diff --git a/pr-preview/pr-62/_/css/site.css b/pr-preview/pr-62/_/css/site.css new file mode 100644 index 0000000..c39da22 --- /dev/null +++ b/pr-preview/pr-62/_/css/site.css @@ -0,0 +1,2608 @@ +@font-face { + font-family: Roboto; + font-style: normal; + font-weight: 400; + src: url(../font/roboto-latin-400-normal.woff2) format("woff2"), url(../font/roboto-latin-400-normal.woff) format("woff"); + unicode-range: U+00??, U+0131, U+0152-0153, U+02bb-02bc, U+02c6, U+02da, U+02dc, U+2000-206f, U+2074, U+20ac, U+2122, U+2191, U+2193, U+2212, U+2215, U+feff, U+fffd +} + +@font-face { + font-family: Roboto; + font-style: italic; + font-weight: 400; + src: url(../font/roboto-latin-400-italic.woff2) format("woff2"), url(../font/roboto-latin-400-italic.woff) format("woff"); + unicode-range: U+00??, U+0131, U+0152-0153, U+02bb-02bc, U+02c6, U+02da, U+02dc, U+2000-206f, U+2074, U+20ac, U+2122, U+2191, U+2193, U+2212, U+2215, U+feff, U+fffd +} + +@font-face { + font-family: Roboto; + font-style: normal; + font-weight: 500; + src: url(../font/roboto-latin-500-normal.woff2) format("woff2"), url(../font/roboto-latin-500-normal.woff) format("woff"); + unicode-range: U+00??, U+0131, U+0152-0153, U+02bb-02bc, U+02c6, U+02da, U+02dc, U+2000-206f, U+2074, U+20ac, U+2122, U+2191, U+2193, U+2212, U+2215, U+feff, U+fffd +} + +@font-face { + font-family: Roboto; + font-style: italic; + font-weight: 500; + src: url(../font/roboto-latin-500-italic.woff2) format("woff2"), url(../font/roboto-latin-500-italic.woff) format("woff"); + unicode-range: U+00??, U+0131, U+0152-0153, U+02bb-02bc, U+02c6, U+02da, U+02dc, U+2000-206f, U+2074, U+20ac, U+2122, U+2191, U+2193, U+2212, U+2215, U+feff, U+fffd +} + +@font-face { + font-family: Roboto Mono; + font-style: normal; + font-weight: 400; + src: url(../font/roboto-mono-latin-400-normal.woff2) format("woff2"), url(../font/roboto-mono-latin-400-normal.woff) format("woff"); + unicode-range: U+00??, U+0131, U+0152-0153, U+02bb-02bc, U+02c6, U+02da, U+02dc, U+2000-206f, U+2074, U+20ac, U+2122, U+2191, U+2193, U+2212, U+2215, U+feff, U+fffd +} + +@font-face { + font-family: Roboto Mono; + font-style: normal; + font-weight: 500; + src: url(../font/roboto-mono-latin-500-normal.woff2) format("woff2"), url(../font/roboto-mono-latin-500-normal.woff) format("woff"); + unicode-range: U+00??, U+0131, U+0152-0153, U+02bb-02bc, U+02c6, U+02da, U+02dc, U+2000-206f, U+2074, U+20ac, U+2122, U+2191, U+2193, U+2212, U+2215, U+feff, U+fffd +} + +*, +::after, +::before { + -webkit-box-sizing: inherit; + box-sizing: inherit +} + +html { + -webkit-box-sizing: border-box; + box-sizing: border-box; + font-size: 1.0625em; + height: 100%; + scroll-behavior: smooth +} + +@media screen and (min-width:1024px) { + html { + font-size: 1.125em + } +} + +body { + background: #fff; + color: #222; + font-family: Roboto, sans-serif; + line-height: 1.15; + margin: 0; + -moz-tab-size: 4; + -o-tab-size: 4; + tab-size: 4; + word-wrap: anywhere +} + +a { + text-decoration: none +} + +a:hover { + text-decoration: underline +} + +a:active { + background-color: none +} + +code, +kbd, +pre { + font-family: Roboto Mono, monospace +} + +b, +dt, +strong, +th { + font-weight: 500 +} + +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline +} + +sub { + bottom: -.25em +} + +sup { + top: -.5em +} + +em em { + font-style: normal +} + +strong strong { + font-weight: 400 +} + +button { + cursor: pointer; + font-family: inherit; + font-size: 1em; + line-height: 1.15; + margin: 0 +} + +button::-moz-focus-inner { + border: none; + padding: 0 +} + +summary { + cursor: pointer; + -webkit-tap-highlight-color: transparent; + outline: none +} + +table { + border-collapse: collapse; + word-wrap: normal +} + +object[type="image/svg+xml"]:not([width]) { + width: -webkit-fit-content; + width: -moz-fit-content; + width: fit-content +} + +::-webkit-input-placeholder { + opacity: .5 +} + +::-moz-placeholder { + opacity: .5 +} + +:-ms-input-placeholder { + opacity: .5 +} + +::-ms-input-placeholder { + opacity: .5 +} + +::placeholder { + opacity: .5 +} + +@media (pointer:fine) { + @supports (scrollbar-width:thin) { + html { + scrollbar-color: #c1c1c1 #fafafa + } + + body * { + scrollbar-width: thin; + scrollbar-color: #c1c1c1 transparent + } + } + + html::-webkit-scrollbar { + background-color: #fafafa; + height: 12px; + width: 12px + } + + body ::-webkit-scrollbar { + height: 6px; + width: 6px + } + + ::-webkit-scrollbar-thumb { + background-clip: padding-box; + background-color: #c1c1c1; + border: 3px solid transparent; + border-radius: 12px + } + + body ::-webkit-scrollbar-thumb { + border-width: 1.75px; + border-radius: 6px + } + + ::-webkit-scrollbar-thumb:hover { + background-color: #9c9c9c + } +} + +@media screen and (min-width:1024px) { + .body { + display: -webkit-box; + display: -ms-flexbox; + display: flex + } +} + +.nav-container { + position: fixed; + top: 3.5rem; + left: 0; + width: 100%; + font-size: .94444rem; + z-index: 1; + visibility: hidden +} + +@media screen and (min-width:769px) { + .nav-container { + width: 15rem + } +} + +@media screen and (min-width:1024px) { + .nav-container { + font-size: .86111rem; + -webkit-box-flex: 0; + -ms-flex: none; + flex: none; + position: static; + top: 0; + visibility: visible + } +} + +.nav-container.is-active { + visibility: visible +} + +.nav { + background: #fafafa; + position: relative; + top: 2.5rem; + height: calc(100vh - 6rem) +} + +@media screen and (min-width:769px) { + .nav { + -webkit-box-shadow: .5px 0 3px #c1c1c1; + box-shadow: .5px 0 3px #c1c1c1 + } +} + +@media screen and (min-width:1024px) { + .nav { + top: 3.5rem; + -webkit-box-shadow: none; + box-shadow: none; + position: sticky; + height: calc(100vh - 3.5rem) + } +} + +.nav a { + color: inherit +} + +.nav .panels { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + height: inherit +} + +html.is-clipped--nav { + overflow-y: hidden +} + +.nav-panel-menu { + overflow-y: scroll; + -ms-scroll-chaining: none; + overscroll-behavior: none; + height: calc(100% - 2.5rem) +} + +.nav-panel-menu:not(.is-active) .nav-menu { + opacity: .75 +} + +.nav-panel-menu:not(.is-active)::after { + content: ""; + background: rgba(0, 0, 0, .5); + display: block; + position: absolute; + top: 0; + right: 0; + bottom: 0; + left: 0 +} + +.nav-menu { + min-height: 100%; + padding: .5rem .75rem; + line-height: 1.35; + position: relative +} + +.nav-menu h3.title { + color: #424242; + font-size: inherit; + font-weight: 500; + margin: 0; + padding: .25em 0 .125em +} + +.nav-list { + list-style: none; + margin: 0 0 0 .75rem; + padding: 0 +} + +.nav-menu>.nav-list+.nav-list { + margin-top: .5rem +} + +.nav-item { + margin-top: .5em +} + +.nav-item-toggle~.nav-list { + padding-bottom: .125rem +} + +.nav-item[data-depth="0"]>.nav-list:first-child { + display: block; + margin: 0 +} + +.nav-item:not(.is-active)>.nav-list { + display: none +} + +.nav-item-toggle { + background: transparent url(../img/caret.svg) no-repeat 50%/50%; + border: none; + outline: none; + line-height: inherit; + padding: 0; + position: absolute; + height: 1.35em; + width: 1.35em; + margin-top: -.05em; + margin-left: -1.35em +} + +.nav-item.is-active>.nav-item-toggle { + -webkit-transform: rotate(90deg); + transform: rotate(90deg) +} + +.is-current-page>.nav-link, +.is-current-page>.nav-text { + font-weight: 500 +} + +.nav-panel-explore { + background: #fafafa; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + position: absolute; + top: 0; + right: 0; + bottom: 0; + left: 0 +} + +.nav-panel-explore:not(:first-child) { + top: auto; + max-height: calc(50% + 2.5rem) +} + +.nav-panel-explore .context { + font-size: .83333rem; + -ms-flex-negative: 0; + flex-shrink: 0; + color: #5d5d5d; + -webkit-box-shadow: 0 -1px 0 #e1e1e1; + box-shadow: 0 -1px 0 #e1e1e1; + padding: 0 .5rem; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: justify; + -ms-flex-pack: justify; + justify-content: space-between; + line-height: 1; + height: 2.5rem +} + +.nav-panel-explore:not(:first-child) .context { + cursor: pointer +} + +.nav-panel-explore .context .version { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-align: inherit; + -ms-flex-align: inherit; + align-items: inherit +} + +.nav-panel-explore .context .version::after { + content: ""; + background: url(../img/chevron.svg) no-repeat 100%/auto 100%; + width: 1.25em; + height: .75em +} + +.nav-panel-explore .components { + line-height: 1.6; + -webkit-box-flex: 1; + -ms-flex-positive: 1; + flex-grow: 1; + -webkit-box-shadow: inset 0 1px 5px #e1e1e1; + box-shadow: inset 0 1px 5px #e1e1e1; + background: #f0f0f0; + padding: .5rem .75rem 0; + margin: 0; + overflow-y: scroll; + -ms-scroll-chaining: none; + overscroll-behavior: none; + max-height: 100%; + display: block +} + +.nav-panel-explore:not(.is-active) .components { + display: none +} + +.nav-panel-explore .component { + display: block +} + +.nav-panel-explore .component+.component { + margin-top: .5rem +} + +.nav-panel-explore .component:last-child { + margin-bottom: .75rem +} + +.nav-panel-explore .component .title { + font-weight: 500 +} + +.nav-panel-explore .versions { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + padding-left: 0; + margin-top: -.25rem; + line-height: 1; + list-style: none +} + +.nav-panel-explore .component .version { + margin: .375rem .375rem 0 0 +} + +.nav-panel-explore .component .version a { + border: 1px solid #c1c1c1; + border-radius: .25rem; + opacity: .75; + white-space: nowrap; + padding: .125em .25em; + display: inherit +} + +.nav-panel-explore .component .is-current a { + border-color: currentColor; + opacity: .9; + font-weight: 500 +} + +@media screen and (max-width:1023.5px) { + aside.toc.sidebar { + display: none + } + + main>.content { + overflow-x: auto + } +} + +@media screen and (min-width:1024px) { + main { + -webkit-box-flex: 1; + -ms-flex: auto; + flex: auto; + min-width: 0 + } + + main>.content { + display: -webkit-box; + display: -ms-flexbox; + display: flex + } + + aside.toc.embedded { + display: none + } + + aside.toc.sidebar { + -webkit-box-flex: 0; + -ms-flex: 0 0 9rem; + flex: 0 0 9rem; + -webkit-box-ordinal-group: 2; + -ms-flex-order: 1; + order: 1 + } +} + +@media screen and (min-width:1216px) { + aside.toc.sidebar { + -ms-flex-preferred-size: 12rem; + flex-basis: 12rem + } +} + +.toolbar { + color: #5d5d5d; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + background-color: #fafafa; + -webkit-box-shadow: 0 1px 0 #e1e1e1; + box-shadow: 0 1px 0 #e1e1e1; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + font-size: .83333rem; + height: 2.5rem; + -webkit-box-pack: start; + -ms-flex-pack: start; + justify-content: flex-start; + position: sticky; + top: 3.5rem; + z-index: 2 +} + +.toolbar a { + color: inherit +} + +.nav-toggle { + background: url(../img/menu.svg) no-repeat 50% 47.5%; + background-size: 49%; + border: none; + outline: none; + line-height: inherit; + padding: 0; + height: 2.5rem; + width: 2.5rem; + margin-right: -.25rem +} + +@media screen and (min-width:1024px) { + .nav-toggle { + display: none + } +} + +.nav-toggle.is-active { + background-image: url(../img/back.svg); + background-size: 41.5% +} + +.home-link { + display: block; + background: url(../img/home-o.svg) no-repeat 50%; + height: 1.25rem; + width: 1.25rem; + margin: .625rem +} + +.home-link.is-current, +.home-link:hover { + background-image: url(../img/home.svg) +} + +.edit-this-page { + display: none; + padding-right: .5rem +} + +@media screen and (min-width:1024px) { + .edit-this-page { + display: block + } +} + +.toolbar .edit-this-page a { + color: #8e8e8e +} + +.breadcrumbs { + display: none; + -webkit-box-flex: 1; + -ms-flex: 1 1; + flex: 1 1; + padding: 0 .5rem 0 .75rem; + line-height: 1.35 +} + +@media screen and (min-width:1024px) { + .breadcrumbs { + display: block + } +} + +a+.breadcrumbs { + padding-left: .05rem +} + +.breadcrumbs ul { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + margin: 0; + padding: 0; + list-style: none +} + +.breadcrumbs li { + display: inline; + margin: 0 +} + +.breadcrumbs li::after { + content: "/"; + padding: 0 .5rem +} + +.breadcrumbs li:last-of-type::after { + content: none +} + +.page-versions { + margin: 0 .2rem 0 auto; + position: relative; + line-height: 1 +} + +@media screen and (min-width:1024px) { + .page-versions { + margin-right: .7rem + } +} + +.page-versions .version-menu-toggle { + color: inherit; + background: url(../img/chevron.svg) no-repeat; + background-position: right .5rem top 50%; + background-size: auto .75em; + border: none; + outline: none; + line-height: inherit; + padding: .5rem 1.5rem .5rem .5rem; + position: relative; + z-index: 3 +} + +.page-versions .version-menu { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + min-width: 100%; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + -webkit-box-align: end; + -ms-flex-align: end; + align-items: flex-end; + background: -webkit-gradient(linear, left top, left bottom, from(#f0f0f0), to(#f0f0f0)) no-repeat; + background: linear-gradient(180deg, #f0f0f0 0, #f0f0f0) no-repeat; + padding: 1.375rem 1.5rem .5rem .5rem; + position: absolute; + top: 0; + right: 0; + white-space: nowrap +} + +.page-versions:not(.is-active) .version-menu { + display: none +} + +.page-versions .version { + display: block; + padding-top: .5rem +} + +.page-versions .version.is-current { + display: none +} + +.page-versions .version.is-missing { + color: #8e8e8e; + font-style: italic; + text-decoration: none +} + +.toc-menu { + color: #5d5d5d +} + +.toc.sidebar .toc-menu { + margin-right: .75rem; + position: sticky; + top: 6rem +} + +.toc .toc-menu h3 { + color: #333; + font-size: .88889rem; + font-weight: 500; + line-height: 1.3; + margin: 0 -.5px; + padding-bottom: .25rem +} + +.toc.sidebar .toc-menu h3 { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + height: 2.5rem; + -webkit-box-pack: end; + -ms-flex-pack: end; + justify-content: flex-end +} + +.toc .toc-menu ul { + font-size: .83333rem; + line-height: 1.2; + list-style: none; + margin: 0; + padding: 0 +} + +.toc.sidebar .toc-menu ul { + max-height: calc(100vh - 8.5rem); + overflow-y: auto; + -ms-scroll-chaining: none; + overscroll-behavior: none +} + +@supports (scrollbar-width:none) { + .toc.sidebar .toc-menu ul { + scrollbar-width: none + } +} + +.toc .toc-menu ul::-webkit-scrollbar { + width: 0; + height: 0 +} + +@media screen and (min-width:1024px) { + .toc .toc-menu h3 { + font-size: .83333rem + } + + .toc .toc-menu ul { + font-size: .75rem + } +} + +.toc .toc-menu li { + margin: 0 +} + +.toc .toc-menu li[data-level="2"] a { + padding-left: 1.25rem +} + +.toc .toc-menu li[data-level="3"] a { + padding-left: 2rem +} + +.toc .toc-menu a { + color: inherit; + border-left: 2px solid #e1e1e1; + display: inline-block; + padding: .25rem 0 .25rem .5rem; + text-decoration: none +} + +.sidebar.toc .toc-menu a { + display: block; + outline: none +} + +.toc .toc-menu a:hover { + color: #1565c0 +} + +.toc .toc-menu a.is-active { + border-left-color: #1565c0; + color: #333 +} + +.sidebar.toc .toc-menu a:focus { + background: #fafafa +} + +.toc .toc-menu .is-hidden-toc { + display: none !important +} + +.doc { + color: #333; + font-size: inherit; + -webkit-hyphens: auto; + -ms-hyphens: auto; + hyphens: auto; + line-height: 1.6; + margin: 0 auto; + max-width: 40rem; + padding: 0 1rem 4rem +} + +@media screen and (min-width:1024px) { + .doc { + -webkit-box-flex: 1; + -ms-flex: auto; + flex: auto; + font-size: .94444rem; + margin: 0 2rem; + max-width: 46rem; + min-width: 0 + } +} + +.doc h1, +.doc h2, +.doc h3, +.doc h4, +.doc h5, +.doc h6 { + color: #191919; + font-weight: 400; + -webkit-hyphens: none; + -ms-hyphens: none; + hyphens: none; + line-height: 1.3; + margin: 1rem 0 0 +} + +.doc>h1.page:first-child { + font-size: 2rem; + margin: 1.5rem 0 +} + +@media screen and (min-width:769px) { + .doc>h1.page:first-child { + margin-top: 2.5rem + } +} + +.doc>h1.page:first-child+aside.toc.embedded { + margin-top: -.5rem +} + +.doc>h2#name+.sectionbody { + margin-top: 1rem +} + +#preamble+.sect1, +.doc .sect1+.sect1 { + margin-top: 2rem +} + +.doc h1.sect0 { + background: #f0f0f0; + font-size: 1.8em; + margin: 1.5rem -1rem 0; + padding: .5rem 1rem +} + +.doc h2:not(.discrete) { + border-bottom: 1px solid #e1e1e1; + margin-left: -1rem; + margin-right: -1rem; + padding: .4rem 1rem .1rem +} + +.doc h3:not(.discrete), +.doc h4:not(.discrete) { + font-weight: 500 +} + +.doc h1 .anchor, +.doc h2 .anchor, +.doc h3 .anchor, +.doc h4 .anchor, +.doc h5 .anchor, +.doc h6 .anchor { + position: absolute; + text-decoration: none; + width: 1.75ex; + margin-left: -1.5ex; + visibility: hidden; + font-size: .8em; + font-weight: 400; + padding-top: .05em +} + +.doc h1 .anchor::before, +.doc h2 .anchor::before, +.doc h3 .anchor::before, +.doc h4 .anchor::before, +.doc h5 .anchor::before, +.doc h6 .anchor::before { + content: "\00a7" +} + +.doc h1:hover .anchor, +.doc h2:hover .anchor, +.doc h3:hover .anchor, +.doc h4:hover .anchor, +.doc h5:hover .anchor, +.doc h6:hover .anchor { + visibility: visible +} + +.doc dl, +.doc p { + margin: 0 +} + +.doc a { + color: #1565c0 +} + +.doc a:hover { + color: #104d92 +} + +.doc a.bare { + -webkit-hyphens: none; + -ms-hyphens: none; + hyphens: none +} + +.doc a.unresolved { + color: #d32f2f +} + +.doc i.fa { + -webkit-hyphens: none; + -ms-hyphens: none; + hyphens: none; + font-style: normal +} + +.doc .colist>table code, +.doc p code, +.doc thead code { + color: #222; + background: #fafafa; + border-radius: .25em; + font-size: .95em; + padding: .125em .25em +} + +.doc code, +.doc pre { + -webkit-hyphens: none; + -ms-hyphens: none; + hyphens: none +} + +.doc pre { + font-size: .88889rem; + line-height: 1.5; + margin: 0 +} + +.doc blockquote { + margin: 0 +} + +.doc .paragraph.lead>p { + font-size: 1rem +} + +.doc .right { + float: right +} + +.doc .left { + float: left +} + +.doc .float-gap.right { + margin: 0 1rem 1rem 0 +} + +.doc .float-gap.left { + margin: 0 0 1rem 1rem +} + +.doc .float-group::after { + content: ""; + display: table; + clear: both +} + +.doc .stretch { + width: 100% +} + +.doc .underline { + text-decoration: underline +} + +.doc .line-through { + text-decoration: line-through +} + +.doc .dlist, +.doc .exampleblock, +.doc .hdlist, +.doc .imageblock, +.doc .listingblock, +.doc .literalblock, +.doc .olist, +.doc .paragraph, +.doc .partintro, +.doc .quoteblock, +.doc .sidebarblock, +.doc .tabs, +.doc .ulist, +.doc .verseblock, +.doc .videoblock, +.doc details, +.doc hr { + margin: 1rem 0 0 +} + +.doc table.tableblock { + font-size: .83333rem +} + +.doc .tablecontainer, +.doc .tablecontainer+*, +.doc :not(.tablecontainer)>table.tableblock, +.doc :not(.tablecontainer)>table.tableblock+* { + margin-top: 1.5rem +} + +.doc p.tableblock+p.tableblock { + margin-top: .5rem +} + +.doc td.tableblock>.content>:first-child { + margin-top: 0 +} + +.doc table.tableblock td, +.doc table.tableblock th { + padding: .5rem +} + +.doc table.tableblock, +.doc table.tableblock>*>tr>* { + border: 0 solid #e1e1e1 +} + +.doc table.grid-all>*>tr>* { + border-width: 1px +} + +.doc table.grid-cols>*>tr>* { + border-width: 0 1px +} + +.doc table.grid-rows>*>tr>* { + border-width: 1px 0 +} + +.doc table.grid-all>thead th, +.doc table.grid-rows>thead th { + border-bottom-width: 2.5px +} + +.doc table.frame-all { + border-width: 1px +} + +.doc table.frame-ends { + border-width: 1px 0 +} + +.doc table.frame-sides { + border-width: 0 1px +} + +.doc table.frame-none>colgroup+*>:first-child>*, +.doc table.frame-sides>colgroup+*>:first-child>* { + border-top-width: 0 +} + +.doc table.frame-sides>:last-child>:last-child>* { + border-bottom-width: 0 +} + +.doc table.frame-ends>*>tr>:first-child, +.doc table.frame-none>*>tr>:first-child { + border-left-width: 0 +} + +.doc table.frame-ends>*>tr>:last-child, +.doc table.frame-none>*>tr>:last-child { + border-right-width: 0 +} + +.doc table.stripes-all>tbody>tr, +.doc table.stripes-even>tbody>tr:nth-of-type(2n), +.doc table.stripes-hover>tbody>tr:hover, +.doc table.stripes-odd>tbody>tr:nth-of-type(odd) { + background: #fafafa +} + +.doc table.tableblock>tfoot { + background: -webkit-gradient(linear, left top, left bottom, from(#f0f0f0), to(#fff)); + background: linear-gradient(180deg, #f0f0f0 0, #fff) +} + +.doc .halign-left { + text-align: left +} + +.doc .halign-right { + text-align: right +} + +.doc .halign-center { + text-align: center +} + +.doc .valign-top { + vertical-align: top +} + +.doc .valign-bottom { + vertical-align: bottom +} + +.doc .valign-middle { + vertical-align: middle +} + +.doc .admonitionblock { + margin: 1.4rem 0 0 +} + +.doc .admonitionblock p, +.doc .admonitionblock td.content { + font-size: .88889rem +} + +.doc .admonitionblock td.content>.title+*, +.doc .admonitionblock td.content>:not(.title):first-child { + margin-top: 0 +} + +.doc .admonitionblock pre { + font-size: .83333rem +} + +.doc .admonitionblock>table { + table-layout: fixed; + position: relative; + width: 100% +} + +.doc .admonitionblock td.content { + padding: 1rem 1rem .75rem; + background: #fafafa; + width: 100%; + word-wrap: anywhere +} + +.doc .admonitionblock .icon { + position: absolute; + top: 0; + left: 0; + font-size: .83333rem; + padding: 0 .5rem; + height: 1.25rem; + line-height: 1; + font-weight: 500; + text-transform: uppercase; + border-radius: .45rem; + -webkit-transform: translate(-.5rem, -50%); + transform: translate(-.5rem, -50%) +} + +.doc .admonitionblock.caution .icon { + background-color: #a0439c; + color: #fff +} + +.doc .admonitionblock.important .icon { + background-color: #d32f2f; + color: #fff +} + +.doc .admonitionblock.note .icon { + background-color: #217ee7; + color: #fff +} + +.doc .admonitionblock.tip .icon { + background-color: #41af46; + color: #fff +} + +.doc .admonitionblock.warning .icon { + background-color: #e18114; + color: #fff +} + +.doc .admonitionblock .icon i { + display: -webkit-inline-box; + display: -ms-inline-flexbox; + display: inline-flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + height: 100% +} + +.doc .admonitionblock .icon i::after { + content: attr(title) +} + +.doc .imageblock, +.doc .videoblock { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center +} + +.doc .imageblock.text-left, +.doc .videoblock.text-left { + -webkit-box-align: start; + -ms-flex-align: start; + align-items: flex-start +} + +.doc .imageblock.text-right, +.doc .videoblock.text-right { + -webkit-box-align: end; + -ms-flex-align: end; + align-items: flex-end +} + +.doc .image>img, +.doc .image>object, +.doc .image>svg, +.doc .imageblock img, +.doc .imageblock object, +.doc .imageblock svg { + display: inline-block; + height: auto; + max-width: 100%; + vertical-align: middle +} + +.doc .image:not(.left):not(.right)>img { + margin-top: -.2em +} + +.doc .videoblock iframe { + max-width: 100%; + vertical-align: middle +} + +#preamble .abstract blockquote { + background: #f0f0f0; + border-left: 5px solid #e1e1e1; + color: #4a4a4a; + font-size: .88889rem; + padding: .75em 1em +} + +.doc .quoteblock, +.doc .verseblock { + background: #fafafa; + border-left: 5px solid #5d5d5d; + color: #5d5d5d +} + +.doc .quoteblock { + padding: .25rem 2rem 1.25rem +} + +.doc .quoteblock .attribution { + color: #8e8e8e; + font-size: .83333rem; + margin-top: .75rem +} + +.doc .quoteblock blockquote { + margin-top: 1rem +} + +.doc .quoteblock .paragraph { + font-style: italic +} + +.doc .quoteblock cite { + padding-left: 1em +} + +.doc .verseblock { + font-size: 1.15em; + padding: 1rem 2rem +} + +.doc .verseblock pre { + font-family: inherit; + font-size: inherit +} + +.doc ol, +.doc ul { + margin: 0; + padding: 0 0 0 2rem +} + +.doc ol.none, +.doc ol.unnumbered, +.doc ol.unstyled, +.doc ul.checklist, +.doc ul.no-bullet, +.doc ul.none, +.doc ul.unstyled { + list-style-type: none +} + +.doc ol.unnumbered, +.doc ul.no-bullet { + padding-left: 1.25rem +} + +.doc ol.unstyled, +.doc ul.unstyled { + padding-left: 0 +} + +.doc ul.circle { + list-style-type: circle +} + +.doc ul.disc { + list-style-type: disc +} + +.doc ul.square { + list-style-type: square +} + +.doc ul.circle ul:not([class]), +.doc ul.disc ul:not([class]), +.doc ul.square ul:not([class]) { + list-style: inherit +} + +.doc ol.arabic { + list-style-type: decimal +} + +.doc ol.decimal { + list-style-type: decimal-leading-zero +} + +.doc ol.loweralpha { + list-style-type: lower-alpha +} + +.doc ol.upperalpha { + list-style-type: upper-alpha +} + +.doc ol.lowerroman { + list-style-type: lower-roman +} + +.doc ol.upperroman { + list-style-type: upper-roman +} + +.doc ol.lowergreek { + list-style-type: lower-greek +} + +.doc ul.checklist { + padding-left: 1.75rem +} + +.doc ul.checklist p>i.fa-check-square-o:first-child, +.doc ul.checklist p>i.fa-square-o:first-child { + display: -webkit-inline-box; + display: -ms-inline-flexbox; + display: inline-flex; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + width: 1.25rem; + margin-left: -1.25rem +} + +.doc ul.checklist i.fa-check-square-o::before { + content: "\2713" +} + +.doc ul.checklist i.fa-square-o::before { + content: "\274f" +} + +.doc .dlist .dlist, +.doc .dlist .olist, +.doc .dlist .ulist, +.doc .olist .dlist, +.doc .olist .olist, +.doc .olist .ulist, +.doc .olist li+li, +.doc .ulist .dlist, +.doc .ulist .olist, +.doc .ulist .ulist, +.doc .ulist li+li { + margin-top: .5rem +} + +.doc .admonitionblock .listingblock, +.doc .olist .listingblock, +.doc .ulist .listingblock { + padding: 0 +} + +.doc .admonitionblock .title, +.doc .exampleblock .title, +.doc .imageblock .title, +.doc .listingblock .title, +.doc .literalblock .title, +.doc .openblock .title, +.doc .tableblock caption, +.doc .videoblock .title { + color: #5d5d5d; + font-size: .88889rem; + font-style: italic; + font-weight: 500; + -webkit-hyphens: none; + -ms-hyphens: none; + hyphens: none; + letter-spacing: .01em; + padding-bottom: .075rem +} + +.doc .tableblock caption { + text-align: left +} + +.doc .olist .title, +.doc .ulist .title { + font-style: italic; + font-weight: 500; + margin-bottom: .25rem +} + +.doc .imageblock .title { + margin-top: .5rem; + padding-bottom: 0 +} + +.doc details { + margin-left: 1rem +} + +.doc details>summary { + display: block; + position: relative; + line-height: 1.6; + margin-bottom: .5rem +} + +.doc details>summary::-webkit-details-marker { + display: none +} + +.doc details>summary::before { + content: ""; + border: solid transparent; + border-left: solid; + border-width: .3em 0 .3em .5em; + position: absolute; + top: .5em; + left: -1rem; + -webkit-transform: translateX(15%); + transform: translateX(15%) +} + +.doc details[open]>summary::before { + border-color: currentColor transparent transparent; + border-width: .5rem .3rem 0; + -webkit-transform: translateY(15%); + transform: translateY(15%) +} + +.doc details>summary::after { + content: ""; + width: 1rem; + height: 1em; + position: absolute; + top: .3em; + left: -1rem +} + +.doc details.result { + margin-top: .25rem +} + +.doc details.result>summary { + color: #5d5d5d; + font-style: italic; + margin-bottom: 0 +} + +.doc details.result>.content { + margin-left: -1rem +} + +.doc .exampleblock>.content, +.doc details.result>.content { + background: #fff; + border: .25rem solid #5d5d5d; + border-radius: .5rem; + padding: .75rem +} + +.doc .exampleblock>.content::after, +.doc details.result>.content::after { + content: ""; + display: table; + clear: both +} + +.doc .exampleblock>.content>:first-child, +.doc details>.content>:first-child { + margin-top: 0 +} + +.doc .sidebarblock { + background: #e1e1e1; + border-radius: .75rem; + padding: .75rem 1.5rem +} + +.doc .sidebarblock>.content>.title { + font-size: 1.25rem; + font-weight: 500; + line-height: 1.3; + margin-bottom: -.3em; + text-align: center +} + +.doc .sidebarblock>.content>:not(.title):first-child { + margin-top: 0 +} + +.doc .listingblock.wrap pre, +.doc .tableblock pre { + white-space: pre-wrap +} + +.doc .listingblock pre:not(.highlight), +.doc .literalblock pre, +.doc pre.highlight code { + background: #fafafa; + -webkit-box-shadow: inset 0 0 1.75px #e1e1e1; + box-shadow: inset 0 0 1.75px #e1e1e1; + display: block; + overflow-x: auto; + padding: .875em +} + +.doc .listingblock>.content { + position: relative +} + +.doc .source-toolbox { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + visibility: hidden; + position: absolute; + top: .25rem; + right: .5rem; + color: grey; + font-family: Roboto, sans-serif; + font-size: .72222rem; + line-height: 1; + white-space: nowrap; + z-index: 1 +} + +.doc .listingblock:hover .source-toolbox { + visibility: visible +} + +.doc .source-toolbox .source-lang { + text-transform: uppercase; + letter-spacing: .075em +} + +.doc .source-toolbox>:not(:last-child)::after { + content: "|"; + letter-spacing: 0; + padding: 0 1ch +} + +.doc .source-toolbox .copy-button { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + background: none; + border: none; + color: inherit; + outline: none; + padding: 0; + font-size: inherit; + line-height: inherit; + width: 1em; + height: 1em +} + +.doc .source-toolbox .copy-icon { + -webkit-box-flex: 0; + -ms-flex: none; + flex: none; + width: inherit; + height: inherit +} + +.doc .source-toolbox img.copy-icon { + -webkit-filter: invert(50.2%); + filter: invert(50.2%) +} + +.doc .source-toolbox svg.copy-icon { + fill: currentColor +} + +.doc .source-toolbox .copy-toast { + -webkit-box-flex: 0; + -ms-flex: none; + flex: none; + position: relative; + display: -webkit-inline-box; + display: -ms-inline-flexbox; + display: inline-flex; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + margin-top: 1em; + background-color: #333; + border-radius: .25em; + padding: .5em; + color: #fff; + cursor: auto; + opacity: 0; + -webkit-transition: opacity .5s ease .5s; + transition: opacity .5s ease .5s +} + +.doc .source-toolbox .copy-toast::after { + content: ""; + position: absolute; + top: 0; + width: 1em; + height: 1em; + border: .55em solid transparent; + border-left-color: #333; + -webkit-transform: rotate(-90deg) translateX(50%) translateY(50%); + transform: rotate(-90deg) translateX(50%) translateY(50%); + -webkit-transform-origin: left; + transform-origin: left +} + +.doc .source-toolbox .copy-button.clicked .copy-toast { + opacity: 1; + -webkit-transition: none; + transition: none +} + +.doc .language-console .hljs-meta { + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none +} + +.doc .dlist dt { + font-style: italic +} + +.doc .dlist dd { + margin: 0 0 .25rem 1.5rem +} + +.doc .dlist dd:last-of-type { + margin-bottom: 0 +} + +.doc td.hdlist1, +.doc td.hdlist2 { + padding: .5rem 0 0; + vertical-align: top +} + +.doc tr:first-child>.hdlist1, +.doc tr:first-child>.hdlist2 { + padding-top: 0 +} + +.doc td.hdlist1 { + font-weight: 500; + padding-right: .25rem +} + +.doc td.hdlist2 { + padding-left: .25rem +} + +.doc .colist { + font-size: .88889rem; + margin: .25rem 0 -.25rem +} + +.doc .colist>table>tbody>tr>:first-child, +.doc .colist>table>tr>:first-child { + padding: .25em .5rem 0; + vertical-align: top +} + +.doc .colist>table>tbody>tr>:last-child, +.doc .colist>table>tr>:last-child { + padding: .25rem 0 +} + +.doc .conum[data-value] { + border: 1px solid; + border-radius: 100%; + display: inline-block; + font-family: Roboto, sans-serif; + font-size: .75rem; + font-style: normal; + line-height: 1.2; + text-align: center; + width: 1.25em; + height: 1.25em; + letter-spacing: -.25ex; + text-indent: -.25ex +} + +.doc .conum[data-value]::after { + content: attr(data-value) +} + +.doc .conum[data-value]+b { + display: none +} + +.doc hr { + border: solid #e1e1e1; + border-width: 2px 0 0; + height: 0 +} + +.doc b.button { + white-space: nowrap +} + +.doc b.button::before { + content: "["; + padding-right: .25em +} + +.doc b.button::after { + content: "]"; + padding-left: .25em +} + +.doc kbd { + display: inline-block; + font-size: .66667rem; + background: #fafafa; + border: 1px solid #c1c1c1; + border-radius: .25em; + -webkit-box-shadow: 0 1px 0 #c1c1c1, 0 0 0 .1em #fff inset; + box-shadow: 0 1px 0 #c1c1c1, inset 0 0 0 .1em #fff; + padding: .25em .5em; + vertical-align: text-bottom; + white-space: nowrap +} + +.doc .keyseq, +.doc kbd { + line-height: 1 +} + +.doc .keyseq { + font-size: .88889rem +} + +.doc .keyseq kbd { + margin: 0 .125em +} + +.doc .keyseq kbd:first-child { + margin-left: 0 +} + +.doc .keyseq kbd:last-child { + margin-right: 0 +} + +.doc .menuseq, +.doc .path { + -webkit-hyphens: none; + -ms-hyphens: none; + hyphens: none +} + +.doc .menuseq i.caret::before { + content: "\203a"; + font-size: 1.1em; + font-weight: 500; + line-height: .90909 +} + +.doc :not(pre).nowrap { + white-space: nowrap +} + +.doc .nobreak { + -webkit-hyphens: none; + -ms-hyphens: none; + hyphens: none; + word-wrap: normal +} + +#footnotes { + font-size: .85em; + line-height: 1.5; + margin: 2rem -.5rem 0 +} + +.doc td.tableblock>.content #footnotes { + margin: 2rem 0 0 +} + +#footnotes hr { + border-top-width: 1px; + margin-top: 0; + width: 20% +} + +#footnotes .footnote { + margin: .5em 0 0 1em +} + +#footnotes .footnote+.footnote { + margin-top: .25em +} + +#footnotes .footnote>a:first-of-type { + display: inline-block; + margin-left: -2em; + text-align: right; + width: 1.5em +} + +nav.pagination { + border-top: 1px solid #e1e1e1; + line-height: 1; + margin: 2rem -1rem -1rem; + padding: .75rem 1rem 0 +} + +nav.pagination, +nav.pagination span { + display: -webkit-box; + display: -ms-flexbox; + display: flex +} + +nav.pagination span { + -webkit-box-flex: 50%; + -ms-flex: 50%; + flex: 50%; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column +} + +nav.pagination .prev { + padding-right: .5rem +} + +nav.pagination .next { + margin-left: auto; + padding-left: .5rem; + text-align: right +} + +nav.pagination span::before { + color: #8e8e8e; + font-size: .75em; + padding-bottom: .1em +} + +nav.pagination .prev::before { + content: "Prev" +} + +nav.pagination .next::before { + content: "Next" +} + +nav.pagination a { + font-weight: 500; + line-height: 1.3; + position: relative +} + +nav.pagination a::after, +nav.pagination a::before { + color: #8e8e8e; + font-weight: 400; + font-size: 1.5em; + line-height: .75; + position: absolute; + top: 0; + width: 1rem +} + +nav.pagination .prev a::before { + content: "\2039"; + -webkit-transform: translateX(-100%); + transform: translateX(-100%) +} + +nav.pagination .next a::after { + content: "\203a" +} + +html.is-clipped--navbar { + overflow-y: hidden +} + +body { + padding-top: 3.5rem +} + +.navbar { + background: #191919; + color: #fff; + font-size: .88889rem; + height: 3.5rem; + position: fixed; + top: 0; + width: 100%; + z-index: 4 +} + +.navbar a { + text-decoration: none +} + +.navbar-brand { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-flex: 1; + -ms-flex: auto; + flex: auto; + padding-left: 1rem +} + +.navbar-brand .navbar-item { + color: #fff +} + +.navbar-brand .navbar-item:first-child { + -ms-flex-item-align: center; + align-self: center; + padding: 0; + font-size: 1.22222rem; + -ms-flex-wrap: wrap; + flex-wrap: wrap; + line-height: 1 +} + +.navbar-brand .navbar-item:first-child a { + color: inherit; + word-wrap: normal +} + +.navbar-brand .navbar-item:first-child :not(:last-child) { + padding-right: .375rem +} + +.navbar-brand .navbar-item.search { + -webkit-box-flex: 1; + -ms-flex: auto; + flex: auto; + -webkit-box-pack: end; + -ms-flex-pack: end; + justify-content: flex-end +} + +#search-input { + color: #333; + font-family: inherit; + font-size: .95rem; + width: 150px; + border: 1px solid #dbdbdb; + border-radius: .1em; + line-height: 1.5; + padding: 0 .25em +} + +#search-input:disabled { + background-color: #dbdbdb; + cursor: not-allowed; + pointer-events: all !important +} + +#search-input:disabled::-webkit-input-placeholder { + color: #4c4c4c +} + +#search-input:disabled::-moz-placeholder { + color: #4c4c4c +} + +#search-input:disabled:-ms-input-placeholder { + color: #4c4c4c +} + +#search-input:disabled::-ms-input-placeholder { + color: #4c4c4c +} + +#search-input:disabled::placeholder { + color: #4c4c4c +} + +#search-input:focus { + outline: none +} + +.navbar-burger { + background: none; + border: none; + outline: none; + line-height: 1; + position: relative; + width: 3rem; + padding: 0; + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-orient: vertical; + -webkit-box-direction: normal; + -ms-flex-direction: column; + flex-direction: column; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + -webkit-box-pack: center; + -ms-flex-pack: center; + justify-content: center; + margin-left: auto; + min-width: 0 +} + +.navbar-burger span { + background-color: #fff; + height: 1.5px; + width: 1rem +} + +.navbar-burger:not(.is-active) span { + -webkit-transition: opacity 0s .25s, margin-top .25s ease-out .25s, -webkit-transform .25s ease-out; + transition: opacity 0s .25s, margin-top .25s ease-out .25s, -webkit-transform .25s ease-out; + transition: transform .25s ease-out, opacity 0s .25s, margin-top .25s ease-out .25s; + transition: transform .25s ease-out, opacity 0s .25s, margin-top .25s ease-out .25s, -webkit-transform .25s ease-out +} + +.navbar-burger span+span { + margin-top: .25rem +} + +.navbar-burger.is-active span+span { + margin-top: -1.5px +} + +.navbar-burger.is-active span:first-child { + -webkit-transform: rotate(45deg); + transform: rotate(45deg) +} + +.navbar-burger.is-active span:nth-child(2) { + opacity: 0 +} + +.navbar-burger.is-active span:nth-child(3) { + -webkit-transform: rotate(-45deg); + transform: rotate(-45deg) +} + +.navbar-item, +.navbar-link { + color: #222; + display: block; + line-height: 1.6; + padding: .5rem 1rem +} + +.navbar-item.has-dropdown { + padding: 0 +} + +.navbar-item .icon { + width: 1.25rem; + height: 1.25rem; + display: block +} + +.navbar-item .icon img, +.navbar-item .icon svg { + fill: currentColor; + width: inherit; + height: inherit +} + +.navbar-link { + padding-right: 2.5em +} + +.navbar-dropdown .navbar-item { + padding-left: 1.5rem; + padding-right: 1.5rem +} + +.navbar-dropdown .navbar-item.has-label { + display: -webkit-box; + display: -ms-flexbox; + display: flex; + -webkit-box-pack: justify; + -ms-flex-pack: justify; + justify-content: space-between +} + +.navbar-dropdown .navbar-item small { + color: #8e8e8e; + font-size: .66667rem +} + +.navbar-divider { + background-color: #e1e1e1; + border: none; + height: 1px; + margin: .25rem 0 +} + +.navbar .button { + display: -webkit-inline-box; + display: -ms-inline-flexbox; + display: inline-flex; + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + background: #fff; + border: 1px solid #e1e1e1; + border-radius: .15rem; + height: 1.75rem; + color: #222; + padding: 0 .75em; + white-space: nowrap +} + +@media screen and (max-width:768.5px) { + .navbar-brand .navbar-item.search { + padding-left: 0; + padding-right: 0 + } +} + +@media screen and (min-width:769px) { + #search-input { + width: 200px + } +} + +@media screen and (max-width:1023.5px) { + .navbar-brand { + height: inherit + } + + .navbar-brand .navbar-item { + -webkit-box-align: center; + -ms-flex-align: center; + align-items: center; + display: -webkit-box; + display: -ms-flexbox; + display: flex + } + + .navbar-menu { + background: #fff; + -webkit-box-shadow: 0 8px 16px rgba(10, 10, 10, .1); + box-shadow: 0 8px 16px rgba(10, 10, 10, .1); 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+ +
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+ +
+ +
+

Installing Dependencies Using the Web Console

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+
+
+

As described in the General Information about Installation section, you may need to install other operators depending on the components and features of OpenShift AI you want to use. This section will discuss about installing and configuring those components.

+
+
+

It is generally recommended to install any dependent operators prior to installing the Red Hat OpenShift AI operator.

+
+
+
+
Red Hat OpenShift Pipelines Operator
+
+

The Red Hat OpenShift Pipelines Operator is required if you want to install the Data Science Pipelines component.

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+
NVIDIA GPU Operator
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+

The NVIDIA GPU Operator is required for GPU support in Red Hat OpenShift AI.

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+
Node Feature Discovery Operator
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+

The Node Feature Discovery Operator is a prerequisite for the NVIDIA GPU Operator.

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+
+
+
+

This section will discuss the process for installing the dependent operators using the OpenShift Web Console.

+
+
+
+
+

Installation of Data Science Pipelines Dependencies

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The Data Science Pipelines component utilizes Red Hat OpenShift Pipelines as an execution engine for all pipeline runs, and is required to be installed to take advantage of the Data Science Pipelines component.

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+
+

The following section discusses installing the Red Hat OpenShift Pipelines operator.

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+
+

Lab: Installation of the Red Hat OpenShift Pipelines operator

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    +
  1. +

    Login to Red Hat OpenShift using a user which has the cluster-admin role assigned.

    +
    2. +
  2. +

    Navigate to Operators → OperatorHub and search for Red Hat OpenShift Pipelines

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    +
    +pipeline search +
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    +
    3. +
  3. +

    Click on the Red Hat OpenShift Pipelines operator. In the pop up window, select the latest channel and the most recent version of the pipelines operator. Click on Install to open the operator’s installation view.

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    +pipeline install1 +
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    4. +
  4. +

    In the Install Operator page, select the default values for all the fields and click Install.

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    +pipeline install2 +
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    5. +
  5. +

    A window showing the installation progress will pop up.

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    +pipeline install3 +
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    6. +
  6. +

    When the installation finishes the operator is ready to be used by Red Hat OpenShift AI.

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    +pipeline install4 +
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    +
    7. +
+
+
+

Red Hat OpenShift Pipelines is now successfully installed.

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+
+ + + + + +
+ + +For assistance in installing OpenShift Pipelines from YAML or via ArgoCD, refer to examples found in the redhat-cop/gitops-catalog/openshift-pipelines-operator GitHub repo. +
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+
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Lab: Installation of GPU Dependencies

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Red Hat OpenShift AI makes it easy to expose GPUs to end users to help accelerate training and serving machine learning models.

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Currently, Red Hat OpenShift AI supports accelerated compute with NVIDIA GPUs using the NVIDIA GPU Operator which relies on the Node Feature Discovery operator as a dependency.

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The following section will discuss the installation and a basic configuration of both NVIDIA GPU Operator and the Node Feature Discovery operator.

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+ + + + + +
+ + +Node Feature Discovery and the NVIDIA GPU Operator can both be installed in a cluster that does not have a node with a GPU. This can be helpful when you plan to add GPUs at a later date. If a GPU is not present in the cluster, the Dashboard will not present the user an option to deploy using a GPU. +
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+ + + + + +
+ + +To view the list of GPU models supported by the NVIDIA GPU Operator refer to the Supported NVIDIA GPUs and Systems docs. +
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+
+

Demo: Installation of the Node Feature Discovery operator

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    +
  1. +

    Login to Red Hat OpenShift using a user which has the cluster-admin role assigned.

    +
    2. +
  2. +

    Navigate to Operators → OperatorHub and search for Node Feature Discovery

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    +nfd search +
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    +
    3. +
  3. +

    Two options for the Node Feature Discovery operator will be available. Click on the one with Red Hat in the top right hand corner, and in the pop up window click on Install to open the operator’s installation view.

    +
    + + + + + +
    + + +Make sure you select Node Feature Discovery from Red Hat NOT the Community version. +
    +
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    +nfd install1 +
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    +
    4. +
  4. +

    In the Install Operator page, select the option to Enable Operator recommended cluster monitoring on this Namespace, and keep all the rest of the parameters at their default values.

    +
    + + + + + +
    + + +Some of these options may vary slightly depending on your version of OpenShift. Please refer to the official Node Feature Discovery Documentation for your version of OpenShift for the recommended settings. +
    +
    +
    +
    +nfd install2 +
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    +
    5. +
  5. +

    Click the Install button at the bottom of the page to proceed with the installation. A window showing the installation progress will pop up.

    +
    6. +
  6. +

    When the installation finishes, click View Operator to configure the Node Feature Discovery operator.

    +
    7. +
  7. +

    Click the Create instance button for the NodeFeatureDiscovery object.

    +
    +
    +nfd configure1 +
    +
    +
    8. +
  8. +

    In the Create NodeFeatureDiscovery page, leave all fields at their default values, and click the Create button.

    +
    9. +
  9. +

    A new set of pods should appear in the Workloads → Pods section managed by the nfd-worker DaemonSet. Node Feature Discovery will now be able to automatically detect information about the nodes in the cluster and apply labels to those nodes.

    +
    +
    +nfd verify +
    +
    +
    10. +
+
+
+ + + + + +
+ + +For assistance in installing the Node Feature Discovery Operator from YAML or via ArgoCD, refer to examples found in the redhat-cop/gitops-catalog/nfd GitHub repo. +
+
+
+

Node Feature Discovery is now successfully installed and configured.

+
+
+
+

Lab: Installation of the NVIDIA GPU Operator

+
+
    +
  1. +

    Login to Red Hat OpenShift using a user which has the cluster-admin role assigned.

    +
    2. +
  2. +

    Navigate to Operators → OperatorHub and search for NVIDIA GPU Operator

    +
    +
    +gpu search +
    +
    +
    3. +
  3. +

    Click the NVIDIA GPU Operator tile. In the pop up window leave all fields at their default values, and click on Install to open the operator’s installation view.

    +
    +
    +gpu install1 +
    +
    +
    4. +
  4. +

    In the Install Operator page, keep all the parameters at their default values, and click the Install button at the bottom of the page to proceed with the installation.

    +
    +
    +gpu install2 +
    +
    +
    5. +
  5. +

    A window showing the installation progress will pop up. Wait while the operator finishes installing.

    +
    6. +
  6. +

    When the installation finishes, click the View Operator button.

    +
    7. +
  7. +

    Click the Create instance button for the ClusterPolicy object.

    +
    +
    +gpu configure1 +
    +
    +
    8. +
  8. +

    In the Create ClusterPolicy page, leave all fields at their default values, and click the Create button.

    +
    9. +
  9. +

    After the ClusterPolicy is created, the NVIDIA GPU Operator will update the status State: ready.

    +
    +
    +gpu verify1 +
    +
    +
    10. +
  10. +

    After the Red Hat OpenShift AI operator has been installed and configured, users will be able to see an option for "Number of GPUs" when creating a new workbench.

    +
    +
    +gpu verify2 +
    +
    +
    11. +
+
+
+ + + + + +
+ + +The Dashboard may initially show "All GPUs are currently in use, try again later." when Red Hat OpenShift AI is first installed. It may take a few minutes after Red Hat OpenShift AI is installed before the GPUs are initially detected. +
+
+
+ + + + + +
+ + +The NVIDIA GPU Operator supports many advanced use cases such as Multi-Instance GPU (MIG) and Time Slicing that are configurable using the ClusterPolicy. For information about advanced GPU configuration capabilities, refer to the official NVIDIA Documentation. +
+
+
+ + + + + +
+ + +For assistance installing the NVIDIA GPU Operator from YAML or via ArgoCD, refer to examples found in the redhat-cop/gitops-catalog/gpu-operator-certified GitHub repo. +
+
+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter1/index.html b/pr-preview/pr-62/rhods-admin/1.33/chapter1/index.html new file mode 100644 index 0000000..cb1f731 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter1/index.html @@ -0,0 +1,178 @@ + + + + + + Installation of Red Hat OpenShift AI :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Installation of Red Hat OpenShift AI

+
+

Supported configurations

+
+
+

OpenShift AI is supported in two configurations:

+
+
+
    +
  • +

    A managed cloud service add-on for Red Hat OpenShift Dedicated (with a Customer Cloud Subscription for AWS or GCP) or for Red Hat OpenShift Service on Amazon Web Services (ROSA). +For information about OpenShift AI on a Red Hat managed environment, see Product Documentation for Red Hat OpenShift AI Cloud Service 1

    +
    • +
  • +

    Self-managed software that you can install on-premise or on the public cloud in a self-managed environment, such as OpenShift Container Platform. +For information about OpenShift AI as self-managed software on your OpenShift cluster in a connected or a disconnected environment, see Product Documentation for Red Hat OpenShift AI Self-Managed 2.8

    +
    • +
+
+
+

In this course we cover installation of Red Hat OpenShift AI self-managed using the OpenShift Web Console.

+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter1/install-general-info.html b/pr-preview/pr-62/rhods-admin/1.33/chapter1/install-general-info.html new file mode 100644 index 0000000..a637112 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter1/install-general-info.html @@ -0,0 +1,185 @@ + + + + + + General Information about Installation :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

General Information about Installation

+
+

Red Hat OpenShift AI is available to install as an operator through the OperatorHub, or as a fully managed solution through the OpenShift Marketplace.

+
+
+
+
+

The product name has been recently changed to Red Hat OpenShift AI (RHOAI) (old name Red Hat OpenShift Data Science). In this course, most references to the product use the new name. However, references to some UI elements might still use the previous name.

+
+
+
+
+

In addition to the Red Hat OpenShift AI Operator there are some other operators that you may need to install depending on which features and components of Red Hat OpenShift AI you want to install and use.

+
+
+
+
Red Hat OpenShift Pipelines Operator
+
+

The Red Hat OpenShift Pipelines Operator is required if you want to install the Red Hat OpenShift AI Pipelines component.

+
+
NVIDIA GPU Operator
+
+

The NVIDIA GPU Operator is required for GPU support in Red Hat OpenShift AI.

+
+
Node Feature Discovery Operator
+
+

The Node Feature Discovery Operator is a prerequisite for the NVIDIA GPU Operator.

+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter1/rhods-install-web-console.html b/pr-preview/pr-62/rhods-admin/1.33/chapter1/rhods-install-web-console.html new file mode 100644 index 0000000..4e8b259 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter1/rhods-install-web-console.html @@ -0,0 +1,285 @@ + + + + + + Installing Red Hat OpenShift AI Using the Web Console :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Installing Red Hat OpenShift AI Using the Web Console

+
+
+
+

Red Hat OpenShift AI is available as an operator via the OpenShift Operator Hub. You will install the Red Hat OpenShift AI operator using the OpenShift web console in this section.

+
+
+
+
+

Lab: Installation of Red Hat OpenShift AI

+
+
+ + + + + +
+ + +The installation requires a user with the cluster-admin role +
+
+
+
    +
  1. +

    Login to the Red Hat OpenShift using a user which has the cluster-admin role assigned.

    +
    2. +
  2. +

    Navigate to Operators → OperatorHub and search for OpenShift AI.

    +
    +
    +rhods install1 +
    +
    Figure 1. Search for OpenShift AI operator
    +
    +
    3. +
  3. +

    Click on the Red Hat OpenShift AI operator. In the pop up window that opens, ensure you select the latest version in the stable channel and click on Install to open the operator’s installation view.

    +
    +
    +rhods install2 +
    +
    Figure 2. OpenShift AI Operator Details
    +
    +
    4. +
  4. +

    In the Install Operator page, leave all of the options as default and click on the Install button to start the installation.

    +
    5. +
  5. +

    The operator Installation progress window will pop up. The installation may take a couple of minutes.

    +
    6. +
  6. +

    When the operator’s installation is finished, click on the Create DataScienceCluster button to create and configure your cluster.

    +
    7. +
  7. +

    In the Create DataScienceCluster page, a number of components have been pre-selected. You can explore the components offered by expanding the Components field. Once you finish reviewing, click on the Create button.

    +
    8. +
  8. +

    After creating the DataScienceCluster, a view showing the DataScienceCluster details opens. Wait until the status of the cluster reads Phase: Ready. This represents the status of the whole cluster.

    +
    +
    +rhods2 clusters +
    +
    Figure 3. DataScienceCluster Instance Ready
    +
    +
    9. +
  9. +

    The operator should be installed and configured now. +In the applications window in the right upper corner of the screen the Red Hat OpenShift AI dashboard should be available.

    +
    +
    +rhods verify1 +
    +
    Figure 4. RHOAI Dashboard
    +
    +
    10. +
  10. +

    Click the Red Hat OpenShift AI button to log in to the Red Hat OpenShift AI dashboard. Log in as the admin user (With the same password that you used to log in to the OpenShift web console).

    +
    +
    +rhods verify2 +
    +
    Figure 5. Red Hat OpenShift AI Log in
    +
    +
    11. +
  11. +

    You should be able to see the Red Hat OpenShift AI home page.

    +
    +
    +rhoai home +
    +
    Figure 6. Red Hat OpenShift AI Home Page
    +
    +
    + + + + + +
    + + +It may take a while to start all the service pods hence the login window may not be accessible immediately. If you are getting an error, check the status of the pods in the project redhat-ods-applications. +Navigate to Workloads → pods and select project redhat-ods-applications. All pods must be running and be ready. If they are not, wait until they become running and ready. +
    +
    +
    +
    +rhods verify pods +
    +
    Figure 7. Pods in Running state
    +
    +
    12. +
+
+
+ + + + + +
+ + +For assistance installing the Red Hat Openshift AI from YAML or via ArgoCD, refer to examples found in the redhat-cop/gitops-catalog/rhods-operator GitHub repo. +
+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter1/uninstalling-rhods.html b/pr-preview/pr-62/rhods-admin/1.33/chapter1/uninstalling-rhods.html new file mode 100644 index 0000000..1fa9cea --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter1/uninstalling-rhods.html @@ -0,0 +1,366 @@ + + + + + + Uninstalling Red Hat OpenShift AI :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Uninstalling Red Hat OpenShift AI

+
+
+
+

The Red Hat OpenShift AI operator manages Red Hat OpenShift AI components, hence uninstalling components requires uninstalling the Red Hat OpenShift AI operator and cleaning some of the resources that have been created by the operator or users.

+
+
+
+
+

Demo: Uninstalling Red Hat OpenShift AI

+
+
+ + + + + +
+ + +These steps are for demonstration purposes only! Do NOT run these steps in your classroom because you will continue to work with the installed version of the product in the hands-on labs in the course. +
+
+
+ + + + + +
+ + +Make sure that you have installed the Red Hat OpenShift AI operator using one of the previous demonstrations (Web based or CLI). They both install a version of the operator from the stable channel. The screenshots in this section were taken on an older version of the product and may not exactly match yours. +
+
+
+
    +
  1. +

    Log in to Red Hat OpenShift web console using a user which has the cluster-admin role assigned.

    +
    2. +
  2. +

    Delete the DataScienceCluster object.

    +
    +

    Navigate to Operators → Installed Operators → Red Hat OpenShift AI → DataScienceCluster and delete all the DSC resources.

    +
    +
    +
    +rhods2 delete dsc +
    +
    Figure 1. Delete DSC Resource
    +
    +
    +

    Alternatively, you can delete the DataScienceCluster objects from the CLI.

    +
    +
    +
    +
    $ oc delete datasciencecluster default-dsc
+
+datasciencecluster.datasciencecluster.opendatahub.io "default-dsc" deleted
    +
    +
    +
    3. +
  3. +

    Delete the DSCInitialization object that the Operator created during installation.

    +
    +

    Navigate to the DSCInitialization tab and delete all DSCI resources.

    +
    +
    +
    +rhods2 delete dsci +
    +
    Figure 2. Delete DSCI Resource
    +
    +
    +

    Alternatively you can delete the DCSI objects from the CLI.

    +
    +
    +
    +
    $ oc delete dscinitialization default-dsci
+
+dscinitialization.dscinitialization.opendatahub.io "default-dsci" deleted
    +
    +
    +
    4. +
  4. +

    Uninstall the Red Hat OpenShift AI operator.

    +
    +

    Navigate to Operators → Installed Operators and uninstall the Red Hat OpenShift AI operator.

    +
    +
    +
    +rhods2 uninstall operator +
    +
    Figure 3. Uninstall RHOAI Operator
    +
    +
    +

    Alternatively you can delete the operator’s subscription from the CLI. OLM will uninstall the operator.

    +
    +
    +
    +
    $ oc delete subscription rhods-operator -n redhat-ods-operator
+
+subscription.operators.coreos.com "rhods-operator" deleted
    +
    +
    +
    5. +
  5. +

    Delete the namespace where the operator was installed.

    +
    +

    Navigate to Administration → Namespaces, search for the redhat-ods-operator and delete it.

    +
    +
    +
    +rhods2 delete ope ns +
    +
    +
    +

    Alternatively you can delete the namespace from the CLI.

    +
    +
    +
    +
    $ oc delete ns redhat-ods-operator
+namespace "redhat-ods-operator" deleted
    +
    +
    +
    6. +
  6. +

    Delete the namespaces that the Operator created during +installation. They are labeled with label opendatahub.io/generated-namespace=true. +Deleting namespace rhods-notebooks leads to Persistent Volume Claims (PVC) being used by Workbench getting deleted as well.

    +
    +

    Navigate to Administration → Namespaces, filter the namespaces using the label opendatahub.io/generated-namespace=true and delete them.

    +
    +
    +
    +rhods2 delete generated ns +
    +
    +
    +

    Alternatively you can delete the namespaces from the CLI.

    +
    +
    +
    +
    $ oc delete ns -l opendatahub.io/generated-namespace
+namespace "redhat-ods-applications" deleted
+namespace "redhat-ods-monitoring" deleted
    +
    +
    +
    7. +
  7. +

    Delete all remaining namespaces created for Datascience projects. These namespaces are labeled by the label opendatahub.io/dashboard=true.

    +
    +

    Navigate to Home → Projects, filter namespaces using the label opendatahub.io/dashboard=true and delete them.

    +
    +
    +
    +rhods2 delete projects +
    +
    +
    +

    Alternatively you can delete them from the CLI.

    +
    +
    +
    +
    $ oc delete ns -l opendatahub.io/dashboard=true
+namespace "my-rhods-project" deleted
    +
    +
    +
    8. +
+
+
+
+
+

Uninstalling the Red Hat OpenShift AI dependencies.

+
+
+

If you have installed some dependencies you can remove them as long as they are not used by other deployments. +The following demonstration shows uninstallation of the Red Hat OpenShift Pipelines operator.

+
+
+

Demo: Uninstallation of the Red Hat OpenShift Pipelines operator

+
+
    +
  1. +

    Log in to Red Hat OpenShift web console using a user which has the cluster-admin role assigned.

    +
    2. +
  2. +

    Navigate to Operators → Installed Operators, choose All Projects and click on the three dots on the right side of the Red Hat OpenShift Pipelines operator.

    +
    +
    +pipelines uninstall +
    +
    +
    +

    Click on Uninstall operator.

    +
    +
    3. +
  3. +

    In the pop-up window scroll down, check Delete all operand instances for this operator and click on Uninstall.

    +
    +
    +piplines uninstall confirm +
    +
    +
    4. +
+
+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter1/upgrading-rhods.html b/pr-preview/pr-62/rhods-admin/1.33/chapter1/upgrading-rhods.html new file mode 100644 index 0000000..468acc9 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter1/upgrading-rhods.html @@ -0,0 +1,312 @@ + + + + + + Upgrading Red Hat OpenShift AI :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Upgrading Red Hat OpenShift AI

+
+
+
+

Red Hat OpenShift AI upgrades are handled by the Red Hat OpenShift AI operator.

+
+
+

When an upgrade is available, OLM creates an Installplan for the new version.

+
+
+
+
$ oc get installplan -n redhat-ods-operator
+NAME            CSV                    APPROVAL   APPROVED
+install-sp49w   rhods-operator.2.7.0   Manual     false   (1)
+install-w6lqv   rhods-operator.2.6.0   Manual     true    (2)
+
+
+
+ + + + + + + + + +
1Installplan for the new version of the operator which has not been approved yet. It has to be approved in order to start the upgrade.
2Installplan for the currently installed version. It’s been approved and the version is currently installed.
+
+
+

Installplan is approved either automatically when a new version is available without user’s intervention or requires a manual approval. Whether the approval is automatic or manual, depends on the value of the installPlanApproval attribute of the operator’s subscription. When it is set to Automatic the installplan is approved automatically and installation starts without user’s intervention. Manual value requires a manual approval.

+
+
+

Approvals can be set from the web console as well as from the CLI.

+
+
+

The following demonstration shows an upgrade with a manual approval done from the web console.

+
+
+
+
+

Demo: Upgrading Red Hat OpenShift AI

+
+
+ + + + + +
+ + +These steps are for demonstration purposes only! Do NOT run these steps in your classroom because you will continue to work with the stable version of the product in the hands-on labs in the course. +
+
+
+ + + + + +
+ + +Make sure that you have installed the Red Hat OpenShift AI operator using one of the previous demonstrations (Web based or CLI). They both install a version of the operator from the stable channel. The screenshots in this section were taken on an older version of the product and may not exactly match yours. +
+
+
+
    +
  1. +

    Log in to Red Hat OpenShift web console using a user which has cluster-admin role assigned.

    +
    2. +
  2. +

    Navigate to Operators → Installed Operators, select project redhat-ods-operator, click on the Red Hat OpenShift AI and choose the Subscription tab. +Edit the Update channel - change it to alpha and click Save.

    +
    +
    +rhods2 channel change +
    +
    +
    + + + + + +
    + + +There may not be any updates available at the time when you do this. In which case, your screen will look different in the following steps. +
    +
    +
    3. +
  3. +

    Navigate again to Operators → Installed Operators and select project redhat-ods-operator. You should see Upgrade available under the Status column.

    +
    +
    +rhods2 upgrade available +
    +
    +
    + + + + + +
    + + +You will see a similar screen only if there is an update available. The screenshots in this demo were taken on an older version of the product when there were frequent updates available. +
    +
    +
    4. +
  4. +

    Click on the Upgrade available status link to open operator’s Install plan

    +
    +
    +rhods2 upgrade install plan +
    +
    +
    5. +
  5. +

    Click on the Preview InstallPlan button to review changes to be made by the upgrade.

    +
    +
    +rhods2 upgrade preview +
    +
    +
    6. +
  6. +

    Click on the Approve button to approve and start the upgrade and navigate to Operators → Installed Operators to view the upgrade status.

    +
    +
    +rhods2 upgrade status +
    +
    +
    7. +
  7. +

    Wait until the Status changes to Succeeded

    +
    +
    +rhods2 upgrade succeeded +
    +
    +
    8. +
+
+
+
+
+

Backup considerations

+
+
+

Permanent storage for Jupyter notebooks is done using OpenShift Persistent Volume Claims (PVC) attached to the Jupyter notebook pod. The operator does not manage PVCs hence there is no need to take a backup as long as the OpenShift Namespace where the Jupyter notebook pod is running is not deleted. Even in an event of accidental operator uninstallation all PVCs in namespaces created by either the operator or an OpenShift AI user remain untouched.

+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/create-datascience-project.png b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/create-datascience-project.png new file mode 100644 index 0000000..aa4d1d4 Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/create-datascience-project.png differ diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/datascience-project-permissions.png b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/datascience-project-permissions.png new file mode 100644 index 0000000..db1e2be Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/datascience-project-permissions.png differ diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/idle-notebook-culling.png b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/idle-notebook-culling.png new file mode 100644 index 0000000..6f61750 Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/idle-notebook-culling.png differ diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/login-with-openshift.png b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/login-with-openshift.png new file mode 100644 index 0000000..b94ca8a Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/login-with-openshift.png differ diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/user-management.png b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/user-management.png new file mode 100644 index 0000000..3935e85 Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter2/_images/user-management.png differ diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter2/index.html b/pr-preview/pr-62/rhods-admin/1.33/chapter2/index.html new file mode 100644 index 0000000..5c91606 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter2/index.html @@ -0,0 +1,177 @@ + + + + + + Controlling User Access and Managing Resources :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Controlling User Access and Managing Resources

+
+

This chapter teaches you how to manage data science projects and controller access with users and groups within OpenShift AI. You will also learn about different administrative configuration options from within OpenShift AI.

+
+
+

Goals:

+
+
+
    +
  • +

    Manage access to the OpenShift AI Dashboard

    +
    • +
  • +

    Control access to data science projects with users and groups

    +
    • +
  • +

    Configure automatic cleanup of idle workbenches

    +
    • +
  • +

    Manage default workbench and model server sizes

    +
    • +
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter2/resources.html b/pr-preview/pr-62/rhods-admin/1.33/chapter2/resources.html new file mode 100644 index 0000000..ec626d1 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter2/resources.html @@ -0,0 +1,620 @@ + + + + + + Managing Resources :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Managing Resources

+
+
+
+

OpenShift AI allows for end users to create many different types of resources as part of the development and deployment process. This section will review the different types of resources users are able to create in OpenShift AI and how to manage them.

+
+
+
+
+

Kubernetes Resources

+
+
+

The majority of actions taken in the OpenShift AI will create a kubernetes object based on that action. This section will discuss the different components supported by OpenShift AI and the different resources they create or interact with.

+
+
+

Data Science Projects

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
OpenShift AI Resource NameKubernetes Resource NameCustom ResourceDescription

Data Science Project

projects.project.openshift.io

No

A Data Science Project is synonym with an OpenShift Project or a Namespace. See the users section for more information on how to create and manage Data Science Projects.

Users

users.user.openshift.io

No

A user is an object that corresponds to a specific user that can be granted permissions within OpenShift.

Groups

groups.user.openshift.io

No

A group is a collection of users that like users, can be granted permissions within OpenShift.

Permissions

rolebindings.rbac.authorization.k8s.io

No

Users and Groups are granted permission to a Data Science Project via a RoleBinding. RoleBindings associate the user or group with a specific Kubernetes Role. Only the Edit and Admin roles are available within the Dashboard.

+
+
+

Notebook Controller

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
OpenShift AI Resource NameKubernetes Resource NameCustom ResourceDescription

Workbench

notebooks.kubeflow.org

Yes

A workbench is a IDE running in a pod using the Kubeflow Notebook Controller. Workbenches can run a number of web based IDEs including JupyterLab, VSCode, and R Studio, depending on what IDE was built into the workbench image.

Cluster Storage

persistentvolumeclaims

No

Workbenches utilize cluster storage to persist a users environment when the workbench pod is not running. When creating a workbench a default cluster storage instance will be created automatically to store any code or files a user creates in the workbench. Additional cluster storage can be added to the workbench for things like a dedicated PVC for training data.

Data Connection

secrets

No

A data connection is a secret that includes fields required to connect to an S3 bucket.

+
+
+

Data Science Pipelines

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
OpenShift AI Resource NameKubernetes Resource NameCustom ResourceDescription

Data Science Pipeline Application

datasciencepipelinesapplications.datasciencepipelinesapplications.opendatahub.io

Yes

DSPA’s create an instance of Data Science Pipelines. DSPA’s require a data connection and an S3 bucket to create the instance. DSPA’s are namespace scoped to prevent leaking data across multiple projects.

Pipelines

N/A

N/A

When developing a pipeline, depending on the tool, users may generate a YAML based PipelineRun object that is then uploaded into the Dashboard to create an executable pipeline. Even though this yaml object is a valid Tekton PipelineRun it is intended to be uploaded to the Dashboard, and not applied directly to the cluster.

Pipeline Runs

pipelineruns.tekton.dev

Yes

A pipeline can be executed in a number of different ways, including from the Dashboard, which will result in the creation of a pipelinerun.

+
+
+

Model Mesh

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + +
OpenShift AI Resource NameKubernetes Resource NameCustom ResourceDescription

Model Server

servingruntimes.serving.kserve.io

Yes

A model server is used to create a pod to serve models. A single model server can serve multiple models from a single instance.

Models

inferenceservices.serving.kserve.io

Yes

Models are associated with a specific model server, from which an endpoint will be created. Models require a data connection and a location where the model file is stored in the s3 bucket.

+
+
+
+
+

Configuring Idle Notebook Culling

+
+
+

Notebooks or Workbenches are intended to be user interactive development environments. Often times they can consume a large number of compute, memory, or GPU resources, and users may forget to shut them off when they are finished working with them, which can lead to issues where clusters begin to run out of available resources if not cleaned up.

+
+
+

The Idle Notebook Culler is intended to help reduce the number of inactive notebooks running on the cluster, by detecting the last time an action was taken in a notebook and shutting down the pods if the notebook has been inactive for a period of time.

+
+
+

An admin can enable culling for notebooks though the Dashboard under the Settings  Cluster settings section:

+
+
+
+idle notebook culling +
+
+
+

Alternatively, the idle notebook culling can be configured by creating the following config map:

+
+
+
+
kind: ConfigMap
+apiVersion: v1
+metadata:
+  name: notebook-controller-culler-config (1)
+  namespace: redhat-ods-applications (2)
+  labels:
+    opendatahub.io/dashboard: 'true'
+data:
+  CULL_IDLE_TIME: '240' (3)
+  ENABLE_CULLING: 'true' (4)
+  IDLENESS_CHECK_PERIOD: '1' (5)
+
+
+
+ + + + + + + + + + + + + + + + + + + + + +
1The name of the config map, which must match notebook-controller-culler-config
2The namespaces where the Dashboard is installed
3The time an notebook is idle before it is automatically stopped (in minutes)
4An option to enable or disable the automatic culling
5How frequently the culler will check if the notebook is idle (in minutes)
+
+
+

A notebook is considered idle when no logged-in user has taken any action inside of the notebook such as executing a cell, creating files, or interacting with the user interface in general.

+
+
+ + + + + +
+ + +
+

If your cluster is configured to end user sessions after a certain period of time, then this setting overrides the idle notebook time limit configured in RHOAI.

+
+
+

For example, assume you have configured your cluster to time out user sessions after one hour of inactivity, and also configured the RHOAI idle notebook time limit to five hours. +In this case, RHOAI will stop a notebook if no logged-in user activity is detected after one hour.

+
+
+
+
+
+
+

Managing Workbench and Model Server Sizes

+
+
+

When launching Workbenches or Model Servers from the Dashboard, users are presented with several default sizes they can select from. The default options may not suit your organizations needs.

+
+
+
+
apiVersion: opendatahub.io/v1alpha
+kind: OdhDashboardConfig
+metadata:
+  annotations:
+    internal.config.kubernetes.io/previousKinds: OdhDashboardConfig
+    internal.config.kubernetes.io/previousNames: odh-dashboard-config
+    internal.config.kubernetes.io/previousNamespaces: default
+  name: odh-dashboard-config (1)
+  namespace: redhat-ods-applications (2)
+  labels:
+    app.kubernetes.io/part-of: rhods-dashboard
+    app.opendatahub.io/rhods-dashboard: 'true'
+spec:
+  dashboardConfig:
+    modelMetricsNamespace: ''
+    enablement: true
+    disableProjects: false
+    disableSupport: false
+    disablePipelines: false
+    disableProjectSharing: false
+    disableModelServing: false
+    disableCustomServingRuntimes: false
+    disableISVBadges: false
+    disableUserManagement: false
+    disableInfo: false
+    disableClusterManager: false
+    disableBYONImageStream: false
+    disableTracking: false
+  groupsConfig:
+    adminGroups: rhods-admins
+    allowedGroups: 'system:authenticated'
+  modelServerSizes: (3)
+    - name: Small
+      resources:
+        limits:
+          cpu: '2'
+          memory: 8Gi
+        requests:
+          cpu: '1'
+          memory: 4Gi
+    - name: Medium
+      resources:
+        limits:
+          cpu: '8'
+          memory: 10Gi
+        requests:
+          cpu: '4'
+          memory: 8Gi
+    - name: Large
+      resources:
+        limits:
+          cpu: '10'
+          memory: 20Gi
+        requests:
+          cpu: '6'
+          memory: 16Gi
+  notebookController:
+    enabled: true
+    notebookNamespace: rhods-notebooks
+    notebookTolerationSettings:
+      enabled: false
+      key: NotebooksOnly
+    pvcSize: 20Gi
+  notebookSizes: (4)
+    - name: Small
+      resources:
+        limits:
+          cpu: '2'
+          memory: 8Gi
+        requests:
+          cpu: '1'
+          memory: 8Gi
+    - name: Medium
+      resources:
+        limits:
+          cpu: '6'
+          memory: 24Gi
+        requests:
+          cpu: '3'
+          memory: 24Gi
+    - name: Large
+      resources:
+        limits:
+          cpu: '14'
+          memory: 56Gi
+        requests:
+          cpu: '7'
+          memory: 56Gi
+    - name: X Large
+      resources:
+        limits:
+          cpu: '30'
+          memory: 120Gi
+        requests:
+          cpu: '15'
+          memory: 120Gi
+  templateOrder: []
+
+
+
+ + + + + + + + + + + + + + + + + +
1The name of the OdhDashboardConfig, which must match odh-dashboard-config
2The namespaces where the Dashboard is installed
3The default model server sizes, provided as a list
4The default workbench sizes, provided by a list
+
+
+ + + + + +
+ + +
+

Users will most often select the largest option available to them, despite what their actual needs are. Customizing these options based on the kinds of use cases your organization typically tackles can help to reduce the overall consumption of resources on a cluster.

+
+
+
+
+ + + + + +
+ + +
+

OpenShift provides the ability to restrict how many resources, including CPU, memory, and GPUs, are consumed by each project on a cluster with a LimitRange. To learn more about these capabilities refer to the +Restrict resource consumption with limit ranges documentation.

+
+
+
+
+
+
+

Removing an Unused Project

+
+
+

Over time, projects may become abandoned as users leave your organization or move onto other initiatives. Old projects may need to be cleaned up and removed from the cluster in order to free up those resources.

+
+
+

The following oc commands can be used to help cleanup OpenShift AI related resources prior to deleting the project:

+
+
+
+
# select the project
+oc project my-project
+
+# delete any workbenches
+oc delete notebooks --all
+# delete any models and model servers
+oc delete inferenceservices,servingruntimes --all
+# delete any data science pipeline instances
+oc delete dspa,pipelineruns --all
+
+# cleanup any other non OpenShift AI resources deployed in the project
+
+# delete any other common objects
+oc delete pvc,configmaps,secrets,rolebindings,roles --all
+
+# delete the project
+oc delete project my-project
+
+
+
+

Similarly, you can use the RHOAI dashboard to delete a data science project. +In the Data Science Projects section, locate the project to be deleted, click â‹® and then click Delete project.

+
+
+ + + + + +
+ + +
+

There is no need to manually delete all the resources in a data science project before deleting the project.

+
+
+

When you delete a data science project, the finalizer deletes all namespaced resources in this project, including notebooks, model servers, PVCs, configuration maps, secrets, and other common project-scoped objects.

+
+
+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter2/users.html b/pr-preview/pr-62/rhods-admin/1.33/chapter2/users.html new file mode 100644 index 0000000..ec2723a --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter2/users.html @@ -0,0 +1,468 @@ + + + + + + Users, Projects, and Permissions :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Users, Projects, and Permissions

+
+
+
+

As discussed in the previous section, Red Hat OpenShift AI allows users to install several different components to enable ML workloads. One of the components is the OpenShift AI Dashboard, which acts as the primary user interface to help guide users to working with, deploying, and managing ML solutions.

+
+
+

This section discusses of the intricacies of managing users with OpenShift AI and how to modify the default permissions for a project.

+
+
+
+
+

Logging Into The OpenShift AI Dashboard

+
+
+

OpenShift AI utilizes OpenShift OAuth as the authentication method for all OpenShift AI related resources. When attempting to log into the OpenShift AI Dashboard or other UI components, users will be presented with a page prompting them to Log in with OpenShift.

+
+
+
+login with openshift +
+
+
+

By default, any user with the ability to login to OpenShift will be able to access the OpenShift AI Dashboard.

+
+
+ + + + + +
+ + +
+

For assistance with configuring user access to OpenShift with various identity providers, please refer to the official Preparing for users OpenShift documentation.

+
+
+
+
+
+
+

Admin Access

+
+
+

By default, users with the cluster-admin role are automatically granted admin permissions in the OpenShift AI Dashboard. Additionally, a group called rhods-admins is automatically created, and any user added to that group will be granted admin permissions.

+
+
+

Admin access in the OpenShift AI Dashboard allows users to control various settings of OpenShift AI through the Dashboard UI, including:

+
+
+
    +
  • +

    Configuring custom notebook images

    +
    • +
  • +

    Configuring custom serving runtimes

    +
    • +
  • +

    Configuring the idle notebook culler (automatic removal of inactive notebooks)

    +
    • +
  • +

    Other notebook configuration settings

    +
    • +
+
+
+

To add users to the rhods-admins group, run the following command:

+
+
+
+
oc adm groups add-users rhods-admins my-user1 my-user2
+
+
+
+
+
+

Updating Default User Permissions

+
+
+

The default permissions for the OpenShift AI Dashboard can be updated through the Settings  User management section.

+
+
+
+user management +
+
+
+

The admin group can be updated to another group other than the default rhods-admins group, such as one that is configured to sync with an external system. Additionally, admin permissions to the Dashboard are always granted to users assigned the cluster-admin ClusterRole.

+
+
+ + + + + +
+ + +
+

It is highly recommended that dedicated admin users are configured for OpenShift AI and that organizations do not rely on the cluster-admin role for exclusive permissions to admin configurations of OpenShift AI. Dedicated Admin users should be added to the existing rhods-admins group, or another group which already contains the correct users that should be configured in lieu of the rhods-admins group.

+
+
+
+
+

The normal Data Science user group can also be updated to change what users are able to sign into the OpenShift AI Dashboard. By default, the Dashboard is configured to allow all authenticated users with the system:authenticated role. A custom group can be configured to allow only specific users belonging to that group to access the Dashboard.

+
+
+ + + + + +
+ + +
+

Updating the access in the User and group settings in the Dashboard will only impact a user’s abilities to access the Dashboard, and will not impact any permissions granted by regular Kubernetes based RBAC.

+
+
+

For example, if the normal user group is updated to only allow specific users to access the Dashboard, and a user that is not part of that group as admin may still have the ability to create Data Science related objects such as Notebooks, Data Science Pipelines, or Model Servers in a namespace they have permission in using the associated k8s objects without the UI.

+
+
+
+
+

Managing Dashboard Permissions with GitOps

+
+

The User and group settings in the OpenShift AI Dashboard can also be managed using the OdhDashboardConfigs object called odh-dashboard-config located in the redhat-ods-applications namespace along with many of the other settings found in the Dashboard’s Settings section:

+
+
+
+
oc get odhdashboardconfigs odh-dashboard-config -n redhat-ods-applications -o yaml
+
+
+
+

A truncated version of the OdhDashboardConfig object with the groupConfig settings can be found below:

+
+
+
+
apiVersion: opendatahub.io/v1alpha
+kind: OdhDashboardConfig
+metadata:
+  name: odh-dashboard-config
+  namespace: redhat-ods-applications
+spec:
+  groupsConfig:
+    adminGroups: rhods-admins
+    allowedGroups: 'system:authenticated'
+
+
+
+

This object can be exported from the cluster and stored in a git repository to enable managing these settings with GitOps.

+
+
+
+
+
+

Default Data Science Project Permissions

+
+
+

The OpenShift AI Dashboard permits users of the Dashboard to create new Data Science Projects when self-provisioning is enabled on the cluster:

+
+
+
+create datascience project +
+
+
+ + + + + +
+ + +
+

The terms Namespaces, OpenShift Projects, and Data Science Projects are often used synonymously. While there are technical differences between these objects, a project is essentially a namespace with some additional features provided by OpenShift. OpenShift will always create a corresponding project for every namespace, and vice versa.

+
+
+

Similarly, a Data Science Project is just an OpenShift Project that has a specific label, which enables it to be used with the Dashboard.

+
+
+
+
+ + + + + +
+ + +
+

For information on how to disable self-provisioner on OpenShift refer to the official Disabling project self-provisioning OpenShift documentation.

+
+
+
+
+

When a user creates a new Data Science Project through the Dashboard, the Dashboard will create a corresponding OpenShift Project and Kubernetes Namespace. The user who created the Data Science Project will automatically be granted the OpenShift admin role for the Namespace.

+
+
+ + + + + +
+ + +
+

OpenShift allows for both "cluster admins" who have the ability to manage all cluster and namespace scoped objects on a cluster, and "namespace admins" that have permission to manage all namespace scoped objects in a specific namespace.

+
+
+

All users with the cluster-admin role have the ability to see all other users namespaces.

+
+
+
+
+

Managing Permissions on a Data Science Project

+
+

A Data Science Project is nothing more than a normal OpenShift Project with a few special labels. These labels are required for the project to appear in the Dashboard. As such, permissions to the project function the same as any other project/namespace in OpenShift.

+
+
+

Users and Groups permissions to a project are managed using Kubernetes Role Based Access Control (RBAC). The Dashboard provides a user interface to easily assign additional users or groups permissions to a project.

+
+
+

An admin user on the project can add additional users or groups to a project by navigating to a specific project under the Data Science Projects menu, and selecting the Permissions tab.

+
+
+
+datascience project permissions +
+
+
+

From the permissions tab, project admin users are able to add additional users or groups and grant them the edit or admin role on the project.

+
+
+ + + + + +
+ + +
+

The Dashboard Permissions user interface only displays users and groups that were granted permissions directly through the Dashboard. Any users or groups that have been granted permission to the Project by any traditional OpenShift role management such as creating RoleBindings on the project or who are granted higher level cluster permissions will not be displayed in the user interface.

+
+
+
+
+
+

Manually Creating Data Science Projects

+
+

When self-provisioner is disabled on a cluster, a cluster administrator will be required to manually create Data Science Projects for users.

+
+
+

As mentioned previously, a Data Science Project is a normal OpenShift project/namespace with a few special labels that allow to be managed by the RHOAI Dashboard, allowing it to be managed using any existing processes or tools your organization already utilizes to manage namespaces.

+
+
+

To manually create a Data Science Project from the cli, you can run the following commands to create a namespace and apply the necessary labels:

+
+
+
+
oc create namespace myproject
+oc label namespace myproject opendatahub.io/dashboard='true' modelmesh-enabled='true'
+
+
+
+

Alternatively, the following YAML object can be used to create the Data Science Project:

+
+
+
+
kind: Namespace
+apiVersion: v1
+metadata:
+  name: myproject
+  labels:
+    modelmesh-enabled: 'true'
+    opendatahub.io/dashboard: 'true'
+
+
+
+

Once the Data Science Project has been created, access to the project will need to be configured for the necessary user or group using either the cli, or a namespace RoleBinding. Refer to the OpenShift documentation for Adding roles to users for additional instructions.

+
+
+
+
+
+

Exercise

+
+
+

Create a new Data Science Project and add a group with edit permissions.

+
+
+
    +
  1. +

    Create a Data Science Project called group-demo.

    +
    2. +
  2. +

    Navigate to the OpenShift Web Console, and create a Group called my-team.

    +
    3. +
  3. +

    Add user1 and user2 to the Group my-team.

    +
    4. +
  4. +

    Back in the OpenShift AI Dashboard, grant the team my-team the edit role on the project group-demo.

    +
    5. +
+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/customContainerFile.png b/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/customContainerFile.png new file mode 100644 index 0000000..eda28d7 Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/customContainerFile.png differ diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/podmanBuildCommand.png b/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/podmanBuildCommand.png new file mode 100644 index 0000000..4864917 Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/podmanBuildCommand.png differ diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/podmanImages.png b/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/podmanImages.png new file mode 100644 index 0000000..2d5bb48 Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/podmanImages.png differ diff --git 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b/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/testSeaborn2.png new file mode 100644 index 0000000..4a87aca Binary files /dev/null and b/pr-preview/pr-62/rhods-admin/1.33/chapter3/_images/testSeaborn2.png differ diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter3/custom.html b/pr-preview/pr-62/rhods-admin/1.33/chapter3/custom.html new file mode 100644 index 0000000..0a59a94 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter3/custom.html @@ -0,0 +1,402 @@ + + + + + + Create a Custom Notebook Image :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Create a Custom Notebook Image

+
+
+
+

Custom notebook images allow you to add specific packages and libraries for your projects. This level of customization provides you with a consistent development environment for your teams to work in and ensures that your projects are reproducible between team members. You may also want to create custom notebooks that are more secure than the default notebook image provided in OpenShift AI.

+
+
+
+
+

Image source and Pre-built images

+
+
+

The default OpenShift AI notebook images can be found in the Default OpenShift AI notebooks repository. This repository contains the source code, pre-built images, and examples to help you create your own image.

+
+
+

Additional images are available at the ODH contributions repository (opendatahub-io-contrib/workbench-images). This is a place to source additional images, as well as a great resource for best practices for building custom images. Workbench and runtime images are available as well as a script to generate custom images (https://github.com/opendatahub-io-contrib/workbench-images#building-an-image).

+
+
+
+
+

Exercise

+
+
+

We will now build our own custom image. We’ll use CUDA Notebook as our base image. This image contains artifacts that we will be using later in the course.

+
+
+

We will be adding the seaborn python package to the base image. It is a data visualization library based on matplotlib.

+
+
+ + + + + +
+ + +
+
    +
  • +

    On OpenShift, every container in a standard namespace (unless you modify security) runs with a random user id (uid), and the group id (gid) 0. Therefore, all the folders that you want to write in, and all the files you want to modify (temporarily) in your image must be accessible by this user. The best practice is to set the ownership as 1001:0 (user "default", group "0").

    +
    • +
  • +

    If you don’t want/can’t do that, another solution is to set permissions properly for any user, like chmod 775.

    +
    • +
  • +

    When launching a notebook in OpenShift AI, the working directory of a user is mounted at /opt/app-root/src. This is not configurable, so make sure to build your images with this default location for the data that you want persisted.

    +
    • +
+
+
+
+
+

Requirements

+
+

To build:

+
+
+
    +
  • +

    You need podman

    +
    +
      +
    • +

      You could also use buildah, docker, OpenShift BuildConfigs, or a Tekton pipeline, but we’ll just build the image using podman for this course.

      +
      • +
    +
    +
    • +
  • +

    You need a container image registry

    +
    +
      +
    • +

      We’ll use Quay as our image registry. You can sign up for a free account at Quay.io if you don’t already have one.

      +
      • +
    +
    +
    • +
+
+
+
+

Containerfile Overview

+
+

Before you start to edit the Containerfile lets go over what this Containerfile is doing.

+
+
+
+
Containerfile
+
+
+
+
+
+
+
#This is the base image we'll be working from
+FROM quay.io/modh/cuda-notebooks:cuda-jupyter-tensorflow-ubi9-python-3.9-20240329-3538b44  (1)
+
+# Copying custom packages
+# The requirements.txt file contains additional package dependencies we want installed
+COPY requirements.txt ./  (2)
+
+# Install packages and cleanup
+# (all commands are chained to minimize layer size)
+RUN echo "Installing softwares and packages" && \     (3)
+    pip install micropipenv && \
+    # Install Python packages \
+    micropipenv install && \
+    rm -f ./requirements.txt
+
+# Fix permissions to support pip in OpenShift environments \
+RUN chmod -R g+w /opt/app-root/lib/python3.9/site-packages && \    (4)
+    fix-permissions /opt/app-root -P
+
+
+
+ + + + + + + + + + + + + + + + + +
1The FROM command contains the base image that we’ll be using to build our custom image from. This will already contain common python packages that Data Scientists use and artifacts needed later in the course. You can find more base images at https://quay.io/organization/modh.
2The COPY command copies a requirements.txt file with the extra package that we will need in our notebook. The requirements.txt file in Python is a text file that lists all the third-party Python packages or libraries that a project depends on. micropipenv is a small footprint alternative to pip that allows us to install packages from our requirements.txt file.
3The next step is to install the packages. A lightweight wrapper for pip, micropipenv, allows us to install packages from our requirements.txt file. After the seaborn package is installed we then remove the requirements.txt to clean up the files.
4We’ll fix the permissions to support pip in OpenShift.
+
+
+
+
+
+

Build Steps

+
+
    +
  1. +

    Clone the https://github.com/RedHatQuickCourses/rhods-qc-apps Git repository to your workstation (using the git CLI, not in the Jupyter Notebook interface).

    +
    2. +
  2. +

    Navigate to the cloned repository, and open the requirements.txt file under the 6.rhod-admin/custom-images folder . The only package we’re installing is seaborn. It’s a Python data visualization library that we’ll use in our notebook.

    +
    +

    requirements.txt

    +
    +
    +
    +
    seaborn==0.13.0
    +
    +
    +
    +

    Multiple packages can be installed with the requirements.txt file by adding them on the next line. For example, the below requirements.txt file would install seaborn and the trino packages.

    +
    +
    +

    requirements.txt

    +
    +
    +
    +
    seaborn==0.13.0
+trino==0.305.0
    +
    +
    +
    3. +
  3. +

    Open the Containerfile under the 6.rhod-admin/custom-images folder and add the 3 commands below to the appropriate sections in the Containerfile. Refer to the Containerfile Overview section above to verify the correct location.

    +
    +
    +
       FROM quay.io/modh/cuda-notebooks:cuda-jupyter-tensorflow-ubi9-python-3.9-20240329-3538b44
    +
    +
    +
    +
    +
       COPY requirements.txt ./
    +
    +
    +
    +
    +
       RUN pip install --no-cache-dir -r requirements.txt
    +
    +
    +
    4. +
  4. +

    We’ll now build the image with podman. Open a terminal and go to 6.rhod-admin/custom-images. Run the below command.

    +
    +
    +
    podman build -t rhods-admin-custom-image .
    +
    +
    +
    +

    You should see a similar message in the logs:

    +
    +
    +
      +
    • +

      The seaborn package is installed successfully.

      +
      +
      +podmanSeabornInstalled +
      +
      +
      • +
    +
    +
    5. +
  5. +

    Once the image is done building run the below command to make sure the image is in the list.

    +
    +
    +
    podman images
    +
    +
    +
    +
    +podmanImages +
    +
    +
    6. +
  6. +

    Login to quay.io so we can push the image you just built up to the quay repository.

    +
    +
    +
    podman login quay.io
    +
    +
    +
    7. +
  7. +

    Push the image to your quay repository.

    +
    +
    +
    podman push rhods-admin-custom-image:latest quay.io/<YOUR_USERNAME>/rhods-admin-custom-image
    +
    +
    +
    8. +
+
+
+

Now you’re ready to import your image into OpenShift AI! See the next section to learn how to import your custom image and test it out.

+
+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter3/importcustom.html b/pr-preview/pr-62/rhods-admin/1.33/chapter3/importcustom.html new file mode 100644 index 0000000..4462ae0 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter3/importcustom.html @@ -0,0 +1,367 @@ + + + + + + Importing Custom Notebook Images :: Red Hat OpenShift AI Administration + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Importing Custom Notebook Images

+
+
+
+

In this section we will go over importing a custom notebook through the OpenShift AI dashboard and test it to make sure our dependencies are included.

+
+
+
+
+

Import the Notebook Image

+
+
+
    +
  1. +

    Before we import the image into OpenShif AI we need to set the quay repository we just created to public. In a browser, login to quay.io and go to the rhods-admin-custom-image repository. Select the Settings gear icon.

    +
    +
    +Quay Repository Settings +
    +
    +
    2. +
  2. +

    Scroll down until you see the Repository Visibility section. Click the Make Public button and click Ok to make the repository public.

    +
    +
    +Quay Visibility Setting +
    +
    +
    + + + + + +
    + + +
    +

    To use private container images, you can create a dockerconfigjson secret in the redhat-ods-applications project and assign this secret to the default service account in this project.

    +
    +
    +

    The secret can contain the registry credentials in dockerconfig JSON format, which, if you use Quay, you can download from the Account Settings page, by clicking Generate Encrypted Password and then downloading the credentials JSON file from the Docker Configuration option.

    +
    +
    +

    After downloading the credentials file, use this file to create a secret:

    +
    +
    +
    +
    $ oc create secret generic quay-pull-secret \
+ --from-file=.dockerconfigjson=path-to-credentials-file.json \
+ --type=kubernetes.io/dockerconfigjson \
+ -n redhat-ods-applications
    +
    +
    +
    +

    Finally, link the secret to the default service account as a pull secret:

    +
    +
    +
    +
    $ oc secrets link default quay-pull-secret --for=pull \
+ -n redhat-ods-applications
    +
    +
    +
    +

    For more information, refer to Importing images and image streams from private registries.

    +
    +
    +
    +
    3. +
  3. +

    Login to the OpenShift AI dashboard as the admin user. Expand Settings, click Notebook Images, and then click Import Image.

    +
    +
    +OpenShift AI Admin Notebook Images +
    +
    +
    4. +
  4. +

    Enter the path to the your repository and give the image a name. Optionally add a description and package information. Click the Import button.

    +
    +
    +OpenShift AI Import Notebook +
    +
    +
    +

    You should now see the image in the list and enabled.

    +
    +
    +
    +OpenShift AI Custom Notebooks +
    +
    +
    5. +
+
+
+
+
+

Test the Image in a Workbench

+
+
+
    +
  1. +

    Now we’ll test out the image we just imported. Go to Data Science Projects and then click the Create data science project button.

    +
    +
    +OpenShift AI Data Science Projects +
    +
    +
    2. +
  2. +

    Give the project a name and resource name. Click the Create button.

    +
    +
    +OpenShift AI Project +
    +
    +
    3. +
  3. +

    Click the Create button.

    +
    +
    +OpenShift AI Workbench +
    +
    +
    4. +
  4. +

    Give the workbench a name and select the rhods-admin-custom-image from the Image selection drop down list. Click the Create Workbench button.

    +
    +
    +OpenShift AI Workbench Details +
    +
    +
    5. +
  5. +

    Once the Status changes to Running click the Open link.

    +
    +
    +OpenShift AI Workbench Start +
    +
    +
    6. +
  6. +

    Login to the notebook and click the Python 3.9.16 notebook tile.

    +
    +
    +OpenShift AI Python Notebook +
    +
    +
    7. +
  7. +

    In the first cell of the notebook add the following

    +
    +
    +
    pip list | grep "^s"
    +
    +
    +
    +

    Press Shift + Enter on your keyboard to execute the cell. This will find all of the dependencies that start with "s". Note that seaborn is an installed dependency.

    +
    +
    +
    +pip list +
    +
    +
    8. +
  8. +

    Now let’s test out the seaborn package to make sure it works. Copy the following code to the cell below.

    +
    +
    +
    import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+
+# Load the dowjones dataset
+dowjones = sns.load_dataset("dowjones")
+
+# Create a line plot with the year on the x-axis
+sns.lineplot(
+    x="Date",
+    y="Price",
+    data=dowjones,
+)
+
+# Set the title and labels
+plt.title("Dow Jones Industrial Average by Year - 1914 to 1968")
+plt.xlabel("Date")
+plt.ylabel("Price")
+
+# Show the plot
+plt.show()
    +
    +
    +
    +
    +Seaborn Code Snippet +
    +
    +
    +

    Click Shift + Enter to execute the cell. You should see the graph below. You can now close the notebook. Don’t worry about saving it.

    +
    +
    +
    +Seaborn Graph +
    +
    +
    9. +
+
+
+

Congratulations! You’ve succesfully built, imported, and tested a custom image in OpenShift AI.

+
+
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/chapter3/index.html b/pr-preview/pr-62/rhods-admin/1.33/chapter3/index.html new file mode 100644 index 0000000..d500279 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/chapter3/index.html @@ -0,0 +1,171 @@ + + + + + + Building Custom Notebook Images :: Red Hat OpenShift AI Administration + + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Building Custom Notebook Images

+
+

In this chapter, you learn how to build custom notebook images.

+
+
+

Goals:

+
+
+
    +
  • +

    Create custom notebook images

    +
    • +
  • +

    Import custom notebook images into OpenShift AI

    +
    • +
+
+ +
+
+
+
+
+ Red Hat +
+ + + diff --git a/pr-preview/pr-62/rhods-admin/1.33/index.html b/pr-preview/pr-62/rhods-admin/1.33/index.html new file mode 100644 index 0000000..9ad08e3 --- /dev/null +++ b/pr-preview/pr-62/rhods-admin/1.33/index.html @@ -0,0 +1,480 @@ + + + + + + Red Hat OpenShift AI Administration :: Red Hat OpenShift AI Administration + + + + + + +
+ +
+
+ +
+
+ +
+ +
+

Red Hat OpenShift AI Administration

+
+
+
+

Welcome to this quick course on the Red Hat OpenShift AI Administration. +This course is the second in a series of five courses about Red Hat OpenShift AI:

+
+
+
    +
  1. +

    Introduction to Red Hat OpenShift AI

    +
    2. +
  2. +

    Red Hat OpenShift AI Administration (This course)

    +
    3. +
  3. +

    Creating Machine Learning Models with Red Hat OpenShift AI

    +
    4. +
  4. +

    Deploying Machine Learning Models with Red Hat OpenShift AI

    +
    5. +
  5. +

    Automation using Data Science Pipelines

    +
    6. +
+
+
+ + + + + +
+ + +After you have completed all the courses in the learning path, you can attempt the "Workshop final hands-on lab", that tests your understanding of the concepts taught in all the courses. +
+
+
+
+
+

Authors

+
+
+

The PTL team acknowledges the valuable contributions of the following Red Hat associates:

+
+
+
    +
  • +

    Jiri Tesar

    +
    • +
  • +

    Jaime Ramirez Castillo

    +
    • +
  • +

    Max Murakami

    +
    • +
  • +

    Trevor Royer

    +
    • +
  • +

    John Hurlocker

    +
    • +
  • +

    Ravi Srinivasan

    +
    • +
+
+
+
+
+

Classroom Environment for Red Hat Associates

+
+
+

Red Hat associates should use the Red Hat Demo Platform to provision a classroom.

+
+
+

Use the Red Hat OpenShift Container Platform Cluster catalog item from Red Hat Demo Platform (RHDP).

+
+
+

When ordering this demo item:

+
+
+
    +
  • +

    Select Practice/Enablement for the Activity field, and Learning about the product for the Purpose.

    +
    • +
  • +

    If you have a Salesforce ID for a customer sales opportunity, then copy it to the Salesforce ID field. +Otherwise, add some random text like "Learning RHOAI".

    +
    • +
  • +

    Select 5 users in the User Count field. +Having at least two users is required for the user management exercise.

    +
    • +
  • +

    In the OpenShift worker memory size field, select 64Gi. +This size might be required if you run multiple workbenches and pipelines simultaneously.

    +
    • +
  • +

    In the OpenShift version field, select the latest stable version available.

    +
    • +
  • +

    Review the auto-stop and auto-destroy timers. You can change this later after classroom provisioning by opening a ticket with the RHDP team.

    +
    • +
+
+
+ + + + + +
+ + +
+

RHOAI workbenches and pipelines use large container images. +Dependencies such as Tensorflow, PyTorch, or CUDA result in image sizes of several gigabytes.

+
+
+

The data ingestion and training stages can demand large amounts of memory and CPU/GPU resources. +Make sure that your worker nodes have enough memory to accommodate pods that consume dozens of GB.

+
+
+
+
+

This classroom does NOT have RHOAI pre-installed. +You install and configure a basic RHOAI instance, and then continue using this instance with other courses in the learning path.

+
+
+

Alternative classroom environment on AWS for Red Hat Associates

+
+

You can also use an AWS cluster by ordering the AWS with OpenShift Open Environment.

+
+
+

This classroom does NOT have RHOAI pre-installed. +You install and configure a basic RHOAI instance, and then continue using this instance with other courses in the learning path.

+
+
+

If you use this item, you must:

+
+
+
    +
  • +

    Select Practice/Enablement for the Activity field, and Learning about the product for the Purpose.

    +
    • +
  • +

    If you have a Salesforce ID for a customer sales opportunity, then copy it to the Salesforce ID field. +Otherwise, add some random text like "Learning RHOAI".

    +
    • +
  • +

    Make sure that you select 3 as the control plane count and the latest OpenShift version.

    +
    • +
  • +

    Select your nearest AWS region and choose m6a.4xlarge as the instance type (Ensure that whatever instance type you choose has at least 16 vCPUs and 64GB RAM).

    +
    • +
  • +

    Review the auto-stop and auto-destroy timers. You can change this later after classroom provisioning by opening a ticket with the RHDP team.

    +
    • +
  • +

    Configure users and authentication. +The AWS item is only pre-configured with the kubeadmin user, which does not have the cluster-admin role assigned. +The exercises in this course, however, assume the existence of a user named admin with the cluster-admin role and five non-admin users (user1…​user5). +Download the following two scripts to your local machine, and execute the following commands.

    +
    + +
    +
    • +
+
+
+
+
+

The password for both the admin and user1…​user5 users is openshift23.

+
+
+
+
+
    +
  • +

    You may have to wait for 5 minutes while the changes take effect. You will see a log in prompt with the htpasswd_provider displayed.

    +
    +
    +htpasswd provider +
    +
    Figure 1. htpasswd_provider prompt
    +
    +
    • +
  • +

    Click the htpasswd_provider button, and log in as the admin user with password openshift23 to access the OpenShift web console as a cluster administrator.

    +
    • +
+
+
+
+
+
+

Classroom Environment for Red Hat Partners

+
+
+

Red Hat partners should provision their own Red Hat OpenShift cluster using the Red Hat Hybrid Cloud Console at https://console.redhat.com/openshift/overview.

+
+
+
+
+

The following compute resource requirements are recommendations for this lab environment. For the official compute requirements for OpenShift AI Self_Managed on OpenShift Container platform visit the product documentation.

+
+
+
+
+
    +
  • +

    Ensure that you provision an OpenShift 4.13 or later cluster with a minimum of 3 control plane nodes (each with 8vCPU and 16GB RAM), and 3 worker nodes (each with 16 vCPU, and 64GB of RAM).

    +
    • +
  • +

    Configure users and authentication. +A default OpenShift cluster installation has a user called kubeadmin. +The exercises in this course, however, assume the existence of a user named admin with the cluster-admin role and five non-admin users (user1…​user5). +Download the following two scripts to your local machine, and execute the following commands after logging in to your cluster as the kubeadmin user using the oc CLI tool.

    +
    + +
    +
    • +
+
+
+
+
+

The password for both the admin and user1…​user5 users is openshift23.

+
+
+
+
+
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    You may have to wait for 5 minutes while the changes take effect. You will see a log in prompt with the htpasswd_provider displayed.

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    Figure 2. htpasswd_provider prompt
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    Click the htpasswd_provider button, and log in as the admin user with password openshift23 to access the OpenShift web console as a cluster administrator.

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Prerequisites

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    Basic knowledge of OpenShift (or Kubernetes) administration

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    Ability to build and deploy container images

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    Knowledge of OpenShift User and Role administration

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    Basic knowledge of AWS EC2 and S3 services

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Objectives

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The overall objectives of this course include:

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    Install RedHat OpenShift AI using the web console and CLI

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    Upgrade RedHat OpenShift AI components

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    Manage RedHat OpenShift AI users and controlling access

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    Enable GPU support in RedHat OpenShift AI

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    Stop idle notebooks

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    Create and configure a custom notebook image

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