• Author: Fantine Huot

After cloning the repository, make sure to run the following commands to initialize and update the submodules.

git submodule init
git submodule update

• TensorFlow

• bin: Scripts to run jobs.
• config: Configuration files.
• log: Log files.
• trainer: Machine learning model trainer.

This repository provides a parameterized, modular framework for creating and running ML jobs.

To train a machine learning model, use the following command:

bin/train.sh model_config dataset

• model_config: Name of ML model configuration to use. This should correspond to a configuration file named config/model_config.sh.
• dataset: Dataset identifier. Check the variables datapath, train_file, and eval_file in bin/train.sh to ensure that this maps to the correct input data.
• label: Optional label to add to the job name.

Parameters for an ML job can be set by creating a corresponding configuration file: config/your_model_config.sh.

• Create a new your_model.py file inside the trainer/model folder. Look at other models inside the folder for examples.
• Reference your new model in trainer/model/__init__.py.
• Set the model argument to your new model's name in your model configuration file config/your_model_config.sh.

The hyperparameters are tuned using bayesian optimization.

To tune the hyperparameters for a machine learning model, use the following command:

bin/tunehp.sh model_config dataset

• model_config: Name of ML model configuration to use. This should correspond to a configuration file named config/model_config.sh.
• dataset: Dataset identifier. Check the variables datapath, train_file, and eval_file in bin/train.sh to ensure that this maps to the correct input data.

You can define the domain to explore for hyperparameter tuning by creating a corresponding configuration file: config/your_model_config_hptuning.yaml. Look at other hyperparameter tuning configuration files for examples.