The biggest, baddest pipelines around.
./sbt/sbt assembly
make # This builds the native libraries used in KeystoneML
# Get the data from S3
wget http://mnist-data.s3.amazonaws.com/train-mnist-dense-with-labels.data
wget http://mnist-data.s3.amazonaws.com/test-mnist-dense-with-labels.data
KEYSTONE_MEM=4g ./bin/run-pipeline.sh \
keystoneml.pipelines.images.mnist.MnistRandomFFT \
--trainLocation ./train-mnist-dense-with-labels.data \
--testLocation ./test-mnist-dense-with-labels.data \
--numFFTs 4 \
--blockSize 2048
To run KeystoneML pipelines on large datasets you will need a Spark cluster. KeystoneML pipelines run on the cluster using spark-submit.
You need to export SPARK_HOME to run KeystoneML using spark-submit. Having done
that you can similarly use run-pipeline.sh to launch your pipeline.
export SPARK_HOME=~/spark-1.3.1-bin-cdh4 # should match the version keystone is built with
KEYSTONE_MEM=4g ./bin/run-pipeline.sh \
keystoneml.pipelines.images.mnist.MnistRandomFFT \
--trainLocation ./train-mnist-dense-with-labels.data \
--testLocation ./test-mnist-dense-with-labels.data \
--numFFTs 4 \
--blockSize 2048