Deployment and verification of machine learning tool-chain based on kubernetes distributed clusters This paper is submitted for possible publication in the special issue on high performance distributed computing
In the field of software engineering, the environmental dependency conflict is a significant problem facing software engineers. Containerization (Pahl 2015 ) was proposed to resolve environmental dependency conflicts, Currently widely used in cloud computing and distributed systems. Simultaneously,...
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| Vydané v: | CCF transactions on high performance computing (Online) Ročník 3; číslo 2; s. 157 - 170 |
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| Jazyk: | English |
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| Abstract | In the field of software engineering, the environmental dependency conflict is a significant problem facing software engineers. Containerization (Pahl
2015
) was proposed to resolve environmental dependency conflicts, Currently widely used in cloud computing and distributed systems. Simultaneously, in the process of large-scale application development and deployment, the microservice (Nadareishvili et al.
2016
) architecture has the advantages of robust scalability and low coupling. Therefore, it is becoming increasingly favored by software developers. For example, Google is one of the few companies that need to manage the deployment and development of a large number of service components on hundreds of thousands of servers. With the concept of containerization and microservices at the core, an open-source distributed container management system called Kubernetes was developed. Kubernetes can not only maintain complete independence of applications but also improve the utilization of hardware resources, so it is affected by Internet companies and widely used by many institutions. In recent years, the demand for computing resources for machine learning-related applications is increasing, and the stand-alone computing for machine learning tasks is often unsustainable. Many data scientists will rely on distributed systems to provide sufficient computing resources for machine learning tasks. Kubernetes can be used for machine learning related applications and provides support for deployment on distributed systems. Meanwhile, it has many excellent features such as containerization and microservices. Therefore, the idea of developing and deploying machine-learning applications based on Kubernetes is favored by data scientists. Google has developed Kubeflow, a machine learning tool suite based on the Kubernetes system. Kubeflow can help data scientists run machine learning workloads on distributed clusters. For historical reasons, Kubeflow’s support for Tensorflow is quite complete, but the support of the framework is not perfect for Pytorch. Besides, although the pipeline component included in Kubeflow can build an entire machine learning workflow, this component is still dependent on the Google Cloud Platform. Therefore, Kubeflow pipeline is not friendly enough for developers who have no conditions to lease Google Cloud Service. This paper designs a complete solution to the problem of Kubeflow pipeline, and verifies the feasibility of the solution through an example, so that Kubernetes no longer depends on Google Cloud Service and better supports machine learning applications based on Pytorch. This solution ensures that data scientists can accomplish Pytorch-based deep learning applications on Kubernetes-based distributed systems development, demonstration, construction workflow, deployment services, operation, maintenance, and other functions. In principle, any data scientist who wants to use Pytorch to develop a machine-learning project can deploy their machine “learning applications on distributed systems by following such a set of solutions. The proposed approach makes the application stably and continuously running and reasonably scheduled on the cluster containing heterogeneous computing resources. |
|---|---|
| AbstractList | In the field of software engineering, the environmental dependency conflict is a significant problem facing software engineers. Containerization (Pahl
2015
) was proposed to resolve environmental dependency conflicts, Currently widely used in cloud computing and distributed systems. Simultaneously, in the process of large-scale application development and deployment, the microservice (Nadareishvili et al.
2016
) architecture has the advantages of robust scalability and low coupling. Therefore, it is becoming increasingly favored by software developers. For example, Google is one of the few companies that need to manage the deployment and development of a large number of service components on hundreds of thousands of servers. With the concept of containerization and microservices at the core, an open-source distributed container management system called Kubernetes was developed. Kubernetes can not only maintain complete independence of applications but also improve the utilization of hardware resources, so it is affected by Internet companies and widely used by many institutions. In recent years, the demand for computing resources for machine learning-related applications is increasing, and the stand-alone computing for machine learning tasks is often unsustainable. Many data scientists will rely on distributed systems to provide sufficient computing resources for machine learning tasks. Kubernetes can be used for machine learning related applications and provides support for deployment on distributed systems. Meanwhile, it has many excellent features such as containerization and microservices. Therefore, the idea of developing and deploying machine-learning applications based on Kubernetes is favored by data scientists. Google has developed Kubeflow, a machine learning tool suite based on the Kubernetes system. Kubeflow can help data scientists run machine learning workloads on distributed clusters. For historical reasons, Kubeflow’s support for Tensorflow is quite complete, but the support of the framework is not perfect for Pytorch. Besides, although the pipeline component included in Kubeflow can build an entire machine learning workflow, this component is still dependent on the Google Cloud Platform. Therefore, Kubeflow pipeline is not friendly enough for developers who have no conditions to lease Google Cloud Service. This paper designs a complete solution to the problem of Kubeflow pipeline, and verifies the feasibility of the solution through an example, so that Kubernetes no longer depends on Google Cloud Service and better supports machine learning applications based on Pytorch. This solution ensures that data scientists can accomplish Pytorch-based deep learning applications on Kubernetes-based distributed systems development, demonstration, construction workflow, deployment services, operation, maintenance, and other functions. In principle, any data scientist who wants to use Pytorch to develop a machine-learning project can deploy their machine “learning applications on distributed systems by following such a set of solutions. The proposed approach makes the application stably and continuously running and reasonably scheduled on the cluster containing heterogeneous computing resources. |
| Author | Cai, Haoyu Wang, Chao Zhou, Xuehai |
| Author_xml | – sequence: 1 givenname: Haoyu orcidid: 0000-0002-5010-3396 surname: Cai fullname: Cai, Haoyu email: caihaoyu@mail.ustc.edu.cn organization: USTC – sequence: 2 givenname: Chao surname: Wang fullname: Wang, Chao organization: USTC – sequence: 3 givenname: Xuehai surname: Zhou fullname: Zhou, Xuehai organization: USTC |
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| Cites_doi | 10.1109/MCC.2014.51 10.1007/978-3-540-30218-6_19 10.1007/978-3-319-33383-0_5 10.1109/COMPSAC.2018.00026 10.1109/MCC.2015.51 10.1007/978-1-4842-4470-8_46 10.3139/9783446456020 10.1145/2966884.2966912 10.1109/TSE.1987.232562 10.1007/978-1-4842-2766-4_12 10.1109/CVPR.2019.00075 10.1109/MS.2016.64 10.1007/978-1-4842-4470-8 |
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| References_xml | – reference: Kramer, O.: Scikitlearn. Machine learning for evolution strategies. Springer, New York, pp. 45–53 (2016) – reference: MerkelDDocker: lightweight linux containers for consistent development and deploymentLinux J.201420142392 – reference: Rezatofighi, H. et al.: Generalized intersection over union: A metric and a loss for bounding box regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. (2019) – reference: Zhang, Y., Yin, G., Wang, T., et al.: An insight into the impact of dockerfile evolutionary trajectories on quality and latency[C]. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). IEEE 1, 138–143 (2018) – reference: Nadareishvili, I., Mitra, R., McLarty, M., et al.: Microservice architecture: aligning principles, practices, and culture[M]. O’Reilly Media, Inc. (2016) – reference: Abadi, M., Barham, P., Chen, J., et al.: Tensorflow: a system for largescale machine learning. In: 12thUSENIX Symposium on Operating Systems Design and Implementation (OSDI16), pp. 265–283 (2016) – reference: Gabriel, E., Fagg, G.E., Bosilca, G., et al.: Open MPI: Goals, concept, and design of a next generation MPI implementation. European Parallel Virtual Machine/Message Passing Interface Users’ Group Meeting, pp. 97–104. Springer, Berlin (2004) – reference: Sandberg, R., Goldberg, D., Kleiman, S., et al.: Design and implementation of the sun network filesystem. In: Proceedings of the Summer USENIX conference, pp. 119–130 (1985) – reference: Ketkar, N.:Introduction to pytorch. Deep learning with python. Springer, New York, pp. 95–208 (2017) – reference: Luksa, M.: Kubernetes in action. Manning Publications (2017) – reference: BernsteinDContainers and cloud: from lxc to docker to kubernetesIEEE Cloud Comput201413818410.1109/MCC.2014.51 – reference: BisongEKubeflow and Kubeflow Pipelines[M]//Building Machine Learning and Deep Learning Models on Google Cloud Platform2019BerkeleyApress67168510.1007/978-1-4842-4470-8 – reference: KooRTouegSCheckpointing and rollback-recovery for distributed systemsIEEE Trans. Softw. Eng.19871233110.1109/TSE.1987.232562 – reference: Kluyver, T., Ragankelley, B., PÉREZ, F., et al.: Jupyter notebooks a publishing format for reproducible computational workflows. ELPUB, pp. 87–90 (2016) – reference: Bisong, E.: Kubeflow and Kubeflow pipelines. Building Machine Learning and Deep Learning Models on Google Cloud Platform. Springer, New York, pp. 671–685 (2019) – reference: Hindman, B., Konwinski, A., Zaharia, M., et al.: Mesos: a platform for finegrained resource sharing in the data center. NSDI: volume 11:22–22 (2011) – reference: BalalaieAHeydarnooriAJamshidiPMicroservices architecture enables devops: migration to a cloud-native architectureIEEE Softw.2016333425210.1109/MS.2016.64 – reference: Mané D.: TensorBoard: TensorFlow’s visualization toolkit (2015) – reference: Chen, T., Li, M., Li, Y., et al.: Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. (2015). arXiv:1512.01274 – reference: PahlCContainerization and the paas cloudIEEE Cloud Comput.201523243110.1109/MCC.2015.51 – reference: Vitux.com. Install nfs server and client on ubuntu 18.04 lts[EB/OL] . (2020). http://vitux.com/installNFSserverandclientonubuntu/ – reference: Redmon, J., Farhadi, A.: Yolov3: An incremental improvement. arXiv:1804.02767 (2018) – reference: Awan, A.A., Hamidouche, K., Venkatesh, A., et al.: Efficient large message broadcast using NCCL and CUDA-aware MPI for deep learning. Proceedings of the 23rd European MPI Users’ Group Meeting. pp. 5–22 (2016) – ident: 65_CR22 – volume: 2014 start-page: 2 issue: 239 year: 2014 ident: 65_CR16 publication-title: Linux J. – ident: 65_CR21 – volume: 1 start-page: 81 issue: 3 year: 2014 ident: 65_CR4 publication-title: IEEE Cloud Comput doi: 10.1109/MCC.2014.51 – ident: 65_CR19 – ident: 65_CR8 doi: 10.1007/978-3-540-30218-6_19 – ident: 65_CR13 doi: 10.1007/978-3-319-33383-0_5 – ident: 65_CR9 – ident: 65_CR23 doi: 10.1109/COMPSAC.2018.00026 – volume: 2 start-page: 24 issue: 3 year: 2015 ident: 65_CR18 publication-title: IEEE Cloud Comput. doi: 10.1109/MCC.2015.51 – ident: 65_CR7 – ident: 65_CR1 – ident: 65_CR11 – ident: 65_CR6 doi: 10.1007/978-1-4842-4470-8_46 – ident: 65_CR14 doi: 10.3139/9783446456020 – ident: 65_CR2 doi: 10.1145/2966884.2966912 – volume: 1 start-page: 23 year: 1987 ident: 65_CR12 publication-title: IEEE Trans. Softw. Eng. doi: 10.1109/TSE.1987.232562 – ident: 65_CR17 – ident: 65_CR10 doi: 10.1007/978-1-4842-2766-4_12 – ident: 65_CR15 – ident: 65_CR20 doi: 10.1109/CVPR.2019.00075 – volume: 33 start-page: 42 issue: 3 year: 2016 ident: 65_CR3 publication-title: IEEE Softw. doi: 10.1109/MS.2016.64 – start-page: 671 volume-title: Kubeflow and Kubeflow Pipelines[M]//Building Machine Learning and Deep Learning Models on Google Cloud Platform year: 2019 ident: 65_CR5 doi: 10.1007/978-1-4842-4470-8 |
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| Subtitle | This paper is submitted for possible publication in the special issue on high performance distributed computing |
| Title | Deployment and verification of machine learning tool-chain based on kubernetes distributed clusters |
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