Model Fine-tune
If you are using Clara-Train to train your model, AIAA provides functionality to let you fine-tune your trained models based on new annotation samples you make.
AIAA will take care of fine-tuning the model and re-load it back to AIAA for serving.
This is an Admin API.
If you have only 1 GPU in your system, then AIAA can’t serve inference through segmentation/annotation APIs during fine-tuning.
You need to make sure you put new samples in /workspace/samples and have your MMAR in /workspace/mmars
To see an example of MMAR check Medical Model Archive (MMAR).
Below is an example directory for clara_ct_seg_spleen_amp model
workspace/
samples/
clara_ct_seg_spleen_amp/
options.conf #optional
dataset.json #optional
images/
spleen_2.nii.gz
spleen_3.nii.gz
...
labels/
spleen_2.nii.gz
spleen_3.nii.gz
...
mmars/
clara_ct_seg_spleen_amp/
commands/
train_finetune.sh
configs/
...
The uri to call is /admin/finetune/[model]. Note that you can pass all options that train.sh takes. There are two ways of passing those options, one is via curl, which is demonstrated below.
# basic call
curl -X POST "http://127.0.0.1:$LOCAL_PORT/admin/finetune/clara_ct_seg_spleen_amp"
# fine-tune for 5 epochs, %3D means space
curl -X POST "http://127.0.0.1:$LOCAL_PORT/admin/finetune/clara_ct_seg_spleen_amp?options=epochs%3D5"
The other is to edit the options.conf file. An example is below. You should put this
file inside your /workspace/samples/[model]/options.conf.
epochs=5
learning_rate=0.00001
If you want to pick up specific images for training and validation,
then you can provide your own configuration at: /workspace/samples/{model}/dataset.json.
You can also set automatic fine-tuning by adding flag --fine_tune true when starting AIAA.
AIAA will run model fine-tune on the fine_tune_hour every day for all the models.