951 research outputs found
Prototype of machine learning “as a service” for CMS physics in signal vs background discrimination
Big volumes of data are collected and analysed by LHC experiments at CERN. The success of this scientific challenges is ensured by a great amount of computing power and storage capacity, operated over high performance networks, in very complex LHC computing models on the LHC Computing Grid infrastructure. Now in Run-2 data taking, LHC has an ambitious and broad experimental programme for the coming decades: it includes large investments in detector hardware, and similarly it requires commensurate investment in the R&D in software and com- puting to acquire, manage, process, and analyse the shear amounts of data to be recorded in the High-Luminosity LHC (HL-LHC) era.
The new rise of Artificial Intelligence - related to the current Big Data era, to the technological progress and to a bump in resources democratization and efficient allocation at affordable costs through cloud solutions - is posing new challenges but also offering extremely promising techniques, not only for the commercial world but also for scientific enterprises such as HEP experiments. Machine Learning and Deep Learning are rapidly evolving approaches to characterising and describing data with the potential to radically change how data is reduced and analysed, also at LHC.
This thesis aims at contributing to the construction of a Machine Learning “as a service” solution for CMS Physics needs, namely an end-to-end data-service to serve Machine Learning trained model to the CMS software framework. To this ambitious goal, this thesis work contributes firstly with a proof of concept of a first prototype of such infrastructure, and secondly with a specific physics use-case: the Signal versus Background discrimination in the study of CMS all-hadronic top quark decays, done with scalable Machine Learning techniques
Predicting CMS datasets popularity with machine learning
In CMS è stato lanciato un progetto di Data Analytics e, all’interno di
esso, un’attività specifica pilota che mira a sfruttare tecniche di Machine Learning
per predire la popolarità dei dataset di CMS. Si tratta di un’osservabile molto
delicata, la cui eventuale predizione premetterebbe a CMS di costruire modelli di
data placement più intelligenti, ampie ottimizzazioni nell’uso dello storage a tutti i
livelli Tiers, e formerebbe la base per l’introduzione di un solito sistema di data
management dinamico e adattivo. Questa tesi descrive il lavoro fatto sfruttando un
nuovo prototipo pilota chiamato DCAFPilot, interamente scritto in python, per
affrontare questa sfida
Predicting dataset popularity for the CMS experiment
The CMS experiment at the LHC accelerator at CERN relies on its computing
infrastructure to stay at the frontier of High Energy Physics, searching for
new phenomena and making discoveries. Even though computing plays a significant
role in physics analysis we rarely use its data to predict the system behavior
itself. A basic information about computing resources, user activities and site
utilization can be really useful for improving the throughput of the system and
its management. In this paper, we discuss a first CMS analysis of dataset
popularity based on CMS meta-data which can be used as a model for dynamic data
placement and provide the foundation of data-driven approach for the CMS
computing infrastructure.Comment: Submitted to proceedings of 17th International workshop on Advanced
Computing and Analysis Techniques in physics research (ACAT
MLaaS4HEP: Machine Learning as a Service for HEP
Machine Learning (ML) will play a significant role in the success of the
upcoming High-Luminosity LHC (HL-LHC) program at CERN. An unprecedented amount
of data at the exascale will be collected by LHC experiments in the next
decade, and this effort will require novel approaches to train and use ML
models. In this paper, we discuss a Machine Learning as a Service pipeline for
HEP (MLaaS4HEP) which provides three independent layers: a data streaming layer
to read High-Energy Physics (HEP) data in their native ROOT data format; a data
training layer to train ML models using distributed ROOT files; a data
inference layer to serve predictions using pre-trained ML models via HTTP
protocol. Such modular design opens up the possibility to train data at large
scale by reading ROOT files from remote storage facilities, e.g. World-Wide LHC
Computing Grid (WLCG) infrastructure, and feed the data to the user's favorite
ML framework. The inference layer implemented as TensorFlow as a Service
(TFaaS) may provide an easy access to pre-trained ML models in existing
infrastructure and applications inside or outside of the HEP domain. In
particular, we demonstrate the usage of the MLaaS4HEP architecture for a
physics use-case, namely the Higgs analysis in CMS originally
performed using custom made Ntuples. We provide details on the training of the
ML model using distributed ROOT files, discuss the performance of the MLaaS and
TFaaS approaches for the selected physics analysis, and compare the results
with traditional methods.Comment: 16 pages, 10 figures, 2 tables, submitted to Computing and Software
for Big Science. arXiv admin note: text overlap with arXiv:1811.0449
Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses
With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community.
The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner.
Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services
Prototype of a cloud native solution of Machine Learning as Service for HEP
To favor the usage of Machine Learning (ML) techniques in High-Energy Physics (HEP) analyses it would be useful to have a service allowing to perform the entire ML pipeline (in terms of reading the data, training a ML model, and serving predictions) directly using ROOT files of arbitrary size from local or remote distributed data sources. The MLaaS4HEP framework aims to provide such kind of solution. It was successfully validated with a CMS physics use case which gave important feedback about the needs of analysts. For instance, we introduced the possibility for the user to provide pre-processing operations, such as defining new branches and applying cuts. To provide a real service for the user and to integrate it into the INFN Cloud, we started working on MLaaS4HEP cloudification. This would allow to use cloud resources and to work in a distributed environment. In this work, we provide updates on this topic, and in particular, we discuss our first working prototype of the service. It includes an OAuth2 proxy server as authentication/authorization layer, a MLaaS4HEP server, an XRootD proxy server for enabling access to remote ROOT data, and the TensorFlow as a Service (TFaaS) service in charge of the inference phase. With this architecture the user is able to submit ML pipelines, after being authenticated and authorized, using local or remote ROOT files simply using HTTP call
Machine Learning as a Service for High Energy Physics on heterogeneous computing resources
Machine Learning (ML) techniques in the High-Energy Physics (HEP) domain are ubiquitous and will play a significant role also in the upcoming High-Luminosity LHC (HL-LHC) upgrade foreseen at CERN: a huge amount of data will be produced by LHC and collected by the ex- periments, facing challenges at the exascale. Despite ML models are successfully applied in many use-cases (online and offline reconstruction, particle identification, detector simulation, Monte Carlo generation, just to name a few) there is a constant seek for scalable, performant, and production-quality operations of ML-enabled workflows. In addition, the scenario is complicated by the gap among HEP physicists and ML experts, caused by the specificity of some parts of the HEP typical workflows and solutions, and by the difficulty to formulate HEP problems in a way that match the skills of the Computer Science (CS) and ML community and hence its potential ability to step in and help. Among other factors, one of the technical obstacles resides in the difference of data-formats used by ML-practitioners and physicists, where the former use mostly flat-format data representations while the latter use to store data in tree-based objects via the ROOT data format. Another obstacle to further development of ML techniques in HEP resides in the difficulty to secure the adequate computing resources for training and inference of ML models, in a scalable and transparent way in terms of CPU vs GPU vs TPU vs other resources, as well as local vs cloud resources. This yields a technical barrier that prevents a relatively large portion of HEP physicists from fully accessing the potential of ML-enabled systems for scientific research. In order to close this gap, a Machine Learning as a Service for HEP (MLaaS4HEP) solution is presented as a product of R&D activities within the CMS experiment. It offers a service that is capable to directly read ROOT-based data, use the ML solution provided by the user, and ultimately serve predictions by pre-trained ML models “as a service” accessible via HTTP protocol. This solution can be used by physicists or experts outside of HEP domain and it provides access to local or remote data storage without requiring any modification or integration with the experiment specific framework. Moreover, MLaaS4HEP is built with a modular design allowing independent resource allocation that opens up a possibility to train ML models on PB-size datasets remotely accessible from the WLCG sites without physically downloading data into local storage.
To prove the feasibility and utility of the MLaaS4HEP service with large datasets and thus be ready for the next future when an increase of data produced is expected, an exploration of different hardware resources is required. In particular, this work aims to provide the MLaaS4HEP service transparent access to heterogeneous resources, which opens up the usage of more powerful resources without requiring any effort from the user side during the access and use phase
Cloud native approach for Machine Learning as a Service for High Energy Physics
Nowadays Machine Learning (ML) techniques are widely adopted in many areas of High Energy Physics (HEP) and certainly will play a significant role also in the upcoming High-Luminosity LHC (HL-LHC) upgrade foreseen at CERN. A huge amount of data will be produced by LHC and collected by the experiments, facing challenges at the exascale.
Here, we present Machine Learning as a Service solution for HEP (MLaaS4HEP) to perform an entire ML pipeline (in terms of reading data, processing data, training ML models, serving predictions) in a completely model-agnostic fashion, directly using ROOT files of arbitrary size from local or distributed data sources.
With the new version of MLaaS4HEP code based on uproot4, we provide new features to improve users’ experience with the framework and their workflows, e.g. users can provide some preprocessing operations to be applied to ROOT data before starting the ML pipeline. Then our approach is extended to use local and cloud resources via HTTP proxy which allows physicists to submit their workflows using the HTTP protocol. We discuss how this pipeline could be enabled in the INFN Cloud Provider and what could be the final architecture
Progress on cloud native solution of Machine Learning as a Service for HEP
Nowadays Machine Learning (ML) techniques are successfully used in many areas of High-Energy Physics (HEP) and will play a significant role also in the upcoming High-Luminosity LHC upgrade foreseen at CERN, when a huge amount of data will be produced by LHC and collected by the experiments, facing challenges at the exascale. To favor the usage of ML in HEP analyses, it would be useful to have a service allowing to perform the entire ML pipeline (in terms of reading the data, processing data, training a ML model, and serving predictions) directly using ROOT files of arbitrary size from local or remote distributed data sources. The Machine Learning as a Service for HEP (MLaaS4HEP) solution we have already proposed aims to provide such kind of service and to be HEP experiment agnostic. To provide users with a real service and to integrate it into the INFN Cloud, we started working on MLaaS4HEP cloudification. This would allow to use cloud resources and to work in a distributed environment. In this work, we provide updates on this topic and discuss a working prototype of the service running on INFN Cloud. It includes an OAuth2 proxy server as authentication/authorization layer, a MLaaS4HEP server, an XRootD proxy server for enabling access to remote ROOT data, and the TensorFlow as a Service (TFaaS) service in charge of the inference phase. With this architecture a HEP user can submit ML pipelines, after being authenticated and authorized, using local or remote ROOT files simply using HTTP calls
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