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Setup

gcloud init

Login

interactive

gcloud auth login

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Calendar Settings

• Set timescale to 15 minutes (Settings > Calendar > View)
• Set declined items to remain visible (Settings > Calendar > Events and invitatations)
• create named views with filters (Calendar > Views )
  • Note: this does not save filter settings, only layout

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Notes on Azure ML environments

(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.

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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.

Create required vertices

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

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# 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 {

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Last updated: July 2021

-- disclaimer: This is my personal preference, always review the official documentation for updates.

Where to go for AzureML Docs

Examples of how to get started

This is a very currently active repo with examples and tutorials.

Use this also to raise github issues with any specific questions or issues.

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#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)

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Pre-reqs

From the terminal running on the AML compute instance, ensure that conda is accessible in your current shell session.

which conda
#-> should return a path to a conda executable

If conda is not found, then verify that the executable is present where expected

ls /anaconda/bin/conda

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Connect Azure ML to Azure DataLake Gen 2 with a Service Principal

Note that service principal role assignments may take a short while to become available, so give it a few minutes before testing the access.

Create a service principal

az ad sp create-for-rbac --name myAMLWorkspaceRep

Note the appId (client ID) and password (client secret) returned:

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Databricks CLI Cheatsheet - managing secrets

(optional) install cli into a virtual python env

virtualenv -p /usr/bin/python2.7 databrickscli
source databrickscli/bin/activate
pip install databricks-cli

Alternatively just install natively