Kubeflow

This guide describes how to integrate Azure Blob Storage within your Charmed Kubeflow deployment.

Requirements

• A Charmed Kubeflow deployment
• An Azure account
• An Azure Storage account

Assign permissions

Before starting to work with Azure Blob Storage, ensure that you have the necessary permissions to access the storage account. Follow this guide to self-assign the Storage Blob Data Contributor role.

Create and connect to a new notebook

From the Kubeflow dashboard, navigate to Notebooks, and click on New Notebook. Select a JupyterLab environment, and connect to the newly created notebook.

Install required packages

On the JupyterLab launcher, click on Terminal to start a new terminal session. Next, install the packages required to connect to your Azure account and interact with it using the Python client library:

pip install azure-cli azure-storage-blob azure-identity

Sign in to your Azure account

Sign in to Azure through the Azure CLI using the following command:

az login

Confirm that you have successfully logged in to your account:

az account show

Connect to your Azure account via the Python client

Add a new tab in your JupyterLab environment, and then create a new Python 3 notebook. Within a notebook cell, run the following code to connect to your Azure account:

import os, uuid
from azure.identity import AzureCliCredential
from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient

try:
    print("Azure Blob Storage Python quickstart sample")

    account_url = "https://<storageaccountname>.blob.core.windows.net"
    default_credential = AzureCliCredential()

    blob_service_client = BlobServiceClient(account_url, credential=default_credential)

except Exception as ex:
    print('Exception:')
    print(ex)

Replace the <storageaccountname> token with the storage account name you want to interact with.

Create a new blob container

You can create a new blob container by creating a new text file in the data directory and upload it as follows:

local_path = "./data"
os.mkdir(local_path)

local_file_name = str(uuid.uuid4()) + ".txt"
upload_file_path = os.path.join(local_path, local_file_name)

file = open(file=upload_file_path, mode='w')
file.write("Hello, World!")
file.close()

container_name = str(uuid.uuid4())
blob_client = blob_service_client.get_blob_client(container=container_name, blob=local_file_name)

print("\nUploading to Azure Storage as blob:\n\t" + local_file_name)

with open(file=upload_file_path, mode="rb") as data:
    blob_client.upload_blob(data)

Establish the local file name defining the local_file_name variable and the new container name defining the container_name variable.

List the blobs in a container

You can list all blobs in a specified container as follows:

print("\nListing blobs...")

blob_list = container_client.list_blobs()
for blob in blob_list:
    print("\t" + blob.name)

Download blobs

You can download blobs and save them to your local file system. Use the following code to download the blob specified by its name:

download_file_path = os.path.join(local_path, str.replace(local_file_name ,'.txt', 'DOWNLOAD.txt'))
container_client = blob_service_client.get_container_client(container= container_name) 
print("\nDownloading blob to \n\t" + download_file_path)

with open(file=download_file_path, mode="wb") as download_file:
 download_file.write(container_client.download_blob(blob.name).readall())

Clean up resources

Clean up the resources created throughout this guide by running the following code:

print("\nPress the Enter key to begin clean up")
input()

print("Deleting blob container...")
container_client.delete_container()

print("Deleting the local source and downloaded files...")
os.remove(upload_file_path)
os.remove(download_file_path)
os.rmdir(local_path)

print("Done")

Alternatively, you can also use the Azure CLI along with this guide to do so.

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