DARPA D3M TA1 primitives written by ArrayFire. These primitives are built such that it utilizes the GPU to accelerate computation by using the arrayfire-python package.

The following command assumes that you have the following:

• Docker installed
• nvidia-docker installed
• A location in your file system that a D3M docker container can bind-mount and access
• This repository accessible from the docker bind-mount location
• The D3M datasets repository accessible from the docker bind-mount location (this can be cloned from the public D3M datasets or the internal version of it)
• Inside the D3M datasets repository, the 185_baseball dataset's learningData.csv downloaded through git LFS (see the README of the datasets repo)

Change the following in the command according to your environment:

• -v parameter of docker run
• -r, -i, -t, -a, -p parameters of python3 -m d3m runtime fit-score

docker run \
    --gpus all \
    --rm \
    -v /home/mark/Documents/d3m:/mnt/d3m \
    registry.gitlab.com/datadrivendiscovery/images/primitives:ubuntu-bionic-python36-v2020.1.9 \
    /bin/bash -c \
        "pip3 install -e /mnt/d3m/d3m-arrayfire-primitives && \
        LD_PRELOAD=/opt/arrayfire/lib64/libafcuda.so \
        LD_LIBRARY_PATH=/opt/arrayfire/lib64 \
        python3 -m d3m \
            runtime \
            fit-score \
                -r /mnt/d3m/datasets/seed_datasets_current/185_baseball/185_baseball_problem/problemDoc.json \
                -i /mnt/d3m/datasets/seed_datasets_current/185_baseball/TRAIN/dataset_TRAIN/datasetDoc.json \
                -t /mnt/d3m/datasets/seed_datasets_current/185_baseball/TEST/dataset_TEST/datasetDoc.json \
                -a /mnt/d3m/datasets/seed_datasets_current/185_baseball/SCORE/dataset_TEST/datasetDoc.json \
                -p /mnt/d3m/d3m-arrayfire-primitives/pipelines/classification.logistic_regression.ArrayFire/d1523250-1597-4f71-bebf-738cb6e58217.json"

Notice that for now, LD_PRELOAD and LD_LIBRARY_PATH has to be defined for the d3m runtime to correctly load the ArrayFire libraries.

These primitives can be used on image datasets (such as 124_120_mnist), but the pipeline for accomplishing this must use the data_preprocessing.ndarray_flatten.Common primitive as a step after data_preprocessing.image_reader.Common. This primitive is currently in a merge request to the d3m common-primitives repo. Note that this is necessary as well for other primitives (such as sklearn-wrap's version of the logit classifier primitive) when dealing with image datasets. See the 90630b51-52b1-4439-b1bf-eb470d6a88b4.yml pipeline for an example.