This example shows how to build an unets image segmentation model on the Oxford-IIIT Pet dataset using Determined's tf.keras API. This example is adapted from this TensorFlow Image Segmentation example.
- model_def.py: The core code for the model. This includes building and compiling the model.
- startup-hook.sh: This script will automatically be run by Determined during startup of every container launched for this experiment. This script installs some additional dependencies and downloads the training data.
- const.yaml: Train the model with constant hyperparameter values.
- distributed.yaml: Same as
const.yaml, but trains the model with multiple GPUs (distributed training).
The data used for this script was fetched via TensorFlow Datasets as done by the tutorial itself. The original Oxford-IIIT Pet dataset is linked here.
If you have not yet installed Determined, installation instructions can be found
under docs/install-admin.html or at https://docs.determined.ai/latest/index.html
Run the following command: det -m <master host:port> experiment create -f const.yaml .. The other configurations can be run by specifying the appropriate
configuration file in place of const.yaml.
Note: The purpose of these graphs is to show a Unets model running in Determined for a set number of epochs, demonstrating the acceleration of model training time achieved with Determined’s distributed training.
