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@ARTICLE{Seidler:1037642,
      author       = {Seidler, Thomas and Emmerich, Fabian and Ehlert, Kristian
                      and Berner, Rico and Nagel-Kanzler, Oliver and Schultz,
                      Norbert and Quade, Markus and Schultz, Martin G. and Abel,
                      Markus},
      title        = {{M}antik: {A} {W}orkflow {P}latform for the {D}evelopment
                      of {A}rtificial {I}ntelligence on {H}igh-{P}erformance
                      {C}omputing {I}nfrastructures},
      journal      = {The journal of open source software},
      volume       = {9},
      number       = {98},
      issn         = {2475-9066},
      reportid     = {FZJ-2025-00807},
      pages        = {6136},
      year         = {2024},
      abstract     = {The use of machine learning (ML) approaches is
                      exponentially increasing, and for manyscientific
                      applications, high-performance computing (HPC)
                      infrastructure is used to train largemodels. However, the
                      tooling for an easy deployment of models for training or
                      inference onHPC infrastructures is not satisfactory, e.g.
                      reproducibility, collaboration and monitoring ofML models
                      are not addressed in existing toolsets. With Mantik, we
                      provide an open-sourcecloud platform, which simplifies the
                      development of and collaboration on ML models on
                      HPCfacilities, and enhances reproducibility by supporting
                      data and code versioning as well asexperiment tracking. The
                      users are able to develop their applications in the
                      environment theyare most comfortable with – their local
                      machine. Usage of the best-choice IDE and mostrecent
                      software versions allow to leverage the full potential of
                      the software stack for theirresearch. Using Mantik’s
                      remote file service allows for simple management of data in
                      remotestorages and keeping track of it. As soon as an
                      application is ready for training or inference,users can
                      immediately submit it to an HPC cluster. During application
                      development, userscan train and/or evaluate their models on
                      HPC clusters via CLI on their local machine or
                      ourbrowser-based Mantik cloud platform. The latter only
                      requires an internet browser such thate.g., ML training from
                      your phone becomes feasible. Once training or inference has
                      begun, auser is able to monitor the application in real time
                      on the Mantik cloud platform.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Earth System Data
                      Exploration (ESDE) / MAELSTROM - MAchinE Learning for
                      Scalable meTeoROlogy and cliMate (955513)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE /
                      G:(EU-Grant)955513},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.21105/joss.06136},
      url          = {https://juser.fz-juelich.de/record/1037642},
}