The code on this repo allows to build a MLaaS4HEP server: the user uploads the required files and runs a ML workflow using MLaaS4HEP.
To launch the server, clone the repo and run in a terminal:
FLASK_ENV=development FLASK_APP=server.py flask run -p 8080
The server has the following APIs:
/uploadto push a tarball file containing all the files necessary to run MLaaS4HEP. The name of the folder must be provided as argument.
curl -F "file=@folder_to_upload.tar.gz" http://localhost:8080/upload?name=luca
/submitto submit a MLaaSHEP workflow. A json file, like input_new.json, must be provided in the curl call with information about which files from which uploaded folder must be used. The user can also specify the memory and CPU usage available for the process and if run on a CPU or GPU (not fully implemented).
curl -H "Content-Type: application/json" -d @input_new.json http://localhost:8080/submit
/status_dockerto get back information about the status of an ongoing process. The process name must be provided as argument.
curl http://localhost:8080/status_docker?process_name=luca_1
/logsto get back and save in a given file the logs of a process. The process name must be provided as argument.
curl -L -o logs.txt http://localhost:8080/logs?process_name=luca_1
/modelto get back the tarball of the output model delivered by MLaaS4HEP. The process name must be provided as argument.
curl -L -o luca_1.tar.gz http://localhost:8080/model?process_name=luca_1
/delete_specsto delete specs files produced by MLaaS4HEP in a previously uploaded folder. The name of the folder must be provided as argument.
curl http://localhost:8080/delete_specs?name=luca
/delete_folderto delete a previously uploaded folder. The name of the folder must be provided as argument.
curl http://localhost:8080/delete_folder?name=luca
Since the reading of remote files from grid data centers can be done only using an x509 proxy, an xrootd proxy server and renewer can be used and integrated with MLaaS4HEP_server following this recipe.
In order to manage the user's authentication we decided to integrate a proxy server in front of the MLaaS4HEP one. We succesfully used auth-proxy-server and OAuth2-Proxy server. For the instructions about their usage see here.
We implemented a working prototype connecting OAuth2-Proxy server, MLaaS4HEP_server, xrootd proxy-cache server, X509 proxy renewer and TFaaS, hosted by a VM of the INFN Cloud. The MLaaS4HEP_server APIs can be reached at the following address https://90.147.174.27:4433 while TFaaS at https://90.147.174.27:8081. To get an access token for the MLaaS4HEP_server and TFaaS you have to install oidc-agent, edit your .bashrc to make oidc-agent persistent, adding the following line:
test -e ~/.oidc-agent.env && . ~/.oidc-agent.env
Then run the agent and load the environment again:
oidc-agent > ~/.oidc-agent.env
source ~/.bashrc
In the following we provide a practical example about how to perform the entire pipeline, from preparing the data to get predictions. You can find a demo version here. The commands used for the demo are here.
### setup oidc-agent
oidc-agent
eval 'oidc-agent'
OIDC_SOCK=/tmp/oidc-VfwO5P/oidc-agent.55537; export OIDC_SOCK;
OIDCD_PID=55542; export OIDCD_PID;
echo Agent pid $OIDCD_PID
Agent pid 55546
### Obtain a token for MLaaS4HEP_server and TFaaS
TOKEN_MLAAS=$(oidc-token luca_api2 --aud=a15f41c9-a974-48ec-967a-2a36d255d524)
TOKEN_TFAAS=$(oidc-token luca_api2 --aud=f343be72-8479-4c95-892b-9dfcda0faac1)
### Check if there are models loaded in the TFaaS server
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" https://90.147.174.27:8081/models
null
### Prepare the folder with the necessary files
ls folder_to_upload
QCD_HT1000to1500.root QCD_HT2000toInf.root QCD_HT500to700.root TTBar.root files_local.txt labels_local.txt params_local.json ttH_noDRmatch.root
QCD_HT1500to2000.root QCD_HT300to500.root QCD_HT700to1000.root ex_keras_model.py files_remote.txt labels_remote.txt params_remote.json ttH_signal.root
### File with the definition of the ML model
cat ex_keras_model.py
"""
Basic example of ML model implemented via Keras framework
"""
from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense
def model(idim):
"Simple Keras model for testing purposes"
ml_model = keras.Sequential([keras.layers.Dense(128, activation='relu',input_shape=(idim,),name='inputs'),
keras.layers.Dropout(0.5),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.5),
keras.layers.Dense(1, activation='sigmoid')])
ml_model.compile(optimizer=keras.optimizers.Adam(lr=1e-3), loss=keras.losses.BinaryCrossentropy(),
metrics=[keras.metrics.BinaryAccuracy(name='accuracy'), keras.metrics.AUC(name='auc')])
return ml_model
### File with the name of local ROOT files
cat files_local.txt
ttH_signal.root
ttH_noDRmatch.root
TTBar.root
QCD_HT700to1000.root
QCD_HT1500to2000.root
QCD_HT1000to1500.root
QCD_HT2000toInf.root
QCD_HT300to500.root
QCD_HT500to700.root
### File with the path and name of remote ROOT files
cat files_remote.txt
/store/user/lgiommi/flatTree_ttHJetTobb_M125_13TeV_amcatnloFXFX_madspin_pythia8.root
/store/user/lgiommi/flatTree_TT_TuneCUETP8M2T4_13TeV-powheg-pythia8.root
/store/user/lgiommi/flatTree_QCD_HT300to500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8.root
/store/user/lgiommi/flatTree_QCD_HT500to700_TuneCUETP8M1_13TeV-madgraphMLM-pythia8.root
/store/user/lgiommi/flatTree_QCD_HT700to1000_TuneCUETP8M1_13TeV-madgraphMLM-pythia8.root
/store/user/lgiommi/flatTree_QCD_HT1000to1500_TuneCUETP8M1_13TeV-madgraphMLM-pythia8.root
/store/user/lgiommi/flatTree_QCD_HT1500to2000_TuneCUETP8M1_13TeV-madgraphMLM-pythia8.root
/store/user/lgiommi/flatTree_QCD_HT2000toInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8.root
### File with labels of the ROOT files
cat labels_local.json
1
0
0
0
0
0
0
0
0
### File with MLaaS4HEP parameters
cat params_local.json
{
"nevts": 30000,
"shuffle": true,
"chunk_size": 10000,
"epochs": 5,
"batch_size": 100,
"identifier":["runNo", "evtNo", "lumi"],
"branch": "events",
"selected_branches":"",
"exclude_branches": "",
"hist": "pdfs",
"redirector": "root://stormgf1.pi.infn.it",
"verbose": 1
}
### Load the folder to the MLaaS4HEP server
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -F "file=@folder_to_upload.tar.gz" https://90.147.174.27:4433/upload?name=luca
Successfully uploaded!
### Prepare a submission file
cat submit.json
{
"name": "luca",
"device": "cpu",
"memory": "3gb",
"cpus": "2",
"files": "files_local.txt",
"labels": "labels_local.txt",
"model": "ex_keras_model.py",
"params": "params_local.json"
}
### Submit a MLaaS4HEP workflow using the loaded ROOT files
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -H "Content-Type: application/json" -d @submit.json https://90.147.174.27:4433/submit
{
"process_name": "luca_1",
"job_id": 5557
}
### Verify the status of the process
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" https://90.147.174.27:4433/status_docker?process_name=luca_1
{
"process_name": "luca_1",
"status": "Up 8 seconds"
}
### Get back and save the logs of the process
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -o logs.txt https://90.147.174.27:4433/logs?process_name=luca_1
cat logs.txt | head -20
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 27881 100 27881 0 0 124k 0 --:--:-- --:--:-- --:--:-- 126k
2022-06-14 15:28:44.016611: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-06-14 15:28:44.016674: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
load ex_keras_model.py <function model at 0x7fd34d81df28> Simple Keras model for testing purposes
DataGenerator: <MLaaS4HEP.generator.RootDataGenerator object at 0x7fd34d7d76a0> [14/Jun/2022:15:28:46] 1655220526.0
model parameters: {"nevts": 30000, "shuffle": true, "chunk_size": 10000, "epochs": 5, "batch_size": 100, "identifier": ["runNo", "evtNo", "lumi"], "branch": "events", "selected_branches": "", "exclude_branches": "", "hist": "pdfs", "redirector": "root://stormgf1.pi.infn.it", "verbose": 1}
Reading ttH_signal.root
# 10000 entries, 29 branches, 1.10626220703125 MB, 0.05584907531738281 sec, 19.808066664389877 MB/sec, 179.05399405758 kHz
# 10000 entries, 29 branches, 1.10626220703125 MB, 0.019169092178344727 sec, 57.7107249909827 MB/sec, 521.6731135184885 kHz
# 10000 entries, 29 branches, 1.10626220703125 MB, 0.016225099563598633 sec, 68.18215214612141 MB/sec, 616.3290376618224 kHz
###total time elapsed for reading + specs computing: 0.09430861473083496; number of chunks 3
###total time elapsed for reading: 0.09124016761779785; number of chunks 3
--- first pass: 38036 events, (29-flat, 0-jagged) branches, 29 attrs
<MLaaS4HEP.reader.RootDataReader object at 0x7fd34d7d79e8> init is complete in 0.1035768985748291 sec
writing specs specs-ttH_signal.json
write specs-ttH_signal.json
Reading ttH_noDRmatch.root
# 10000 entries, 29 branches, 1.10626220703125 MB, 0.046973228454589844 sec, 23.550908537204343 MB/sec, 212.8872195716171 kHz
# 10000 entries, 29 branches, 1.10626220703125 MB, 0.017837047576904297 sec, 62.020477450744515 MB/sec, 560.6308979602749 kHz
# 10000 entries, 29 branches, 1.10626220703125 MB, 0.01699066162109375 sec, 65.11001347103726 MB/sec, 588.5585990121239 kHz
### Download the trained ML model
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -o luca_1.tar.gz https://90.147.174.27:4433/model?process_name=luca_1
mkdir -p luca_1 && tar -xvf luca_1.tar.gz -C luca_1
ls luca_1
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 147k 100 147k 0 0 680k 0 --:--:-- --:--:-- --:--:-- 717k
x ./
x ./assets/
x ./saved_model.pb
x ./keras_metadata.pb
x ./params.json
x ./variables/
x ./variables/variables.index
x ./variables/variables.data-00000-of-00001
assets keras_metadata.pb params.json saved_model.pb variables
### Check the models loaded in the TFaaS server
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" https://90.147.174.27:8081/models
[{"name":"luca_1","model":"saved_model.pb","labels":"","options":null,"inputNode":"","outputNode":"","description":"","timestamp":"2022-06-14 15:29:17.456513917 +0000 UTC m=+261933.688268477"}]
### Choose an event to test inference using the trained ML model
cat predict_bkg.json
{
"values": [0.19563319290790765, 0.8628343629750731, 0.20469675544301077, 0.5979233885840486, 0.5624403641089002, 0.4966360687831127, 0.9971232134875923, 0.9641571184466814, 0.016140890033353065, 0.012642983007060364, 0.044779417127065256, 0.04623121102305415, 0.027365998536597175, 0.004034759345313149, 0.07267125173331217, 0.4668842294559054, 0.10894376909915392, 0.044679238817932156, 1.0, 0.9496461903794253, 0.9982200258458422, 0.5, 0.0, 0.0, 0.35235498377667923, 0.6612158851740676, 0.6065199265679636, 0.3931907707503391, 0.37482121042755157],
"model": "luca_1"
}
### Obtain prediction for the selected event using TFaaS
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" -X POST -H "Content-type: application/json" -d @predict_bkg.json https://90.147.174.27:8081/json
[0.08601278]
### Delete specs files to be prepared for reading remote ROOT files
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" https://90.147.174.27:4433/delete_specs?name=luca
Specs deletion completed!
### Submit a MLaaS4HEP workflow using remote ROOT files
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -H "Content-Type: application/json" -d @submit_remote.json https://90.147.174.27:4433/submit
{
"process_name": "luca_2",
"job_id": 6138
}
### Get back and save the logs of the process
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -o logs_remote.txt https://90.147.174.27:4433/logs?process_name=luca_2
cat logs_remote.txt | head -20
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 2021 100 2021 0 0 12608 0 --:--:-- --:--:-- --:--:-- 13123
2022-06-14 15:29:40.371538: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-06-14 15:29:40.371628: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
load ex_keras_model.py <function model at 0x7fc254249f28> Simple Keras model for testing purposes
DataGenerator: <MLaaS4HEP.generator.RootDataGenerator object at 0x7fc2541fe630> [14/Jun/2022:15:29:42] 1655220582.0
model parameters: {"nevts": 30000, "shuffle": true, "chunk_size": 10000, "epochs": 5, "batch_size": 100, "identifier": ["runNo", "evtNo", "lumi"], "branch": "boostedAk8/events", "selected_branches": "", "exclude_branches": "", "hist": "pdfs", "redirector": "root://stormgf1.pi.infn.it", "verbose": 1}
Reading root://stormgf1.pi.infn.it//store/user/lgiommi/flatTree_ttHJetTobb_M125_13TeV_amcatnloFXFX_madspin_pythia8.root
# 10000 entries, 77 branches, 4.863739013671875 MB, 3.1020548343658447 sec, 1.5679087809117251 MB/sec, 3.223669642849594 kHz
# 10000 entries, 77 branches, 4.863739013671875 MB, 2.2151570320129395 sec, 2.1956633066560243 MB/sec, 4.514352642039505 kHz
# 10000 entries, 77 branches, 4.863739013671875 MB, 2.0409839153289795 sec, 2.3830364252958383 MB/sec, 4.899597652335311 kHz
###total time elapsed for reading + specs computing: 8.13843321800232; number of chunks 3
###total time elapsed for reading: 7.35819149017334; number of chunks 3
--- first pass: 948348 events, (22-flat, 52-jagged) branches, 328 attrs
<MLaaS4HEP.reader.RootDataReader object at 0x7fc2541fe978> init is complete in 8.150626182556152 sec
writing specs specs-flatTree_ttHJetTobb_M125_13TeV_amcatnloFXFX_madspin_pythia8.json
write specs-flatTree_ttHJetTobb_M125_13TeV_amcatnloFXFX_madspin_pythia8.json
Reading root://stormgf1.pi.infn.it//store/user/lgiommi/flatTree_TT_TuneCUETP8M2T4_13TeV-powheg-pythia8.root
### Delete the loaded folder with all the material and the trained ML models
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" https://90.147.174.27:4433/delete_folder?name=luca
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" -X DELETE https://90.147.174.27:8081/delete -F "model=luca_1"
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" -X DELETE https://90.147.174.27:8081/delete -F "model=luca_2"
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" https://90.147.174.27:8081/models
Folder deletion completed!
null
The MLaaS4HEP APIs can be used in the following way:
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -F "file=@folder_to_upload.tar.gz" https://90.147.174.27:4433/upload?name=luca
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -H "Content-Type: application/json" -d @input_new.json https://90.147.174.27:4433/submit
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" https://90.147.174.27:4433/status_docker?process_name=luca_1
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -o logs.txt https://90.147.174.27:4433/logs?process_name=luca_1
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" -o luca_1.tar.gz https://90.147.174.27:4433/model?process_name=luca_1
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" https://90.147.174.27:4433/delete_specs?name=luca
curl -L -k -H "Authorization: Bearer ${TOKEN_MLAAS}" https://90.147.174.27:4433/delete_folder?name=luca
The TFaaS APIs can be used in the following way:
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" https://90.147.174.27:8081/models
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" -X POST -H "Content-Encoding: gzip" -H "content-type: application/octet-stream" --data-binary @model.tar.gz https://90.147.174.27:8081/upload
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" -X POST -H "Content-type: application/json" -d @predict_bkg.json https://90.147.174.27:8081/json
curl -L -k -H "Authorization: Bearer ${TOKEN_TFAAS}" -X DELETE https://90.147.174.27:8081/delete -F "model=luca_1"
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