In v37, models for flows created in v35 and above are forward compatible, meaning that you can use them in v37 without having to retrain them during the upgrade process. As a result, forward-compatible models allow you to:
upgrade v36 models after upgrading the application to v37,
maintain the automation levels achieved in v36, and
continue existing training efforts for any use cases that may be in implementation when upgrading to v37.
Which models are forward compatible
In v37, the following types of models are forward-compatible:
Locator models
Transcription models (i.e., sets of finetuning models)
Classification models
The following types of models are not forward compatible, so you need to train v37 versions of these models in order to use them in v37:
Text Classification models
To learn more about Text Classification, see Text Classification.
Forward-compatible models and upgrades
When upgrading to v37, keep in mind that some aspects of the v37-upgrade process differ from upgrades to previous versions.
How the upgrade process changes
If you're upgrading from v34 or earlier, you need to upgrade to v35 first and complete any submissions created in v34 or earlier before upgrading to v36 or v37.
When upgrading to v37, you do not need to attach a v37 version of the trainer to your previous application version.
Models for flows created in v36 are not removed from the system during the upgrade process; they remain intact and work "out of the box" after upgrading to v37, with no retraining required.
In v36 and v37, Hyperscience supports flows created in v35, but not flows created in v34 or earlier.
If you want to use flows created in v34 or earlier, you need to upgrade those flows in v35 prior to upgrading to v36 or v37 — they are not supported in v36 or v37, nor are their models forward compatible.
Using v35 models and flows in v37
As mentioned earlier, you can continue using v35 flows in v37 without interruption or loss of automation. However, v35 flows can only use the models they were using before upgrading to v36 or v37, and you cannot import models for these flows. You cannot import a v35 model for use with a v36 or v37 flow.
If you need to change a model for a v35 flow (e.g., train it for a new Semi-structured layout), you must create a new model, along with a new flow in v37 to go with it. When changing a model, all of the models for the model's flow must be upgraded for the new v37 flow. In other words, you cannot upgrade a flow's models selectively or in a piecemeal fashion. You also cannot attach a v35 trainer to update the models for a v37 flow.
When using v35 flows, you can use cloned versions of the releases you used in v35. However, if you add Semi-structured layouts to those releases, there is no automation for the submissions matched to these layouts. That is, the system generates Supervision tasks for these submissions.
Memory Management
If you are using v35 flows and out-of-memory errors occur, you should enable the Memory Management feature. This feature assigns flows that were created in specific application versions to specific application machines in your instance.
To learn more about Memory Management, see Memory Management.
How to know when to train forward-compatible models
When you open a v35 flow in Flow Studio, the system lets you know which models for the flow need to be trained.
From there, you can click on an issue's Go to Model Management button.
If the affected model is a Locator Model, you are redirected to the Model Library. There, you can find the model mentioned in the issue's description and click on its name. On the model’s details page, you can take action to correct the issue.
If the model mentioned in the issue is a Classification or Transcription mode, clicking Go to Model Management takes you directly to the model’s details page, where you can take corrective action.
Models from v34 or earlier do not appear in the Model Library.