143 research outputs found
Prototype of Machine Learning “as a Service” for CMS Physics in Signal vs Background discrimination
No full textMLaaS4HEP: Machine Learning as a Service for HEP
No full textMachine 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
Exploring Patterns and Correlations in CMS Computing Operations Data with Big Data Analytics Techniques
No full textMachine Learning as a Service for High Energy Physics on heterogeneous computing resources
No full textMachine 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
Prototype of a cloud native solution of Machine Learning as Service for HEP
No full textTo 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
Cloud native approach for Machine Learning as a Service for High Energy Physics
No full textNowadays 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
Big Data Analysis for Predictive Maintenance at the INFN-CNAF Data Center using Machine Learning Approaches
No full textCollection and harmonization of system logs and prototypal Analytics services with the Elastic (ELK) suite at the INFN-CNAF computing centre
No full textThe distributed Grid infrastructure for High Energy Physics experiments at the Large Hadron Collider (LHC) in Geneva comprises a set of computing centres, spread all over the world, as part of the Worldwide LHC Computing Grid (WLCG). In Italy, the Tier-1 functionalities are served by the INFN-CNAF data center, which provides also computing and storage resources to more than
twenty non-LHC experiments. For this reason, a high amount of logs are collected each day from various sources, which are highly heterogeneous and difficult to harmonize. In this contribution, a working implementation of a system that collects, parses and displays the log information from
CNAF data sources and the investigation of a Machine Learning based predictive maintenance system, is presented
Towards Predictive Maintenance with Machine Learning at the INFN-CNAF computing centre
No full textThe INFN-CNAF computing center, one of the Worldwide LHC Computing Grid Tier-1 sites, is serving a large set of scientific communities, in High Energy Physics and beyond. In order to increase efficiency and to remain competitive in the long run, CNAF is launching various activities aiming at implementing a global predictive maintenance solution for the site.
This requires a site-wide effort in collecting, cleaning and structuring all possibly useful data coming from log files of the various Tier-1 services and systems, as a necessary step prior to designing machine learning based approaches for predictive maintenance.
Among the Tier-1 services, efficient storage systems are one of the key ingredients of Tier-1 operations. CNAF uses the StoRM service as a Grid Storage Resource Manager solution: its operations are logged in a very complex manner, as the log content is deeply unstructured and hard to be exploited for analytics purposes. Despite such difficulty, the StoRM logs are a precious source of information for operators (e. g. real-time monitoring and anomaly detection), for developers (e. g. debugging, service stability, code improvements) and for site managers (service optimization, storage usage efficiency, time and money saving ways to spot and prevent unwanted behaviors).
Based on previous experiences on Big Data Analytics and Machine/Deep learning in the CMS experiment, this work describes how the StoRM logs can be handled and parsed to extract the relevant information, how such log handling can be designed to work automatically, how to define and implement metrics to tag critical states of the service, how to correlate StoRM events with external services events, and ultimately how to contribute to the future CNAF-wide predictive maintenance system.
Initial results in this activity are presented and discussed. Furthermore, a mention to ongoing complementary work at the CNAF center is also mentioned
TSimpleAnalysis: histogramming many trees in parallel
No full textI worked inside the ROOT team of EP-SFT group. My project focuses on writing a ROOT class that has the aim of creating histograms from a TChain. The name of the class is TSimpleAnalysis and it is already integrated in ROOT. The work that I have done was to write the source, the header le of the class and also a python script, that allows to the user to use the class through the command line. This represents a great improvement respect to the usual user code that counts lines and lines of code to do the same thing. (Link for the class: https://root.cern.ch/doc/master/classTSimpleAnalysis.html
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