The cfa_azure
python module is intended to ease the challenge of working with Azure via multiple Azure python modules which require the correct steps and many lines of code to execute. cfa_azure
simplifies many repeated workflows when interacting with Azure, Blob Storage, Batch, and more. For example, creating a pool in Azure may take different credentials and several clients to complete, but with cfa_azure
, creating a pool is reduced to a single function with only a few parameters.
The cfa_azure
module is composed of three submodules: batch
, clients
, and helpers
. The module clients
contains what we call the AzureClient, which combines the multiple Azure Clients needed to interact with Azure and consolidates to a single client. The module helpers
contains more fine-grained functions which are used within the batch
and clients
modules or independently for more control when working with Azure.
Classes:
- AzureClient: a client object used for interacting with Azure. It initializes based on a supplied configuration file and creates various Azure clients under the hood. It can be used to upload containers, upload files, run jobs, and more. To customize the logging capabilities of cfa_azure, two environment variables can be set. These are LOG_LEVEL and LOG_OUTPUT.
LOG_LEVEL: sets the logging level. Choices are:
- debug
- info
- warning
- error
LOG_OUTPUT: sets the output of the logs. Choices are:
- file: saves log output to a file, nested within a ./logs/ folder
- stdout: saves log output to stdout
- both: saves log output to both file and stdout
Example:
export LOG_LEVEL="info"
export LOG_OUTPUT="stdout"
Functions:
- read_config: reads in a configuration toml file to a python object
- create_container: creates an Azure Blob container
- get_autoscale_formula: finds and reads autoscale_formula.txt from working directory or subdirectory
- get_sp_secret: retrieves the user's service principal secret
- get_sp_credential: retrieves the service principal credential
- get_blob_service_client: creates a Blob Service Client for interacting with Azure Blob
- get_batch_mgmt_client: creates a Batch Management Client for interacting with Azure Batch
- create_blob_containers: uses create_container() to create input and output containers in Azure Blob
- get_batch_pool_json: creates a dict based on config for working in Azure
- create_batch_pool: creates a Azure Batch Pool based on info stored in the config file
- list_containers: lists the containers in Azure Blob
- upload_files: uploads all files in specified folder to the specified container
- get_batch_service_client: creates a Batch Service Client objectfor interacting with Batch jobs
- add_job: creates a job in Azure Batch
- add_task_to_job: adds a task to the specified job based on user-input Docker command
- monitor_tasks: monitors the tasks running in a job
- list_files_in_container: lists out all files stored in the specified Azure container
- df_to_yaml: converts a pandas dataframe to yaml file
- yaml_to_df: converts a yaml file to pandas dataframe
- edit_yaml_r0: takes in a yaml file and produces replicate yaml files with the r0 changed based on the start, stop, and step provided
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