This example shows how to build a simple CNN on the CIFAR-10 dataset using Determined's PyTorch API. This example is adapted from this Keras CNN example.
- model_def.py: The core code for the model. This includes building and compiling the model.
- const.yaml: Train the model with constant hyperparameter values.
- adaptive.yaml: Perform a hyperparameter search using Determined's state-of-the-art adaptive hyperparameter tuning algorithm.
- distributed.yaml: Same as
const.yaml, but trains the model with multiple GPUs (distributed training). - distributed_inference.yaml: Use the distributed training workflow with PyTorchTrial to accelerate batch inference workloads.
The CIFAR-10 dataset is downloaded from https://www.cs.toronto.edu/~kriz/cifar.html.
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.
Training the model with the hyperparameter settings in const.yaml should yield a validation accuracy of ~74%.