gcloud init
interactive
gcloud auth login
(Ref: https://docs.microsoft.com/en-us/azure/machine-learning/reference-yaml-environment#yaml-syntax)
The environment item in an AzureML cli v2 job specification yml file can leverage an AzureML curated environment directly, referenced as follows:
environment: azureml:AzureML-pytorch-1.10-ubuntu18.04-py38-cuda11-gpu@latest
A list of available curated environments can be found here, or from wtihin the environments tab in AzureML Studio.
Context: this question
The following commands can be exectuted from within the Data Explorer interface of an Azure Cosmos DB Gremlin API database resource, or from other gremlin consoles such as a local TinkerPop installation.
This can be in an empty graph, like that created when adding a graph in Azure Cosmos DB Gremlin API or added to an exsisting graph, like for example the 'modern' sample graph from the Tinkerpop Getting Started documentation.
g.addV("person").property("pk","pk").property("id","a").property("name","Alice")
g.addV("person").property("pk","p
# This terraform spec provisions | |
# - Azure resource group | |
# - Azure Cosmos DB Account (SQL API, Analytics Storage enabled) | |
# - Azure Log Analytics Workspace | |
# - Diagnostic Setting that sends Cosmos DB logs to the Log Analytics Workspace | |
# - A Cosmos database in the account (SQL API document db) | |
# - A container in the database, with throughput and indexing configurations | |
# Set up terraform (basic example, local backend) | |
terraform { |
Last updated: July 2021
-- disclaimer: This is my personal preference, always review the official documentation for updates.
This is a very currently active repo with examples and tutorials.
Use this also to raise github issues with any specific questions or issues.
#create a dataframe from filepaths
directory = '/path/on/dbfs'
file_paths = dbutils.fs.ls(directory)
#e.g
print(file_paths[0].path)
print(file_paths[0].name)
Note that service principal role assignments may take a short while to become available, so give it a few minutes before testing the access.
az ad sp create-for-rbac --name myAMLWorkspaceRep
Note the appId (client ID) and password (client secret) returned: