SaulLu/test
2
1<!doctype html>2<html lang="en">3<head>4 <meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>5 <title>learning@home</title>6 <meta name="description" content="A library to train large neural networks across the internet. Imagine training GPT-3 on thousands of nodes from universities, companies, and volunteers.">7 <link rel="mask-icon" href="logo_small.png">8 <link rel="alternate icon" class="js-site-favicon" type="image/png" href="logo.png">9 <link rel="icon" class="js-site-favicon" type="image/png" href="logo.png">10 <meta property="og:url" content="https://learning-at-home.github.io">11 <meta property="og:site_name" content="learning@home">12 <meta property="og:title" content="Train vast neural networks together">13 <meta property="og:description" content="A library to train large neural networks across the internet. 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Imagine training one huge transformer on thousands of computers from universities, companies, and volunteers.197 <br><br>198 <div style="width:100%; margin: 0 auto;">199 <a class="github-button" href="https://github.com/learning-at-home/hivemind" data-size="large" data-show-count="false" aria-label="Star learning-at-home/hivemind on GitHub">Code</a>200 <div style="overflow: hidden; white-space: nowrap; margin: 0 auto; display: inline-block;">201 <button onclick="window.open('https://arxiv.org/abs/2002.04013');" 202 class="arxiv_button">Paper</button>203 </div>204 <a href="https://twitter.com/intent/tweet?hashtags=learningathome,joinhivemind&text=Join%20the%20deep%20learning%20hivemind!%0Alearning-at-home.github.io"205 class="twitter-hashtag-button" data-show-count="true" data-size="large">Tweet</a>206 <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>207 208 <script async defer src="https://buttons.github.io/buttons.js"></script>209 </div>210 </div>211 <hr class="faded" style="width: 70%; margin-top:20px;"><br>212 213 <div class="faded" style="margin-top:35px; display: inline-block; text-align:center;">214 <div style="width: 100%; max-width: 440px; display: inline-block; vertical-align: top; text-align:left; padding-right: 5px">215 <p class="faded title" style="font-size:28px; margin-bottom:12px">216 Why should you care?</p>217 218 <span class="faded text" style="margin-top: 4px;">219 Larger neural networks are winning:220 <ul style="text-align: left; list-style-position: inside; margin-top: 12px; margin-left: -32px;">221 <li style="margin-top: 12px;">222 pretrained transformers <a href=https://w4ngatang.github.io/static/papers/superglue.pdf target="_blank" rel="noopener noreferrer">dominate</a> most NLP tasks;</li>223 <li style="margin-top: 12px;">224 bigger CNNs <a href="https://arxiv.org/abs/1912.11370" target="_blank" rel="noopener noreferrer">perform better</a> at computer vision;</li>225 <li style="margin-top: 12px;">226 GPT-3 has <a href="https://arxiv.org/abs/2005.14165" target="_blank" rel="noopener noreferrer">175B</a> parameters and <a target="_blank" rel="noopener noreferrer" href="https://arxiv.org/abs/2006.16668">the race continues</a></li>227 </ul>228 229 With transfer learning, these large models can harness nearly unlimited raw data to improve performance on both <a href=https://paperswithcode.com/task/language-modelling target="_blank" rel="noopener noreferrer">academic benchmarks</a> and solve <a href=https://medium.com/towards-artificial-intelligence/crazy-gpt-3-use-cases-232c22142044 target="_blank" rel="noopener noreferrer">new unexpected</a> tasks.230 231 232 <center>233 <span style="margin-top: 16px; font-style: italic; font-size: 14px;">234 Image credit: [1] <a href="https://arxiv.org/abs/2001.08361" target="_blank" rel="noopener noreferrer">Kaplan et al. (2020)</a>, [2, 3] <a href="https://arxiv.org/abs/1811.06965" target="_blank" rel="noopener noreferrer">Huang et al. (2018)</a>235 </span>236 </center>237 </span>238 239 </div>240 <div class style="width: 100%; max-width: 440px; vertical-align: middle; display: inline-block;">241 <div class="carousel" 242 data-flickity='{ "draggable": ">1", "pageDots": false, "groupCells": true}'>243 <div class="carousel-cell" style="padding-left:30px; padding-right:30px;">244 <img width=340px height=250px src="kaplan1.png">245 </div>246 <div class="carousel-cell" style="padding-left:30px; padding-right:40px;">247 <img width=320px height=250px src="gpipe1.png"></div>248 <div class="carousel-cell" style="padding-left:40px; padding-right:30px;">249 <img width=320px height=250px src="gpipe2.png"></div>250 </div>251 </div>252 253 <p class="faded text" style="width: 100%; max-width: 900px; margin-top:16px; text-align: left">254 That said, training large neural networks isn't cheap. The hardware used for the <a href="https://arxiv.org/abs/1909.08053" target="_blank" rel="noopener noreferrer">previous largest</a> language model costs over $25 million. A single training run for GPT-3 will set you back <a href="https://lambdalabs.com/blog/demystifying-gpt-3/" target="_blank" rel="noopener noreferrer">at least $4.6M</a> in cloud GPUs. As a result, researchers can't contribute to state-of-the-art deep learning models and practitioners can't build applications without <a href=https://blogs.microsoft.com/ai/openai-azure-supercomputer target="_blank" rel="noopener noreferrer">being supported</a> by a megacorporation. If we want the future of AI to be bright, it can't be private.255 </p>256 257 </div>258 <br>259 260 <div class="faded" style="margin-top:32px; width: 100%; max-width: 900px; display: inline-block; text-align: left;">261 <p class="faded title" style="font-size:28px;">262 What is hivemind?</p>263 <br>264 <p class="faded text" style="margin-top:16px;">265 Hivemind is a library for decentralized training of large neural networks. In a nutshell, you want to train a neural network, but all you have is a bunch of enthusiasts with unreliable computers that communicate over the internet. Any peer may fail or leave at any time, but the training must go on. To meet this objective, hivemind models use a specialized layer type: the <b>D</b>ecentralized <b>M</b>ixture of <b>E</b>xperts (DMoE). Here's how it works:<br>266 </p>267 <div class="container">268 <video src="dmoe_demonstration.mp4" style="width:100%; max-width:880px; min-width:320px; margin-top:12px; float:center;" controls poster="poster.png">269 </video>270 </div>271 <br>272 <div style="text-align: center">273 <div style="width: 100%; max-width: 500px; vertical-align: top; display: inline-block; padding-right: 5px; text-align: left;">274 <span class="faded text" style="margin-top:16px">275 In a hivemind experiment, all peers:276 <ul style="text-align: left; list-style-position: inside; margin-top: 16px; margin-left: -32px;">277 <li style="margin-top: 12px;">278 host one or more experts depending on their hardware;</li>279 <li style="margin-top: 12px;">280 run asynchronous training, calling experts from other peers,</li>281 <li style="margin-top: 12px;">282 form a Distributed Hash Table to discover each other's experts<br>283 <span style="padding-left:24px">284 - the same type of protocol that powers BitTorrent file sharing.</span>285 </li>286 </ul>287 <p> Hivemind uses <a href=https://pdos.csail.mit.edu/~petar/papers/maymounkov-kademlia-lncs.pdf target="_blank" rel="noopener noreferrer">Kademlia</a>-based DHT that can scale to tens of thousands of peers with logarithmic search complexity.</p>288 </span>289 </div>290 <div style="width:100%; max-width: 385px; vertical-align: top; display: inline-block; margin-top: 0px; align:center;">291 <img src="hivemind_components.png" style="width:100%; max-width:240px">292 </div>293 294 <p class="faded text" style="text-align: left;">295 On each forward pass, a peer first determines what "speciality" of experts is needed to process the current inputs using a small "gating function" module. Then it finds <i>k</i> (e.g. 4) most suitable experts from other peers in the network using the DHT protocol. Finally, it sends forward pass requests to the selected experts, collects their outputs and averages them for the final prediction. Compared to traditional architectures, the Mixture-of-Experts needs much less bandwidth as every input is only sent to a small fraction of all experts.296 </p>297 <div style="width:100%; max-width: 900px; vertical-align: top; display: inline-block; margin-top: 0px; align:center;">298 <img src="dmoe-forward-backward.png" style="width:100%;">299 </div>300 <p class="faded text" style="text-align: left; margin-top:5px">301 More importantly, the decentralized Mixture-of-Experts layers are inherently fault-tolerant: if some of the chosen experts fail to respond, the model will simply average the remaining ones and call that <a href=https://jmlr.org/papers/v15/srivastava14a.html target="_blank" rel="noopener noreferrer">dropout</a>. In the event that all <i>k</i> experts fail simultaneously, a peer will backtrack and find another <i>k</i> experts across the DHT. Finally, since every input is likely to be processed by different experts, hivemind peers run several <a href=https://papers.nips.cc/paper/4390-hogwild-a-lock-free-approach-to-parallelizing-stochastic-gradient-descent target="_blank" rel="noopener noreferrer">asynchronous training</a> batches to better utilize their hardware.302 </p>303 304 </div>305 </div>306 <br>307 308 <div class="faded" style="margin-top:35px; width: 100%; max-width: 900px; align: center; vertical-align: top; display: inline-block; text-align:left;">309 <p class="faded title" style="font-size:28px;">310 What is hivemind for?311 </p>312 <br>313 314 <span class="faded text" style="margin-top:15px">315 Hivemind is designed for you to:316 <ul style="text-align: middle; list-style-position: inside; margin-top: 16px; margin-left: -32px;">317 <li style="margin-top: 12px;">318 run crowdsourced deep learning using compute from volunteers or decentralized participants; </li>319 <li style="margin-top: 12px;">320 train neural networks on multiple servers with varying compute, bandwidth and reliability; </li>321 <li style="margin-top: 12px;">322 <i>[to be announced]</i> join a worldwide open deep learning experiment. </li>323 </ul>324 <br>325 326 Conversely, here's what it <b>isn't</b> for:327 <ul style="text-align: middle; list-style-position: inside; margin-top: 16px; margin-left: -32px;">328 <li style="margin-top: 12px;">329 splitting your model between 2-3 servers that you fully control: use <a href=https://pytorch.org/docs/stable/rpc.html target="_blank"rel="noopener noreferrer">torch.distributed.rpc</a>;</li>330 <li style="margin-top: 12px;">331 distributed training for a reliable, uniform and highly connected cluster: use <a href=https://github.com/microsoft/DeepSpeed target="_blank"rel="noopener noreferrer">DeepSpeed</a>; </li>332 <li style="margin-top: 12px;">333 training <span class="tooltip">small <span class="tooltiptext">More specifically, models that fit into a single worker's memory.</span></span> models with dynamically allocated of in-house workers: use <a href=https://pytorch.org/elastic/0.2.0/index.html>torch.elastic</a>.</li>334 335 </ul>336 337 338 <p style="margin-top: 16px; text-align:left">339 Hivemind v0.8 is in the early alpha stage: the core functionality to train340 decentralized models is there, but the inferface is still in active development.341 If you want to try hivemind for yourself or contribute to its development,342 take a look at the <a href=https://learning-at-home.readthedocs.io/en/latest/user/quickstart.html><u>quickstart tutorial</u></a>.343 Feel free to contact us <a href=https://github.com/learning-at-home/hivemind/issues target="_blank" rel="noopener noreferrer">on github</a> with any questions, feedback and issues.344 </p>345 </span>346 </div>347 348 349 <div style="margin: 0 auto; margin-top: 0px;">350 <a class="github-button" href="https://github.com/learning-at-home/hivemind" data-size="large" data-show-count="false" aria-label="Star learning-at-home/hivemind on GitHub">Code</a>351 <div style="overflow: hidden; white-space: nowrap; margin: 0 auto; display: inline-block;">352 <button onclick="window.open('https://arxiv.org/abs/2002.04013');" 353 class="arxiv_button">Paper</button>354 </div>355 <a href="https://twitter.com/intent/tweet?hashtags=joinhivemind,learningathome&text=Join%20the%20deep%20learning%20hivemind!%0Alearning-at-home.github.io"356 class="twitter-hashtag-button" data-show-count="true" data-size="large">Tweet</a>357 <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>358 359 <script async defer src="https://buttons.github.io/buttons.js"></script>360 </div>361 <hr style="margin-bottom: 64px; width:0%; border: 0 solid white">362 </div>363 </div>364 </div>365 366 367 <script>368// draw background; Note: this background is based on https://codepen.io/pawelqcm/pen/oxPYox by Pawel369// Note 2: Pawel, you're awesome.370(function() {371 var content_element = document.getElementById("overlay");372 var canvas = document.querySelector('canvas');373 var title_elem = document.getElementsByClassName("faded title")[0];374 var title_text = document.getElementById("title_text");375 if (screen.width < 637)376 title_text.innerHTML = "<br>hivemind<br>"377 378 ctx = canvas.getContext('2d');379 if (!ctx)380 console.warn("Your browser does not support canvas, content may be broken :'(");381 382 var SENSITIVITY, SIBLINGS_LIMIT, DENSITY, TOTAL_NODES, ANCHOR_LENGTH, CURSOR_HEIGHT, CURSOR_WIDTH;383 css_opts = getComputedStyle(document.documentElement);384 SENSITIVITY = css_opts.getPropertyValue('--background-sensitivity') || 120;385 SIBLINGS_LIMIT = css_opts.getPropertyValue('--background-siblings') || 7;386 NODE_DENSITY = css_opts.getPropertyValue('--background-node-density') || 6;387 CURSOR_WIDTH = css_opts.getPropertyValue('--background-cursor-width') || 250;388 CURSOR_HEIGHT = css_opts.getPropertyValue('--background-cursor-height') || 250;389 CURSOR_VERTICAL_SHRINK = css_opts.getPropertyValue('--background-cursor-vertical-shrink') || 0.1;390 SPEED_COEF = css_opts.getPropertyValue('--background-speed') || 1;391 ENERGY_DECAY = css_opts.getPropertyValue('--energy-decay') || 2;392 SHOW_IF_WIDER_THAN = css_opts.getPropertyValue('--background-show-if-wider-than') || 500;393 MOVE_ON_CURSOR = css_opts.getPropertyValue('--background-move-on-cursor').includes("true") || false;394 395 var nodes = [];396 choice = (choices => choices[Math.floor(Math.random() * choices.length)])397 sample_color = () => choice([[40, 40, 40], [133, 133, 133]])398 399 ANCHOR_LENGTH = 20;400 401 var cursor = {x: 0, y: 0};402 403 function centralize_cursor() {404 var rect = document.getElementById("bug-logo").getBoundingClientRect()405 var window_left = window.pageXOffset || document.documentElement.scrollLeft;406 var window_top = window.pageYOffset || document.documentElement.scrollTop;407 cursor.x = window_left + rect.left + rect.width / 2;408 cursor.y = window_top + rect.top + rect.height / 2;409 }410 411 function Node(x, y) {412 this.anchorX = x;413 this.anchorY = y;414 this.x = Math.random() * (x - (x - ANCHOR_LENGTH)) + (x - ANCHOR_LENGTH);415 this.y = Math.random() * (y - (y - ANCHOR_LENGTH)) + (y - ANCHOR_LENGTH);416 this.vx = (Math.random() * 2 - 1) * SPEED_COEF;417 this.vy = (Math.random() * 2 - 1) * SPEED_COEF;418 this.energy = Math.random() * 100;419 this.radius = Math.random();420 this.siblings = [];421 [this.r, this.g, this.b] = sample_color()422 this.brightness = 0;423 }424 425 Node.prototype.drawNode = function() {426 var color = `rgba(${this.r}, ${this.g}, ${this.b}, ${this.brightness})`;427 ctx.beginPath();428 ctx.arc(this.x, this.y, 2 * this.radius + 2 * this.siblings.length / SIBLINGS_LIMIT, 0, 2 * Math.PI);429 ctx.fillStyle = color;430 ctx.fill();431 };432 433 Node.prototype.drawConnections = function() {434 for (var i = 0; i < this.siblings.length; i++) {435 var color = `rgba(133, 133, 133, ${this.brightness})`;436 ctx.beginPath();437 ctx.moveTo(this.x, this.y);438 ctx.lineTo(this.siblings[i].x, this.siblings[i].y);439 ctx.lineWidth = 1 - calcDistance(this, this.siblings[i]) / SENSITIVITY;440 ctx.strokeStyle = color;441 ctx.stroke();442 }443 };444 445 446 Node.prototype.moveNode = function() {447 this.energy -= ENERGY_DECAY;448 if (this.energy < 1) {449 this.energy = Math.random() * 100;450 if (this.x - this.anchorX < -ANCHOR_LENGTH) {451 this.vx = Math.random() * SPEED_COEF;452 } else if (this.x - this.anchorX > ANCHOR_LENGTH) {453 this.vx = Math.random() * -SPEED_COEF;454 } else {455 this.vx = Math.random() * SPEED_COEF * 2 - SPEED_COEF;456 }457 if (this.y - this.anchorY < -ANCHOR_LENGTH) {458 this.vy = Math.random() * SPEED_COEF;459 } else if (this.y - this.anchorY > ANCHOR_LENGTH) {460 this.vy = Math.random() * -SPEED_COEF;461 } else {462 this.vy = Math.random() * SPEED_COEF * 2 - SPEED_COEF;463 }464 }465 relative_speed_rate = Math.min(canvas.height / 100, 10.0)466 this.x += this.vx * this.energy * relative_speed_rate;467 this.y += this.vy * this.energy * relative_speed_rate;468 };469 470 function initNodes() {471 centralize_cursor();472 ctx.clearRect(0, 0, canvas.width, canvas.height);473 if (canvas.width >= SHOW_IF_WIDER_THAN)474 total_nodes = Math.round(NODE_DENSITY * (canvas.width / 100 * canvas.height / 100));475 else476 total_nodes = 0;477 nodes = [];478 for (var i = 0; i < total_nodes; i++)479 nodes.push(new Node(50 + Math.random() * (canvas.width - 100), 480 5 + Math.random() * (canvas.height - 10)));481 }482 483 function calcDistance(node1, node2) {484 return Math.sqrt(Math.pow(node1.x - node2.x, 2) + (Math.pow(node1.y - node2.y, 2)));485 }486 487 function findSiblings() {488 var node1, node2, distance;489 for (var i = 0; i < nodes.length; i++) {490 node1 = nodes[i];491 node1.siblings = [];492 for (var j = 0; j < nodes.length; j++) {493 node2 = nodes[j];494 if (node1 !== node2) {495 distance = calcDistance(node1, node2);496 if (distance < SENSITIVITY) {497 if (node1.siblings.length < SIBLINGS_LIMIT) {498 node1.siblings.push(node2);499 } else {500 var node_sibling_distance = 0;501 var max_distance = 0;502 var s;503 for (var k = 0; k < SIBLINGS_LIMIT; k++) {504 node_sibling_distance = calcDistance(node1, node1.siblings[k]);505 if (node_sibling_distance > max_distance) {506 max_distance = node_sibling_distance;507 s = k;508 }509 }510 if (distance < max_distance) {511 node1.siblings.splice(s, 1);512 node1.siblings.push(node2);513 }514 }515 }516 }517 }518 }519 }520 521 function redrawScene() {522 resizeWindow();523 ctx.clearRect(0, 0, canvas.width, canvas.height);524 findSiblings();525 var i, node, distance;526 for (i = 0; i < nodes.length; i++) {527 node = nodes[i];528 scaled_distance = calcDistance({x: cursor.x / CURSOR_WIDTH, y: cursor.y / CURSOR_HEIGHT},529 {x: node.x / CURSOR_WIDTH, y: node.y / CURSOR_HEIGHT});530 531 node.brightness = Math.max(1 - scaled_distance, 0);532 }533 for (i = 0; i < nodes.length; i++) {534 node = nodes[i];535 if (node.brightness) {536 node.drawConnections();537 node.drawNode();538 }539 node.moveNode();540 }541 requestAnimationFrame(redrawScene);542 }543 544 function initHandlers() {545 document.addEventListener('resize', resizeWindow);546 document.addEventListener('orientationchange', resizeWindow);547 if (MOVE_ON_CURSOR) {548 document.addEventListener('mousemove', moveHandler);549 document.addEventListener('touchmove', moveHandler);550 }551 }552 553 function resizeWindow(evt) {554 var new_width, new_height;555 new_width = Math.round(Math.max(title_elem.getBoundingClientRect().right, window.innerWidth))556 557 if (!MOVE_ON_CURSOR)558 new_height = Math.round(title_elem.getBoundingClientRect().top - canvas.getBoundingClientRect().top);559 else560 new_height = Math.round(Math.max(561 content_element.offsetHeight, content_element.scrollHeight,562 content_element.clientHeight, window.innerHeight));563 564 if (canvas.width != new_width || canvas.height != new_height) {565 canvas.width = new_width;566 canvas.height = new_height;567 initNodes();568 }569 if (!MOVE_ON_CURSOR)570 centralize_cursor();571 }572 573 function moveHandler(evt) {574 if (evt.type == "mousemove") {575 cursor.x = window.pageXOffset + evt.clientX;576 cursor.y = window.pageYOffset + evt.clientY;577 }578 else { // touch event579 cursor.x = window.pageXOffset + evt.changedTouches[0].clientX;580 cursor.y = window.pageYOffset + evt.changedTouches[0].clientY;581 }582 }583 584 initHandlers();585 initNodes();586 redrawScene();587 588})();589 </script>590</body>591</html>592