autogluon.bench 0.0.3

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AutoGluon-Bench

Welcome to AutoGluon-Bench, a suite for benchmarking your AutoML frameworks.

Setup

Follow the steps below to set up autogluon-bench:

# create virtual env
python3.9 -m venv .venv_agbench
source .venv_agbench/bin/activate

Install autogloun-bench from PyPI:

python -m pip install autogluon.bench

Or install autogluon-bench from source:

git clone https://github.com/autogluon/autogluon-bench.git
cd autogluon-bench

# install from source in editable mode
pip install -e .

Also, replace all pip install autogluon.bench with pip install -e . in the source code.

Run benchmarks locally

To run the benchmarks on your local machine, use the following command:

agbench run path/to/local_config_file

Check out our sample configuration file for local runs.

The results are stored in the following directory: {WORKING_DIR}/{root_dir}/{module}/{benchmark_name}_{timestamp}.

Tabular Benchmark

To perform tabular benchmarking, set the module to tabular. You must set both Benchmark Configurations and Tabular Specific configurations, and each should have a single value. Refer to the sample configuration file for more details.

The tabular module leverages the AMLB benchmarking framework. Required and optional AMLB arguments are specified via the configuration file mentioned previously.

To benchmark a custom branch of AutoGluon on tabular module, use amlb_custom_branch: https://github.com/REPO/autogluon#BRANCH in the configuration file.

Multimodal Benchmark

For multimodal benchmarking, set the module to multimodal. We currently support benchmarking multimodal on a custom branch. Note that multimodal benchmarking directly calls the MultiModalPredictor, bypassing the extra layer of AMLB. Therefore, the required arguments are different from those for tabular.

You can add more datasets to your benchmarking jobs. We provided sample multimodal datasets and object detection dataset. Follow these samples to add custom datasets, then specify dataset_name in your local config file. Please follow the section Install From Source for more instructions on how to develop with source.

Run benchmarks on AWS

AutoGluon-Bench uses the AWS CDK to build an AWS Batch compute environment for benchmarking.

To get started, install Node.js and AWS CDK with the following commands:

curl https://raw.githubusercontent.com/creationix/nvm/master/install.sh | bash  # replace bash with other shell (e.g. zsh) if you are using a different one
source ~/.bashrc
nvm install 18.16.0  # install Node.js
npm install -g aws-cdk  # install aws-cdk
cdk --version  # verify the installation, you might need to update the Node.js version depending on the log.

To initiate benchmarking on the cloud, use the command below:

agbench run /path/to/cloud_config_file

This command automatically sets up an AWS Batch environment using instance specifications defined in the cloud_config_file. It also creates a lambda function named with your chosen LAMBDA_FUNCTION_NAME. This lambda function is automatically invoked with the cloud config file you provided, submitting multiple AWS Batch jobs to the job queue (named with the PREFIX you provided).

In order for the Lambda function to submit multiple jobs simultaneously, you need to specify a list of values for each module-specific key. Each combination of configurations is saved and uploaded to your specified METRICS_BUCKET in S3, stored under S3://{METRICS_BUCKET}/configs/{BENCHMARK_NAME}_{timestamp}/{BENCHMARK_NAME}_split_{UID}.yaml. Here, UID is a unique ID assigned to the split.

The AWS infrastructure configurations and submitted job IDs are saved locally at {WORKING_DIR}/{root_dir}/{module}/{benchmark_name}_{timestamp}/aws_configs.yaml. You can use this file to check the job status at any time:

agbench get-job-status --config-file /path/to/aws_configs.yaml

You can also check the job status using job IDs:

agbench get-job-status --job-ids JOB_ID_1 --job-ids JOB_ID_2 —cdk_deploy_region AWS_REGION

Job logs can be viewed on the AWS console. Each job has an UID attached to the name, which you can use to identify the respective config split. After the jobs are completed and reach the SUCCEEDED status in the job queue, you'll find metrics saved under S3://{METRICS_BUCKET}/{module}/{benchmark_name}_{timestamp}/{benchmark_name}_{timestamp}_{UID}.

By default, the infrastructure created is retained for future use. To automatically remove resources after the run, use the --remove_resources option:

agbench run path/to/cloud_config_file --remove_resources

This will check the job status every 2 minutes and remove resources after all jobs succeed. If any job fails, resources will be kept.

If you want to manually remove resources later, use:

agbench destroy-stack STATIC_RESOURCE_STACK_NAME BATCH_STACK_NAME CDK_DEPLOY_ACCOUNT CDK_DEPLOY_REGION

where you can find all argument values in {WORKING_DIR}/{root_dir}/{module}/{benchmark_name}_{timestamp}/aws_configs.yaml.

Configure the AWS infrastructure

The default infrastructure configurations are located here.

CDK_DEPLOY_ACCOUNT: dummy
CDK_DEPLOY_REGION: dummy
PREFIX: ag-bench-test
RESERVED_MEMORY_SIZE: 15000
MAX_MACHINE_NUM: 20
BLOCK_DEVICE_VOLUME: 100
INSTANCE: g4dn.2xlarge
METRICS_BUCKET: autogluon-benchmark-metrics
DATA_BUCKET: automl-mm-bench
VPC_NAME: automm-batch-stack/automm-vpc
LAMBDA_FUNCTION_NAME: ag-bench-test-job-function

where:

• CDK_DEPLOY_ACCOUNT and CDK_DEPLOY_REGION should be overridden with your AWS account ID and desired region to create the stack.
• PREFIX is used as an identifier for the stack and resources created.
• RESERVED_MEMORY_SIZE is used together with the instance memory size to calculate the container shm_size.
• BLOCK_DEVICE_VOLUME is the size of storage device attached to instance.
• METRICS_BUCKET is the bucket to upload benchmarking metrics.
• DATA_BUCKET is the bucket to download dataset from.
• VPC_NAME is used to look up an existing VPC.
• LAMBDA_FUNCTION_NAME lambda function to submit jobs to AWS Batch.

To override these configurations, use the cdk_context key in your custom config file. See our sample cloud config for reference.

Evaluating bechmark runs

Innixma's autogluon-benchmark repository can be used to evaluate tabular benchmark runs whose results are in S3. Using these utilities is ad-hoc at this time, but in a coming release we will integrate this capability into autogluon-bench and support evaulation of multimodal benchmarks.

Evaluation Steps

Clone the autogluon-benchmark repository:

git clone https://github.com/gidler/autogluon-benchmark.git

Confirm that AWS credentials are setup for the AWS account that has the benchmark results in S3.

Run the aggregate_all.py script

python scripts/aggregate_all.py --s3_bucket {AWS_BUCKET} --s3_prefix {AWS_PREFIX} --version_name {BENCHMARK_VERSION_NAME}

# example: python scripts/aggregate_all.py --s3_bucket autogluon-benchmark-metrics --s3_prefix tabular/ --version_name test_local_20230330T180916

This will create a new file in S3 with this signature:

s3://{AWS_BUCKET}/aggregated/{AWS_PREFIX}/{BENCHMARK_VERSION_NAME}/results.csv

Run the run_generate_clean_openml python utility. You will need to manually set the run_name_arg and path_prefix variables in the script.

python autogluon_benchmark/evaluation/runners/run_generate_clean_openml.py 

This will create a local file of results in the data/results/input/prepared/openml/ directory.

Run the benchmark_evaluation python script. You will need to manually update the frameworks_run and paths variables in the script.

python autogluon_benchmark/evaluation/runners/run_evaluation_openml.py