Have you ever wanted to run your EC algorithm in the cloud? Discouraged by the complexity of EC2? We will deploy your EC algorithm on the cloud for you with our FCUBE framework!
FCUBE supports a Bring Your Own Learner (BYOL) model: it deploys your EC algorithm to hundreds of machines and does all the data management for you. No scripts, no launch hassles, no tedious result collection. FCUBE is (EC) deployment as a service:
For this activity, our goal is to unite the developers of interesting EC classifier algorithms to solve relevant problems of public domain. We seek an experienced informed discussion on the various approaches and techniques without being distracted by one problem at hand. Therefore, we have set up the following format:
In a first step, you will adapt your learner to be compliant with FCUBE's interface; we provide an example learner GPFunction (one of our Java learners for numerical features) together with a split of the higgs dataset that you can use to debug yours:
Example with our GPFunction learner:
$ java -jar gpfunction.jar -train higgs_noheader_02.csv -minutes 10 -properties params.properties
Example: the GPFunction learner produces several models. Let is pick the model called mostAccurate.txt and generate predictions for the same split higgs_noheader_02.csv.
$ java -jar gpfunction.jar -predict higgs_noheader_02.csv -model mostAccurate.txt -o predictions.csv
The executable gpfunction.jar will generate a csv file named predictions.csv containing one label per line.
Once we have the final number of participants, each participant will be assigned a budget in Amazon EC2. Then, each participant will be asked to choose a combination of:
In the last step prior to deployment, participants will have the option to expose a range of possible choices for their learner-specific parameters. This way, it will possible to assign different parameters to the different instances running on the cloud. More details to come on this aspect.
Finally, we will deploy your learners in EC2. We will analyze the predictions of your learner and communicate performance metrics.
In this edition of the workshop, we will target binary classification problems. For each dataset, we will release samples of different sizes. These samples will allow to estimate the running time of the classifier learning algorithms given the size of the data.
The Higgs dataset is a public dataset. It is composed of 11000000 exemplars and 28 real-valued features. Expect a CSV file with any number of features where the last column is the label (0 or 1).
The BP dataset is a dataset composed of 4569200 exemplars. The goal is to predict acute hypotension episodes from physiological signals of thousands of patients. These signals are processed to identify beats and extract 100 real-valued features from those beats. Expect a CSV file with any number of features where the last column is the label (0 or 1).
The Contact Map dataset comes from the Protein Structure Prediction field, and it was originally generated to train a predictor for the residue-residue contact prediction track of the CASP9 competiton. The dataset has 32 million instances, 631 attributes, 2 classes, 98% of negative examples. For this activity, we will only consider the numerical features of this dataset (539 real-valued features). Expect a CSV file with any number of features where the last column is the label (0 or 1).
Having trouble with the adaptation of your learner? Feel free to contact us by email at email@example.com and we’ll try to help you sort it out.
This collaborative activity is organized by the Any-Scale Learning For All (ALFA) group at MIT.