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Cloud Execution

Azure

A key benefit and design motivation of OpticSim is being able to execute many simulations/optimizations at once. This is best enabled through the use of cloud computing services such as Azure.

As part of the base package, we provide support for cloud execution using an Azure Machine Learning workspace.

To use this functionally you'll first need to set up an AML workspace with a compute cluster. Then you'll need to provide a few bits of information to OpticSim:

  • Subscription ID, of the form XXXXXXXX-XXX-XXX-XXXX-XXXXXXXXXXXX
  • Resource group name
  • Workspace name
  • Compute cluster name

This information can be cached either to a specific file, or globally:

OpticSim.Cloud.cache_run_config
OpticSim.Cloud.get_cached_run_config

You should also include an .amlignore file in the root of your project. This is similar to a .gitignore file and should include any files which should not be uploaded to AML as part of your source snapshot, for examples test/.

!!! note Manifest.toml must be listed in your .amlignore file.

If an .amlignore doesn't already exist then one will be created on the first submission of a run to AML.

Once everything is configured, you can submit a run:

OpticSim.Cloud.submit_run_to_AML

To retrieve outputs from your run simply write files to the outputs/ directory and the files will automatically appear as part of the AML run.

Examples

using OpticSim.Cloud

cache_run_config([subscription_id], [resource_group_name], [workspace_name], [compute_name], [path_to_config])

submit_run_to_AML("example-run", [path_to_script], ["--arg1", "1", "--arg2", "2"], nothing, [path_to_config])

submit_run_to_AML("example-hyperdrive-run", [path_to_script], ["--arg1", "1"], Dict("--arg2" => ["1", "2", "3"]), [path_to_config])

Other Cloud Services

Currently no other services are supported, though it should be reasonably straightforward to add similar functionality to that for AML.