This example is an EfficientDet port using Determined's PyTorchTrial API. The original example can be found on rwightman's EfficientDet Github.
- 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.
- efficientdet_files/: This folder includes original EfficientDet files that have been slightly altered to work with Determined.
- const.yaml: Train the model with constant hyperparameter values.
- distributed.yaml: Same as
const.yaml, but trains the model with multiple GPUs (distributed training). - adaptive.yaml: Runs an adaptive search with the configured restraints.
The coco dataset is the primary dataset for this port. Information on how to download the dataset can be found here. Once downloaded, copy the data on all the Determined agents. Then configure the bind mount in the configuration files to the appropriate location. More information on bind mounts can be found 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.
The default configurations are based on rwightman's reported results. After 5 training days, the model efficientdet_d0's mAP should be about .326. To help optimize sync_batch_norm, set tensor_fusion_threshold: 1 and tensor_fusion_cycle_time: 2 to improve training performance time.
Note: The purpose of these graphs is to show the efficientdet_d0 model running in Determined
for a set number of epochs, demonstrating the acceleration of model training time
achieved with Determined’s distributed training. The plots are based on 4x2080tis
