AI and HPC Containers | NVIDIA Developer

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bars.attr("width", 0); if(barLabels) { barLabels.style("opacity", 0); } // Fade in chart chart.svg.selectAll("g") .style("opacity", 0) .transition() .duration(1000) .style("opacity", 1); // Animate in bars bars.transition() .duration(barAnimationDuration) .delay(function (d, i) { return 1000 + animationDelay * i; }) .attr("width", function(d) { return chart.x(d.value); }); // Fade in intra-bar labels if (barLabels) { barLabels.transition() .duration(1000) .delay(function (d, i) { return 1000 + animationDelay * chart.data.length; }) .style("opacity", 1); } } function drawYAxis(g, y, tickPadding, lineHeight, yAxisLabelClass) { g.append("g") .attr("class", "y axis y-axis-label") .call(d3.axisLeft(y).tickSize(0).tickPadding(tickPadding)); var yAxisLabels = g.selectAll(".y .tick text"); if (yAxisLabelClass) { yAxisLabels.attr("class", yAxisLabelClass); } yAxisLabels.each(function(t) { var lines = t.split("\n"); if (lines.length == 2) { var x = -tickPadding; var offset = "-.4em"; if (yAxisLabelClass == "title-subtitle") { offset = "-.2em"; } var self = d3.select(this); self.text(null); self.append("tspan") .attr("x", x) .attr("dy", offset) .text(lines[0]); self.append("tspan") .attr("x", x) .attr("dy", lineHeight + "em") .text(lines[1]); } }); } /** * Add a legend of squares and labels to the provided d3 selected div. * * @param legend - A d3 selection of the div to add the legend to. * @param data - One row per legend entry, each with values * "label" and "class". * @param milestone - Optional, an object with a "color" value for a * milestone line in the chart, will be added as the last legend entry. */ function addLegend(legend, data, milestone, greenScale, numGreens) { // Copy data before modifying data = data.slice(); if (milestone) { data.push({"label": milestone.label, "class": "milestone-square"}); } var keys = data.map(function(l) { return l.label; }); var color = d3.scaleOrdinal() .range(data.map(function(l) { return l.class; })) .domain(keys); // Add a div for each legend entry var legendEntry = legend.selectAll("div") .data(data) .enter() .append("div").attr("class", "legend-entry"); // Add the square for each legend entry legendEntry.append("div") .attr("class", function(l) { return "legend-square " + color(l.label); }) .style("background-color", function(l, i) { if (color(l.label) == "green") { return greenScale(i / numGreens); } }); // Add the text for each legend entry legendEntry.append("div") .attr("class", "legend-text") .html(function(d) { return d.label; }); if (milestone) { var milestoneSquare = legend.select(".milestone-square") var boundingRect = milestoneSquare.node().getBoundingClientRect(); var svg = milestoneSquare.append("svg"); svg.attr("width", boundingRect.width); svg.attr("height", boundingRect.height); svg.append("g") .append("line") .attr("class", "milestone " + milestone.class) .attr("x1", 0) .attr("x2", boundingRect.width) .attr("y1", 1) .attr("y2", 1); } } /** * Add a simple horizontal bar chart to the provided svg element. * * @param svg - A d3 selection of the svg to add the chart to. * @param data - One row per horizontal bar, each with attributes * "label", "value", and "class". * E.g. For a two-bar chart comparing Quadro P5000 to CPU: * [ * {"label": "Quadro P5000", "value": 7.3, "class": "green"}, * {"label": "CPU", "value": 1, "class": "blue"} * ] * @param ticks - The ticks to show on the x-axis, each with attributes * "tickLabel", "value", and "class". * E.g. For 3 ticks, with the middle tick hidden for mobile: * [ * {"tickLabel": "0", "value": 0, "class": ""}, * {"tickLabel": "1X", "value": 1, "class": "mobile-hide"}, * {"tickLabel": "2X", "value": 2, "class": ""} * ] * @param xAxisLabel - The label to show under the x axis. * @param yAxisLabelClass - Passing an option to this parameter turns on * special styling for the y-axis, which is useful when the labels * for the bars in the y-axis spill over to two lines. Labels will * be split into lines on "\n", extra padding will be added between * bars, and font will be smaller to accommodate the extra line. * Supported options: * "" - For default styling, leaving labels as single lines. * "two-lines" - Both lines will be slightly smaller in font than default. * "title-subtitle" - Second line will be smaller than first line. * @param isDark - true to enable styling for dark background, otherwise false. */ function createHorizontalBarChart(chartContainer, data, ticks, xAxisLabel, yAxisLabelClass, isDark) { var barAnimationDuration = 1000; var animationDelay = 200; var chart = drawHorizontalBarChart(chartContainer, data, ticks, xAxisLabel, yAxisLabelClass, isDark); // If the chart should be immediately visible, begin animation. var rendered = false; if (chartContainer.node().getBoundingClientRect().top - window.innerHeight <= 0) { rendered = true; animate(chart, barAnimationDuration, animationDelay); } // Begin animation the first time the chart scrolls into view. window.addEventListener("scroll", function() { if (!rendered && chartContainer.node().getBoundingClientRect().top - window.innerHeight <= 0) { rendered = true; animate(chart, barAnimationDuration, animationDelay); } }); // Redraw whenever the browser window is resized. var width = window.innerWidth; window.addEventListener("resize", function() { if (window.innerWidth != width) { chartContainer.selectAll("*").remove(); drawHorizontalBarChart(chartContainer, data, ticks, xAxisLabel, yAxisLabelClass, isDark); width = window.innerWidth; } }); } i function drawHorizontalBarChart(chartContainer, data, ticks, xAxisLabel, yAxisLabelClass, isDark) { // Right margin leaves room for last tick label var margin = {top: 0, right: 20, bottom: 0, left: 0}; var barHeight = 20; var barPadding = 5; if (yAxisLabelClass == "two-lines" || yAxisLabelClass == "title-subtitle") { barPadding = 10; } var tickPadding = 12; var lineHeight = 1.1; // ems var svg = chartContainer.append("svg"); // The graph's width is set to fit within its parent container. var svgWidth = svg.node().parentNode.getBoundingClientRect().width; svg.attr("width", svgWidth); if (isDark) { svg.attr("class", yAxisLabelClass + " dark"); } else { svg.attr("class", yAxisLabelClass); } // The graph's height is a function of the number of bars. var height = (barHeight * data.length + barPadding * (data.length + 1)); var g = svg.append("g").attr("class", "graph"); var y = d3.scaleBand().range([0, height]); y.domain(data.map(function(d) { return d.axisLabel; })) .padding(0.2); // Draw the y axis to know how much space to leave for it, // then immediately delete it. Redrawing it last ensures // the correct ordering of layers for display. drawYAxis(g, y, tickPadding, lineHeight, yAxisLabelClass); var yAxisWidth = svg.select(".y").node().getBoundingClientRect().width; svg.select("g.y.axis").remove(); var width = svgWidth - margin.left - yAxisWidth - margin.right; g.attr("transform", "translate(" + (yAxisWidth + margin.left) + "," + margin.top + ")"); var x = d3.scaleLinear().range([0, width]); x.domain([d3.min(ticks, function(d) { return d.value; }), d3.max(ticks, function(d) { return d.value; })]); var xAxis = d3.axisBottom(x) .tickValues(ticks.map(function(tick) { return tick.value; })) .tickFormat(function(d, i) { return ticks[i].tickLabel; }) .tickSizeInner([-height]) .tickSizeOuter(0) .tickPadding(tickPadding); g.append("g") .attr("class", "x axis tick-label") .attr("transform", "translate(0," + height + ")") .call(xAxis); svg.selectAll(".x .tick").each(function(t, i) { d3.select(this).attr("class", ticks[i].class); }); var greenBegin = "#76b900"; var greenEnd = "#c8e399"; if (isDark) { greenEnd = "#3b5d00"; } var greenScale = d3.interpolateLab(greenBegin, greenEnd); var numGreens = 0; for (var i = 0; i < data.length; i++) { if (data[i].class == "green") { numGreens++; } } var bars = g.selectAll(".bar") .data(data).enter().append("g") .attr("transform", function(d, i) { return "translate(0," + y(d.axisLabel) + ")"; }); bars.append("rect") .attr("class", function(d) { return "bar " + d.class; }) .attr("fill", function(d, i) { if (d.class == "green") { return greenScale(i/numGreens); } }) .attr("x", 0) .attr("height", barHeight) .attr("width", function(d) { return x(d.value); }); // Add labels on bars var barLabels = bars.append("text") .attr("class", "bar-label") .attr("x", function(d) { return x(d.value) - 6; }) .attr("y", barHeight / 2) .attr("dy", ".35em") .text(function(d) { return d.barLabel; }); barLabels.each(function(d, i) { if (this.getBoundingClientRect().width + 12 > bars.nodes()[i].getBoundingClientRect().width) { var labelWidth = this.getBoundingClientRect().width; var bar = d3.select(this.parentNode).select(".bar"); var barWidth = bar.node().getBoundingClientRect().width; if (labelWidth + 13 > width - barWidth) { this.remove(); } else { d3.select(this) .attr("transform", "translate(" + 12 + ",0)") .attr("class","outer-bar-label") .style("text-anchor", "start"); } } }); var xAxisHeight = svg.select(".x.axis.tick-label").node().getBoundingClientRect().height; // Add a label to the x axis. if (xAxisLabel) { g.append("text") .attr("x", width / 2) .attr("y", xAxisHeight + 5) .attr("class", "axis-label") .attr("alignment-baseline", "hanging") .text(xAxisLabel) .call(wrap, width, lineHeight); } drawYAxis(g, y, tickPadding, lineHeight, yAxisLabelClass); var svgHeight = margin.top + margin.bottom + svg.select(".graph").node().getBoundingClientRect().height; svg.attr("height", svgHeight); return { svg: svg, data: data, x: x, }; } /** * Add a grouped horizontal bar chart to the provided svg element. * * @param svg - A d3 selection of the svg to add the chart to. * @param legend - A d3 selection of the div to add the legend to. * @param barData - One row per horizontal bar, each with attributes * "label", "values". * @param legendData - One row per horizontal bar, each with attributes * "label" and "class". * E.g. For a two-bar chart comparing Quadro P5000 to CPU: * [ * {"label": "Quadro P5000", "class": "green"}, * {"label": "CPU", "class": "green"} * ] * @param ticks - The ticks to show on the x-axis, each with attributes * "tickLabel", "value", and "class". * E.g. For 3 ticks, with the middle tick hidden for mobile: * [ * {"tickLabel": "0", "value": 0, "class": ""}, * {"tickLabel": "1X", "value": 1, "class": "mobile-hide"}, * {"tickLabel": "2X", "value": 2, "class": ""} * ] * @param milestone - Optional, an object with a "color" and "value" * for a milestone line in the chart. Set null for no milestone. * @param xAxisLabel - The label to show under the x axis. * @param isDark - true to enable styling for dark background, otherwise false. */ function createGroupedHorizontalBarChart(chartContainer, barData, legendData, ticks, milestone, xAxisLabel, isDark) { var barAnimationDuration = 800; var animationDelay = 30; var chart = drawHorizontalGroupedBarChart(chartContainer, barData, legendData, ticks, milestone, xAxisLabel, isDark); // If the chart should be immediately visible, begin animation. var rendered = false; if (chartContainer.node().getBoundingClientRect().top - window.innerHeight <= 0) { rendered = true; animate(chart, barAnimationDuration, animationDelay); } // Begin animation the first time the chart scrolls into view. window.addEventListener("scroll", function() { if (!rendered && chartContainer.node().getBoundingClientRect().top - window.innerHeight <= 0) { rendered = true; animate(chart, barAnimationDuration, animationDelay); } }); // Redraw whenever the browser window is resized. var width = window.innerWidth; window.addEventListener("resize", function() { if (window.innerWidth != width) { chartContainer.selectAll("*").remove(); drawHorizontalGroupedBarChart(chartContainer, barData, legendData, ticks, milestone, xAxisLabel, isDark); width = window.innerWidth; } }); } function drawHorizontalGroupedBarChart(chartContainer, barData, legendData, ticks, milestone, xAxisLabel, isDark) { // Right margin leaves room for last tick label var margin = {top: 0, right: 20, bottom: 0, left: 0}; var barHeight = 10; var barPadding = 2; var groupPadding = 10; var tickPadding = 12; var lineHeight = 1.1; var keys = legendData.map(function(l) { return l.label; }); var legend = chartContainer.append("div"); var svg = chartContainer.append("svg"); var greenBegin = "#76b900"; var greenEnd = "#c8e399"; if (isDark) { greenEnd = "#3b5d00"; } var greenScale = d3.interpolateLab(greenBegin, greenEnd); var numGreens = 0; for (var i in legendData) { if (legendData[i].class == "green") { numGreens++; } } // The graph's width is set to fit within its parent container. var svgWidth = svg.node().parentNode.getBoundingClientRect().width; svg.attr("width", svgWidth); if (isDark) { svg.attr("class", "dark"); legend.attr("class", "legend dark"); } else { legend.attr("class", "legend"); } // The graph's height is a function of the number of bars. var height = (barHeight * legendData.length * barData.length + groupPadding * (barData.length + 1) + barPadding * (legendData.length - 1) * barData.length); var g = svg.append("g").attr("class", "graph"); // Map groups to graph height var y0 = d3.scaleBand().range([0, height]).padding(0.1); y0.domain(barData.map(function(d) { return d.label; })); // Map bars to group height var y1 = d3.scaleBand().padding(0.2); y1.domain(keys).range([0, y0.bandwidth()]); // Draw the y axis to know how much space to leave for it, // then immediately delete it. Redrawing it last ensures // the correct ordering of layers for display. drawYAxis(g, y0, tickPadding, lineHeight, null); var yAxisWidth = svg.select(".y").node().getBoundingClientRect().width; svg.select("g.y.axis").remove(); var width = svgWidth - margin.left - yAxisWidth - margin.right; g.attr("transform", "translate(" + (yAxisWidth + margin.left) + "," + margin.top + ")"); var x = d3.scaleLinear().range([0, width]); x.domain([0, d3.max(ticks, function(d) { return d.value; })]); var xAxis = d3.axisBottom(x) .tickValues(ticks.map(function(tick) { return tick.value; })) .tickFormat(function(d, i) { return ticks[i].tickLabel; }) .tickSizeInner([-height]) .tickSizeOuter(0) .tickPadding(tickPadding); g.append("g") .attr("class", "x axis tick-label") .attr("transform", "translate(0," + height + ")") .call(xAxis); var z = d3.scaleOrdinal() .range(legendData.map(function(l) { return l.class; })) .domain(keys); // Add a label to the x axis. var xAxisHeight = svg.select(".x.axis.tick-label").node().getBoundingClientRect().height; // Add a label to the x axis. if (xAxisLabel) { g.append("text") .attr("x", width / 2) .attr("y", xAxisHeight + 5) .attr("class", "axis-label") .attr("alignment-baseline", "hanging") .text(xAxisLabel) .call(wrap, width, lineHeight); } g.append("g") .selectAll("g") .data(barData) .enter().append("g") .attr("transform", function(d) { return "translate(0," + y0(d.label) + ")"; }) .selectAll("rect") .data(function(d) { return keys.map(function(key, i) { return {key: key, value: d.values[i]}; }); }) .enter().append("rect") .attr("class", function(d) { return z(d.key) + " bar"; }) .attr("y", function(d, i) { return y1(d.key) }) .attr("x", function(d) { return 0; }) .attr("height", barHeight) .attr("width", function(d, i) { return x(d.value); }) .attr("fill", function(d, i) { if (z(d.key) == "green") { return greenScale(i / numGreens); } }) .attr("width", function(d) { return x(d.value); }); // Add milestone line if (milestone) { // It appears D3 adds .5 to ticks to get crispEdges for Safari // (see https://github.com/d3/d3-axis/issues/8) var milestoneX = x(milestone.value) + .5; g.append("line") .attr("class", "milestone " + milestone.class) .attr("x1", milestoneX) .attr("x2", milestoneX) .attr("y1", 0) .attr("y2", height); } addLegend(legend, legendData, milestone, greenScale, numGreens); drawYAxis(g, y0, tickPadding, lineHeight, null); var svgHeight = margin.top + margin.bottom + svg.select(".graph").node().getBoundingClientRect().height; svg.attr("height", svgHeight); return { svg: svg, x: x, }; } </script> <div id="join-nvd-banner" style="background: linear-gradient(rgb(153, 153, 153) 0%, rgb(102, 102, 102) 100%); padding: 1em 0px; color: white;" class=""> <div class="container"> <div class="col-12 text-center"> Just Announced—Run Jupyter Notebooks on Google Cloud with NGC's New One Click Deploy Feature. <a href="https://developer.nvidia.com/blog/run-jupyter-notebooks-on-google-cloud-with-new-one-click-deploy-feature-in-the-ngc-catalog/" target="_blank" class="cta--prim m-0 ml-2">Read More</a> </div> </div> </div> <div class="sct-bg--s sct-vert-cntr sct-vert-top--m hero-image--1"> <div class="cntnr--cw"> <div class="row"> <div class="col-lg-6 col-md-6 col-sm-6 col-xs-12"> <h1 class="h--large txt-clr--blck">AI and HPC Containers</h1> <p class="content-l txt-clr--blck">Develop and deploy applications faster with GPU-optimized containers from the NVIDIA NGC™ catalog.</p> </div> </div> </div> </div> <style> .img-center{ display: block; 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background-size: 1366px 280px; } } .list { list-style: disc; margin-left: 16px; } sup{ font-size: 0.7em; } </style> <section> <div class="cntnr--cw"> <div class="p-t-60 p-b-60"> <div class="cntnr--cozy"> <div class="txt-cntr"> <h2 class="h--medium txt-cntr text-transform-none p-b-15" style="color: #1a1a1a;">What Are Containers?</h2> <p class="content-m">A container is a portable unit of software that combines the application and all its dependencies into a single package that’s agnostic to the underlying host OS. It removes the need to build complex environments and simplifies the application development-to-deployment process.</p> <p class="content-m">The <a href="https://www.nvidia.com/en-us/gpu-cloud/" target="_blank">NVIDIA NGC catalog</a> contains a host of GPU-optimized containers for deep learning, machine learning, visualization, and high-performance computing (HPC) applications that are tested for performance, security, and scalability.</p>  <strong><a href="https://ngc.nvidia.com/catalog/containers?orderBy=modifiedDESC&pageNumber=1&query=&quickFilter=containers&filters=" target="_blank">Browse NGC Containers <span class="fas fa-angle-right fa-fw"></span></a></strong> </div> </div> </div> </div> </section> <section style="background-color: #eee;padding-bottom:30px;"> <div class="cntnr--cw"> <div class="p-t-60"> <div class="cntnr--cw"> <div class="txt-cntr"> <h2 class="h--medium txt-cntr text-transform-none p-b-15" style="color: #1a1a1a;">Benefits of Containers from the NGC Catalog</h2> </div> </div> </div> <div class="row"> <div class="col-md-3"> <div> <img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/ngc-containers/deploy-and-run-applications-icon.svg" class="img-responsive img-center" alt="Deploy and Run Application Icon" style="width:96px;"> <h3 class="h--smallest txt-clr--blck">Deploy Easily</h3> <p class="content-m">Built-in libraries and dependencies allow you to easily deploy and run applications. Deploy AI/ML containers to Vertex AI using the <a target="_blank" href="https://catalog.ngc.nvidia.com/orgs/nvidia/collections/vertexaiworkbench">quick deploy feature in the NGC catalog</a>.</p> </div> </div> <div class="col-md-3"> <div> <img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/ngc-containers/inference-icon.svg" class="img-responsive img-center" alt="Interface Icon" style="width:96px;"> <h3 class="h--smallest txt-clr--blck">Train Faster</h3> <p class="content-m">NVIDIA AI containers like <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow" target="_blank">TensorFlow</a> and <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch" target="_blank">PyTorch</a> provide performance-optimized monthly releases for faster AI training and inference. </p> </div> </div> <div class="col-md-3"> <div> <img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/ngc-containers/check-mark-approved-icon.svg" class="img-responsive img-center" alt="Check Mark Approve Icon" style="width:96px;"> <h3 class="h--smallest txt-clr--blck">Run Anywhere</h3> <p class="content-m">Deploy the containers on multi-GPU/multi-node systems anywhere—in the cloud, on premises, and at the edge—on bare metal, virtual machines (VMs), and Kubernetes. </p> </div> </div> <div class="col-md-3"> <div> <img src="https://d29g4g2dyqv443.cloudfront.net/sites/default/files/akamai/ngc-containers/security-icon.svg" class="img-responsive img-center" alt="Check Mark Security Icon" style="width:96px;"> <h3 class="h--smallest txt-clr--blck">Deploy with Confidence</h3> <p class="content-m">Containers are scanned for common vulnerabilities and exposures (CVEs), come with security reports, and are backed by optional enterprise support through NVIDIA AI Enterprise. </p> </div> </div> </div> </div> </section> <section> <div class="cntnr--cozy p-b-30 p-t-60"> <div class="txt-cntr"> <h2 class="h--medium txt-clr--blck">Optimized for Performance </h2> <p class="content-m">NVIDIA-built docker containers are updated monthly and third-party software is updated regularly to deliver the features needed to extract maximum performance from your existing infrastructure and reduce time to solution.</p> </div> </div> <div class="row"> <div class="col-md-5"></div> <div class="col-md-2"> <a href="https://developer.nvidia.com/deep-learning-performance-training-inference" class="cta--tert cta--l" target="_blank">View more performance metrics <span class="fas fa-angle-right fa-fw"></span> </a> </div> <div class="col-md-5"></div> </div> </section> <section> <div class="cntnr--cw p-b-30"> <div class="row"> <div class="col-md-4"> <h3 class="h--smallest txt-clr--blck">BERT-Large for Natural Language Processing </h3> <p class="content-m">BERT-Large leverages mixed precision arithmetic and Tensor Cores on Volta V100 and Ampere A100 GPUs for faster training times while maintaining target accuracy.</p> <div id="bert-large-fine-tuning"></div> <p class="content-s">BERT-Large and Training performance with TensorFlow on a single node 8x V100 (16GB) & A100 (40GB). Mixed Precision. Batch size for BERT: 3 (V100), 24 (A100)</p> <script> var chart = d3.select("#bert-large-fine-tuning"); var data = [ {"axisLabel": "A100 (v21.02)", "value": 4.3, "barLabel": "4.3X", "class": "green"}, {"axisLabel": "V100 (v21.02)", "value": 1.2, "barLabel": "1.2X", "class": "green"}, {"axisLabel": "V100 (v20.06)", "value": 1, "barLabel": "", "class": "green"} ]; var ticks = [ {"value": 0, "tickLabel": "0", "class": ""}, {"value": 1, "tickLabel": "1X", "class": ""}, {"value": 2, "tickLabel": "2X", "class": ""}, {"value": 3, "tickLabel": "3X", "class": ""}, {"value": 4, "tickLabel": "4X", "class": ""}, {"value": 5, "tickLabel": "5X", "class": ""} ]; var xAxisLabel = "Relative Performance"; var yAxisLabelClass = ""; var isDark = false; createHorizontalBarChart(chart, data, ticks, xAxisLabel, yAxisLabelClass, isDark); </script> <br> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://ngc.nvidia.com/catalog/resources/nvidia:bert_for_pytorch" target="_blank"><strong>Explore BERT-Large for PyTorch <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://ngc.nvidia.com/catalog/resources/nvidia:bert_for_tensorflow" target="_blank"><strong>Explore BERT-Large for TensorFlow <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> </div> <div class="col-md-4"> <h3 class="h--smallest txt-clr--blck" style="min-height: 60px;">ResNet50 v1.5 for Image Processing</h3> <p class="content-m">This model is trained with mixed precision using Tensor Cores on Volta, Turing and NVIDIA Ampere GPU architectures for faster training. </p> <div id="resnet-50"></div> <p class="content-s">ResNet 50 performance with TensorFlow on single-node 8x V100 (16GB) and A100 (40 GB). Mixed Precision. Batch size for ResNet50: 26</p> <script> var chart = d3.select("#resnet-50"); var data = [ {"axisLabel": "A100 (v21.02)", "value": 1.8, "barLabel": "1.8X", "class": "green"}, {"axisLabel": "V100 (v21.02)", "value": 1.3, "barLabel": "1.3X", "class": "green"}, {"axisLabel": "V100 (v20.06)", "value": 1, "barLabel": "", "class": "green"} ]; var ticks = [ {"value": 0, "tickLabel": "0", "class": ""}, {"value": 1, "tickLabel": "1X", "class": ""}, {"value": 2, "tickLabel": "2X", "class": ""} ]; var xAxisLabel = "Relative Performance"; var yAxisLabelClass = ""; var isDark = false; createHorizontalBarChart(chart, data, ticks, xAxisLabel, yAxisLabelClass, isDark); </script> <br> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://ngc.nvidia.com/catalog/models/nvidia:resnet50_pyt_amp" target="_blank"><strong>Explore ResNet50 for PyTorch <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://ngc.nvidia.com/catalog/models/nvidia:rn50_tf_amp_ckpt" target="_blank"><strong>Explore ResNet50 for TensorFlow <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> </div> <div class="col-md-4"> <h3 class="h--smallest txt-clr--blck" style="min-height: 60px;">Matlab for Deep Learning</h3> <p class="content-m">Continuous development of Matlab’s Deep Learning container improves performance for training and inference </p> <div id="matlab"></div> <p class="content-s">Windows 10, Intel Xeon E5-2623 @2.4GHz, NVIDIA Titan V 12GB GPUs</p> <script> var chart = d3.select("#matlab"); var legendData = [ {"label": "R2020a", "class": "green"}, {"label": "R2020b", "class": "green"}, ]; var barData = [ {"label": "4 GPU", "values": [1.25, 2]}, {"label": "2 GPU", "values": [1.05, 1.29]}, {"label": "1 GPU", "values": [1, 1.025]}, ]; var ticks = [ {"value": 0, "tickLabel": "0", "class": ""}, {"value": 1, "tickLabel": "1X", "class": ""}, {"value": 2, "tickLabel": "2X", "class": ""}, ] // Specify "value" and "color" of milestone line. // Set null for no milestone line. var milestone = null; var xAxisLabel = "Relative Speedup"; var isDark = false; createGroupedHorizontalBarChart(chart, barData, legendData, ticks, milestone, xAxisLabel, isDark); </script> <br> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://ngc.nvidia.com/catalog/containers/partners:matlab" target="_blank"><strong>Explore Matlab <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> </div> </div> </div> </section> <section class="sct--xs"> <div class="cntnr--cw"> <h3 class="h--medium txt-cntr">Quick Deploy</h3> <div class="row"> <div class="col-md-6"> <p class="p--medium">The quick deploy feature in the NGC catalog automatically sets up the Vertex AI instance with an optimal configuration, preloads the dependencies, runs the software from NGC without any need to set up the infrastructure.</p> <br> <p class="p--medium">Deploy popular DL and ML containers, models, and SDKs directly from the <a target="_blank" href="https://catalog.ngc.nvidia.com/orgs/nvidia/collections/vertexaiworkbench">NGC catalog.</a></p> </div> <div class="col-md-6"> <iframe width="560" height="315" src="https://www.youtube.com/embed/D-RHjpSF0GI" title="Click Deploy Demo" alt="Click Deploy Demo" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> </div> </div> </div> </section> <section style="background-color:#eee"> <div class="cntnr--cozy p-b-30 p-t-60"> <div class="txt-cntr"> <h2 class="h--medium txt-cntr text-transform-none" style="color: #1a1a1a;">Containers for Diverse Workloads </h2> <p class="content-m">Get started today by selecting from over 80 containerized software applications and SDKs, developed by NVIDIA and our ecosystem of partners. </p> <div class="row"> <div class="col-md-5"></div> <div class="col-md-2" style="margin-left: -26px;"> <a href="https://ngc.nvidia.com/catalog/containers?orderBy=modifiedDESC&pageNumber=1&query=&quickFilter=containers&filter=" class="cta--tert cta--l">Pull containers now <span class="fas fa-angle-right fa-fw"></span></a> </div> <div class="col-md-5"></div> </div>  </div> </div> </section> <section class="sct--s" style="background-color:#eee;padding-bottom:1px;padding-top:2px;"> <div class="cntnr--cw"> <div class="cntnr--cozy"> <div class="txt-cntr"> <h3 class="h--small txt-cntr text-transform-none" style="color: #1a1a1a;">AI Containers</h3> </div> </div> </div> </section> <section style="background-color:#eee"> <div class="cntnr--cw"> <div class="row"> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">TensorFlow</h4> <p class="content-m"><a href="https://www.nvidia.com/en-sg/data-center/gpu-accelerated-applications/tensorflow/">TensorFlow</a> is an open-source software library for high-performance numerical computation. </p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">PyTorch</h4> <p class="content-m">PyTorch is a GPU-accelerated tensor computational framework with a Python front end.</p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">NVIDIA Triton Inference Server</h4> <p class="content-m"><a href="/nvidia-triton-inference-server">NVIDIA Triton™ Inference Server</a> is an open-source inference solution that maximizes utilization of and performance on GPUs.</p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorrt';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">NVIDIA TensorRT</h4> <p class="content-m"><a href="/TensorRT">NVIDIA TensorRT<sup>®</sup></a> is a C++ library that facilitates high-performance inference on NVIDIA GPUs. </p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorrt" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> </div> </div> </section> <section class="sct--s" style="background-color:#eee;padding-bottom:1px;"> <div class="cntnr--cw"> <div class="cntnr--cozy"> <div class="txt-cntr"> <h3 class="h--small txt-cntr text-transform-none" style="color: #1a1a1a;">Application Frameworks</h3> </div> </div> </div> </section> <section style="background-color:#eee"> <div class="cntnr--cw"> <div class="row"> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/nvidia/collections/claratrainframework';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">NVIDIA Clara</h4> <p class="content-m">NVIDIA Clara™ Train for medical imaging is an application framework with over 20 state-of-the-art pre-trained models, transfer learning and federated learning tools, AutoML, and AI-assisted annotation. </p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/nvidia/collections/claratrainframework" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/nvidia/containers/deepstream';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">DeepStream</h4> <p class="content-m"><a href="https://developer.nvidia.com/Deepstream-sdk">DeepStream</a> is the streaming analytics toolkit for AI-based video, audio, and image understanding for multi-sensor processing.</p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/deepstream" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/resources/riva_quickstart';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">NVIDIA Riva</h4> <p class="content-m"><a href="https://developer.nvidia.com/riva">NVIDIA Riva</a>, is an application framework for multimodal conversational AI services that delivers real-time performance on GPUs.</p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/riva/resources/riva_quickstart" target="_blank"> Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://ngc.nvidia.com/catalog/containers/nvidia:hugectr';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">Merlin Training</h4> <p class="content-m">Merlin HugeCTR, a component of <a href="https://developer.nvidia.com/nvidia-merlin">NVIDIA Merlin™</a>, is a deep neural network training framework designed for recommender systems.</p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/?filters=&orderBy=scoreDESC&pageNumber=0&query=merlin&quickFilter=" target="_blank">Explore container<span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> </div> </div> </section> <section class="sct--s" style="background-color:#eee;padding-bottom:1px;"> <div class="cntnr--cw"> <div class="cntnr--cozy"> <div class="txt-cntr"> <h3 class="h--medium txt-cntr text-transform-none" style="color: #1a1a1a;">HPC Containers</h3> </div> </div> </div> </section> <section class="sct--s" style="background-color:#eee;padding-top:0;"> <div class="cntnr--cw"> <div class="row"> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/hpc/containers/namd';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">NAMD</h4> <p class="content-m">NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems. </p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/hpc/containers/namd" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/hpc/containers/gromacs';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">GROMACS</h4> <p class="content-m">GROMACS is a popular molecular dynamics application used to simulate proteins and lipids.</p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/hpc/containers/gromacs" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/hpc/containers/relion';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">RELION</h4> <p class="content-m">RELION implements an empirical Bayesian approach for analysis of cryogenic electron microscopy (cryo-EM). </p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/hpc/containers/relion" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> <div class="col-lg-3 col-md-3 col-sm-6 col-xs-12"> <div class="card" onclick="location.href='https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nvhpc';"> <div class="card-cntnt-cntnr"> <h4 class="h--smallest txt-clr--blck">NVIDIA HPC SDK</h4> <p class="content-m">The <a href="https://developer.nvidia.com/hpc-sdk">NVIDIA HPC SDK</a> is a comprehensive suite of compilers, libraries, and tools for building, deploying, and managing HPC applications.</p> <a class="cta--tert" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nvhpc" target="_blank">Explore container <span class="fas fa-angle-right fa-fw"></span></a> </div> </div> </div> </div> </div> </section> <section> <div class="cntnr--cozy p-b-30 p-t-60"> <h2 class="h--medium txt-cntr txt-clr--blck">Frequently Asked Questions</h2> <div class="panel-group p-t-60" id="accordion"> <div class="panel panel-default"> <div class="panel-heading"> <a data-parent="#accordion" data-toggle="collapse" href="#collapseOne" aria-expanded="true" aria-controls="collapseOne">Why are the containers from the NGC catalog useful for Data Scientists, Researchers, and Developers? <i class="fas fa-angle-down rotate-icon" style="float: right;"></i> </a> </div> <div class="panel-collapse collapse in" id="collapseOne" aria-labelledby="headingOne" data-parent="#accordion"> <div class="panel-body"> <ul class="fa-ul"> <li> <span class="fa-li"><i class="far fa-angle-right fa-fw"></i></span>A diverse set of containers span a multitude of use cases with built-in libraries and dependencies for easy compiling of custom applications.</li> <li> <span class="fa-li"><i class="far fa-angle-right fa-fw"></i></span>They offer faster training with Automatic Mixed Precision (AMP) and minimal code changes.</li> <li> <span class="fa-li"><i class="far fa-angle-right fa-fw"></i></span>Reduced time to solution with the ability to scaleup from single-node to multi-node systems.</li> <li> <span class="fa-li"><i class="far fa-angle-right fa-fw"></i></span>Extremely portable, allowing you to develop faster by running containers in the cloud, on premises, or at the edge.</li> </ul> </div> </div> </div> <br> <div class="panel panel-default"> <div class="panel-heading" id="headingTwo"> <a data-parent="#accordion" data-toggle="collapse" href="#collapseTwo" aria-expanded="false" aria-controls="collapseTwo">Why are the containers from the NGC catalog useful for Machine Learning Engineers and IT? <i class="fas fa-angle-down rotate-icon" style="float: right;"></i></a> </div> <div class="panel-collapse collapse" id="collapseTwo" aria-labelledby="headingTwo" data-parent="#accordion"> <div class="panel-body"> <p class="content-m" style="line-height: 1.5em">Containers from the NGC catalog make it seamless for machine learning engineers and IT to deploy to production.</p> <ul class="fa-ul"> <li> <span class="fa-li"><i class="far fa-angle-right fa-fw"></i></span>They are tested on various platforms and architectures, enabling seamless deployment on a wide variety of systems and platforms. </li> <li> <span class="fa-li"><i class="far fa-angle-right fa-fw"></i></span>They can be deployed to run on bare metal, virtual machines (VMs), and Kubernetes, including various architectures such as x86, ARM, and IBM Power. </li> <li> <span class="fa-li"><i class="far fa-angle-right fa-fw"></i></span>They can run easily on various container runtimes such as Docker, Singularity, cri-o, and containerd. </li> <li> <span class="fa-li"><i class="far fa-angle-right fa-fw"></i></span>The container images are scanned for common vulnerabilities and exposures (CVEs) and are backed by optional enterprise support to troubleshoot issues for NVIDIA-built software. </li> </ul> </div> </div> </div> </div> </div> </section> <section style="background-color:#eee"> <div class="cntnr--cozy p-b-30 p-t-60"> <div class="txt-cntr"> <h2 class="h--medium txt-cntr txt-clr--blck">NGC Catalog Resources </h2> </div> </div> <div class="container p-b-60"> <div class="row"> <div class="col-md-3"> <h3 class="h--smallest txt-clr--blck">Developer Blogs</h3> <p>Learn how to use the NGC catalog with these step-by-step instructions.</p> <br> <br> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://developer.nvidia.com/blog?r=1&tags=technical-walkthrough,ngc&categories=" target="_blank" style="text-transform: none;"><strong>Explore technical blogs <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> </div> <div class="col-md-3"> <h3 class="h--smallest txt-clr--blck">Developer News</h3> <p>Read about the latest NGC catalog updates and announcements.</p> <br> <br> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://developer.nvidia.com/blog?r=1&tags=news,ngc&categories=" target="_blank" style="text-transform: none;"><strong>Read news <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> </div> <div class="col-md-3"> <h3 class="h--smallest txt-clr--blck">GTC Sessions</h3> <p>Watch all the top NGC sessions on demand. </p> <br> <br> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://www.nvidia.com/en-us/on-demand/playlist/playList-89d9d0e7-9daa-42c3-b332-c0f653290e49/" target="_blank" style="text-transform: none;"><strong>Watch GTC Sessions <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> </div> <div class="col-md-3"> <h3 class="h--smallest txt-clr--blck">Webinars</h3> <p>Walk through how to use the NGC catalog with these video tutorials.</p> <br> <br> <span style="margin-top: 0;" class="row-of-buttons"> <a class="cta--tert" href="https://resources.nvidia.com/l/en-us-ngc?contentType=Webinar" target="_blank" style="text-transform: none;"><strong>Watch webinars <span class="fas fa-angle-right fa-fw"></span></strong></a> </span> </div> </div> </div> </section> <section class="sct--xs sct--drk-gry4"> <div class="cntnr--cozy"> <p class="content-l text-white txt-cntr">Accelerate your AI development with Containers from the NGC catalog.</p> <center><a class="cta--prim" href="https://catalog.ngc.nvidia.com/containers" role="button" target="_blank">Get Started</a></center> <br> </div> </section> <script> document.addEventListener('DOMContentLoaded', () => { const allLinks = document.querySelectorAll('.dz3-main-section.dz-new-theme a'); allLinks.forEach((link) => { let hasIcon = link.querySelector('span.fas'); if(hasIcon) { link.classList.add('has-cta-icon'); } }); }); </script> </main> <div id='footer' class='mt-auto'></div> <script 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