hpogrid 0.0.4

A workflow for hyperparamter optimization using the ATLAS grid resources

Hyperparameter Optimization on the Grid

This package provides a framework for performing hyperparameter optimization (HPO) using the ATLAS grid resources.

Table of Contents

1. Basic Workflow
2. Getting the code
3. Setup and Installation
   • Using the default conda environment
   • Inside a custom virtual environment
4. How to
5. Adapting training scripts for hyperparameter optimization
6. Managing Configuration Files
   • HPO Configuration
   • Grid Configuration
   • Model Configuration
   • Search Space onfiguration
7. Running Hyperparameter Optimization Jobs
8. Monitoring Job Status
9. Visualizing Hyperparamter Optimization Results
10. Command line options
11. Important Notes
    • Working Directory of Container
    • Location of Input Dataset
    • Digest of Grid Site Errors

Basic Workflow

The execution of the above hyperparameter optimization workflow can be done entirely using the hpogrid tool provided by this repository. The sample usage and instruction for using the hpogrid tool is given in the next few sections.

The workflow can be divided into the following steps:

Step 1: Prepare the configuration files for a hyperparameter optimization task which will submitted to the ATLAS grid site. A total of four configuration files are required. They are:

1. HPO Configuration: Configurations that define how the hyperparameter optimization is performed. This may include the algorithm of hyperparameter optimization, the scheduling method for choosing the next hyperparameter points, the number of hyperparameter points to be evaluated and so on.
2. Search Space Configuration: Configurations that define the hyperparameter search space. This includes the sampling method for a particular hyperparameter (such as uniform or normal sampling or logirthmic based uniform sampling) and its range of allowed values.
3. Model Configuration: Configurations that contains information of the training model that is called by the hyperparameter optimization alogrithm. This should include
   • The name of the training script which contains the class/function defining the training model
   • The name of the class/function that defines the training model
   • The parameters that should be passed to the training model. For details please refer to the section (Adaptation of Training Script)
4. Grid Configuration: Configurations that define settings for grid job submission. This may include the container inside which the scripts are run, the name of input and output datasets and the name of the grid site where the hyperparameter optimization jobs are run.

Step 2: Upload the input dataset via rucio which will be retrieved by the grid site when the hyperparameter optimization task is executed.

Step 3: Adapt the training script(s) to conform with the format required by the hyperparameter optimization library (Ray Tune).

Step 4: Submit the hyperparamter optimization task and monitor its progress.

Step 5: Retrieve the hyperparamter optimization results after completion. The results can be output into various formats supported by the hpogrid tool for visualization.

Getting the code

To get the code, use the following command:

git clone ssh://[e-mail]:7999/aml/hyperparameter-optimization/alkaid-qt/hpogrid/hpogrid.git

Setup and Installation

Using the default conda environment

To setup just use the command (from the base path of the project):

source setupenv.sh

This will setup the default conda environment ml-base which contains all necessary machine-learning packages and the latest version of ROOT.

Inside a custom virtual environment

Activate your custom environment then use the command (from the base path of the project):

source setupenv.sh no-conda

Then install the hpogrid package:

pip install hpogrid

How to

• For details of how to adapt a training script to the HPO library, please refer to this section
• For details of how to create, update, display a configuration file, please refer to this section

Example 1: Optimizing a simple objective function

Given a simple objective function loss = (height - 14)**2 - abs(width - 3) with -100 < height < 100 and 0 < width < 20. The goal is to minimize the metric loss. The training script with the above objective function can be found in example/scripts/simple_objective.py.

# Create Configuration Files
hpogrid model_config create simple_objective_model --script simple_objective.py --model simple_objective
hpogrid search_space create simple_objective_space '{"height":{"method":"uniform","dimension":{"low":-100,"high":100}}, "width":{"method":"uniform","dimension":{"low":0,"high":20}}}'
hpogrid hpo_config create random_search_min_loss --algorithm random --metric loss --mode min --trials 200
hpogrid grid_config create manc_standard --site ANALY_MANC_GPU_TEST
# Create a Project with the Configuration Files
hpogrid project create simple_objective --scripts_path ${HPOGRID_BASE_PATH}/example/scripts/simple_objective.py --model_config simple_objective_model --search_space simple_objective_space --hpo_config random_search_min_loss --grid_config manc_standard
# Submit Grid Jobs corresponding to the Project
hpogrid run simple_objective --n_jobs 3

Example 2: Optimizing a simple trainable class

The training script with the above trainable class can be found in example/scripts/simple_trainable.py

# Create Configuration Files
hpogrid model_config create simple_trainable_model --script simple_trainable.py --model MyTrainableClass
hpogrid search_space create simple_trainable_space '{"alpha":{"method":"uniform","dimension":{"low":-10,"high":10}}, "beta": {"method":"categorical","dimension":{"categories":[-1,0,1,2,3,4,5]}}}'
hpogrid hpo_config create bayesian_min_loss --algorithm bayesian --metric loss --mode min --trials 200
# If you have already created the grid config manc_standard, you may skip the following line
hpogrid grid_config create manc_standard --site ANALY_MANC_GPU_TEST
# Create a Project with the Configuration Files
hpogrid project create simple_trainable --scripts_path ${HPOGRID_BASE_PATH}/example/scripts/simple_trainable.py --model_config simple_trainable_model --search_space simple_trainable_space --hpo_config bayesian_min_loss --grid_config manc_standard
# Submit Grid Jobs corresponding to the Project
hpogrid run simple_trainable --n_jobs 3

Example 3: Optimizing a convolutional neural network for the MNIST dataset

# Create Configuration Files
hpogrid model_config create mnist_cnn --script mnist.py --model MNISTTrainable
hpogrid search_space create mnist_cnn_space '{"hidden": {"method": "categorical", "dimension": {"categories": [32, 64, 128]}},"batchsize": {"method": "categorical", "dimension": {"categories": [32, 64, 128, 256, 512]}}, "lr": {"method": "loguniform", "dimension": {"low": 4.5e-05, "high": 1.83e-2}}, "beta": {"method": "uniform", "dimension": {"low": 0.5, "high": 1.0}}}'
hpogrid hpo_config create hyperopt_min_loss --algorithm hyperopt --mode min --trials 200
# If you have already created the grid config manc_standard, you may skip the following line
hpogrid grid_config create manc_standard --site ANALY_MANC_GPU_TEST
# Create a Project with the Configuration Files
hpogrid project create mnist_cnn --scripts_path ${HPOGRID_BASE_PATH}/example/scripts/mnist.py --model_config mnist_cnn --search_space mnist_cnn_space --hpo_config hyperopt_min_loss --grid_config manc_standard
# Submit Grid Jobs corresponding to the Project
hpogrid run simple_trainable --n_jobs 3

Adapting training scripts for hyperparameter optimization

Managing Configuration Files

In general, the command for managing configuraton file takes the form:

hpogrid <config_type> <action> <config_name> [<options>]

The <config_type> argument specifies the type of configuration to be handled. The avaliable types are

• hpo_config : Configuration for hyperparamter optimization
• grid_config : Configuration for grid job submission
• model_config : Configuration for the machine learning model (which the hyperparameters are to be optimized)
• search_space : Configuration for the hyperparameter search space

The <action> argument specifies the action to be performed. The available actions are

• create : Create a new configuration
• recreate : Recreate an existing configuration (the old configuration will be overwritten)
• update : Update an existing configuration (the old configuration except those to be updated will be kept)
• remove : Remove an existing configuration
• list : List the name of existing configurations (the <config_name> argument is omitted)
• show : Display the content of an existing configuration

The <config_name> argument specifies name given to a configuration file.

The [<options>] arguments specify the configuration settings for the corresponding configuration type. The available options are explained below.

HPO Configuration

Option	Description	Default	Choices
algortihm	Algorithm for hyperparameter optimization	'random'	'hyperopt', 'skopt', 'bohb', 'ax', 'tune', 'random', 'bayesian'
metric	Evaluation metric to be optimized	'accuracy'	-
mode	Optimization mode (either 'min' or 'max')	'max'	'max', 'min'
scheduler	Trial scheduling method for hyperparameter optimization	'asynchyperband'	'asynchyperband', 'bohbhyperband', 'pbt'
trials	Number of trials (search points)	100	-
log_dir	Logging directory	"./log"	-
verbose	Check to enable verbosity	-	-
stop	Stopping criteria	'{"training_iteration": 1}'	-
scheduler_param	extra parameters for the trial scheduler	'{"max_concurrent": 4}'	-
algorithm_param	extra parameters for hyperparameter optimization algorithm	{}	-

Grid Configuration

Option	Description	Default
site	Grid site where the jobs are submitted	ANALY_MANC_GPU_TEST
container	Docker or singularity container which the jobs are run	/cvmfs/unpacked.cern.ch/gitlab-registry.cern.ch/aml/hyperparameter-optimization/alkaid-qt/hpogrid:latest
retry	Check to enable retrying faild jobs	-
inDS	Name of input dataset	-
outDS	Name of output dataset	user.${{RUCIO_ACCOUNT}}.hpogrid.{HPO_PROJECT_NAME}.out.$(date +%Y%m%d%H%M%S)

Model Configuration

This defines the parameters and settings for the machine learning model which the hyperparameters are to be optimized.

Option	Description
script	Name of the training script where the function or class that defines the training model will be called to perform the training
model	Name of the function or class that defines the training model
param	Extra parameters to be passed to the training model

Search Space Configuration

This defines the search space for hyperparameter optimization.

The format for defining a search space in command line is through a json decodable string:

'{"NAME_OF_HYPERPARAMETER":{"method":"SAMPLING_METHOD","dimension":{"DIMENSION":"VALUE"}},
"NAME_OF_HYPERPARAMETER":{"method":"SAMPLING_METHOD","dimension":{"DIMENSION":"VALUE"}}, ...}'

Supported sampling methods for a hyperparameter:

Method	Description	Dimension
categorical	Returns one of the values in categories, which should be a list. If grid_searchis set to 1, each value must be sampled once.	categories, grid_search
uniform	Returns a value uniformly between low and high	low, high
uniformint	Returns an integer value uniformly between low and high	low, high
quniform	Returns a value like round(uniform(low, high) / q) * q	low, high, q
loguniform	Returns a value drawn according to exp(uniform(low, high)) so that the logarithm of the return value is uniformly distributed.	low, high, base
qloguniform	Returns a value like round(exp(uniform(low, high)) / q) * q	low, high, base, q
normal	Returns a real value that's normally-distributed with mean mu and standard deviation sigma.	mu, sigma
qnormal	Returns a value like round(normal(mu, sigma) / q) * q	mu, sigma, q
lognormal	Returns a value drawn according to exp(normal(mu, sigma)) so that the logarithm of the return value is normally distributed.	mu, sigma, base
qlognormal	Returns a value like round(exp(normal(mu, sigma)) / q) * q	mu, sigma, base, q

Examples:

hpogrid search_space create my_search_space '{ "lr":{"method":"loguniform","dimension":{"low":1e-5,"high":1e-2, "base":10}},\
"batchsize":{"method":"categorical","dimension":{"categories":[32,64,128,256,512,1024]}},\
"num_layers":{"method":"uniformint","dimension":{"low":3,"high":10}},\
"momentum":{"method":"uniform","dimension":{"low":0.5,"high":1.0}} }'

Running Hyperparameter Optimization Jobs

Step 1: Create a custom project with the configuration files:

hpogrid project create <project_name> [--options]

Option	Description
scripts_path	the path to where the training scripts (or the directory containing the training scripts) are located
hpo_config	the hpo configuration to use for this project
grid_config	the grid configuration to use for this project
model_config	the model configuration to use for this project
search_space	the search space configuration to use for this project

Step 2: Run the project:

• To run locally:

hpogrid local_run PROJECT_NAME

• To run on the grid:

hpogrid run <project_name> [--options]

Option	Description	Default
n_jobs	the number of grid jobs to be submitted (useful for random search, i.e. to run a single search point per job)	1
site	the site to where the jobs are submitted (this will override the site setting in the grid configuration	-

Monitoring Job Status

To get the status of recent grid jobs:

hpogrid tasks show [--options]

Option	Description	Default
username	filter tasks by username	1
limit	the maximum number of tasks to query	1000
days	filter tasks within the recent N days	30
taskname	filter tasks by taskname (accept wildcards)	-
jeditaskid	only show the task with the specified jeditaskid	-
metadata	print out the metadata of a task	False
sync	force no caching on the PanDA server	False
range	filter tasks by jeditaskid range	-
output	output result with the filename if specified	-
outcol	data columns to be saved in output	'jeditaskid', 'status', 'taskname', 'computingsite', 'metastruct'

Visualizing Hyperparamter Optimization Results

To get the hpo result for a specific project:

hpogrid report <project_name> [--options]

Option	Description	Default	Choices
limit	the maximum number of tasks to query	1000	-
days	filter tasks within the recent N days	30	-
taskname	filter tasks by taskname (accept wildcards)	-	-
range	filter tasks by jeditaskid range	-	-
extra	extra data columns to be displayed and saved	-	'site', 'task_time_s', 'time_s', 'taskid'
outname	output file name (excluding extension)	hpo_result	-
to_json	output result to a json file	False	-
to_html	output result to an html file	False	-
to_csv	output result to a csvle	False	-
to_pcp	output result as a parallel coordinate plot	False	-

Command Line Options

• To kill a job by jeditaskid

hpogrid tasks kill <jeditaskid>

• To retry a job by jeditaskid

hpogrid tasks retry <jeditaskid>

• To see a list of available GPU sites

hpogrid sites

Important NOTES

Working Directory of Container

• By default, the working directory (where the training scripts are located) of the container is /hpogrid/project/

Location of Input Dataset

• By default, the location of input dataset inside the container is /hpogrid/project/ (same as working directory)

Digest of Grid Site Errors

• To be updated