The Data Scientist in a Box (DSBox) system is an AutoML system that automates the generation of machine learning pipelines, including data augmentation, data cleaning, data featurization, model selection and hyperparameter tuning. DSBox uses a highly customizable pipeline template representation that allows advance users to easily incorporate their machine learning knowledge to configure the pipeline search space. For non-expert users DSBox generates high quality machine learning models with minimal need of intervention.
DSBox is part of the Data Driven Discovery of Models (D3M) program. More information about the program and about related projects can be found at the D3M website.
This is the final release version of DSBox AutoML system. The dockerlized version is available at https://hub.docker.com/repository/docker/ckxz105/dsbox-ta2 (public). This docker has all primitives installed and ready to run except some speicial primitives that required static files.
If running with some special primitives(most are images/audio related), you need to download the static files.
To download them, please add extra python3 -m d3m index download inside the docker build file (available at dockerBuildFiles folder of this repo).
To use the dsbox-ta2 system only. Run with
docker run --entrypoint /user_opt/client.sh \
--name $docker_name \
-m 200G \
--shm-size=50g \
--cpus=20 \
-e D3MRUN=ta2 \
-e D3MINPUTDIR=/input \
-e D3MOUTPUTDIR=/output \
-e D3MLOCALDIR=/tmp \
-e D3MSTATICDIR=/static \
-e D3MPROBLEMPATH=/input/TRAIN/problem_TRAIN/problemDoc.json \
-e CUDA_VISIBLE_DEVICES=$gpu_id \
-e D3MCPU=$D3MCPU \
-e D3MRAM=50 \
-e D3MTIMEOUT=$D3MTIMEOUT \
-e DSBOX_LOGGING_LEVEL="dsbox=DEBUG:console_logging_level=WARNING:file_logging_level=DEBUG" \
-v ${dataset_dir}/${problem}:/input \
-v ${output_dir}:/output \
-v /data/1/dsbox/static_files/static/:/static \
$docker_image
The cpu / memory size can be modified by the users. According to different problems given. The logging level can also be changed. The important part is to sent corresponding dataset path {dataset_dir}, problem name {problem} and output path {output_dir}.
The input dataset should follow the requirement of D3M projects.
There is one sample dataset available at dsbox-ta2/unit_tests/resources
For detail structures of the dataset, please refer to https://gitlab.com/datadrivendiscovery/data-supply/-/tree/v4.0.0 (public repo)
Existed datasets: https://gitlab.datadrivendiscovery.org/d3m/datasets. (D3M users only)
The main focus output will be some json pipeline files at output/pipelines_ranked. Those pipeline files can then be used to run and make predictions. For detail using of those pipelines, please refer to https://gitlab.com/datadrivendiscovery/d3m/-/tree/v2020.1.9/docs
There is also a score folder which has the prediction scores based on the score dataset. If the score file is empty, it means the pipeline failed.