Azure Blob Store Setup

Driverless AI lets you explore Azure Blob Store data sources from within the Driverless AI application.

Note: Depending on your Docker install version, use either the docker run --runtime=nvidia (>= Docker 19.03) or nvidia-docker (< Docker 19.03) command when starting the Driverless AI Docker image. Use docker version to check which version of Docker you are using.

Supported Data Sources Using the Azure Blob Store Connector

The following data sources can be used with the Azure Blob Store connector.

• Azure Blob Storage (general purpose v1)

• Blob Storage

• Azure Files (File Storage)

• Azure Data Lake Storage Gen 2 (Storage V2)

The following data sources can be used with the Azure Blob Store connector when also using the HDFS connector.

• Azure Data Lake Gen 1 (HDFS connector required)

• Azure Data Lake Gen 2 (HDFS connector optional)

Description of Configuration Attributes

The following configuration attributes are specific to enabling Azure Blob Storage.

• azure_blob_account_name: The Microsoft Azure Storage account name. This should be the dns prefix created when the account was created (for example, “mystorage”).

• azure_blob_account_key: Specify the account key that maps to your account name.

• azure_connection_string: Optionally specify a new connection string. With this option, you can include an override for a host, port, and/or account name. For example,

  azure_connection_string = "DefaultEndpointsProtocol=http;AccountName=<account_name>;AccountKey=<account_key>;BlobEndpoint=http://<host>:<port>/<account_name>;"
  

• azure_blob_init_path: Specifies the starting Azure Blob store path displayed in the UI of the Azure Blob store browser.

• enabled_file_systems: The file systems you want to enable. This must be configured in order for data connectors to function properly.

The following additional configuration attributes can be used for enabling an HDFS Connector to connect to Azure Data Lake Gen 1 (and optionally with Azure Data Lake Gen 2).

• hdfs_config_path: The location the HDFS config folder path. This folder can contain multiple config files.

• hdfs_app_classpath: The HDFS classpath.

• hdfs_app_supported_schemes: Supported schemas list is used as an initial check to ensure valid input to connector.

Example 1: Enabling the Azure Blob Store Data Connector

Docker Image InstallsDocker Image with the config.tomlNative Installs

This example enables the Azure Blob Store data connector by specifying environment variables when starting the Driverless AI Docker image. This lets users reference data stored on your Azure storage account using the account name, for example: https://mystorage.blob.core.windows.net.

 nvidia-docker run \
   --pid=host \
   --init \
   --rm \
   --shm-size=2g --cap-add=SYS_NICE --ulimit nofile=131071:131071 --ulimit nproc=16384:16384 \
   -e DRIVERLESS_AI_ENABLED_FILE_SYSTEMS="file,azrbs" \
   -e DRIVERLESS_AI_AZURE_BLOB_ACCOUNT_NAME="mystorage" \
   -e DRIVERLESS_AI_AZURE_BLOB_ACCOUNT_KEY="<access_key>" \
   -p 12345:12345 \
   -v /tmp/dtmp/:/tmp \
   -v /tmp/dlog/:/log \
   -v /tmp/dlicense/:/license \
   -v /tmp/ddata/:/data \
   -u $(id -u):$(id -g) \
   h2oai/dai-ubi8-x86_64:1.11.1.1-cuda11.8.0.xx

Example 2: Mount Azure File Shares to the Local File System

Supported Data Sources Using the Local File System

• Azure Files (File Storage)

Mounting Azure File Shares

Azure file shares can be mounted into the Local File system of Driverless AI. To mount the Azure file share, follow the steps listed on https://docs.microsoft.com/en-us/azure/storage/files/storage-how-to-use-files-linux.

Example 3: Enable HDFS Connector to Connect to Azure Data Lake Gen 1

This example enables the HDFS Connector to connect to Azure Data Lake Gen1. This lets users reference data stored on your Azure Data Lake using the adl uri, for example: adl://myadl.azuredatalakestore.net.

Docker Image with the config.tomlNative Installs

1. Create an Azure AD web application for service-to-service authentication: https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-service-to-service-authenticate-using-active-directory

2. Add the information from your web application to the Hadoop core-site.xml configuration file:

<configuration>
  <property>
    <name>fs.adl.oauth2.access.token.provider.type</name>
    <value>ClientCredential</value>
  </property>
  <property>
    <name>fs.adl.oauth2.refresh.url</name>
    <value>Token endpoint created in step 1.</value>
  </property>
  <property>
    <name>fs.adl.oauth2.client.id</name>
    <value>Client ID created in step 1</value>
  </property>
  <property>
    <name>fs.adl.oauth2.credential</name>
    <value>Client Secret created in step 1</value>
  </property>
  <property>
    <name>fs.defaultFS</name>
    <value>ADL URIt</value>
  </property>
</configuration>

3. Take note of the Hadoop Classpath and add the azure-datalake-store.jar file. This file can found on any Hadoop version in: $HADOOP_HOME/share/hadoop/tools/lib/*.

echo "$HADOOP_CLASSPATH:$HADOOP_HOME/share/hadoop/tools/lib/*"

4. Configure the Driverless AI config.toml file. Set the following configuration options:

enabled_file_systems = "upload, file, hdfs, azrbs, recipe_file, recipe_url"
hdfs_config_path = "/path/to/hadoop/conf"
hdfs_app_classpath = "/hadoop/classpath/"
hdfs_app_supported_schemes = "['adl://']"

5. Mount the config.toml file into the Docker container.

 nvidia-docker run \
  --pid=host \
  --init \
  --rm \
  --shm-size=2g --cap-add=SYS_NICE --ulimit nofile=131071:131071 --ulimit nproc=16384:16384 \
  --add-host name.node:172.16.2.186 \
  -e DRIVERLESS_AI_CONFIG_FILE=/path/in/docker/config.toml \
  -p 12345:12345 \
  -v /local/path/to/config.toml:/path/in/docker/config.toml \
  -v /etc/passwd:/etc/passwd:ro \
  -v /etc/group:/etc/group:ro \
  -v /tmp/dtmp/:/tmp \
  -v /tmp/dlog/:/log \
  -v /tmp/dlicense/:/license \
  -v /tmp/ddata/:/data \
  -u $(id -u):$(id -g) \
  h2oai/dai-ubi8-x86_64:1.11.1.1-cuda11.8.0.xx

Example 4: Enable HDFS Connector to Connect to Azure Data Lake Gen 2

This example enables the HDFS Connector to connect to Azure Data Lake Gen2. This lets users reference data stored on your Azure Data Lake using the Azure Blob File System Driver, for example: abfs[s]://file_system@account_name.dfs.core.windows.net/<path>/<path>/<file_name>.

Docker Image with the config.tomlNative Installs

1. Create an Azure Service Principal: https://docs.microsoft.com/en-us/azure/active-directory/develop/howto-create-service-principal-portal

2. Grant permissions to the Service Principal created on step 1 to access blobs: https://docs.microsoft.com/en-us/azure/storage/common/storage-auth-aad

3. Add the information from your web application to the Hadoop core-site.xml configuration file:

<configuration>
  <property>
    <name>fs.azure.account.auth.type</name>
    <value>OAuth</value>
  </property>
  <property>
    <name>fs.azure.account.oauth.provider.type</name>
    <value>org.apache.hadoop.fs.azurebfs.oauth2.ClientCredsTokenProvider</value>
  </property>
  <property>
    <name>fs.azure.account.oauth2.client.endpoint</name>
    <value>Token endpoint created in step 1.</value>
  </property>
  <property>
    <name>fs.azure.account.oauth2.client.id</name>
    <value>Client ID created in step 1</value>
  </property>
  <property>
    <name>fs.azure.account.oauth2.client.secret</name>
    <value>Client Secret created in step 1</value>
  </property>
</configuration>

4. Take note of the Hadoop Classpath and add the required jar files. These files can found on any Hadoop version 3.2 or higher at: $HADOOP_HOME/share/hadoop/tools/lib/*

echo "$HADOOP_CLASSPATH:$HADOOP_HOME/share/hadoop/tools/lib/*"

Note: ABFS is only supported for Hadoop version 3.2 or higher.

5. Configure the Driverless AI config.toml file. Set the following configuration options:

enabled_file_systems = "upload, file, hdfs, azrbs, recipe_file, recipe_url"
hdfs_config_path = "/path/to/hadoop/conf"
hdfs_app_classpath = "/hadoop/classpath/"
hdfs_app_supported_schemes = "['abfs://']"

6. Mount the config.toml file into the Docker container.

  nvidia-docker run \
    --pid=host \
    --init \
    --rm \
    --shm-size=2g --cap-add=SYS_NICE --ulimit nofile=131071:131071 --ulimit nproc=16384:16384 \
    --add-host name.node:172.16.2.186 \
    -e DRIVERLESS_AI_CONFIG_FILE=/path/in/docker/config.toml \
    -p 12345:12345 \
    -v /local/path/to/config.toml:/path/in/docker/config.toml \
    -v /etc/passwd:/etc/passwd:ro \
    -v /etc/group:/etc/group:ro \
    -v /tmp/dtmp/:/tmp \
    -v /tmp/dlog/:/log \
    -v /tmp/dlicense/:/license \
    -v /tmp/ddata/:/data \
    -u $(id -u):$(id -g) \
    h2oai/dai-ubi8-x86_64:1.11.1.1-cuda11.8.0.xx

Export MOJO artifact to Azure Blob Storage

In order to export the MOJO artifact to Azure Blob Storage, you must enable support for the shared access signatures (SAS) token. You can enable support for the SAS token by setting the following variables in the config.toml file:

1. enable_artifacts_upload=true

2. artifacts_store="azure"

3. artifacts_azure_sas_token="token"

For instructions on exporting artifacts, see Exporting Artifacts.

FAQ

Can I connect to my storage account using Private Endpoints?

Yes. Driverless AI can use private endpoints if Driverless AI is located in the allowed VNET.

Does Driverless AI support secure transfer?

Yes. The Azure Blob Store Connector make all connections over HTTPS.

Does Driverless AI support hierarchical namespaces?

Yes.

Can I use Azure Managed Identities (MSI) to access the DataLake?

Yes. If Driverless AI is running on an Azure VM with managed identities. To enable the HDFS Connector to use MSI to authenticate, add to the core-site.xml:

For Gen1:

<property>
    <name>fs.adl.oauth2.access.token.provider.type</name>
    <value>MSI</value>
</property>

For Gen2:

<property>
    <name>fs.azure.account.auth.type</name>
    <value>OAuth</value>
</property>
<property>
    <name>fs.azure.account.oauth.provider.type</name>
    <value>org.apache.hadoop.fs.azurebfs.oauth2.MsiTokenProvider</value>
</property>