc3Dd~4Ee5.-Ff6Gg7Hh8Ii9Jj0Kk1_Ll2Mm3Nn4

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_adls_datastore.yml

Langkah 1: Membuat datastore Azure Files

from azure.ai.ml.entities import AzureFileDatastore
from azure.ai.ml.entities import AccountKeyConfiguration
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureFileDatastore(
    name="file_example",
    description="Datastore pointing to an Azure File Share.",
    account_name="mytestfilestore",
    file_share_name="my-share",
    credentials=AccountKeyConfiguration(
        account_key= "aaaaaaaa-0b0b-1c1c-2d2d-333333333333"
    ),
)

ml_client.create_or_update(store)

from azure.ai.ml.entities import AzureFileDatastore
from azure.ai.ml.entities import SasTokenConfiguration
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureFileDatastore(
    name="file_sas_example",
    description="Datastore pointing to an Azure File Share using SAS token.",
    account_name="mytestfilestore",
    file_share_name="my-share",
    credentials=SasTokenConfiguration(
        sas_token="?xx=A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u&xx=C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1wx&xx=Ff6Gg~7Hh8.-Ii9Jj0Kk1Ll2Mm3Nn4_Oo5Pp6Qq7&xx=N7oP8qR9sT0uV1wX2yZ3aB4cD5eF6g&xxx=Ee5Ff~6Gg7.-Hh8Ii9Jj0Kk1Ll2Mm3_Nn4Oo5Pp6&xxx=C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1w"
    ),
)

ml_client.create_or_update(store)

Buat file YAML ini (perbarui nilai):

# my_files_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureFile.schema.json
name: file_example
type: azure_file
description: Datastore pointing to an Azure File Share.
account_name: mytestfilestore
file_share_name: my-share
credentials:
  account_key: aaaaaaaa-0b0b-1c1c-2d2d-333333333333

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_files_datastore.yml

Buat file YAML ini (perbarui nilai):

# my_files_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureFile.schema.json
name: file_sas_example
type: azure_file
description: Datastore pointing to an Azure File Share using a SAS token.
account_name: mytestfilestore
file_share_name: my-share
credentials:
  sas_token: "?xx=A1bC2dE3fH4iJ5kL6mN7oP8qR9sT0u&xx=C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1wx&xx=Ff6Gg~7Hh8.-Ii9Jj0Kk1Ll2Mm3Nn4_Oo5Pp6Qq7&xx=N7oP8qR9sT0uV1wX2yZ3aB4cD5eF6g&xxx=Ee5Ff~6Gg7.-Hh8Ii9Jj0Kk1Ll2Mm3_Nn4Oo5Pp6&xxx=C2dE3fH4iJ5kL6mN7oP8qR9sT0uV1w"

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_files_datastore.yml

Membuat penyimpanan data Azure Data Lake Storage Gen1

Penting

Azure Data Lake Storage Gen1 pensiun pada 29 Februari 2024. Anda tidak dapat membuat akun Gen1 baru, dan sumber daya Gen1 yang ada tidak lagi dapat diakses. Konten berikut disediakan hanya untuk referensi. Untuk penyimpanan data baru, gunakan Azure Data Lake Storage Gen2 sebagai gantinya. Untuk mempelajari tentang memigrasikan data yang ada, lihat Memigrasikan Azure Data Lake Storage dari Gen1 ke Gen2.

from azure.ai.ml.entities import AzureDataLakeGen1Datastore
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureDataLakeGen1Datastore(
    name="",
    store_name="",
    description="",
)

ml_client.create_or_update(store)

from azure.ai.ml.entities import AzureDataLakeGen1Datastore
from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = AzureDataLakeGen1Datastore(
    name="adls_gen1_example",
    description="Datastore pointing to an Azure Data Lake Storage Gen1.",
    store_name="mytestdatalakegen1",
    credentials=ServicePrincipalCredentials(
        tenant_id= "bbbbcccc-1111-dddd-2222-eeee3333ffff",
        client_id= "44445555-eeee-6666-ffff-7777aaaa8888",
        client_secret= "Cc3Dd~4Ee5.-Ff6Gg7Hh8Ii9Jj0Kk1_Ll2Mm3Nn4",
    ),
)

ml_client.create_or_update(store)

Buat file YAML ini (perbarui nilai):

# my_adls_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureDataLakeGen1.schema.json
name: alds_gen1_credless_example
type: azure_data_lake_gen1
description: Credential-less datastore pointing to an Azure Data Lake Storage Gen1 instance.
store_name: mytestdatalakegen1

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_adls_datastore.yml

Buat file YAML ini (perbarui nilai):

# my_adls_datastore.yml
$schema: https://azuremlschemas.azureedge.net/latest/azureDataLakeGen1.schema.json
name: adls_gen1_example
type: azure_data_lake_gen1
description: Datastore pointing to an Azure Data Lake Storage Gen1 instance.
store_name: mytestdatalakegen1
credentials:
  tenant_id: bbbbcccc-1111-dddd-2222-eeee3333ffff
  client_id: 44445555-eeee-6666-ffff-7777aaaa8888
  client_secret: Cc3Dd~4Ee5.-Ff6Gg7Hh8Ii9Jj0Kk1_Ll2Mm3Nn4

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_adls_datastore.yml

Membuat datastore OneLake (Microsoft Fabric) (pratinjau)

Bagian ini menjelaskan berbagai opsi untuk membuat datastore OneLake. Datastore OneLake adalah bagian dari Microsoft Fabric. Saat ini, Azure Pembelajaran Mesin mendukung koneksi ke artefak Microsoft Fabric lakehouse di folder "Files" yang menyertakan folder atau file dan pintasan Amazon S3. Untuk informasi selengkapnya tentang lakehouse, lihat Apa itu lakehouse di Microsoft Fabric?.

Pembuatan datastore OneLake memerlukan informasi berikut dari instans Microsoft Fabric Anda:

Titik akhir
GUID Ruang Kerja
GUID Artefak

Cuplikan layar berikut menjelaskan cara mengambil sumber daya informasi yang diperlukan ini dari instans Microsoft Fabric Anda.

Anda dapat menemukan "Titik Akhir", "GUID Ruang Kerja", dan "GUID Artefak" di "URL" dan "jalur ABFS" dari halaman "Properti":

Format URL: https://{your_one_lake_endpoint}/{your_one_lake_workspace_guid}/{your_one_lake_artifact_guid}/Files
Format jalur ABFS: abfss://{your_one_lake_workspace_guid}@{your_one_lake_endpoint}/{your_one_lake_artifact_guid}/Files

Membuat datastore OneLake

from azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = OneLakeDatastore(
    name="onelake_example_id",
    description="Datastore pointing to a Microsoft fabric artifact.",
    one_lake_workspace_name="bbbbbbbb-7777-8888-9999-cccccccccccc", #{your_one_lake_workspace_guid}
    endpoint="msit-onelake.dfs.fabric.microsoft.com", #{your_one_lake_endpoint}
    artifact=OneLakeArtifact(
        name="cccccccc-8888-9999-0000-dddddddddddd/Files", #{your_one_lake_artifact_guid}/Files
        type="lake_house"
    )
)

ml_client.create_or_update(store)

from azure.ai.ml.entities import OneLakeDatastore, OneLakeArtifact
from azure.ai.ml.entities._datastore.credentials import ServicePrincipalCredentials
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

ml_client = MLClient.from_config(credential=DefaultAzureCredential())

store = OneLakeDatastore(
    name="onelake_example_sp",
    description="Datastore pointing to a Microsoft fabric artifact.",
    one_lake_workspace_name="bbbbbbbb-7777-8888-9999-cccccccccccc", #{your_one_lake_workspace_guid}
    endpoint="msit-onelake.dfs.fabric.microsoft.com", #{your_one_lake_endpoint}
    artifact=OneLakeArtifact(
        name="cccccccc-8888-9999-0000-dddddddddddd/Files", #{your_one_lake_artifact_guid}/Files
        type="lake_house"
    ),
    credentials=ServicePrincipalCredentials(
        tenant_id= "bbbbcccc-1111-dddd-2222-eeee3333ffff",
        client_id= "44445555-eeee-6666-ffff-7777aaaa8888",
        client_secret= "Cc3Dd~4Ee5.-Ff6Gg7Hh8Ii9Jj0Kk1_Ll2Mm3Nn4",
    ),
)

ml_client.create_or_update(store)

Buat file YAML berikut. Perbarui nilai:

# my_onelake_datastore.yml
$schema: http://azureml/sdk-2-0/OneLakeDatastore.json
name: onelake_example_id
type: one_lake
description: Credential-less datastore pointing to a OneLake lakehouse.
one_lake_workspace_name: "eeeeffff-4444-aaaa-5555-bbbb6666cccc"
endpoint: "msit-onelake.dfs.fabric.microsoft.com"
artifact:
  type: lake_house
  name: "1111bbbb-22cc-dddd-ee33-ffffff444444/Files"

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_onelake_datastore.yml

Buat file YAML berikut. Perbarui nilai:

# my_onelakesp_datastore.yml
$schema: http://azureml/sdk-2-0/OneLakeDatastore.json
name: onelake_example_id
type: one_lake
description: Credential-less datastore pointing to a OneLake lakehouse.
one_lake_workspace_name: "eeeeffff-4444-aaaa-5555-bbbb6666cccc"
endpoint: "msit-onelake.dfs.fabric.microsoft.com"
artifact:
  type: lake_house
  name: "1111bbbb-22cc-dddd-ee33-ffffff444444/Files"
credentials:
  tenant_id: bbbbcccc-1111-dddd-2222-eeee3333ffff
  client_id: 44445555-eeee-6666-ffff-7777aaaa8888
  client_secret: Cc3Dd~4Ee5.-Ff6Gg7Hh8Ii9Jj0Kk1_Ll2Mm3Nn4

Buat datastore Pembelajaran Mesin di CLI:

az ml datastore create --file my_onelakesp_datastore.yml

Langkah berikutnya

Saran dan Komentar

Apakah halaman ini membantu?

Last updated on 2026-03-11

Membuat datastore

Prasyarat

Membuat datastore Azure Blob

Membuat datastore Azure Data Lake Storage Gen2

Langkah 1: Membuat datastore Azure Files

Membuat penyimpanan data Azure Data Lake Storage Gen1

Membuat datastore OneLake (Microsoft Fabric) (pratinjau)

Membuat datastore OneLake

Langkah berikutnya

Saran dan Komentar

Sumber Daya Tambahan: