community
pipelines
community | pipelines | |
---|---|---|
1 | 2 | |
151 | 3,457 | |
0.7% | 1.0% | |
7.7 | 9.8 | |
3 days ago | 5 days ago | |
Jsonnet | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
community
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How can we read variables from file and use them in promql?
However I am not able to figure it out, how can I feed the string xyz_stack_1 to grafana. I have setup docker-compose.yaml file to start up all the containers. The configuration is done through prometheus.yaml, grafana.ini, dashboards.yaml and datasources.yaml
pipelines
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Putting an ML model into production using Feast and Kubeflow on Azure (Part I)
Kubeflow Pipelines comes with a pre-defined KFServing component which can be imported from the GitHub repo and reused across the pipelines without the need to define it every time. KFServing is Kubeflow's solution for "productionizing" your ML models and works with a lot of frameworks like Tensorflow, sci-kit, and PyTorch among others.
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Machine Learning Orchestration on Kubernetes using Kubeflow
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.
What are some alternatives?
kserve - Standardized Serverless ML Inference Platform on Kubernetes
kubeflow - Machine Learning Toolkit for Kubernetes
couler - Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
deployKF - deployKF builds machine learning platforms on Kubernetes. We combine the best of Kubeflow, Airflow†, and MLflow† into a complete platform.
elyra - Elyra extends JupyterLab with an AI centric approach.
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.
kfserving - Standardized Serverless ML Inference Platform on Kubernetes [Moved to: https://github.com/kserve/kserve]
fashion-mnist - A MNIST-like fashion product database. Benchmark :point_down:
prometheus - A docker-compose stack for Prometheus monitoring
soopervisor - ☁️ Export Ploomber pipelines to Kubernetes (Argo), Airflow, AWS Batch, SLURM, and Kubeflow.
bodywork - ML pipeline orchestration and model deployments on Kubernetes.