HAMi Adopters
Organisations below all are using HAMi in production.
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HAMi Adopters
All organisations are sorted alphabetically by the first letter of their English names.
Organization | Contact | Environment | Description of Use |
---|---|---|---|
4paradigm | @archlitchi | Production | device-sharing for third-party devices like (GPU,NPU,MLU),etc. |
DaoCloud | @wawa0210 | Production | Used for our cloud-native AI products. |
RiseUnion | @yangshiqi | Production | device-sharing for third-party devices like (GPU,NPU,MLU),etc. |
Linklogis | @rnyrnyrny | Production | Online inference service. |
PingAn Securities | @detongz | Testing/Staging | Used with kubeflow to allocate a single gpu to multiple notebooks, improving work efficiency; occasionally encountered jupyter kernel crashes (later resolved by adjusting parameters). |
Caper | @summerisc | Production | Physical gpu partitioning, used with volcano scheduler for automatic training pipelines. |
Huawei | @AlexPei | Evaluation | Testing resource isolation for multiple deep learning inference services (multi-container) sharing a single card; found that with continuously increasing concurrent requests, video memory continues to increase and does not release after stopping the stress test; the utilization rate of gpu computing units exceeds the set value. |
PingAn Bank | @jamie-liu | Testing | Solved the problem of insufficient gpu resources and improved resource utilization. |
Strongit | @eadou | Testing | Used for testing and training ai algorithms. |
Beijing Chenan | @Chenyangzh | Production | Deep learning algorithm inference. |
Sinochem Modern Agriculture | @mazhaoshuo | Production | Inference. |
XW Bank | @JJwangbilin | Testing | Solved the problem of gpu computing power isolation. |
Haofang | @khw934 | Evaluation | Testing various gpu virtualization scenarios to fully utilize gpu resources; requested support for using cpu resources to replace gpu computing power; requested support for the function of consolidating fragmented resources (e.g., if one card has 0.3 remaining and another has 0.5, it should be possible to apply for 0.7). |
R3 | @Danniez | Production | Infrastructure deployment. |
China Mobile | @ssslkj123 | Staging/Production | Gpu resource pooling, tenant isolation based on gpu time slicing and memory quota control, machine learning operations, and sales scenarios; offline deployment of helm templates is not user-friendly and deployment is complex. |
H3C | @chenxj1997 | Testing | Implemented gpu isolation. |
Chengqi Technology | @x1y2z3456 | Testing | Effective gpu isolation. |
A certain Shenzhen public institution | @NoKnowKonwNo | Testing | Helm deployment successful; the vgpu-scheduler single pod can only apply for gpu units less than or equal to the number of graphics cards. |
Xuanyuan Network Technology Co., Ltd. | @15220036003 | Evaluation/Testing | Using a physical gpu card for teaching, virtualizing multiple vgpus for multiple students; the vgpu-device-plugin plugin could not be installed (later resolved with community help). |
Toodata | @51qzpw | Evaluation | Model inference, image interpretation, and other scenarios. |
Infervision | @freemanke | Evaluation | Model inference. |
China East Telecom | @fangfenghuang | Testing | Gpu virtualization to solve gpu resource sharing problems. |
A certain Chinese enterprise | @18735100708 | Production | Deep learning inference. |
China Unicom Industrial Internet | @zqz199 | Evaluation/Testing | Attempting to build a systematic platform for ai training and inference that can allocate gpu resources with fine-grained partitioning. |
Chengyu Wisdom | @x1y2z3456 | Production | Deep learning training, educational and research institutions. |
Anyuan Huixin | @nice-jiang | Staging/Production | In education, research, and internal enterprise r&d scenarios, vgpu effectively solves the problem of improving gpu resource utilization. |
Technical University of Munich | @Ajexsen | Evaluation | Master's thesis, federated learning test research and development environment. |
Southeast University | @niconical | Evaluation | Preliminary research on gpu resource utilization optimization on the arm64 platform on kubernetes. |
Hangzhou Lianhui | @louyifei8888, @xyy1999 | Evaluation/Testing | Gpu usage isolation, research on maximizing gpu resource utilization. |
Woqu | @zhuziyuan | Evaluation/Testing | Test GPU sharding |
Guangdong University of Technology | @cccusername | Evaluation/Testing | research on GPU virtualization technology and GPU isolation |
Shenzhen Bode Ruijie Health Technology Co., Ltd. | @rainbowechoes | Production | Gpu virtualization inference. |
A certain industrial internet enterprise | @Dravening | Production | Various gpu computing tasks scheduled based on k8s, gpu virtualization greatly helps improve gpu resource utilization. |
ppio/ | @zeta65 | Evaluation | Ai computing to improve resource utilization. |
Beijing Unit Technology Co., Ltd. | @jingzhe6414 | Production | Ai computing platform, refined resource allocation. |
Nankai University - Network Laboratory | @liudsl | Evaluation | Preliminary research on gpu computing resource allocation and isolation for scheduling algorithm research. |
Institute of Information Engineering, Chinese Academy of Sciences | @Crownor | Production | Research resource integration and management |
A certain fund | @hellobiek | Production | Financial scenarios, intelligent customer service, intelligent search, etc. |
Donghua University | @kirakiseki | Evaluation/Testing | Use this plug-in to run high video memory demand tasks, use k8s to schedule gpu resources, and provide flexible resource allocation support for learning and training scenarios. |
China University of Mining and Technology | @hyc-yuchen | Evaluation | Perform gpu virtualization and use k8s to schedule gpu resources. |
iFlytek | @whybeyoung | Production | Public cloud reasoning, training. |
Beijing East China Information Technology Co., Ltd. | @xieyyan | Production | Ai gpu speed limit. |
gsafety | @liuchunhui-c | Production | 3 nodes reasoning training. |
Miaoyun | @erganzi | Evaluation | Perform gpu virtualization and use k8s to schedule gpu resources. |
Chongyue Computer Network Technology Co., Ltd. | @stormdragongardin | Evaluation/Testing | Cloud platform development. |
Guangzhou Pingao | @zhangQiWorr | Evaluation | Gpu heterogeneous resource scheduling research. |
Shanghai Aisha Medical Technology Co., Ltd. | @shown1985 | Testing | Internal testing. |
Kylinsoft | cuiyudong-free | Testing/Staging | Deploy hami functions in ai scenarios of cloud base operating system/server operating system. |
Harbin Institute of Technology | @blackjack2015 | Production | Gpu cluster management of the research group. |
MSXF | @xiaoyao | Testing | Gpu sharing in development environment, heterogeneous gpu management. |
Tongcheng Travel | @devenami | Production | Inference service gpu sharing, improve gpu utilization; gpu model: l40s, a800. |