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fashion-mnist-kfp-lab
A notebook showing how to easily convert a current notebook you have to a notebook that can be run on Kubeflow Pipelines.
Let's try to learn Kubeflow with an example. In this demo, we will try Kubeflow on a local Kind cluster. You should have at least 16GB of RAM, 8 CPUs modern machine to try it on your local machine, otherwise use a VM in cloud. We will use Zalando's fashion MNIST dataset and this notebook by manceps for demo.
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Due to some issue, I had to enable few feature gates and extra API server arguments to make it work. Please use the following Kind configuration to create the cluster.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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About the Dataset Fashion-MNIST is a dataset of Zalando's article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image associated with a label from 10 classes. We intend Fashion-MNIST to serve as a direct drop-in replacement for the original MNIST dataset for benchmarking machine learning algorithms. It shares the exact image size and structure of training and testing splits. source: https://github.com/zalandoresearch/fashion-mnist
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You can run the notebook from the dashboard and create the pipeline. Please note, in Kubeflow v1.2, there is an issue causing RBAC: permission denied error while connecting to the pipeline. This will be fixed in v1.3 and you can read more about the issue here. As a workaround, you need to create Istio ServiceRoleBinding and EnvoyFilter to add an identity in the header. Refer this gist for the patch.
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If you are looking for bringing agility, improved management with enterprise-grade features such as RBAC, multi-tenancy and isolation, security, auditability, collaboration for the machine learning operations in your organization, Kubeflow is an excellent option. It is stable, mature and curated with best-in-class tools and framework which can be deployed in any Kubernetes distribution. See Kubeflow roadmap here to look into what's coming in the next version.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
