terraform-k8s-stack-kubeflow-mlflow

所属分类:微服务
开发工具:HCL
文件大小:19KB
下载次数:0
上传日期:2022-06-21 17:06:14
上 传 者sh-1993
说明:  组合器堆栈:Kubeflow+MLflow
(Combinator stack: Kubeflow + MLflow)

文件列表:
.header.md (2372, 2022-06-22)
.pre-commit-config.yaml (712, 2022-06-22)
.terraform-docs.yml (216, 2022-06-22)
LICENSE (11357, 2022-06-22)
examples (0, 2022-06-22)
examples\basic (0, 2022-06-22)
examples\basic\main.tf (39, 2022-06-22)
examples\testfaster (0, 2022-06-22)
examples\testfaster\.testfaster.yml (4766, 2022-06-22)
examples\testfaster\main.tf (1463, 2022-06-22)
kubeflow.tf (83, 2022-06-22)
locals.tf (11, 2022-06-22)
mlflow.tf (118, 2022-06-22)
poddefault.tf (1660, 2022-06-22)
poddefault.yaml (435, 2022-06-22)
profile.yaml (161, 2022-06-22)
providers.tf (321, 2022-06-22)
variables.tf (140, 2022-06-22)

# Kubeflow + MLFlow **tl; dr;** A [combinator](https://combinator.ml) stack that provides [Kubeflow](https://kubeflow.org) and [MLFlow](https://mlflow.org). - [Introduction](#introduction) - [Test Drive](#test-drive) - [Usage](#usage) ## Introduction [Kubeflow](https://kubeflow.org) is an open-source MLOps platform that combines Jupyter hosting, ML pipelining, and hyperparameter tuning. It is packaged into a single UI to help data scientists train their ML models. Kubeflow Pipelines (KFP) in particular, has emerged as one eminent ML pipelinging technology, mainly thanks to the managed hosting in various clouds. Its opinionated ML-specific API helps data scientists and ML engineers develop robust, repeatable pipelines. This stack adds MLflow for model management and makes it easy to log models to MLflow from kubeflow notebooks and pipelines. ### Kubeflow Version This installation uses Kubeflow version 1.2, which is now out of date. ### Status and Recommendations :warning: **For Testing Only** :warning: This installation method is not recommended for use. It required a lot of work-arounds that are not suitable for production use. Please refer to the [official documentation](https://www.kubeflow.org/docs/started/installing-kubeflow/) for production installation instructions. ## Test Drive The fastest way to get started is to use the test drive functionality provided by [TestFaster](https://testfaster.ci). Click on the "Launch Test Drive" button below (opens a new window). :computer: Launch Test Drive :computer: ## Usage ### Prerequisites Start by preparing your Kubernetes cluster using one of the [infrastructure components](https://combinator.ml/infrastructure/introduction/) or use your own cluster. ### Component Usage ```terraform module "kubeflow_mlflow_stack" { source = "combinator-ml/stack-kubeflow-mlflow/k8s" # Optional settings go here } ``` See the full configuration options below. ### Instructions Kubeflow is big, so it can take some time to start. Once it does connect to the istio ingress gateway service. Once you see the login screen, the username is `admin@kubeflow.org` and the password is `12341234`. ## Requirements | Name | Version | |------|---------| | terraform | >= 0.13 | | helm | >= 2.2.0 | | k8s | >= 0.9.1 | | kubernetes | >= 2.3.2 | ## Providers | Name | Version | |------|---------| | k8s | >= 0.9.1 | | kubernetes | >= 2.3.2 | | null | n/a | | time | n/a | ## Modules | Name | Source | Version | |------|--------|---------| | kubeflow | combinator-ml/kubeflow/k8s | 0.0.2 | | mlflow | combinator-ml/mlflow/k8s | 0.0.3 | ## Resources | Name | |------| | [k8s_manifest](https://registry.terraform.io/providers/banzaicloud/k8s/latest/docs/resources/manifest) | | [kubernetes_cluster_role_binding](https://registry.terraform.io/providers/hashicorp/kubernetes/latest/docs/resources/cluster_role_binding) | | [null_resource](https://registry.terraform.io/providers/hashicorp/null/latest/docs/resources/resource) | | [time_sleep](https://registry.terraform.io/providers/hashicorp/time/latest/docs/resources/sleep) | ## Inputs | Name | Description | Type | Default | Required | |------|-------------|------|---------|:--------:| | mlflow\_namespace | (Optional) The namespace to install into. | `string` | `"mlflow"` | no | ## Outputs No output.

近期下载者

相关文件


收藏者