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ENH: Add Environment Locking #735
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Closes #726
This includes changes to how we handle primary and secondary dependencies in the InnerEye Deep Learning library. Locking is now performed through the use of
conda export, preventing secondary versions from switching unexpectedly. Through the use of a bash script the env is locked and anenvironment.ymlsuitable for ingestion by AzureML is constructed.This is far from a perfect solution, but at the very least significantly reduces the chance of pipelines failing due to secondary dependency version changes.