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| name: CI | ||
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| on: | ||
| push: | ||
| branches: [ master ] | ||
| pull_request: | ||
| branches: [ master ] | ||
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| workflow_dispatch: | ||
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| jobs: | ||
| delphes-tf: | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - uses: actions/checkout@v2 | ||
| - name: Install python deps | ||
| run: | | ||
| sudo apt install python3 python3-pip wget | ||
| sudo python3 -m pip install --upgrade pip | ||
| sudo python3 -m pip install --upgrade setuptools | ||
| sudo python3 -m pip install tensorflow==2.3 setGPU sklearn matplotlib mplhep pandas scipy uproot3 uproot3-methods awkward0 keras-tuner networkx | ||
| - name: Run delphes TF model | ||
| run: ./scripts/local_test_delphes_tf.sh | ||
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| cms-tf: | ||
| runs-on: ubuntu-latest | ||
| steps: | ||
| - uses: actions/checkout@v2 | ||
| - name: Install python deps | ||
| run: | | ||
| sudo apt install python3 python3-pip wget | ||
| sudo python3 -m pip install --upgrade pip | ||
| sudo python3 -m pip install --upgrade setuptools | ||
| sudo python3 -m pip install tensorflow==2.3 setGPU sklearn matplotlib mplhep pandas scipy uproot3 uproot3-methods awkward0 keras-tuner networkx | ||
| - name: Run CMS TF model | ||
| run: ./scripts/local_test_cms_tf.sh |
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| [](https://travis-ci.org/jpata/particleflow) | ||
| [](https://doi.org/10.5281/zenodo.4452283) | ||
| [](https://github.com/jpata/particleflow/actions) | ||
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| Notes on modernizing CMS particle flow with machine learning. Internal documentation and results can be found at https://twiki.cern.ch/twiki/bin/view/CMS/MLParticleFlow. | ||
| <p float="left"> | ||
| <img src="delphes/plots/event.png" alt="Simulated event" width="600"/> | ||
| </p> | ||
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| Quickstart with training: | ||
| <p float="left"> | ||
| <img src="delphes/plots/num_particles.png" alt="Particle multiplicity" width="300"/> | ||
| <img src="delphes/plots/res_pid2.png" alt="Neutral hadron resolution" width="300"/> | ||
| </p> | ||
|
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| ``` | ||
| #get the code | ||
| git clone https://github.com/jpata/particleflow.git | ||
| cd particleflow | ||
| ## MLPF with Delphes | ||
|
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||
| #run a small local test including data prep and training | ||
| ./scripts/local_test.sh | ||
| ``` | ||
| Short instructions with a single test file in [notebooks/delphes-tf-mlpf-quickstart.ipynb](notebooks/delphes-tf-mlpf-quickstart.ipynb). | ||
|
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||
| # Overview | ||
| Long instructions for reproducing the full training from scratch in [README_delphes.md](README_delphes.md). | ||
| The plots can be generated using the notebook [delphes/resolution_checks.ipynb](delphes/resolution_checks.ipynb). | ||
|
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||
| - [x] set up datasets and ntuples for detailed PF analysis | ||
| - [x] advanced CMSSW version with generator truth in [Validation/RecoParticleFlow/PFAnalysis.cc](https://github.com/jpata/cmssw/blob/jpata_pfntuplizer/Validation/RecoParticleFlow/plugins/PFAnalysis.cc) | ||
| - [x] reproduce existing PFCandidates with machine learning | ||
| - [x] end-to-end training of elements to MLPF-candidates using GNN-s | ||
| - [ ] reconstruct genparticles directly from detector elements a la HGCAL, neutrino experiments etc | ||
| - [x] set up datasets for regression genparticles from elements | ||
| - [x] Develop a baseline ML-PF model that is able to regress pions and neutral hadrons | ||
| - [ ] develop improved loss function for event-to-event comparison: EMD, GAN | ||
| - [ ] Improve ML-PF model physics performance | ||
| - [ ] Improve ML-PF model computational performance | ||
| - [x] Create CMSSW EDProducer for ML-PF particles | ||
| - [x] GPU-evaluation of MLPFProducer in CMSSW | ||
| - [x] Implement a simple tensorflow based ML-PF training for evalutation in CMSSW | ||
| - [ ] GPU code for existing PF algorithms | ||
| - [x] test CLUE for element to block clustering | ||
| - [ ] port CLUE to PFBlockAlgo in CMSSW | ||
| - [ ] parallelize PFAlgo calls on blocks | ||
| - [ ] GPU-implementation of PFAlgo | ||
| - [ ] GPU-implementation of PFBlockAlgo distances | ||
| ### Delphes dataset | ||
| The dataset is available from zenodo: https://doi.org/10.5281/zenodo.4452283. | ||
|
|
||
| ## Presentations | ||
|
|
||
| - CMS ML Forum, 2020-09-30: https://indico.cern.ch/event/952419/contributions/4041555/attachments/2113070/3554608/2020_09_30.pdf | ||
| - CMS ML Town Hall, 2020-07-03: https://indico.cern.ch/event/922319/contributions/3928284/attachments/2068518/3472668/2020_07_02.pdf | ||
| - FastML meeting, 2020-05-29: https://indico.cern.ch/event/923986/contributions/3883991/attachments/2047940/3431648/2020_05_28.pdf | ||
| - CMS PF group, 2020-05-22: https://indico.cern.ch/event/921949/contributions/3873351/attachments/2042984/3422056/2020_05_22.pdf | ||
| - CMS scouting group, 2020-05-15: https://indico.cern.ch/event/894101/contributions/3862093/attachments/2038019/3412747/2020_05_13.pdf | ||
| - CMS PF group, 2020-04-24: https://indico.cern.ch/event/912351/contributions/3839281/attachments/2026117/3389540/2020_04_24.pdf | ||
| - Caltech CMS group ML meeting, 2020-04-16: https://indico.cern.ch/event/909688/contributions/3826957/attachments/2021475/3380133/2020_04_15_caltechml.pdf | ||
| - ML4RECO meeting, 2020-04-09: https://indico.cern.ch/event/908361/contributions/3821957/attachments/2017888/3373038/2020_04_08.pdf | ||
| - CMS PF group, 2020-03-13: https://indico.cern.ch/event/897397/contributions/3786360/attachments/2003108/3344534/2020_03_13.pdf | ||
| - ML4RECO meeting, 2020-03-12: https://indico.cern.ch/event/897281/contributions/3784715/attachments/2002839/3343921/2020_03_12.pdf | ||
| - ML4RECO meeting, 2020-03-04: https://indico.cern.ch/event/895228/contributions/3776739/attachments/1998928/3335497/2020_03_04.pdf | ||
| - CMS PF group, 2020-02-28: https://indico.cern.ch/event/892992/contributions/3766807/attachments/1995348/3328771/2020_02_28.pdf | ||
| - CMS PF group, 2020-01-31: https://indico.cern.ch/event/885043/contributions/3730304/attachments/1979098/3295074/2020_01_30_pf.pdf | ||
| - FNAL HGCAL ML meeting, 2020-01-30: https://indico.cern.ch/event/884801/contributions/3730336/attachments/1978912/3294638/2020_01_30.pdf | ||
| - Caltech group meeting, 2020-01-28: https://indico.cern.ch/event/881683/contributions/3714961/attachments/1977131/3291096/2020_01_21.pdf | ||
| - CMS PF group, 2020-01-17: https://indico.cern.ch/event/862200/contributions/3706909/attachments/1971145/3279010/2020_01_16.pdf | ||
| - CMS PF group, 2019-11-22: https://indico.cern.ch/event/862195/contributions/3649510/attachments/1949957/3236487/2019_11_22.pdf | ||
| - CMS PF group, 2019-11-08: https://indico.cern.ch/event/861409/contributions/3632204/attachments/1941376/3219105/2019_11_08.pdf | ||
| - Caltech ML meeting, 2019-10-31: https://indico.cern.ch/event/858644/contributions/3623446/attachments/1936711/3209684/2019_10_07_pf.pdf | ||
| - Caltech ML meeting, 2019-09-19: https://indico.cern.ch/event/849944/contributions/3572113/attachments/1911520/3158764/2019_09_18_pf_ml.pdf | ||
| - CMS PF group, 2019-09-10: https://indico.cern.ch/event/846887/contributions/3557300/attachments/1904664/3145310/2019_09_10_pf_refactoring.pdf | ||
| - Caltech ML meeting, 2019-09-05: https://indico.cern.ch/event/845349/contributions/3554787/attachments/1902837/3141723/2019_09_05_pfalgo.pdf | ||
|
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||
| In case the above links do not load, the presentations are also mirrored on the following CERNBox link: https://cernbox.cern.ch/index.php/s/GkIRJU1YZuai4ix | ||
|
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||
| ## Other relevant issues, repos, PR-s: | ||
|
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||
| - https://github.com/jpata/cmssw/issues/56 | ||
| - https://github.com/cms-sw/cmssw/pull/29361 | ||
|
|
||
| ## Setting up the code | ||
|
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||
| CMSSW recipe from [setup.sh](test/setup.sh): | ||
|
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| ```bash | ||
| source /cvmfs/cms.cern.ch/cmsset_default.sh | ||
| export SCRAM_ARCH=slc7_amd64_gcc820 | ||
|
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| scramv1 project CMSSW CMSSW_11_1_0_pre5 | ||
| cd CMSSW_11_1_0_pre5/src | ||
| eval `scramv1 runtime -sh` | ||
| git cms-init | ||
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| git remote add -f jpata https://github.com/jpata/cmssw | ||
| git fetch -a jpata | ||
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| git cms-addpkg RecoParticleFlow/PFProducer | ||
| git cms-addpkg Validation/RecoParticleFlow | ||
| git cms-addpkg SimGeneral/CaloAnalysis/ | ||
| git cms-addpkg SimGeneral/MixingModule/ | ||
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| git checkout -b jpata_pfntuplizer --track jpata/jpata_pfntuplizer | ||
|
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||
| #just to get an exact version of the code | ||
| git checkout 0fdcc0e8b6d848473170f0dc904468fa8a953aa8 | ||
|
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||
| #download the MLPF weight file | ||
| mkdir -p RecoParticleFlow/PFProducer/data/mlpf/ | ||
| wget http://login-1.hep.caltech.edu/~jpata/particleflow/2020-05/models/mlpf_2020_05_19.pb -O RecoParticleFlow/PFProducer/data/mlpf/mlpf_2020_05_19.pb | ||
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| scram b | ||
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| #Run a small test of ML-PF | ||
| cmsRun RecoParticleFlow/PFProducer/test/mlpf_producer.py | ||
| edmDumpEventContent test.root | grep -i mlpf | ||
|
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| #Run ML-PF within the reco framework up to ak4PFJets / ak4MLPFJets | ||
| cmsDriver.py step3 --runUnscheduled --conditions auto:phase1_2021_realistic \ | ||
| -s RAW2DIGI,L1Reco,RECO,RECOSIM,EI,PAT \ | ||
| --datatier MINIAODSIM --nThreads 1 -n 10 --era Run3 \ | ||
| --eventcontent MINIAODSIM --geometry=DB.Extended \ | ||
| --filein /store/relval/CMSSW_11_0_0_patch1/RelValQCD_FlatPt_15_3000HS_14/GEN-SIM-DIGI-RAW/PU_110X_mcRun3_2021_realistic_v6-v1/20000/087F3A84-A56F-784B-BE13-395D75616CC5.root \ | ||
| --customise RecoParticleFlow/PFProducer/mlpfproducer_customize.customize_step3 \ | ||
| --fileout file:step3_inMINIAODSIM.root | ||
| ``` | ||
|
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| ## Datasets | ||
|
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| - May 2020 | ||
| - TTbar with PU for PhaseI, privately generated, 20k events | ||
| - flat ROOT: `/storage/group/gpu/bigdata/particleflow/TTbar_14TeV_TuneCUETP8M1_cfi/pfntuple_*.root` | ||
| - pickled graph data: `/storage/group/gpu/bigdata/particleflow/TTbar_14TeV_TuneCUETP8M1_cfi/raw/*.pkl` | ||
| - processed pytorch: `/storage/user/jpata/particleflow/data/TTbar_14TeV_TuneCUETP8M1_cfi/processed/*.pt` | ||
| - processed TFRecord: `/storage/group/gpu/bigdata/particleflow/TTbar_14TeV_TuneCUETP8M1_cfi/tfr2/cand/*.tfrecords` | ||
|
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| ## Creating the datasets | ||
|
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| ```bash | ||
| cd mlpf/data | ||
| mkdir TTbar_14TeV_TuneCUETP8M1_cfi | ||
| python prepare_args.py > args.txt | ||
| condor_submit genjob.jdl | ||
| ``` | ||
|
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| ## Contents of the flat ROOT output ntuple | ||
|
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| The ROOT ntuple contains all PFElements, PFCandidates and GenParticles, along with the links. The following code creates the networkx graph data and a normalized data table: | ||
|
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| ```bash | ||
| #process a single file from ROOT to pickle, saving each event into a separate file | ||
| python test/postprocessing2.py --input data/TTbar_14TeV_TuneCUETP8M1_cfi/pfntuple_1.root --events-per-file 1 --save-full-graph --save-normalized-table | ||
|
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| #produce the pytorch processed dataset, merging 5 pickle files into one pytorch file | ||
| python test/graph_data.py --dataset data/TTbar_14TeV_TuneCUETP8M1_cfi --num-files-merge 5 | ||
| ``` | ||
| ### Software setup | ||
| The software setup for the ML training is available in the singularity spec file [scripts/base.singularity](scripts/base.singularity). | ||
|
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| ## Acknowledgements | ||
|
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| Part of this work was conducted at **iBanks**, the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of **iBanks**. | ||
| This project is supported by the Mobilitas Pluss Returning Researcher Grant MOBTP187 of the Estonian Research Council. Part of this work was conducted at **iBanks**, the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of **iBanks**. |
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| ## Presentations | ||
|
|
||
| - CMS ML Forum, 2020-09-30: https://indico.cern.ch/event/952419/contributions/4041555/attachments/2113070/3554608/2020_09_30.pdf | ||
| - CMS ML Town Hall, 2020-07-03: https://indico.cern.ch/event/922319/contributions/3928284/attachments/2068518/3472668/2020_07_02.pdf | ||
| - FastML meeting, 2020-05-29: https://indico.cern.ch/event/923986/contributions/3883991/attachments/2047940/3431648/2020_05_28.pdf | ||
| - CMS PF group, 2020-05-22: https://indico.cern.ch/event/921949/contributions/3873351/attachments/2042984/3422056/2020_05_22.pdf | ||
| - CMS scouting group, 2020-05-15: https://indico.cern.ch/event/894101/contributions/3862093/attachments/2038019/3412747/2020_05_13.pdf | ||
| - CMS PF group, 2020-04-24: https://indico.cern.ch/event/912351/contributions/3839281/attachments/2026117/3389540/2020_04_24.pdf | ||
| - Caltech CMS group ML meeting, 2020-04-16: https://indico.cern.ch/event/909688/contributions/3826957/attachments/2021475/3380133/2020_04_15_caltechml.pdf | ||
| - ML4RECO meeting, 2020-04-09: https://indico.cern.ch/event/908361/contributions/3821957/attachments/2017888/3373038/2020_04_08.pdf | ||
| - CMS PF group, 2020-03-13: https://indico.cern.ch/event/897397/contributions/3786360/attachments/2003108/3344534/2020_03_13.pdf | ||
| - ML4RECO meeting, 2020-03-12: https://indico.cern.ch/event/897281/contributions/3784715/attachments/2002839/3343921/2020_03_12.pdf | ||
| - ML4RECO meeting, 2020-03-04: https://indico.cern.ch/event/895228/contributions/3776739/attachments/1998928/3335497/2020_03_04.pdf | ||
| - CMS PF group, 2020-02-28: https://indico.cern.ch/event/892992/contributions/3766807/attachments/1995348/3328771/2020_02_28.pdf | ||
| - CMS PF group, 2020-01-31: https://indico.cern.ch/event/885043/contributions/3730304/attachments/1979098/3295074/2020_01_30_pf.pdf | ||
| - FNAL HGCAL ML meeting, 2020-01-30: https://indico.cern.ch/event/884801/contributions/3730336/attachments/1978912/3294638/2020_01_30.pdf | ||
| - Caltech group meeting, 2020-01-28: https://indico.cern.ch/event/881683/contributions/3714961/attachments/1977131/3291096/2020_01_21.pdf | ||
| - CMS PF group, 2020-01-17: https://indico.cern.ch/event/862200/contributions/3706909/attachments/1971145/3279010/2020_01_16.pdf | ||
| - CMS PF group, 2019-11-22: https://indico.cern.ch/event/862195/contributions/3649510/attachments/1949957/3236487/2019_11_22.pdf | ||
| - CMS PF group, 2019-11-08: https://indico.cern.ch/event/861409/contributions/3632204/attachments/1941376/3219105/2019_11_08.pdf | ||
| - Caltech ML meeting, 2019-10-31: https://indico.cern.ch/event/858644/contributions/3623446/attachments/1936711/3209684/2019_10_07_pf.pdf | ||
| - Caltech ML meeting, 2019-09-19: https://indico.cern.ch/event/849944/contributions/3572113/attachments/1911520/3158764/2019_09_18_pf_ml.pdf | ||
| - CMS PF group, 2019-09-10: https://indico.cern.ch/event/846887/contributions/3557300/attachments/1904664/3145310/2019_09_10_pf_refactoring.pdf | ||
| - Caltech ML meeting, 2019-09-05: https://indico.cern.ch/event/845349/contributions/3554787/attachments/1902837/3141723/2019_09_05_pfalgo.pdf | ||
|
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| In case the above links do not load, the presentations are also mirrored on the following CERNBox link: https://cernbox.cern.ch/index.php/s/GkIRJU1YZuai4ix | ||
|
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| # CMS training | ||
|
|
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| ``` | ||
| #get the code | ||
| git clone https://github.com/jpata/particleflow.git | ||
| cd particleflow | ||
| #run a small local test including data prep and training | ||
| ./scripts/local_test_cms_tf.sh | ||
| ``` |
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| ## Delphes dataset and training | ||
|
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| The following instructions use singularity, but you may have a different local setup. | ||
|
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| ```bash | ||
| cd delphes | ||
|
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| # Run the simulation step | ||
| # Generate events with pythia, mix them with PU and run a detector simulation using Delphes | ||
| singularity exec http://jpata.web.cern.ch/jpata/centos7hepsim.sif ./run_sim.sh | ||
|
|
||
| # Run the ntuplization step | ||
| # generate X,y input matrices for NN training in out/pythia8_ttbar/*.pkl.bz2 | ||
| singularity exec http://jpata.web.cern.ch/jpata/centos7hepsim.sif ./run_ntuple.sh | ||
|
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| #Alternatively, to skip run_sim.sh and run_ntuple.sh, download everything from https://doi.org/10.5281/zenodo.4452283 and put into out/pythia8_ttbar | ||
|
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| #now move the data into the right place | ||
| mv out/pythia8_ttbar ../data/ | ||
| cd ../data/pythia8_ttbar | ||
| mkdir raw | ||
| mkdir val | ||
| mkdir root | ||
| mv *.root root/ | ||
| mb *.promc root/ | ||
|
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| #these are held out for validation | ||
| mv tev14_pythia8_ttbar_9_*.pkl.bz2 val/ | ||
| mv *.pkl.bz2 raw/ | ||
| cd ../.. | ||
|
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| # Generate the TFRecord datasets needed for larger-than-RAM training | ||
| singularity exec --nv http://jpata.web.cern.ch/jpata/base.simg python3 mlpf/launcher.py --action data --model-spec parameters/delphes-gnn-skipconn.yaml | ||
|
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| # Run the training of the base GNN model using e.g. 5 GPUs in a data-parallel mode | ||
| CUDA_VISIBLE_DEVICES=0,1,2,3,4 singularity exec --nv http://jpata.web.cern.ch/jpata/base.simg python3 mlpf/launcher.py --action train --model-spec parameters/delphes-gnn-skipconn.yaml | ||
|
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| #Run the validation to produce the predictions file | ||
| singularity exec --nv http://jpata.web.cern.ch/jpata/base.simg python3 mlpf/launcher.py --action eval --model-spec parameters/delphes-gnn-skipconn.yaml --weights ./experiments/delphes-gnn-skipconn-*/weights.300-*.hdf5 | ||
|
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| singularity exec --nv http://jpata.web.cern.ch/jpata/base.simg python3 mlpf/launcher.py --action time --model-spec parameters/delphes-gnn-skipconn.yaml --weights ./experiments/delphes-gnn-skipconn-*/weights.300-*.hdf5 | ||
| ``` | ||
|
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| ## Recipe to prepare Delphes singularity image | ||
| NB: The Delphes AngularSmearing module has been modified to correctly take into account the smearing for tracks, see [delphes/install.sh](delphes/install.sh) | ||
|
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| ```bash | ||
| wget http://atlaswww.hep.anl.gov/hepsim/soft/centos7hepsim.img | ||
| sudo singularity build --sandbox centos7hepsim.sandbox centos7hepsim.img | ||
| sudo singularity exec -B /home --writable centos7hepsim.sandbox ./install.sh | ||
| sudo singularity build centos7hepsim.sif centos7hepsim.sandbox | ||
| sudo rm -Rf centos7hepsim.sandbox | ||
| ``` |
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| # S.Chekanov | ||
|
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| # define here PYTHIA and HEPMC directories | ||
| ifndef PYTHIA8_DIR | ||
| $(error PYTHIA8_DIR env variable is not set. Run setup.sh first) | ||
| endif | ||
|
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||
|
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| ifndef PROMC | ||
| $(error PROMC env variable is not set. Run setup.sh first) | ||
| endif | ||
|
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| include ${PROMC}/etc/config.mk | ||
| include ${ROOTSYS}/etc/Makefile.arch | ||
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| # Root variables | ||
| ROOTCFLAGS = $(shell root-config --nonew --cflags) | ||
| ROOTLIBS = $(shell root-config --nonew --libs) | ||
| ROOTGTTLIBS = $(shell root-config --nonew --glibs) | ||
| CXXFLAGS += $(ROOTCFLAGS) | ||
|
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| LIBDIRARCH=lib/ | ||
| OutPutOpt = -o | ||
| LIBS += -L$(PROMC)/lib -lpromc -lprotoc -lprotobuf -lprotobuf-lite -lcbook -lz | ||
| LIBS += -L$(PYTHIA8_DIR)/$(LIBDIRARCH) -lpythia8 | ||
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| SOURCE_FILES1 := $(shell ls -1 main.cc) | ||
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| INCLUDE1=-I./src | ||
| INCLUDE2=-I. | ||
| INCLUDE3=-I$(PROMC)/include -I$(PROMC)/src | ||
| INCLUDE4=-I$(HEPMC)/include | ||
| INCLUDE5=-I$(PYTHIA8_DIR)/include | ||
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| # build object files | ||
| objects1 = $(patsubst %.cc,%.o,$(SOURCE_FILES1)) | ||
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| %.o: %.cc | ||
| $(CXX) $(OPT) $(CXXFLAGS) $(INCLUDE1) $(INCLUDE2) $(INCLUDE3) $(INCLUDE4) $(INCLUDE5) -o $@ -c $< | ||
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| Tasks: clean main.exe | ||
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| LIBOBJS = $(patsubst %.cc,%.o,$(SOURCE_FILES)) | ||
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| main.exe: $(objects1) | ||
| $(LD) $(LDFLAGS) $^ $(LIBS) $(OutPutOpt)$@ | ||
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| clean: | ||
| @rm -f *.o *~ main.exe src/*.o ; echo "Clear.." |
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