KingMaker

KingMaker is the workflow management for producing ntuples with the CROWN framework. The workflow management is based on law, which is using luigi as backend.

⚠ Important: A detailed description of the KingMaker workflow to produce NTuples can be found in the CROWN documentation.

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The ML_train workflow

The ML_train workflow

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Workflow

The ML_train workflow currently contains a number of the tasks necessary for the htt-ml analysis. The-NMSSM analysis is currently not yet supported. It should be noted, that all created files are stored in remote storage and might be subject to file caching under certain circumstances. The tasks , located in are:

1. CreateTrainingDataShard Remote workflow task that creates the process-datasets for the machine learning tasks from the config files you provide. The task uses the ntuples and friend trees described in the Sample setup. These dependencies are currently not checked by LAW. Also uses the create_training_datashard script.
   The task branches each return a root file that consists of one fold of one of the processes described in the provided configs. These files can then be used for the machine learning tasks.
2. RunTraining Remote workflow task that performs the neural network training using GPU resources if possible. Uses the Tensorflow_training script. The hyperparameters of this training are set by the provided config files.
   Each branch task returns a set of files for one fold of one training specified in the configs. Each set includes the trained .h5 model, the preprocessing object as a .pickle file and a graph of the loss as a .pdf and .png. The task also returns a set of files that can be used with the lwtnn package.
3. RunTesting Remote workflow task that performs a number of tests on the trained neural network using GPU resources if possible. Uses the ml_tests scripts. The tests return a number plots and their .json files in a tarball, which is copied to the remote storage. The plots include confusion, efficiency, purity, 1D-taylor and taylor ranking.
4. RunAllAnalysisTrainings Task to run all trainings described in the configs.

Run ML_train

Normally, the ML_train workflow can be run by running the RunAllAnalysisTrainings task:

law run RunAllAnalysisTrainings --analysis-config <Analysis configs>

Alternatively a single training/testing can be performed by using the RunTraining/RunTesting task directly:

law run RunTraining --training-information '[["<Training name>","<Training configs>"]]'
law run RunTesting --training-information '[["<Training name>","<Training configs>"]]'

Similarly it is possible to create only a single data-shard:

law run CreateTrainingDataShard --datashard-information '[["<Process name>", "<Process class>"]]' --process-config-dirs '["<Process dir>"]'

An example of how the above scripts could look like with the example configs:

law run RunAllAnalysisTrainings --analysis-config ml_configs/example_configs/sm.yaml
law run RunTraining --training-information '[["sm_2018_mt","ml_configs/example_configs/trainings.yaml"]]'
law run RunTesting --training-information '[["sm_2018_mt","ml_configs/example_configs/trainings.yaml"]]'
law run CreateTrainingDataShard --datashard-information '[["2018_mt_ggh", "ggh"]]' --process-config-dirs '["ml_configs/example_configs/processes"]'

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Configurations, not set in the ml_config config files.

There are a number of parameters to be set in the luigi and law config files:

• ENV_NAME: The Environment used in all non-batch jobs. Can be set individually for each batch job.
• additional_files: What files should be transfered into a batch job in addition to the usual ones (lawluigi_configs, processor and law).
• production_tag: Can be any string. Used to differentiate the runs. Default is a unique timestamp.
• A number of htcondor specific settings that can be adjusted if necessary.

Useful command line arguments:

1. --workers; The number of tasks that are handled simultaneously.
2. --print-status -1; Return the current status of all tasks involved in the workflow.
3. --remove-output -1; Remove all output files of tasks.

Old Setup readme

Setup

Setting up KingMaker should be straight forward:

git clone --recursive git@github.com:KIT-CMS/KingMaker.git
cd KingMaker
source setup.sh <Analysis Name>

this should setup the environment specified in the luigi.cfg file (located at lawluigi_configs/<Analysis Name>_luigi.cfg), which includes all needed packages. The environment is sourced from the conda instance located at /cvmfs/etp.kit.edu/LAW_envs/conda_envs/miniconda/ if possible. If the relevant environment is not available this way, the environment will be set up in a local conda instance. The environment files are located at conda_environments/<Analysis Name>_env.cfg. In addition other files are installed depending on the analysis.

A list of available analyses can be found in the setup.sh script or by running

source setup.sh -l

In addition a luigid scheduler is also started if there isn't one running already.

When setting up an already cloned version, a

source setup.sh <Analysis Name>

is enough.