Microsoft Azure Batch AI Management Client Library for Python
Microsoft Azure SDK for Python
This is the Microsoft Azure Batch AI Management Client Library.
Azure Resource Manager (ARM) is the next generation of management APIs that replace the old Azure Service Management (ASM).
This package has been tested with Python 2.7, 3.4, 3.5 and 3.6.
For the older Azure Service Management (ASM) libraries, see azure-servicemanagement-legacy library.
For a more complete set of Azure libraries, see the azure bundle package.
IMPORTANT: If you have an earlier version of the azure package (version < 1.0), you should uninstall it before installing this package.
You can check the version using pip:
If you see azure==0.11.0 (or any version below 1.0), uninstall it first:
pip uninstall azure
For code examples, see Batch AI Management on docs.microsoft.com.
If you encounter any bugs or have suggestions, please file an issue in the Issues section of the project.
This version uses 2018-05-01 BatchAI API specification which introduced the following braking changes:
Clusters, FileServers must be created under a workspace;
Jobs must be created under an experiment;
Clusters, FileServers and Jobs do not accept location during creation and belong to the same location as the parent workspace;
Clusters, FileServers and Jobs do not support tags;
BatchAIManagementClient.usage renamed to BatchAIManagementClient.usages;
Job priority changed a type from int to an enum;
File.is_directory is replaced with File.file_type;
Job.priority and JobCreateParameters.priority is replaced with scheduling_priority;
Removed unsupported MountSettings.file_server_type attribute;
OutputDirectory.type unsupported attribute removed;
OutputDirectory.create_new attributes removed, BatchAI will always create output directories if they not exist;
SetupTask.run_elevated attribute removed, the setup task is always executed under root.
Added support to workspaces to group Clusters, FileServers and Experiments and remove limit on number of allocated resources;
Added support for experiment to group jobs and remove limit on number of jobs;
Added support for configuring /dev/shm for jobs which use docker containers;
Added first class support for generic MPI jobs;
Added first class support for Horovod jobs.
Fix some invalid models in Python 3
Compatibility of the sdist with wheel 0.31.0
General Breaking changes
This version uses a next-generation code generator that might introduce breaking changes.
Model signatures now use only keyword-argument syntax. All positional arguments must be re-written as keyword-arguments. To keep auto-completion in most cases, models are now generated for Python 2 and Python 3. Python 3 uses the “*” syntax for keyword-only arguments.
Enum types now use the “str” mixin (class AzureEnum(str, Enum)) to improve the behavior when unrecognized enum values are encountered. While this is not a breaking change, the distinctions are important, and are documented here: https://docs.python.org/3/library/enum.html#others At a glance:
“is” should not be used at all.
“format” will return the string value, where “%s” string formatting will return NameOfEnum.stringvalue. Format syntax should be prefered.
New Long Running Operation:
Return type changes from msrestazure.azure_operation.AzureOperationPoller to msrest.polling.LROPoller. External API is the same.
Return type is now always a msrest.polling.LROPoller, regardless of the optional parameters used.
The behavior has changed when using raw=True. Instead of returning the initial call result as ClientRawResponse, without polling, now this returns an LROPoller. After polling, the final resource will be returned as a ClientRawResponse.
New polling parameter. The default behavior is Polling=True which will poll using ARM algorithm. When Polling=False, the response of the initial call will be returned without polling.
polling parameter accepts instances of subclasses of msrest.polling.PollingMethod.
add_done_callback will no longer raise if called after polling is finished, but will instead execute the callback right away.
added support for job level mounting
added support for environment variables with secret values
added support for performance counters reporting in Azure Application Insights
added support for custom images
added support for pyTorch deep learning framework
added API for usage and limits reporting
added API for listing job files in subdirectories
now user can choose caching type during NFS creation
get cluster now reports a path segment generated for storing start task output logs
get job now reports a path segment generated for job’s output directories
renamed EnvironmentSetting to EnvironmentVariable
credentials_info property got renamed to credentials.
removed unused class FileServerStatus and Code enum
renamed enums for CachingType and VmPriority
removed ‘statuses’ attribute on FileServer
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