--- title: A hands-on guide to work with MindSpore on Kubeflow description: Would you like to get started using MindSpore and Kubeflow? Here is hands-on guide that will allow you to expand your MLOps ecosystem! url: https://canonical.com/blog/kubeflow-mindspore-integration-guide?format=md --- 1. [Blog](https://canonical.com/blog) 2. Article --- [Andreea Munteanu](https://canonical.com/blog/author/munteanuandreea "More about Andreea Munteanu") 8 November 2022 # A hands-on guide to work with MindSpore on Kubeflow [AI/ML](https://canonical.com/blog/tag/ai-ml) [AIML](https://canonical.com/blog/tag/aiml) [Kubeflow](https://canonical.com/blog/tag/kubeflow) [MLOps](https://canonical.com/blog/tag/mlops) [opensource](https://canonical.com/blog/tag/opensource) --- Share the article Looking at the report that Gartner did in 2022 regarding [top technology trends](https://www.gartner.com/en/documents/4006919), AI engineering represents an important pillar in the near future. It is composed of three core technologies: DataOps, MLOps and DevOps.The discipline’s main purpose is to develop AI models that can quickly and continuously provide business value. For instance, models that enable cross-functional collaboration, automation, data analysis, and machine learning. AI engineering shortens the development and deployment processes. ## What is Kubeflow? [Charmed Kubeflow](https://charmed-kubeflow.io/) is a production-grade, end-to-end MLOps platform that translates steps in the data science workflow into Kubernetes jobs. It is one of the official distributions of the [Kubeflow](https://www.kubeflow.org/) upstream project. Using it, data scientists and machine learning engineers benefit from having ML deployments that are simple, portable and scalable. It has capabilities that cover a wide range, from experimentation using Notebooks, to training using the Kubeflow Pipelines or tuning, using Katib. *Learn more about* [*what is Kubeflow?*](https://ubuntu.com/ai/what-is-kubeflow) ## What is MindSpore? [MindSpore](https://github.com/mindspore-ai/mindspore) is an open-source AI framework built for deep learning, training and inference, that is used for edge, mobile and cloud scenarios. It implemented automatic differentiation based on source transformation. This is a technology evolving from the functional programming framework and it supports automatic control flow. *Learn more about*[*Mindspore.*](https://github.com/mindspore-ai/mindspore) ## Why integrate Kubeflow and Mindspore? Charmed Kubeflow is an MLOps platform, composed of various open-source components. Jupyter Notebooks is one of the tools that are natively available. Having integration with an AI Framework enables data scientists and machine learning engineers to benefit from advanced training capabilities and a cost model-splitting strategy. It combines data parallelism, model parallelism and hybrid parallelism. It leads on the one hand to execution efficiency, at the pipeline, deep graph or on-device level, as well as development friendliness, because of the automatic differential, parallelisation and optimisation. Overall, the integration between Charmed Kubeflow and MindSpore highlights the growing MLOps ecosystem that Canonical is working on building. Juggling between tools, especially when working with data can be easily challenging. Thus, charmed Kubeflow aims to solve this problem, by creating an environment that natively offers access to various AI/ML tools. ## Access Charmed Kubeflow If you have not deployed Charmed Kubeflow, please follow the instructions from the [Quickstart guide](https://charmed-kubeflow.io/docs/quickstart) to deploy it. Please bear in mind that you need to run Ubuntu 20.04 or later with, a minimum of 32GB of free memory and 50GB of disk space. During the deployment process, you will * Install MicroK8s * Install Juju * Deploy Charmd Kubeflow Once Charmed Kubeflow is deployed and all Kubeflow components have the active status, please follow the instructions below. ``` juju refresh jupyter-ui --channel=latest/edge ``` This step updates *jupyter-ui* to the latest version, so it includes MindSpore’s Notebook image. ## Configure Charmed Kubeflow After Kubeflow is installed, you need to do a simple configuration to access it. First configure the access address: ``` juju config dex-auth public-url=http://10.64.140.43.nip.io ``` ``` juju config oidc-gatekeeper public-url=http://10.64.140.43.nip.io ``` Then configure the username and password for access: ``` juju config dex-auth static-username=admin ``` ``` juju config dex-auth static-password=ucantseeme ``` Next, enter in the browser,  and the user password you just set. Then you can see the complete Dashboard of Kubeflow. ## Run MindSpore-enabled Notebooks In order to access MindSpore within Charmed Kubeflow, you will need to use the natively enabled Notebooks. On the Notebook tab, from the Charmed Kubeflow dashboard, select Create Notebook, enter the notebook name MindSpore, select *jupyterlab* for the image, use *mindspore/jupyter-mindspore* from the list, allocate appropriate CPU and content resources, and click Create. In the Notebook interface, you can see the prompt that the creation is completed soon. Click Connect, we can open the notebook in another browser tab page. [Here we can use the existing notebook](https://link.zhihu.com/?target=https%3A//obs.dualstack.cn-north-4.myhuaweicloud.com/mindspore-website/notebook/r1.8/tutorials/zh_cn/beginner/mindspore_quick_start.py) on MindSpore’s official website to upload the handwritten digit recognition notebook through the notebook page. Click Execute, and you can see that the notebook is running directly without additional installation of MindSpore and Vision suites. ## Get started with your first project Once you have the MindSpore image natively enabled in your Notebook from Charmed Kubeflow, you can go ahead and start your AI/ML projects. Whereas MindSpore is designed to support a wide range of use cases, computer vision might be an interesting example. #### **Learn more about Charmed Kubeflow and MindSpore** * [Tutorial of MindSpore and Charmed Kubeflow integration](https://charmed-kubeflow.io/docs/mindspore) * [Retail at the edge with MLOps: market basket analysis](https://ubuntu.com/engage/retail-at-the-edge-with-mlops-market-basket-analysis) * [A guide to integrate Charmed Kubeflow and Mindspore](https://zhuanlan.zhihu.com/p/568398333) in Chinese. * [Hyperparameter tuning with MLOps platform](https://ubuntu.com/engage/hyperparameter-tuning-mlops-platform) * [Canonical’s AI Services](https://ubuntu.com/ai/services) ## Sign up for our newsletter Get the latest Canonical news and updates in your inbox. Work email: \*I agree to receive information about Canonical's products and services. By submitting this form, I confirm that I have read and agree to [Canonical's Privacy Policy](https://canonical.com/legal/dataprivacy). Sign up ## Share on --- ## Related posts [### Canonical joins the Open Secure AI Alliance](https://canonical.com/blog/open-secure-ai-alliance) Canonical is now part of the Open Secure AI Alliance, announced by NVIDIA with partners across cloud computing, cybersecurity, enterprise software, open source foundations, and... [Canonical](https://canonical.com/blog/author/canonical) 28 August 2026 [### Beyond tokens per watt – using Ubuntu 26.04 LTS for AI](https://canonical.com/blog/beyond-tokens-per-watt) Tokens per watt (TpW) – the measure of useful AI work produced per watt of energy consumed – is the metric at top of mind for CEOs, heads of AI, and infrastructure teams alike.... 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