NOTE: This benchmarking framework is a WORK IN PROGRESS. Check back later for the completed benchmark suite. Please file an issue with any concerns/questions.
Automatic Machine Learning (AutoML) systems automatically build machine learning pipelines or neural architectures in a data-driven, objective, and automatic way. They automate a lot of drudge work in designing machine learning systems, so that better systems can be developed, faster. However, AutoML research is also slowed down by two factors:
-
We currently lack standardized, easily-accessible benchmarking suites of tasks (datasets) that are curated to reflect important problem domains, practical to use, and sufficiently challenging to support a rigorous analysis of performance results.
-
Subtle differences in the problem definition, such as the design of the hyperparameter search space or the way time budgets are defined, can drastically alter a task’s difficulty. This issue makes it difficult to reproduce published research and compare results from different papers.
This toolkit aims to address these problems by setting up standardized environments for in-depth experimentation with a wide range of AutoML systems.
Documentation: https://openml.github.io/automlbenchmark/
- Curated suites of benchmarking datasets from OpenML (TODO: add study link)
- Includes a wide range of AutoML systems
- New AutoML systems can be added as Docker images
- Execute experiments locally or on AWS (see below)
Future plans:
- Automatic sharing of benchmarking results on OpenML.
- Allow tuning of the AutoML systems (hyper-hyperparameters), beyond their default settings.
- More benchmark datasets, and datasets of other types (e.g. regression).
To run the benchmarks, you will need:
- Python 3.5+.
- PIP3: ensure you have a recent version. If necessary, upgrade your pip using
pip3 install --upgrade pip. - The Python libraries listed in requirements.txt: it is strongly recommended to first create a Python virtual environment (cf. also Pyenv: quick install using
curl https://pyenv.run | bashorbrew install pyenv) and work in it if you don't want to mess up your global Python environment. - Docker, if you plan to run the benchmarks in a container.
Clone the repo:
git clone https://github.com/openml/automlbenchmark.git
cd automlbenchmarkOptional: create a Python3 virtual environment.
- NOTE: we don't recommend to create your virtual environment with
virtualenvlibrary here as the application may create additional virtual environments for some frameworks to run in isolation. Those virtual environments are created internally usingpython -m venvand we encountered issues withpipwhenvenvis used on top of avirtualenvenvironment. Therefore, we rather suggest one of the method below:
using venv:
python3 -m venv ./venv
source venv/bin/activate
# remember to call `deactivate` once you're done using the applicationor using pyenv:
pyenv install {python_version: 3.7.4}
pyenv virtualenv ve-automl
pyenv local ve-automlThen pip install the dependencies:
pip3 install -r requirements.txt- NOTE: in case of issues when installing Python requirements, you may want to try the following:
- on some platforms, we need to ensure that requirements are installed sequentially:
xargs -L 1 pip install < requirements.txt. - enforce the
pip3version above in your virtualenv:pip3 install --upgrade pip==19.3.1.
- on some platforms, we need to ensure that requirements are installed sequentially:
To run a benchmark call the runbenchmark.py script with at least the following arguments:
- The AutoML framework that should be evaluated, see frameworks.yaml for supported frameworks. If you want to add a framework see here.
- The benchmark suite to run should be one implemented in benchmarks folder.
- (Optional) If the benchmark should be run
local(default, tested on Linux and macOS only), in adockercontainer or onawsusing multiple ec2 instances.
Examples:
python3 runbenchmark.py
python3 runbenchmark.py constantpredictor
python3 runbenchmark.py tpot test
python3 runbenchmark.py autosklearn test -m docker
python3 runbenchmark.py h2oautoml validation 1h4c -m awsFor the complete list of supported arguments, run:
python3 runbenchmark.py --helpusage: runbenchmark.py [-h] [-m {local,docker,aws}]
[-t [task_id [task_id ...]]]
[-f [fold_num [fold_num ...]]] [-i input_dir]
[-o output_dir] [-u user_dir] [-p parallel_jobs]
[-s {auto,skip,force,only}] [-k [true|false]]
framework [benchmark] [constraint]
positional arguments:
framework The framework to evaluate as defined by default in
resources/frameworks.yaml.
benchmark The benchmark type to run as defined by default in
resources/benchmarks/{benchmark}.yaml or the path to a
benchmark description file. Defaults to `test`.
constraint The constraint definition to use as defined by default in
resources/constraints.yaml. Defaults to `test`.
optional arguments:
-h, --help show this help message and exit
-m {local,docker,aws}, --mode {local,docker,aws}
The mode that specifies how/where the benchmark tasks
will be running. Defaults to local.
-t [task_id [task_id ...]], --task [task_id [task_id ...]]
The specific task name (as defined in the benchmark
file) to run. If not provided, then all tasks from the
benchmark will be run.
-f [fold_num [fold_num ...]], --fold [fold_num [fold_num ...]]
If task is provided, the specific fold(s) to run. If
fold is not provided, then all folds from the task
definition will be run.
-i input_dir, --indir input_dir
Folder where datasets are loaded by default. Defaults
to `input_dir` as defined in resources/config.yaml
-o output_dir, --outdir output_dir
Folder where all the outputs should be written.
Defaults to `output_dir` as defined in
resources/config.yaml
-u user_dir, --userdir user_dir
Folder where all the customizations are stored.
Defaults to `user_dir` as defined in
resources/config.yaml
-p parallel_jobs, --parallel parallel_jobs
The number of jobs (i.e. tasks or folds) that can run
in parallel. Defaults to 1. Currently supported only
in docker and aws mode.
-s {auto,skip,force,only}, --setup {auto,skip,force,only}
Framework/platform setup mode. Defaults to auto.
•auto: setup is executed only if strictly necessary.
•skip: setup is skipped. •force: setup is always
executed before the benchmark. •only: only setup is
executed (no benchmark).
-k [true|false], --keep-scores [true|false]
Set to true [default] to save/add scores in output
directory.
The script will produce output that records task metadata and the result. The result is the score on the test set, where the score is a specific model performance metric (e.g. "AUC") defined by the benchmark.
task framework fold result mode version utc acc auc logloss
0 iris H2OAutoML 0 1.000000 local 3.22.0.5 2019-01-21T15:19:07 1.000000 NaN 0.023511
1 iris H2OAutoML 1 1.000000 local 3.22.0.5 2019-01-21T15:20:12 1.000000 NaN 0.091685
2 kc2 H2OAutoML 0 0.811321 local 3.22.0.5 2019-01-21T15:21:11 0.811321 0.859307 NaN
3 kc2 H2OAutoML 1 0.886792 local 3.22.0.5 2019-01-21T15:22:12 0.886792 0.888528 NaN
The automlbenchmark app currently allows running benchmarks in various environments:
- in a docker container (running locally or on multiple AWS instances).
- completely locally, if the framework is supported on the local system.
- on AWS, possibly distributing the tasks to multiple EC2 instances, each of them running the benchmark either locally or in a docker container.
The Docker image is automatically built before running the benchmark if it doesn't already exist locally or in a public repository (by default in https://hub.docker.com/orgs/automlbenchmark/repositories). Especially, without docker image, the application will need to download and install all the dependencies when building the image, so this may take some time.
The generated image is usually named automlbenchmark/{framework}:{tag}, but this is customizable per framework: cf. resources/frameworks.yaml and HOWTO for details.
For example, this will build a Docker image for the RandomForest framework and then immediately start a container to run the validation benchmark, using all folds, allocating 1h and 4 cores for each task:
python3 runbenchmark.py RandomForest validation 1h4c -m dockerIf the corresponding image already exists locally and you want it to be rebuilt before running the benchmark, then the setup needs to be forced:
python3 runbenchmark.py {framework} {benchmark} {constraint} -m docker -s forceThe image can also be built without running any benchmark:
python3 runbenchmark.py {framework} -m docker -s onlyIn rare cases, mainly for development, you may want to specify the docker image:
python3 runbenchmark.py {framework} {benchmark} {constraint} -m docker -Xdocker.image={image}If docker allows portability, it is still possible to run the benchmarks locally without container on some environments (currently Linux, and macOS for most frameworks).
A minimal example would be to run the test benchmarks with a random forest:
python3 runbenchmark.py RandomForest testThe majority of frameworks though require a setup step before being able to run a benchmark. Please note that this step may take some time depending on the framework.
This setup is executed by default on first run of the framework, but in this case, it is not guaranteed that the benchmark run following immediately will manage to complete successfully (for most frameworks though, it does).
In case of error, just run the benchmark one more time.
If it still fails, you may need to rerun the setup step manually:
python3 runbenchmark.py {framework} -s onlyYou can then run the benchmarks as many times as you wish.
When testing a framework or a new dataset, you may want to run only a single task and a specific fold, for example:
python3 runbenchmark.py TPOT validation -t bioresponse -f 0To run a benchmark on AWS you additionally need to have a configured AWS account. The application is using the boto3 Python package to exchange files through S3 and create EC2 instances.
If this is your first time setting up your AWS account on the machine that will run the automlbenchmark app, you can use the AWS CLI tool and run:
aws configureYou will need your AWS Access Key ID, AWS Secret Access Key, and pick a default EC2 region.
- NOTE: Currently the AMI is only configured for the following regions so you'll have to set your default region as one of these:
- us-east-1
- us-west-1
- eu-west-1
- eu-central-1
On first use, it is recommended to simply copy the config.yaml from examples/aws to your user ~/.config/automlbenchmark folder (or merge it if you already have a config.yaml in this user folder) and follow the instructions in that file.
To run a test to see if the benchmark framework is working on AWS, do the following:
python3 runbenchmark.py constantpredictor test -m awsThis will create and start an EC2 instance for each benchmark job and run the 4 jobs (2 OpenML tasks * 2 folds) from the test benchmark sequentially, each job running for 1mn in this case (excluding setup time for the EC2 instances).
For longer benchmarks, you'll probably want to run multiple jobs in parallel and distribute the work to several EC2 instances, for example:
python3 runbenchmark.py AUTOWEKA validation 1h4c -m aws -p 4will keep 4 EC2 instances running, monitor them in a dedicated thread, and finally collect all outputs from s3.
- NOTE: each EC2 instance is provided with a time limit at startup to ensure that in any case, the instance is stopped even if there is an issue when running the benchmark task. In this case the instance is stopped, not terminated, and we can therefore inspect the machine manually (ideally after resetting its UserData field to avoid re-triggering the benchmark on the next startup).
The console output is still showing the instances starting, outputs the progress and then the results for each dataset/fold combination:
Running `H2OAutoML_nightly` on `validation` benchmarks in `aws` mode
Loading frameworks definitions from ['/Users/me/repos/automlbenchmark/resources/frameworks.yaml'].
Loading benchmark definitions from /Users/me/repos/automlbenchmark/resources/benchmarks/validationt.yaml.
Uploading `/Users/me/repos/automlbenchmark/resources/benchmarks/validation.yaml` to `ec2/input/validation.yaml` on s3 bucket automl-benchmark.
...
Starting new EC2 instance with params: H2OAutoML_nightly /s3bucket/input/validation.yaml -t micro-mass -f 0
Started EC2 instance i-0cd081efc97c3bf6f
[2019-01-22T11:51:32] checking job aws_validation_micro-mass_0_H2OAutoML_nightly on instance i-0cd081efc97c3bf6f: pending
Starting new EC2 instance with params: H2OAutoML_nightly /s3bucket/input/validation.yaml -t micro-mass -f 1
Started EC2 instance i-0251c1655e286897c
...
[2019-01-22T12:00:32] checking job aws_validation_micro-mass_1_H2OAutoML_nightly on instance i-0251c1655e286897c: running
[2019-01-22T12:00:33] checking job aws_validation_micro-mass_0_H2OAutoML_nightly on instance i-0cd081efc97c3bf6f: running
[2019-01-22T12:00:48] checking job aws_validation_micro-mass_1_H2OAutoML_nightly on instance i-0251c1655e286897c: running
[2019-01-22T12:00:48] checking job aws_validation_micro-mass_0_H2OAutoML_nightly on instance i-0cd081efc97c3bf6f: running
...
[ 731.511738] cloud-init[1521]: Predictions saved to /s3bucket/output/predictions/h2oautoml_nightly_micro-mass_0.csv
[ 731.512132] cloud-init[1521]: H2O session _sid_96e7 closed.
[ 731.512506] cloud-init[1521]: Loading predictions from /s3bucket/output/predictions/h2oautoml_nightly_micro-mass_0.csv
[ 731.512890] cloud-init[1521]: Metric scores: {'framework': 'H2OAutoML_nightly', 'version': 'nightly', 'task': 'micro-mass', 'fold': 0, 'mode': 'local', 'utc': '2019-01-22T12:00:02', 'logloss': 0.6498889633819804, 'acc': 0.8793103448275862, 'result': 0.6498889633819804}
[ 731.513275] cloud-init[1521]: Job local_micro-mass_0_H2OAutoML_nightly executed in 608.534 seconds
[ 731.513662] cloud-init[1521]: All jobs executed in 608.534 seconds
[ 731.514089] cloud-init[1521]: Scores saved to /s3bucket/output/scores/H2OAutoML_nightly_task_micro-mass.csv
[ 731.514542] cloud-init[1521]: Loaded scores from /s3bucket/output/scores/results.csv
[ 731.515006] cloud-init[1521]: Scores saved to /s3bucket/output/scores/results.csv
[ 731.515357] cloud-init[1521]: Summing up scores for current run:
[ 731.515782] cloud-init[1521]: task framework ... acc logloss
[ 731.516228] cloud-init[1521]: 0 micro-mass H2OAutoML_nightly ... 0.87931 0.649889
[ 731.516671] cloud-init[1521]: [1 rows x 9 columns]
...
EC2 instance i-0cd081efc97c3bf6f is stopped
Job aws_validation_micro-mass_0_H2OAutoML_nightly executed in 819.305 seconds
[2019-01-22T12:01:34] checking job aws_validation_micro-mass_1_H2OAutoML_nightly on instance i-0251c1655e286897c: running
[2019-01-22T12:01:49] checking job aws_validation_micro-mass_1_H2OAutoML_nightly on instance i-0251c1655e286897c: running
EC2 instance i-0251c1655e286897c is stopping
Job aws_validation_micro-mass_1_H2OAutoML_nightly executed in 818.463 seconds
...
Terminating EC2 instances i-0251c1655e286897c
Terminated EC2 instances i-0251c1655e286897c with response {'TerminatingInstances': [{'CurrentState': {'Code': 32, 'Name': 'shutting-down'}, 'InstanceId': 'i-0251c1655e286897c', 'PreviousState': {'Code': 64, 'Name': 'stopping'}}], 'ResponseMetadata': {'RequestId': 'd09eeb0c-7a58-4cde-8f8b-2308a371a801', 'HTTPStatusCode': 200, 'HTTPHeaders': {'content-type': 'text/xml;charset=UTF-8', 'transfer-encoding': 'chunked', 'vary': 'Accept-Encoding', 'date': 'Tue, 22 Jan 2019 12:01:53 GMT', 'server': 'AmazonEC2'}, 'RetryAttempts': 0}}
Instance i-0251c1655e286897c state: shutting-down
All jobs executed in 2376.891 seconds
Deleting uploaded resources `['ec2/input/validation.yaml', 'ec2/input/config.yaml', 'ec2/input/frameworks.yaml']` from s3 bucket automl-benchmark.
By default, a benchmark run creates the following subdirectories and files in the output directory (by default a subdirectory of ./results with unique name identifying the benchmark run):
scores: this subdirectory containsresults.csv: a global scoreboard, keeping scores from all benchmark runs. For safety reasons, this file is automatically backed up toscores/backup/results_{currentdate}.csvby the application before any modification.- individual score files keeping scores for each framework+benchmark combination (not backed up).
predictions, this subdirectory contains the last predictions in a standardized format made by each framework-dataset combination. Those last predictions are systematically backed up with current data topredictions/backupsubdirectory before a new prediction is written.logs: this subdirectory contains logs produced by theautomlbenchmarkapp, including when it's been run in Docker container or on AWS.
see HOWTO
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent