This repository utilizes Azure ML to train detectron2
and yolact
based models. Refer to the below diagram for an architecture overview of the implemented method.
Azure Machine Learning Service Demo
- setting up environment
- connect data set
- set up environment
- training
- Detectron2
- Yolact (need to finish)
- Yolov5
- Pytorch.segmentation_models
- model evaluaiton
- model registration
- model deployment and operationation
- Azure ML Experiment Logging, Metrics and Eval Image Samples
Quick start for utilized detectron2 and yolact for AzureML training. Please follow this Azure ML Doc for setting up initial AzureML Environment.
- Finalize training dataset and coco annotations
- update model training configs and dataset info in either the
aml_detectron2
oraml_yolact
aml_code
folder - update required training config in the
experiment_cfg.py
file - run
python aml_new_experiment.py
-
Clone this repository (git installation required)
cd $HOME # or another directory for this project/repo git clone https://github.com/naivelogic/azureml_cv.git cd azureml_cv
-
Tools that I am using
-
Install environment with Anaconda:
conda env create -f azureml-env.yml conda activate azureml ## if using Jupyter Notebooks create custom jupyter kernel for AzureML python -m ipykernel install --user --name=azureml jupyter nbextension install --py --user azureml.widgets jupyter nbextension enable azureml.widgets --user --py
-
Login to Azure on the command line
seems like i need to run
az login --use-device-code
to initialize each session for authentication to the AML workspace (i have multiple AML across different subscriptions and tenants)# azure cli linux ## install az function tools # https://docs.microsoft.com/en-us/cli/azure/install-azure-cli-linux?pivots=apt curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash # now log in az login --use-device-code # may also need to install this package if getting authentication errors pip install azure-cli-core