blob: 30cdf0c9952f7d69e060dc8f8907bbed4760fbee [file] [log] [blame]
(window.webpackJsonp=window.webpackJsonp||[]).push([[61],{128:function(e,t,n){"use strict";n.r(t),n.d(t,"frontMatter",(function(){return l})),n.d(t,"metadata",(function(){return i})),n.d(t,"toc",(function(){return c})),n.d(t,"default",(function(){return u}));var r=n(3),o=n(7),a=(n(0),n(194)),l={title:"MLflow UI"},i={unversionedId:"userDocs/others/mlflow",id:"userDocs/others/mlflow",isDocsHomePage:!1,title:"MLflow UI",description:"\x3c!--",source:"@site/docs/userDocs/others/mlflow.md",slug:"/userDocs/others/mlflow",permalink:"/docs/next/userDocs/others/mlflow",editUrl:"https://github.com/apache/submarine/edit/master/website/docs/userDocs/others/mlflow.md",version:"current",sidebar:"docs",previous:{title:"Tracking",permalink:"/docs/next/userDocs/submarine-sdk/tracking"},next:{title:"Tensorboard",permalink:"/docs/next/userDocs/others/tensorboard"}},c=[{value:"Usage",id:"usage",children:[]},{value:"Example",id:"example",children:[]}],s={toc:c};function u(e){var t=e.components,l=Object(o.a)(e,["components"]);return Object(a.b)("wrapper",Object(r.a)({},s,l,{components:t,mdxType:"MDXLayout"}),Object(a.b)("h3",{id:"usage"},"Usage"),Object(a.b)("p",null,"MLflow UI shows the tracking result of the experiments. When we\nuse the log_param or log_metric in ModelClient API, we could view\nthe result in MLflow UI. Below is the example of the usage of MLflow\nUI."),Object(a.b)("h3",{id:"example"},"Example"),Object(a.b)("ol",null,Object(a.b)("li",{parentName:"ol"},"Run the following code in the cluster")),Object(a.b)("pre",null,Object(a.b)("code",{parentName:"pre",className:"language-python"},'from submarine import ModelsClient\nimport random\nimport time\n\nif __name__ == "__main__":\n modelClient = ModelsClient()\n with modelClient.start() as run:\n modelClient.log_param("learning_rate", random.random())\n for i in range(100):\n time.sleep(1)\n modelClient.log_metric("mse", random.random() * 100, i)\n modelClient.log_metric("acc", random.random(), i)\n')),Object(a.b)("ol",{start:2},Object(a.b)("li",{parentName:"ol"},"In the MLflow UI page, you can see the log_param and the log_metric\nresult. You can also compare the training between different workers.")),Object(a.b)("p",null,Object(a.b)("img",{src:n(211).default})))}u.isMDXComponent=!0},194:function(e,t,n){"use strict";n.d(t,"a",(function(){return m})),n.d(t,"b",(function(){return d}));var r=n(0),o=n.n(r);function a(e,t,n){return t in e?Object.defineProperty(e,t,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[t]=n,e}function l(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function i(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?l(Object(n),!0).forEach((function(t){a(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):l(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function c(e,t){if(null==e)return{};var n,r,o=function(e,t){if(null==e)return{};var n,r,o={},a=Object.keys(e);for(r=0;r<a.length;r++)n=a[r],t.indexOf(n)>=0||(o[n]=e[n]);return o}(e,t);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);for(r=0;r<a.length;r++)n=a[r],t.indexOf(n)>=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(o[n]=e[n])}return o}var s=o.a.createContext({}),u=function(e){var t=o.a.useContext(s),n=t;return e&&(n="function"==typeof e?e(t):i(i({},t),e)),n},m=function(e){var t=u(e.components);return o.a.createElement(s.Provider,{value:t},e.children)},p={inlineCode:"code",wrapper:function(e){var t=e.children;return o.a.createElement(o.a.Fragment,{},t)}},f=o.a.forwardRef((function(e,t){var n=e.components,r=e.mdxType,a=e.originalType,l=e.parentName,s=c(e,["components","mdxType","originalType","parentName"]),m=u(n),f=r,d=m["".concat(l,".").concat(f)]||m[f]||p[f]||a;return n?o.a.createElement(d,i(i({ref:t},s),{},{components:n})):o.a.createElement(d,i({ref:t},s))}));function d(e,t){var n=arguments,r=t&&t.mdxType;if("string"==typeof e||r){var a=n.length,l=new Array(a);l[0]=f;var i={};for(var c in t)hasOwnProperty.call(t,c)&&(i[c]=t[c]);i.originalType=e,i.mdxType="string"==typeof e?e:r,l[1]=i;for(var s=2;s<a;s++)l[s]=n[s];return o.a.createElement.apply(null,l)}return o.a.createElement.apply(null,n)}f.displayName="MDXCreateElement"},211:function(e,t,n){"use strict";n.r(t),t.default=n.p+"assets/images/mlflow-ui-e2fbae31ba60c324e66f00f0ae3caebf.png"}}]);