[FEA] Reducing manual copy/paste between MONAI / MONAI Deploy / MONAI Label

[aihsani]

Is your feature request related to a problem? Please describe.

Currently, manual work (copy/paste code) is required for a MONAI developer to create a MONAI Deploy App. That is, there is no common API between MONAI and MONAI Deploy (outside of importing MONAI utilities/libraries in a MONAI Deploy App). Due to this, an experienced MONAI developer (primarily focused on developing new algorithms/training paradigms) needs to learn the concepts/API in MONAI Deploy and copy/paste code in the appropriate places in a MONAI Deploy Operator and manually ensure that data transfer concepts (e.g. InferContext) in MONAI Deploy are correctly used.

Analogically, in the case of MONAI Label, an experienced MONAI developer must do much the same: familiarize themselves with the concepts of of MONAI Label (e.g InferTask, TrainTask, etc) and copy/paste their code in the appropriate sections to ensure that the MONAI Label App functions as expected.

Describe the solution you'd like

In addition to researchers "freestyling" training scripts using MONAI, they could also optionally use concepts of MONAI Label and Stream such as tasks (InferTask, TrainTask, etc.) and contexts (InputContext, OutputContext, ExecutionContext) to represent the conceptual task the use aims a certain branch of code to accomplish all the while using data transfer types that are portable across MONAI. The aim is ultimately to employ each of the execution branches for the application the user wants to create; this may still require manual adjustment but overall reduce the amount of manual copy/paste and reduce the learning curve across different MONAI products.

Describe alternatives you've considered
Learn each framework in isolation (MONAI, MONAI Label, MONAI Deploy) and copy/paste where appropriate.

[dbericat]

@rahul-imaging @MMelQin @gigony do you see your proposal of having a standalone transform library to be reused across Core and Deploy part of this issue or as a separate one?

@wyli for viz.

[Nic-Ma]

Hi @MMelQin @dbericat ,

May I know why you need to copy / paste the code into MONAI Deploy? Can't we just install MONAI v0.7 package and call the transforms or other components?

Thanks in advance.

[MMelQin]

Just to clarify:

1. It may be a misconception stating "cut and paste" in Deploy App SDK. it is more of Deploy/Inference developer needs to understand the set of transforms (pre and post) required before and after sending the tensors to the model for inference. Training and Deploy take in different raw input, and often times, Deploy needs to add additional transforms, and simply cannot directly use the Compose (a chain of transforms) in Train application.
2. AI inference domain classes in App SDK depends on MONAI core, specifically Transforms, Compose, as well as Inferer modules, but only use it as runtime dependency, not a static dependency of Deploy App SDK. This is an architectural decision such that App SDK package needs not include MONAI core, and the AI inference classes uses the same set of processing module (transforms and inferer) used during train for consistency.
3. Higher level constructs, especially workflow related e.g. InfereTask/Train Task, may not need to be in MONAI core, and more likely than not, cannot be transferred from Train to Deploy (see points above).
4. Additional, Train uses an engine that is not used Deploy, e.g. Deploy does not use Ignite, and does not support Handlers for Ignite. So, pipeline used in Train can NOT be reused in Deploy.
5. The consensus among MONAI WG and MONAI Deploy WG is that there are needs for:

self describing model, i.e. just a TorchScript is not enough for Deploy, besides, meta data is needed to describe the input/output data/format, transforms used in the context of the input/output data/format.
Ideally, a separate, standalone, transfroms only library can be provided by MONAI core.