enyac-group / hyperpower

Hardware-aware Neural Network (Keras+TensorFlow) Hyper-parameter Optimization (Bayesian optimization)

HyperPower (Keras/TensorFlow + Spearmint)

Hardware-aware hyper-parameter search for Keras+TensorFlow neural networks via Spearmint Bayesian optimisation.

Description

Hyper-parameter optimization of neural networks (NN) has emerged as a challenging process. This design problem becomes more daunting if we are find the optimal (in terms of classification error) NN configuration that also satisfies hardware constraints, e.g., maximum inference runtime, maximum GPU energy or power consumption.

HyperPower uses the effectiveness of Bayesian optimization to employ hardware-constrained hyper-parameter optimization. This codebase is as basis in the HyperPower paper:

HyperPower: Power- and Memory-Constrained Hyper-Parameter Optimization for Neural Networks
Dimitrios Stamoulis, Ermao Cai, Da-Cheng Juan, Diana Marculescu
Design, Automation and Test in Europe Conference & Exhibition (DATE), 2018. IEEE, 2018.

General Setup

STEP 1: Installation (prerequisites)

1. Install Keras and TensorFlow
2. Make sure you have nvidia-smi (to read GPU power/energy values)
3. Download and install MongoDB
4. Install Spearmint.

STEP 2: Experimental setup

1. Defining the neural network (and the hyper-parameters) in Keras

   An example of a neural network can be found in experiments/cifar10/keras_model. In a default Keras model (w/o hyper-parameter optimization) the syntax would be:

   <...>
   momentum_val = 0.9
   decay_val = 0.001
   lr_val = 0.01
   
   opt=keras.optimizers.SGD(lr=lr_val, decay=decay_val, momentum=momentum_val)
   <...>
   

   To define hyper-parameters, we instead write:

   <...>
   momentum_val = HYPERPARAM{"type":"FLOAT", "token": "momentum", "min": 0.95, "max": 0.999}
   decay_val = HYPERPARAM{"type":"INT", "token": "weight_decay_base", "transform": "NEGEXP10", "min": 1, "max": 5}
   lr_val = HYPERPARAM{"type":"INT", "token": "base_lr_base", "transform": "NEGEXP10", "min": 1, "max": 5}
   
   opt=keras.optimizers.SGD(lr=lr_val, decay=decay_val, momentum=momentum_val)
   <...>
   

   For example, the learning rate is defined as a hyper-parameter HYPERPARAM{"type":"INT", "token": "base_lr_base", "transform": "NEGEXP10", "min": 1, "max": 5}, with range from 1 to 5; by using a transformation key NEGEXP10, this corresponds to taking the negative of the exponent of these values (1, ..., 5) with base 10, i.e., Spearmint will try {0.1, ..., 0.0001}. For more information on the transformations, we utilize a syntax similar to the one used in CWSM. Please note that our syntax also requires the token entry to be defined, i.e., a unique name for each hyper-parameter.

2. Generating Spearmint-compatible config file

   The provided gener_experiment.py file parses the experiments/myexperiment/keras_model/network_def.py file and generates the config.json file directory with the definitions of the hyper-parameters exposed to Spearmint.

   You should make sure that you set the SPEARMINT_ROOT variable in the gener_experiment.py script so that SPEARMINT_ROOT/spearmint/main.py can be launched from the gener_experiment.py script.

3. Callable objective function.

   The provided hyperpower.py gets the hyper-parameters of the design instance suggested next by Spearmint and translates this to a callable Keras script. The script is executed to obtain (i) the overall classification error and (ii) the inference runtime/power/energy per image of the NN.

STEP 2: Run hardware-aware Bayesian optimization

You are ready to optimize! We provide a hyper-parameter optimization example in experiments/cifar10/. You can launch your hardware-constrained hyper-parameter optimization with the provided run_tool.sh.