Abstract
A new method based on Domain Specific Language (DSL) approach to Deep Learning (DL) lifecycle data management tool support is presented: a very simple DL lifecycle data management tool, which however is usable in practice (it will be called Core tool) and a very advanced extension mechanism which in fact converts the Core tool into domain specific tool (DSL tool) building framework for DL lifecycle data management tasks. The extension mechanism will be based on the metamodel specialization approach to DSL modeling tools introduced by authors. The main idea of metamodel specialization is that we, at first, define the Universal Metamodel (UMM) for a domain and then for each use case define a Specialized Metamodel. But for use in our new domain the specialization concept will be extended: we add a functional specialization where invoking an additional custom program at appropriate points of Core tool is supported.
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References
Barzdins, J., Cerans, K., Grasmanis, M., Kalnins A., et al.: Domain specific languages for business process management: A case study. In: Proceedings of 9th OOPSLA Workshop on Domain-Specific Modeling, pp. 34–40 (2009)
Sprogis, A., Barzdins, J.: Specification, configuration and implementation of DSL tools. Front. Artif. Intell. Appl. 249, 330–343 (2012). https://doi.org/10.3233/978-1-61499-161-8-330
Kalnins, A., Barzdins, J.: Metamodel specialization for graphical modeling language support. In: Proceedings of 19th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2016, pp. 103–112 (2016). https://doi.org/10.1145/2976767.2976779
Kalnins, A., Barzdins, J.: Metamodel specialization for graphical language support. Softw. Syst. Model. J. 18(3), 1699–1735 (2019). https://doi.org/10.1007/s10270-018-0668-3
Bisong, E.: Kubeflow and kubeflow pipelines. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 671–685. Apress (2019). https://doi.org/10.1007/978-1-4842-4470-8_46
Flyte: Cloud Native Machine Learning and Data Processing Platform. https://flyte.org
Dagster: System for building modern data applications. https://github.com/dagster-io/dagster
Metaflow: Framework for real-life data science. https://metaflow.org
DVC: Open-source Version Control System for Machine Learning Projects. https://dvc.org
Haifeng, J., Qingquan. S., Xia, H.: Auto-Keras: An efficient neural architecture search system. In: Proceedings of 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1946–1956 (2019). https://doi.org/10.1145/3292500.3330648
Observatory: Solution for tracking machine learning models. https://github.com/wmeints/observatory
lab: MLearning Lab. https://github.com/beringresearch/lab
Weights&Biases. https://www.wandb.com
comet. https://www.comet.ml
mlflow: An open source platform for the machine learning lifecycle. https://mlflow.org
FGLab: ML Dashboard. https://kaixhin.github.io/FGLab
Sacred. https://github.com/IDSIA/sacred
guild.ai: The ML Engineering Platform. https://guild.ai
Sacredboard: Web dashboard for the Sacred machine learning experiment management tool. https://github.com/chovanecm/sacredboard
Acknowledgements
The research was supported by ERDF project 1.1.1.1/18/A/045 at Institute of Mathematics and Computer Science, University of Latvia.
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Celms, E. et al. (2020). Towards DSL for DL Lifecycle Data Management. In: Robal, T., Haav, HM., Penjam, J., Matulevičius, R. (eds) Databases and Information Systems. DB&IS 2020. Communications in Computer and Information Science, vol 1243. Springer, Cham. https://doi.org/10.1007/978-3-030-57672-1_16
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