kubeflow-workshop | This repository is dedicated for conducting workshop events | Learning library

by Hydrospheredata Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | kubeflow-workshop Summary

kubeflow-workshop is a Python library typically used in Tutorial, Learning, Angular, Nodejs applications. kubeflow-workshop has no bugs, it has no vulnerabilities and it has low support. However kubeflow-workshop build file is not available. You can download it from GitHub.

This repository is dedicated for conducting workshop events

Support

Quality

Security

License

Reuse

Support

kubeflow-workshop has a low active ecosystem.

It has 13 star(s) with 7 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

kubeflow-workshop has no issues reported. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of kubeflow-workshop is current.

Quality

kubeflow-workshop has 0 bugs and 0 code smells.

Security

kubeflow-workshop has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

kubeflow-workshop code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

kubeflow-workshop does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

kubeflow-workshop releases are not available. You will need to build from source code and install.

kubeflow-workshop has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

kubeflow-workshop saves you 563 person hours of effort in developing the same functionality from scratch.

It has 1316 lines of code, 31 functions and 14 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed kubeflow-workshop and discovered the below as its top functions. This is intended to give you an instant insight into kubeflow-workshop implemented functionality, and help decide if they suit your requirements.

Simulate production traffic
Generate image and labels
Download files from base url
Unpack an archive
Apply a config map to a config map
Create a function that uses a config map
Calculate confusion matrix
Construct input_fn
Prettify model structure
Get model versions
Process images
Write image data to disk
Input_fn
Builds the decoder
Computes the layer

Get all kandi verified functions for this library.

kubeflow-workshop Key Features

No Key Features are available at this moment for kubeflow-workshop.

kubeflow-workshop Examples and Code Snippets

No Code Snippets are available at this moment for kubeflow-workshop.

Community Discussions

Trending Discussions on kubeflow-workshop

MLflow Kubernetes Pod Deployment

QUESTION

MLflow Kubernetes Pod Deployment

Asked 2020-Apr-09 at 12:06

I'm attempting to create a kubernetes pod that will run MLflow tracker to store the mlflow artifacts in a designated s3 location. Below is what I'm attempting to deploy with

Dockerfile:

...

ANSWER

Answered 2020-Apr-09 at 12:06

The issue here is related to Persistent Volume Claim that is not provisioned by Your minikube cluster.

You will need to make a decision to switch to platform managed kubernetes service or to stick with minikube and manually satisfy the Persistent Volume Claim or with alternative solutions.

The simplest option would be to use helm charts for mflow installation like this or this.

The first helm chart has listed requirements:

Prerequisites

Kubernetes cluster 1.10+

Helm 2.8.0+

PV provisioner support in the underlying infrastructure.

Just like in the guide You followed this one requires PV provisioner support.

So by switching to EKS You most likely will have easier time deploying mflow with artifact storing with s3.

If You wish to stay on minikube, You will need to modify the helm chart values or the yaml files from the guide You linked to be compatible with You manual configuration of PV. It might also need permissions configuration for s3.

The second helm chart has the following limitation/feature:

Known limitations of this Chart
I've created this Chart to use it in a production-ready environment in my company. We are using MLFlow with a Postgres backend store.

Therefore, the following capabilities have been left out of the Chart:

Using persistent volumes as a backend store.

Using other database engines like MySQL or SQLServer.

You can try to install it on minikube. This setup would result in artifacts being stored on remote a database. It would still need tweaking in order to connect to s3.

Anyway minikube still is a lightweight distribution of kubernetes targeted mainly for learning, so You will eventually reach another limitation if You stick to it for too long.

Hope it helps.

Source https://stackoverflow.com/questions/61089001

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install kubeflow-workshop

You can download it from GitHub.
You can use kubeflow-workshop like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: