tango-23.06

[ML-TANGO]

TANGO 2023 06 Release

This release introduces several enhancements and updates, leveraging the following containers: bms, autonn (YoloE), deploy_codegen, and deploy_target, all integrated into a React.js based project manager front-end.

To improve the efficiency of the autonn container, which typically requires several days for training even on high-performance GPUs, we have implemented a manual workflow step-forward functionality within the project manager. This functionality has been tested and validated through two stages:

• bms + autonn stage: This stage utilizes the dataset and target configurations within the project manager to select the appropriate base model. The selected base model undergoes fine-tuning, generating a trained model and associated codes for the subsequent stage.
• deploy_codegen + deploy_target stage: Building upon the trained model and generated codes from the previous stage, this stage prepares executable neural network codes for deployment on the specified target, as configured in the project manager. Please note that the current release includes the addition of K8s target deployment-related code, although it has not undergone full testing.

Notes on the current release:
We have introduced K8s target deployment-related code, which has been developed and built but requires further testing to ensure seamless integration.
This release will be the last release to feature the React.js based project manager front-end. Starting from the next releases, we will transition to a new Vue.js based project manager front-end, currently developed under teslasystem_vue branch.

BMS and AutoNN

This release includes the BMS (Base Model Selector) container, developed by ETRI, which serves as a simple test for BMS member container role. The BMS container selects the base model from the Yolo v7 series based on target type information (pc, ondevice, cloud, or k8s) specified within the project configuration step of the project manager. The selected base model is utilized in the AutoNN containers for fine-tuning with the dataset, also specifed within the project configuration step. Additionally, the AutoNN container (implemented in autonn\YoloE), developed by ETRI, has been included for testing the AutoNN member container role within the TANGO project workflow (pipeline).

Deployment

Deployment:

We have made changes to the source structure related to deployment codes compared to the previous release. The updates are as follows:

• The codes responsible for generating the executable neural network code have been moved to the deploy_codegen folder.
• The codes for deploying the executable neural network have been relocated to the deploy_target folder.

The deploy_target folder now includes sub-folders based on the deployment target:

• cloud: codes for deployment to cloud environments
• k8s: codes for deployment to Kubernetes clusters (Please note that the k8s deployment functionality is not operational in this release but will be addressed in future updates.)
• ondevice: codes for deployment to on-device platforms

Notes on deployment:

• The codes in the deploy_target/k8s folder can be used to build a Docker image, but integration testing is still ongoing. Therefore, in this release, K8s deployment is not functioning properly.
• In this release, deploy_codegen supports TensorRT, Apache TVM, PyTorch and [ACL] (https://github.com/ARM-software/ComputeLibrary). Support for RKNN . Support for rknn is still in progress.

To Do:

In the next release, the project manager will use a Vue.js-based front-end with support for fully manual/automatic workflow step-forward functionality.

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This discussion was created from the release tango-23.06.