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GitHub - lsds/KungFu: Fast and Adaptive Distributed Machine Learning for TensorFlow, PyTorch and MindSpore.
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d="M12 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 12 1Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2Flsds%2FKungFu" class="HeaderMenu-link HeaderMenu-button d-inline-flex d-lg-none flex-order-1 f5 no-underline border color-border-default rounded-2 px-2 py-1 color-fg-inherit js-prevent-focus-on-mobile-nav" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/lsds/KungFu","user_id":null}}" data-hydro-click-hmac="5a14a13d872d4bfd3826c27a124ec6b084b47d3df0c2c9fb9e9811e51406e8a9" data-analytics-event="{"category":"Marketing nav","action":"click to Sign in","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > Sign in </a> </div> </div> <div class="HeaderMenu js-header-menu height-fit position-lg-relative d-lg-flex flex-column flex-auto top-0"> <div class="HeaderMenu-wrapper d-flex flex-column flex-self-start flex-lg-row flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"github_copilot","context":"product","tag":"link","label":"github_copilot_link_product_navbar"}" href="https://github.com/features/copilot"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 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class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any workflow </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"codespaces","context":"product","tag":"link","label":"codespaces_link_product_navbar"}" href="https://github.com/features/codespaces"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-codespaces color-fg-subtle mr-3"> <path d="M3.5 3.75C3.5 2.784 4.284 2 5.25 2h13.5c.966 0 1.75.784 1.75 1.75v7.5A1.75 1.75 0 0 1 18.75 13H5.25a1.75 1.75 0 0 1-1.75-1.75Zm-2 12c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v4a1.75 1.75 0 0 1-1.75 1.75H3.25a1.75 1.75 0 0 1-1.75-1.75ZM5.25 3.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h13.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Zm-2 12a.25.25 0 0 0-.25.25v4c0 .138.112.25.25.25h17.5a.25.25 0 0 0 .25-.25v-4a.25.25 0 0 0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"code_review","context":"product","tag":"link","label":"code_review_link_product_navbar"}" href="https://github.com/features/code-review"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-review color-fg-subtle mr-3"> <path d="M10.3 6.74a.75.75 0 0 1-.04 1.06l-2.908 2.7 2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M1.5 4.25c0-.966.784-1.75 1.75-1.75h17.5c.966 0 1.75.784 1.75 1.75v12.5a1.75 1.75 0 0 1-1.75 1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 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width="24" data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="product-explore-heading">Explore</span> <ul class="list-style-none f5" 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0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 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6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" value="+iVFde7lddU83FF5VozQHYkO+OaJ+ASgVWIJ9vfh6EL8/+8ahRgD4BJYfq0qYda6Z171S5HuS0X7MAPyrcFTbg==" /> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <input type="hidden" 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href=\"/lsds/KungFu/blob/main/docs/kungfu-logo.png\"\u003e\u003cimg src=\"/lsds/KungFu/raw/main/docs/kungfu-logo.png\" width=\"50%\" height=\"30%\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKungFu\u003c/h1\u003e\u003ca id=\"user-content-kungfu\" class=\"anchor\" aria-label=\"Permalink: KungFu\" href=\"#kungfu\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eMaking adaptive distributed machine learning easy and efficient.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://travis-ci.com/lsds/KungFu\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/652a92bd9223555fe9d9d1e34dce4a16ba11a12367e087f2fc1b8dffe950c9a1/68747470733a2f2f7472617669732d63692e636f6d2f6c7364732f4b756e6746752e7376673f6272616e63683d6d6173746572\" alt=\"Build Status\" data-canonical-src=\"https://travis-ci.com/lsds/KungFu.svg?branch=master\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca href=\"https://kungfu.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/69dc21a5dab63bbbf36b72b6764e4be21b7b3f1540b8f8b6565d43db406e11bd/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6b756e6766752f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/kungfu/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFeatures\u003c/h2\u003e\u003ca id=\"user-content-features\" class=\"anchor\" aria-label=\"Permalink: Features\" href=\"#features\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKungFu aims to help users achieve \u003cem\u003efast\u003c/em\u003e and \u003cem\u003eadaptive\u003c/em\u003e distributed machine learning with \u003cem\u003eminimal\u003c/em\u003e efforts. This is important because a machine learning system must cope with growing complex models and increasingly complicated deployment environments, making it\ndifficult to constantly deliver high performance with an \u003cem\u003eempirical\u003c/em\u003e configuration.\nTo address this, KungFu provides the following unique features:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSimplicity: KungFu permits distributed training by adding minimal code in your training program. KungFu is also simple to install and run. It does not require extra deployment like parameter servers and heavy dependencies like MPI in Horovod.\u003c/li\u003e\n\u003cli\u003eAdaptable distributed training: KungFu provides useful advanced \u003ca href=\"/lsds/KungFu/blob/main/srcs/python/kungfu/tensorflow/optimizers/__init__.py\"\u003edistributed optimizers\u003c/a\u003e such as\ncommunication-efficient \u003ccode\u003ePairAveragingOptimizer\u003c/code\u003e and hyper-parameter-robust \u003ccode\u003eSynchronousAveragingOptimizer\u003c/code\u003e to help you address the cases in which conventional Synchronous SGD does not scale. See \u003ca href=\"https://github.com/lsds/KungFu#choosing-the-right-optimizer\"\u003eOptimizers\u003c/a\u003e for how to choose the right KungFu optimizer for your training scenario.\u003c/li\u003e\n\u003cli\u003eOnline monitoring and control: KungFu aims to support \u003ca href=\"/lsds/KungFu/blob/main/srcs/python/kungfu/tensorflow/optimizers/sync_sgd.py\"\u003edistributed SGD metrics\u003c/a\u003e such as \u003ca href=\"https://openai.com/blog/science-of-ai/\" rel=\"nofollow\"\u003egradient noise scale\u003c/a\u003e to help understand the training process with low overhead.\nKungFu further provides control operators such as \u003ccode\u003ebarrier\u003c/code\u003e and \u003ccode\u003eresize_cluster\u003c/code\u003e to help reconfigure training online, even in response to monitored metrics.\u003c/li\u003e\n\u003cli\u003eFast and scalable: KungFu has a decentralized architecture, an non-blocking runtime, and high-performance implementations of communication, monitoring and control operators. Check out its performance in \u003ca href=\"https://github.com/lsds/KungFu#benchmark\"\u003eBenchmark\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eWe have been using KungFu for scaling out different deep learning models such as ResNet, DenseNet, OpenPose, BERT, CycleGAN and Alpha Zero. Check out their \u003ca href=\"https://github.com/lsds/KungFu#examples\"\u003eexamples\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsage\u003c/h2\u003e\u003ca id=\"user-content-usage\" class=\"anchor\" aria-label=\"Permalink: Usage\" href=\"#usage\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKungFu currently support TensorFlow and Keras. To scale out your TensorFlow program, for example, you need to make two changes:\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eWrap your \u003ccode\u003etf.train.optimizer\u003c/code\u003e in KungFu's \u003ccode\u003eSynchronousSGDOptimizer\u003c/code\u003e, \u003ccode\u003eSynchronousAveragingOptimizer\u003c/code\u003e, or \u003ccode\u003ePairAveragingOptimizer\u003c/code\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eEnsure all workers start with consistent states by broadcasting a worker's initial global variables.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import tensorflow as tf\n\n# Build model...\nloss = ...\nopt = tf.train.AdamOptimizer(0.01)\n\n# KungFu Step 1: Wrap tf.optimizer in KungFu optimizers\nfrom kungfu.tensorflow.optimizers import SynchronousSGDOptimizer\nopt = SynchronousSGDOptimizer(opt)\n\n# Make training operation\ntrain_op = opt.minimize(loss)\n\n# Train your model\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n\n # KungFu Step 2: ensure distributed workers start with consistent states\n from kungfu.tensorflow.initializer import BroadcastGlobalVariablesOp\n sess.run(BroadcastGlobalVariablesOp())\n\n for step in range(10):\n sess.run(train_op)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etensorflow\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Build model...\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ...\n\u003cspan class=\"pl-s1\"\u003eopt\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etrain\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eAdamOptimizer\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e0.01\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# KungFu Step 1: Wrap tf.optimizer in KungFu optimizers\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekungfu\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etensorflow\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eoptimizers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSynchronousSGDOptimizer\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eopt\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSynchronousSGDOptimizer\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eopt\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Make training operation\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etrain_op\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eopt\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eminimize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Train your model\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eSession\u003c/span\u003e() \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esess\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003esess\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eglobal_variables_initializer\u003c/span\u003e())\n\n \u003cspan class=\"pl-c\"\u003e# KungFu Step 2: ensure distributed workers start with consistent states\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekungfu\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etensorflow\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003einitializer\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eBroadcastGlobalVariablesOp\u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003esess\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-en\"\u003eBroadcastGlobalVariablesOp\u003c/span\u003e())\n\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003estep\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003esess\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003erun\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_op\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can find more details in the \u003ca href=\"https://kungfu.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003eDocumentation\u003c/a\u003e, for example, for how to use KungFu with \u003ca href=\"/lsds/KungFu/blob/main/examples/tf1_mnist_session.py\"\u003eSession\u003c/a\u003e, \u003ca href=\"/lsds/KungFu/blob/main/examples/tf1_mnist_keras.py\"\u003eTensorFlow Keras\u003c/a\u003e, \u003ca href=\"/lsds/KungFu/blob/main/examples/tf1_mnist_estimator.py\"\u003eEstimator\u003c/a\u003e, and \u003ca href=\"/lsds/KungFu/blob/main/examples/tf2_mnist_gradient_tape.py\"\u003eGradientTape\u003c/a\u003e in TensorFlow 1 and 2.\nFor KungFu with Keras, check out \u003ca href=\"/lsds/KungFu/blob/main/examples/keras_mnist.py\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall\u003c/h2\u003e\u003ca id=\"user-content-install\" class=\"anchor\" aria-label=\"Permalink: Install\" href=\"#install\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKungFu is implemented in Go and C++.\nCurrently, it has a Python binding for TensorFlow (including v1 and v2) and Keras (assuming you use TensorFlow as the backend).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eKungFu for TensorFlow requires \u003ca href=\"https://www.python.org/downloads/\" rel=\"nofollow\"\u003ePython 3\u003c/a\u003e, \u003ca href=\"https://cmake.org/install/\" rel=\"nofollow\"\u003eCMake 3.5+\u003c/a\u003e, and \u003ca href=\"https://golang.org/dl/\" rel=\"nofollow\"\u003eGolang 1.13+\u003c/a\u003e.\nKungFu has been tested with \u003ca href=\"https://www.tensorflow.org/install/pip#older-versions-of-tensorflow\" rel=\"nofollow\"\u003eTensorFlow\u003c/a\u003e 1.12, 1.13, 1.15 and 2.0.0.\nKungFu has a known installation issue with TensorFlow 1.14.\nAssuming you have the above pre-requites, you can install KungFu as follows:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/lsds/KungFu.git\ncd KungFu\npip3 install --no-index -U --user .\"\u003e\u003cpre\u003egit clone https://github.com/lsds/KungFu.git\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e KungFu\npip3 install --no-index -U --user \u003cspan class=\"pl-c1\"\u003e.\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\n\u003cp dir=\"auto\"\u003eKungFu provides \u003ccode\u003ekungfu-run\u003c/code\u003e to launch a KungFu process on a multi-GPU server.\nIn a cluster, we need to launch \u003ccode\u003ekungfu-run\u003c/code\u003e on each node.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Show the help of kungfu-run\nkungfu-run -help\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Show the help of kungfu-run\u003c/span\u003e\nkungfu-run -help\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can use KungFu with Docker. Check out the docker files for \u003ca href=\"/lsds/KungFu/blob/main/docker/Dockerfile.tf-gpu\"\u003eGPU\u003c/a\u003e and \u003ca href=\"/lsds/KungFu/blob/main/docker/Dockerfile.tf-cpu\"\u003eCPU\u003c/a\u003e machines.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRun\u003c/h2\u003e\u003ca id=\"user-content-run\" class=\"anchor\" aria-label=\"Permalink: Run\" href=\"#run\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe show how to run a KungFu program using a MNIST example.\nDownload the MNIST dataset (\u003ca href=\"/lsds/KungFu/blob/main/scripts/download-mnist.sh\"\u003escript\u003c/a\u003e) first and then run the following training script:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Train a Single Layer Perception (SLP) model for the MNIST dataset using 4 CPUs for 10 data epochs.\nkungfu-run -np 4 python3 examples/tf1_mnist_session.py --data-dir=./mnist\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Train a Single Layer Perception (SLP) model for the MNIST dataset using 4 CPUs for 10 data epochs.\u003c/span\u003e\nkungfu-run -np 4 python3 examples/tf1_mnist_session.py --data-dir=./mnist\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can run this example on two machines (assuming each with 8 GPUs) using the below command (NOTE: this command must be called on each machine):\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Assume the machines have NIC eth0 and their IPs are 192.168.0.1 and 192.168.0.2.\n# Assume NUM_GPU_SLOTS=8, NUM_GPUS=16\nkungfu-run -np $NUM_GPUS \\\n -H 192.168.0.1:$NUM_GPU_SLOTS,192.168.0.2:$NUM_GPU_SLOTS -nic eth0 \\\n python3 examples/tf1_mnist_session.py --data-dir=./mnist\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Assume the machines have NIC eth0 and their IPs are 192.168.0.1 and 192.168.0.2.\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e Assume NUM_GPU_SLOTS=8, NUM_GPUS=16\u003c/span\u003e\nkungfu-run -np \u003cspan class=\"pl-smi\"\u003e$NUM_GPUS\u003c/span\u003e \\\n -H 192.168.0.1:\u003cspan class=\"pl-smi\"\u003e$NUM_GPU_SLOTS\u003c/span\u003e,192.168.0.2:\u003cspan class=\"pl-smi\"\u003e$NUM_GPU_SLOTS\u003c/span\u003e -nic eth0 \\\n python3 examples/tf1_mnist_session.py --data-dir=./mnist\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003ekungfu-run\u003c/code\u003e use the \u003ccode\u003enic\u003c/code\u003e option to infer its IP and thus its role in the cluster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExamples\u003c/h2\u003e\u003ca id=\"user-content-examples\" class=\"anchor\" aria-label=\"Permalink: Examples\" href=\"#examples\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe have been using KungFu in training\ndifferent kinds of AI models.\nThe following are representative examples:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003eImageNet\u003c/strong\u003e\u003c/em\u003e: KungFu can speed up the training\nof ResNet, VGG, DenseNet and others for ImageNet.\nCheck out this in an \u003ca href=\"https://github.com/luomai/benchmarks/tree/cnn_tf_v1.12_compatible_kungfu/scripts/tf_cnn_benchmarks#running-kungfu\"\u003eImageNet benchmark suite\u003c/a\u003e extended from the \u003ca href=\"https://github.com/luomai/benchmarks/tree/cnn_tf_v1.12_compatible_kungfu\"\u003eTensorFlow benchmark\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003ePose estimation\u003c/strong\u003e\u003c/em\u003e: Pose estimation models such as \u003ca href=\"https://github.com/CMU-Perceptual-Computing-Lab/openpose\"\u003eOpenPose\u003c/a\u003e are often batch-size sensitive.\nWe used KungFu in\na popular \u003ca href=\"https://github.com/tensorlayer/openpose-plus\"\u003eOpenPose implementation\u003c/a\u003e and improved time-to-accuracy\nusing the model averaging optimizer which preserves the merits of small batch size.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003eNatural language processing\u003c/strong\u003e\u003c/em\u003e:\nWe have an \u003ca href=\"https://github.com/luomai/bert\"\u003eexample\u003c/a\u003e that shows how you can use few lines to enable distributed training for the Google BERT model.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003eAdversarial learning\u003c/strong\u003e\u003c/em\u003e:\nAdversarial learning trains multiple networks in parallel and prefer using small batches for training.\nKungFu thus become an attractive option, because of its minimal changes to GAN programs\nand its optimizers that decouple batch size and system parallelism.\nSee the \u003ca href=\"https://github.com/tensorlayer/cyclegan\"\u003eCycleGAN example\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003eReinforcement learning\u003c/strong\u003e\u003c/em\u003e:\nWe are working on an Alpha Zero distributed training example and will release it soon.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eChoosing the right optimizer\u003c/h2\u003e\u003ca id=\"user-content-choosing-the-right-optimizer\" class=\"anchor\" aria-label=\"Permalink: Choosing the right optimizer\" href=\"#choosing-the-right-optimizer\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKungFu aims to help users decrease the\ntime to reach a desired accuracy (time-to-accuracy)\nthrough scaling.\nThere are two major ways to improve time-to-accuracy in KungFu:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSynchronous SGD: Adopt parallel workers to improve the estimation of gradients, and\nreach a minima quickly using an increased learning rate.\u003c/li\u003e\n\u003cli\u003eModel Averaging: Adopt parallel workers to explore the solution space and collaborate through averaging diverged models in order\nto find a good minima quickly.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003eSynchronous SGD\u003c/strong\u003e\u003c/em\u003e:\nSynchronous SGD (S-SGD) is implemented as \u003ccode\u003eSynchronousSGDOptimizer\u003c/code\u003e in KungFu, equivalent to\nthe DistributedOptimizer in Horovod.\nThe use of S-SGD, however, poses scalability and accuracy challenges.\nScalability-wise, all S-SGD workers must exchange all gradients per iteration, making\nthem hard to deal with limited bandwidth and stragglers;\n(ii) accuracy-wise, S-SGD \u003cem\u003ecouples\u003c/em\u003e training batch size with the number of workers,\nenforcing users to use large batch sizes, which can adversely\naffect the generality of a trained model (see \u003ca href=\"https://arxiv.org/abs/1609.04836\" rel=\"nofollow\"\u003epaper\u003c/a\u003e).\nTo compensate the loss in generality, users must explore various \u003ca href=\"https://research.fb.com/wp-content/uploads/2017/06/imagenet1kin1h5.pdf\" rel=\"nofollow\"\u003emethods\u003c/a\u003e\nfor tuning hyper-parameters.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003eModel averaging\u003c/strong\u003e\u003c/em\u003e:\nModel averaging is implemented as \u003ccode\u003eSynchronousAveragingOptimizer\u003c/code\u003e and\n\u003ccode\u003ePairAveragingOptimizer\u003c/code\u003e in KungFu.\nThe former realizes the hyper-parameter-robust \u003ca href=\"http://www.vldb.org/pvldb/vol12/p1399-koliousis.pdf\" rel=\"nofollow\"\u003eSMA\u003c/a\u003e\nalgorithm; while the latter implements the \u003ca href=\"https://arxiv.org/abs/1710.06952\" rel=\"nofollow\"\u003eAD-PSGD\u003c/a\u003e algorithm\nwhich reduces bandwidth consumption and tolerates stragglers.\nIn model averaging, each worker trains its local\nmodel using SGD, and average\nits model with peers to speed up the search for minima.\nModel averaging algorithms have a convergence guarantee (see \u003ca href=\"https://arxiv.org/abs/1412.6651\" rel=\"nofollow\"\u003eEA-SGD paper\u003c/a\u003e)\nand can converge fast with DL models (see \u003ca href=\"https://arxiv.org/abs/1907.08610\" rel=\"nofollow\"\u003eLookahead paper\u003c/a\u003e).\nA useful property of model averaging is: it decouples\nbatch size with system parallelism, often making\nit \u003cem\u003ehyper-parameter robust\u003c/em\u003e. We find\nthis property valuable\nas DL users often find it hard and expensive to\ntune synchronous SGD at scale.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003eConvergence evaluation\u003c/strong\u003e\u003c/em\u003e:\nWe have tested KungFu optimizers using ResNet-50 and ResNet-101 for ImageNet.\nWhen using 8 V100, all KungFu optimizers can reach the target 75% accuracy,\nthe same as the baseline Horovod.\nWhen using 16 V100, Horovod and \u003ccode\u003eSynchronousSGDOptimizer\u003c/code\u003e suffer from\nthe increased batch size and their accuracy drop to 59% while\n\u003ccode\u003eSynchronousAveragingOptimizer\u003c/code\u003e and \u003ccode\u003ePairAveragingOptimizer\u003c/code\u003e still\nreach the target 75%.\nAll these tests use a per-GPU batch size as 64 and \u003ca href=\"https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks#getting-started\"\u003ehyper-parameters\u003c/a\u003e\nsuggested by the TensorFlow benchmark authors.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBenchmark\u003c/h2\u003e\u003ca id=\"user-content-benchmark\" class=\"anchor\" aria-label=\"Permalink: Benchmark\" href=\"#benchmark\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe benchmark KungFu in a cluster that has 16 V100 GPUs hosted by 2 DGX-1 machines.\nThe machines are interconnected by a 100 Gbps network. We measure the training throughput of ResNet-50, VGG16 and InceptionV3. These models represent different kinds of training workloads.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn the \u003cem\u003e\u003cstrong\u003esynchronous training\u003c/strong\u003e\u003c/em\u003e case, we compare KungFu (\u003ccode\u003eSynchronousSGDOptimizer\u003c/code\u003e) with \u003ca href=\"https://github.com/horovod/horovod\"\u003eHorovod\u003c/a\u003e (0.16.1). Horovod uses OpenMPI 4.0.0. We evaluate the spectrum of batch size (from 256 to 4096) commonly used by S-SGD users.\nThis batch size is evenly shared by the 16 GPUs.\nKungFu outperforms Horovod on all tested models, in particular with small batch sizes which significantly raise the\nfrequency of synchronization.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/lsds/KungFu/blob/main/benchmarks/system/result/sync-scalability.svg\"\u003e\u003cimg src=\"/lsds/KungFu/raw/main/benchmarks/system/result/sync-scalability.svg\" alt=\"sync\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn the \u003cem\u003e\u003cstrong\u003easynchronous training\u003c/strong\u003e\u003c/em\u003e case, we compare KungFu (\u003ccode\u003ePairAveragingOptimizer\u003c/code\u003e) with TensorFlow parameter servers (1.13.1). We uses the same range of batch sizes as above. KungFu exhibits better scalability as well.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/lsds/KungFu/blob/main/benchmarks/system/result/async-scalability.svg\"\u003e\u003cimg src=\"/lsds/KungFu/raw/main/benchmarks/system/result/async-scalability.svg\" alt=\"async\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe have also run the same benchmark in a 16-server cluster (each has a P100).\nKungFu exhibits better scalability in this communication-challenging environment,\nand we thus only report the 16 V100 result here. You can find the benchmark scripts \u003ca href=\"/lsds/KungFu/blob/main/benchmarks/system\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDevelopment\u003c/h2\u003e\u003ca id=\"user-content-development\" class=\"anchor\" aria-label=\"Permalink: Development\" href=\"#development\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eKungFu is designed with extensibility in mind.\nIt has a low-level API and a modular architecture, making\nit suitable for implementing new distributed training algorithms.\nCheck out the developer \u003ca href=\"/lsds/KungFu/blob/main/CONTRIBUTING.md\"\u003eguideline\u003c/a\u003e for more information.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"KungFu","anchor":"kungfu","htmlText":"KungFu"},{"level":2,"text":"Features","anchor":"features","htmlText":"Features"},{"level":2,"text":"Usage","anchor":"usage","htmlText":"Usage"},{"level":2,"text":"Install","anchor":"install","htmlText":"Install"},{"level":2,"text":"Run","anchor":"run","htmlText":"Run"},{"level":2,"text":"Examples","anchor":"examples","htmlText":"Examples"},{"level":2,"text":"Choosing the right optimizer","anchor":"choosing-the-right-optimizer","htmlText":"Choosing the right optimizer"},{"level":2,"text":"Benchmark","anchor":"benchmark","htmlText":"Benchmark"},{"level":2,"text":"Development","anchor":"development","htmlText":"Development"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Flsds%2FKungFu"}},{"displayName":"LICENSE","repoName":"KungFu","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Flsds%2FKungFu"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_tab":false}}}}</script> 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QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="/lsds/KungFu/blob/main/docs/kungfu-logo.png"><img src="/lsds/KungFu/raw/main/docs/kungfu-logo.png" width="50%" height="30%" style="max-width: 100%;"></a> </div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">KungFu</h1><a id="user-content-kungfu" class="anchor" aria-label="Permalink: KungFu" href="#kungfu"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Making adaptive distributed machine learning easy and efficient.</p> <p dir="auto"><a href="https://travis-ci.com/lsds/KungFu" rel="nofollow"><img src="https://camo.githubusercontent.com/652a92bd9223555fe9d9d1e34dce4a16ba11a12367e087f2fc1b8dffe950c9a1/68747470733a2f2f7472617669732d63692e636f6d2f6c7364732f4b756e6746752e7376673f6272616e63683d6d6173746572" alt="Build Status" data-canonical-src="https://travis-ci.com/lsds/KungFu.svg?branch=master" style="max-width: 100%;"></a> <a href="https://kungfu.readthedocs.io/en/latest/?badge=latest" rel="nofollow"><img src="https://camo.githubusercontent.com/69dc21a5dab63bbbf36b72b6764e4be21b7b3f1540b8f8b6565d43db406e11bd/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6b756e6766752f62616467652f3f76657273696f6e3d6c6174657374" alt="Documentation Status" data-canonical-src="https://readthedocs.org/projects/kungfu/badge/?version=latest" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Features</h2><a id="user-content-features" class="anchor" aria-label="Permalink: Features" href="#features"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">KungFu aims to help users achieve <em>fast</em> and <em>adaptive</em> distributed machine learning with <em>minimal</em> efforts. This is important because a machine learning system must cope with growing complex models and increasingly complicated deployment environments, making it difficult to constantly deliver high performance with an <em>empirical</em> configuration. To address this, KungFu provides the following unique features:</p> <ul dir="auto"> <li>Simplicity: KungFu permits distributed training by adding minimal code in your training program. KungFu is also simple to install and run. It does not require extra deployment like parameter servers and heavy dependencies like MPI in Horovod.</li> <li>Adaptable distributed training: KungFu provides useful advanced <a href="/lsds/KungFu/blob/main/srcs/python/kungfu/tensorflow/optimizers/__init__.py">distributed optimizers</a> such as communication-efficient <code>PairAveragingOptimizer</code> and hyper-parameter-robust <code>SynchronousAveragingOptimizer</code> to help you address the cases in which conventional Synchronous SGD does not scale. See <a href="https://github.com/lsds/KungFu#choosing-the-right-optimizer">Optimizers</a> for how to choose the right KungFu optimizer for your training scenario.</li> <li>Online monitoring and control: KungFu aims to support <a href="/lsds/KungFu/blob/main/srcs/python/kungfu/tensorflow/optimizers/sync_sgd.py">distributed SGD metrics</a> such as <a href="https://openai.com/blog/science-of-ai/" rel="nofollow">gradient noise scale</a> to help understand the training process with low overhead. KungFu further provides control operators such as <code>barrier</code> and <code>resize_cluster</code> to help reconfigure training online, even in response to monitored metrics.</li> <li>Fast and scalable: KungFu has a decentralized architecture, an non-blocking runtime, and high-performance implementations of communication, monitoring and control operators. Check out its performance in <a href="https://github.com/lsds/KungFu#benchmark">Benchmark</a>.</li> </ul> <p dir="auto">We have been using KungFu for scaling out different deep learning models such as ResNet, DenseNet, OpenPose, BERT, CycleGAN and Alpha Zero. Check out their <a href="https://github.com/lsds/KungFu#examples">examples</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Usage</h2><a id="user-content-usage" class="anchor" aria-label="Permalink: Usage" href="#usage"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">KungFu currently support TensorFlow and Keras. To scale out your TensorFlow program, for example, you need to make two changes:</p> <ol dir="auto"> <li> <p dir="auto">Wrap your <code>tf.train.optimizer</code> in KungFu's <code>SynchronousSGDOptimizer</code>, <code>SynchronousAveragingOptimizer</code>, or <code>PairAveragingOptimizer</code>.</p> </li> <li> <p dir="auto">Ensure all workers start with consistent states by broadcasting a worker's initial global variables.</p> </li> </ol> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import tensorflow as tf # Build model... loss = ... opt = tf.train.AdamOptimizer(0.01) # KungFu Step 1: Wrap tf.optimizer in KungFu optimizers from kungfu.tensorflow.optimizers import SynchronousSGDOptimizer opt = SynchronousSGDOptimizer(opt) # Make training operation train_op = opt.minimize(loss) # Train your model with tf.Session() as sess: sess.run(tf.global_variables_initializer()) # KungFu Step 2: ensure distributed workers start with consistent states from kungfu.tensorflow.initializer import BroadcastGlobalVariablesOp sess.run(BroadcastGlobalVariablesOp()) for step in range(10): sess.run(train_op)"><pre><span class="pl-k">import</span> <span class="pl-s1">tensorflow</span> <span class="pl-k">as</span> <span class="pl-s1">tf</span> <span class="pl-c"># Build model...</span> <span class="pl-s1">loss</span> <span class="pl-c1">=</span> ... <span class="pl-s1">opt</span> <span class="pl-c1">=</span> <span class="pl-s1">tf</span>.<span class="pl-c1">train</span>.<span class="pl-c1">AdamOptimizer</span>(<span class="pl-c1">0.01</span>) <span class="pl-c"># KungFu Step 1: Wrap tf.optimizer in KungFu optimizers</span> <span class="pl-k">from</span> <span class="pl-s1">kungfu</span>.<span class="pl-s1">tensorflow</span>.<span class="pl-s1">optimizers</span> <span class="pl-k">import</span> <span class="pl-v">SynchronousSGDOptimizer</span> <span class="pl-s1">opt</span> <span class="pl-c1">=</span> <span class="pl-en">SynchronousSGDOptimizer</span>(<span class="pl-s1">opt</span>) <span class="pl-c"># Make training operation</span> <span class="pl-s1">train_op</span> <span class="pl-c1">=</span> <span class="pl-s1">opt</span>.<span class="pl-c1">minimize</span>(<span class="pl-s1">loss</span>) <span class="pl-c"># Train your model</span> <span class="pl-k">with</span> <span class="pl-s1">tf</span>.<span class="pl-c1">Session</span>() <span class="pl-k">as</span> <span class="pl-s1">sess</span>: <span class="pl-s1">sess</span>.<span class="pl-c1">run</span>(<span class="pl-s1">tf</span>.<span class="pl-c1">global_variables_initializer</span>()) <span class="pl-c"># KungFu Step 2: ensure distributed workers start with consistent states</span> <span class="pl-k">from</span> <span class="pl-s1">kungfu</span>.<span class="pl-s1">tensorflow</span>.<span class="pl-s1">initializer</span> <span class="pl-k">import</span> <span class="pl-v">BroadcastGlobalVariablesOp</span> <span class="pl-s1">sess</span>.<span class="pl-c1">run</span>(<span class="pl-en">BroadcastGlobalVariablesOp</span>()) <span class="pl-k">for</span> <span class="pl-s1">step</span> <span class="pl-c1">in</span> <span class="pl-en">range</span>(<span class="pl-c1">10</span>): <span class="pl-s1">sess</span>.<span class="pl-c1">run</span>(<span class="pl-s1">train_op</span>)</pre></div> <p dir="auto">You can find more details in the <a href="https://kungfu.readthedocs.io/en/latest/?badge=latest" rel="nofollow">Documentation</a>, for example, for how to use KungFu with <a href="/lsds/KungFu/blob/main/examples/tf1_mnist_session.py">Session</a>, <a href="/lsds/KungFu/blob/main/examples/tf1_mnist_keras.py">TensorFlow Keras</a>, <a href="/lsds/KungFu/blob/main/examples/tf1_mnist_estimator.py">Estimator</a>, and <a href="/lsds/KungFu/blob/main/examples/tf2_mnist_gradient_tape.py">GradientTape</a> in TensorFlow 1 and 2. For KungFu with Keras, check out <a href="/lsds/KungFu/blob/main/examples/keras_mnist.py">here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Install</h2><a id="user-content-install" class="anchor" aria-label="Permalink: Install" href="#install"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">KungFu is implemented in Go and C++. Currently, it has a Python binding for TensorFlow (including v1 and v2) and Keras (assuming you use TensorFlow as the backend).</p> <p dir="auto">KungFu for TensorFlow requires <a href="https://www.python.org/downloads/" rel="nofollow">Python 3</a>, <a href="https://cmake.org/install/" rel="nofollow">CMake 3.5+</a>, and <a href="https://golang.org/dl/" rel="nofollow">Golang 1.13+</a>. KungFu has been tested with <a href="https://www.tensorflow.org/install/pip#older-versions-of-tensorflow" rel="nofollow">TensorFlow</a> 1.12, 1.13, 1.15 and 2.0.0. KungFu has a known installation issue with TensorFlow 1.14. Assuming you have the above pre-requites, you can install KungFu as follows:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/lsds/KungFu.git cd KungFu pip3 install --no-index -U --user ."><pre>git clone https://github.com/lsds/KungFu.git <span class="pl-c1">cd</span> KungFu pip3 install --no-index -U --user <span class="pl-c1">.</span></pre></div> <p dir="auto">KungFu provides <code>kungfu-run</code> to launch a KungFu process on a multi-GPU server. In a cluster, we need to launch <code>kungfu-run</code> on each node.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Show the help of kungfu-run kungfu-run -help"><pre><span class="pl-c"><span class="pl-c">#</span> Show the help of kungfu-run</span> kungfu-run -help</pre></div> <p dir="auto">You can use KungFu with Docker. Check out the docker files for <a href="/lsds/KungFu/blob/main/docker/Dockerfile.tf-gpu">GPU</a> and <a href="/lsds/KungFu/blob/main/docker/Dockerfile.tf-cpu">CPU</a> machines.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Run</h2><a id="user-content-run" class="anchor" aria-label="Permalink: Run" href="#run"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We show how to run a KungFu program using a MNIST example. Download the MNIST dataset (<a href="/lsds/KungFu/blob/main/scripts/download-mnist.sh">script</a>) first and then run the following training script:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Train a Single Layer Perception (SLP) model for the MNIST dataset using 4 CPUs for 10 data epochs. kungfu-run -np 4 python3 examples/tf1_mnist_session.py --data-dir=./mnist"><pre><span class="pl-c"><span class="pl-c">#</span> Train a Single Layer Perception (SLP) model for the MNIST dataset using 4 CPUs for 10 data epochs.</span> kungfu-run -np 4 python3 examples/tf1_mnist_session.py --data-dir=./mnist</pre></div> <p dir="auto">You can run this example on two machines (assuming each with 8 GPUs) using the below command (NOTE: this command must be called on each machine):</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Assume the machines have NIC eth0 and their IPs are 192.168.0.1 and 192.168.0.2. # Assume NUM_GPU_SLOTS=8, NUM_GPUS=16 kungfu-run -np $NUM_GPUS \ -H 192.168.0.1:$NUM_GPU_SLOTS,192.168.0.2:$NUM_GPU_SLOTS -nic eth0 \ python3 examples/tf1_mnist_session.py --data-dir=./mnist"><pre><span class="pl-c"><span class="pl-c">#</span> Assume the machines have NIC eth0 and their IPs are 192.168.0.1 and 192.168.0.2.</span> <span class="pl-c"><span class="pl-c">#</span> Assume NUM_GPU_SLOTS=8, NUM_GPUS=16</span> kungfu-run -np <span class="pl-smi">$NUM_GPUS</span> \ -H 192.168.0.1:<span class="pl-smi">$NUM_GPU_SLOTS</span>,192.168.0.2:<span class="pl-smi">$NUM_GPU_SLOTS</span> -nic eth0 \ python3 examples/tf1_mnist_session.py --data-dir=./mnist</pre></div> <p dir="auto"><code>kungfu-run</code> use the <code>nic</code> option to infer its IP and thus its role in the cluster.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Examples</h2><a id="user-content-examples" class="anchor" aria-label="Permalink: Examples" href="#examples"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We have been using KungFu in training different kinds of AI models. The following are representative examples:</p> <ul dir="auto"> <li> <p dir="auto"><em><strong>ImageNet</strong></em>: KungFu can speed up the training of ResNet, VGG, DenseNet and others for ImageNet. Check out this in an <a href="https://github.com/luomai/benchmarks/tree/cnn_tf_v1.12_compatible_kungfu/scripts/tf_cnn_benchmarks#running-kungfu">ImageNet benchmark suite</a> extended from the <a href="https://github.com/luomai/benchmarks/tree/cnn_tf_v1.12_compatible_kungfu">TensorFlow benchmark</a>.</p> </li> <li> <p dir="auto"><em><strong>Pose estimation</strong></em>: Pose estimation models such as <a href="https://github.com/CMU-Perceptual-Computing-Lab/openpose">OpenPose</a> are often batch-size sensitive. We used KungFu in a popular <a href="https://github.com/tensorlayer/openpose-plus">OpenPose implementation</a> and improved time-to-accuracy using the model averaging optimizer which preserves the merits of small batch size.</p> </li> <li> <p dir="auto"><em><strong>Natural language processing</strong></em>: We have an <a href="https://github.com/luomai/bert">example</a> that shows how you can use few lines to enable distributed training for the Google BERT model.</p> </li> <li> <p dir="auto"><em><strong>Adversarial learning</strong></em>: Adversarial learning trains multiple networks in parallel and prefer using small batches for training. KungFu thus become an attractive option, because of its minimal changes to GAN programs and its optimizers that decouple batch size and system parallelism. See the <a href="https://github.com/tensorlayer/cyclegan">CycleGAN example</a>.</p> </li> <li> <p dir="auto"><em><strong>Reinforcement learning</strong></em>: We are working on an Alpha Zero distributed training example and will release it soon.</p> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Choosing the right optimizer</h2><a id="user-content-choosing-the-right-optimizer" class="anchor" aria-label="Permalink: Choosing the right optimizer" href="#choosing-the-right-optimizer"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">KungFu aims to help users decrease the time to reach a desired accuracy (time-to-accuracy) through scaling. There are two major ways to improve time-to-accuracy in KungFu:</p> <ul dir="auto"> <li>Synchronous SGD: Adopt parallel workers to improve the estimation of gradients, and reach a minima quickly using an increased learning rate.</li> <li>Model Averaging: Adopt parallel workers to explore the solution space and collaborate through averaging diverged models in order to find a good minima quickly.</li> </ul> <p dir="auto"><em><strong>Synchronous SGD</strong></em>: Synchronous SGD (S-SGD) is implemented as <code>SynchronousSGDOptimizer</code> in KungFu, equivalent to the DistributedOptimizer in Horovod. The use of S-SGD, however, poses scalability and accuracy challenges. Scalability-wise, all S-SGD workers must exchange all gradients per iteration, making them hard to deal with limited bandwidth and stragglers; (ii) accuracy-wise, S-SGD <em>couples</em> training batch size with the number of workers, enforcing users to use large batch sizes, which can adversely affect the generality of a trained model (see <a href="https://arxiv.org/abs/1609.04836" rel="nofollow">paper</a>). To compensate the loss in generality, users must explore various <a href="https://research.fb.com/wp-content/uploads/2017/06/imagenet1kin1h5.pdf" rel="nofollow">methods</a> for tuning hyper-parameters.</p> <p dir="auto"><em><strong>Model averaging</strong></em>: Model averaging is implemented as <code>SynchronousAveragingOptimizer</code> and <code>PairAveragingOptimizer</code> in KungFu. The former realizes the hyper-parameter-robust <a href="http://www.vldb.org/pvldb/vol12/p1399-koliousis.pdf" rel="nofollow">SMA</a> algorithm; while the latter implements the <a href="https://arxiv.org/abs/1710.06952" rel="nofollow">AD-PSGD</a> algorithm which reduces bandwidth consumption and tolerates stragglers. In model averaging, each worker trains its local model using SGD, and average its model with peers to speed up the search for minima. Model averaging algorithms have a convergence guarantee (see <a href="https://arxiv.org/abs/1412.6651" rel="nofollow">EA-SGD paper</a>) and can converge fast with DL models (see <a href="https://arxiv.org/abs/1907.08610" rel="nofollow">Lookahead paper</a>). A useful property of model averaging is: it decouples batch size with system parallelism, often making it <em>hyper-parameter robust</em>. We find this property valuable as DL users often find it hard and expensive to tune synchronous SGD at scale.</p> <p dir="auto"><em><strong>Convergence evaluation</strong></em>: We have tested KungFu optimizers using ResNet-50 and ResNet-101 for ImageNet. When using 8 V100, all KungFu optimizers can reach the target 75% accuracy, the same as the baseline Horovod. When using 16 V100, Horovod and <code>SynchronousSGDOptimizer</code> suffer from the increased batch size and their accuracy drop to 59% while <code>SynchronousAveragingOptimizer</code> and <code>PairAveragingOptimizer</code> still reach the target 75%. All these tests use a per-GPU batch size as 64 and <a href="https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks#getting-started">hyper-parameters</a> suggested by the TensorFlow benchmark authors.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Benchmark</h2><a id="user-content-benchmark" class="anchor" aria-label="Permalink: Benchmark" href="#benchmark"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We benchmark KungFu in a cluster that has 16 V100 GPUs hosted by 2 DGX-1 machines. The machines are interconnected by a 100 Gbps network. We measure the training throughput of ResNet-50, VGG16 and InceptionV3. These models represent different kinds of training workloads.</p> <p dir="auto">In the <em><strong>synchronous training</strong></em> case, we compare KungFu (<code>SynchronousSGDOptimizer</code>) with <a href="https://github.com/horovod/horovod">Horovod</a> (0.16.1). Horovod uses OpenMPI 4.0.0. We evaluate the spectrum of batch size (from 256 to 4096) commonly used by S-SGD users. This batch size is evenly shared by the 16 GPUs. KungFu outperforms Horovod on all tested models, in particular with small batch sizes which significantly raise the frequency of synchronization.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/lsds/KungFu/blob/main/benchmarks/system/result/sync-scalability.svg"><img src="/lsds/KungFu/raw/main/benchmarks/system/result/sync-scalability.svg" alt="sync" style="max-width: 100%;"></a></p> <p dir="auto">In the <em><strong>asynchronous training</strong></em> case, we compare KungFu (<code>PairAveragingOptimizer</code>) with TensorFlow parameter servers (1.13.1). We uses the same range of batch sizes as above. KungFu exhibits better scalability as well.</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/lsds/KungFu/blob/main/benchmarks/system/result/async-scalability.svg"><img src="/lsds/KungFu/raw/main/benchmarks/system/result/async-scalability.svg" alt="async" style="max-width: 100%;"></a></p> <p dir="auto">We have also run the same benchmark in a 16-server cluster (each has a P100). KungFu exhibits better scalability in this communication-challenging environment, and we thus only report the 16 V100 result here. You can find the benchmark scripts <a href="/lsds/KungFu/blob/main/benchmarks/system">here</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Development</h2><a id="user-content-development" class="anchor" aria-label="Permalink: Development" href="#development"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">KungFu is designed with extensibility in mind. It has a low-level API and a modular architecture, making it suitable for implementing new distributed training algorithms. Check out the developer <a href="/lsds/KungFu/blob/main/CONTRIBUTING.md">guideline</a> for more information.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="UP/50Dr6Ggz/UjFX1OJdMLHNR1mL+uE/HlEEWkkzkI0A56fLqLS2ENj5rOEO2a5dboX2Me49WJpLmfZhoi68Xg==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Fast and Adaptive Distributed Machine Learning for TensorFlow, PyTorch and MindSpore. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/distributed-systems" title="Topic: distributed-systems" data-view-component="true" class="topic-tag topic-tag-link"> distributed-systems 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.216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>293</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/lsds/KungFu/watchers" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-eye mr-2"> <path d="M8 2c1.981 0 3.671.992 4.933 2.078 1.27 1.091 2.187 2.345 2.637 3.023a1.62 1.62 0 0 1 0 1.798c-.45.678-1.367 1.932-2.637 3.023C11.67 13.008 9.981 14 8 14c-1.981 0-3.671-.992-4.933-2.078C1.797 10.83.88 9.576.43 8.898a1.62 1.62 0 0 1 0-1.798c.45-.677 1.367-1.931 2.637-3.022C4.33 2.992 6.019 2 8 2ZM1.679 7.932a.12.12 0 0 0 0 .136c.411.622 1.241 1.75 2.366 2.717C5.176 11.758 6.527 12.5 8 12.5c1.473 0 2.825-.742 3.955-1.715 1.124-.967 1.954-2.096 2.366-2.717a.12.12 0 0 0 0-.136c-.412-.621-1.242-1.75-2.366-2.717C10.824 4.242 9.473 3.5 8 3.5c-1.473 0-2.825.742-3.955 1.715-1.124.967-1.954 2.096-2.366 2.717ZM8 10a2 2 0 1 1-.001-3.999A2 2 0 0 1 8 10Z"></path> </svg> <strong>22</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/lsds/KungFu/forks" data-view-component="true" class="Link Link--muted"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-repo-forked mr-2"> <path d="M5 5.372v.878c0 .414.336.75.75.75h4.5a.75.75 0 0 0 .75-.75v-.878a2.25 2.25 0 1 1 1.5 0v.878a2.25 2.25 0 0 1-2.25 2.25h-1.5v2.128a2.251 2.251 0 1 1-1.5 0V8.5h-1.5A2.25 2.25 0 0 1 3.5 6.25v-.878a2.25 2.25 0 1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>59</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Flsds%2FKungFu&report=lsds+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/lsds/KungFu/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="3" data-view-component="true" class="Counter">3</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/lsds/KungFu/releases/tag/v0.2.1"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">KungFu 0.2.1 Release</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2020-01-11T21:31:21Z" class="no-wrap">Jan 11, 2020</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/lsds/KungFu/releases" data-view-component="true" class="Link">+ 2 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/lsds/packages?repo_name=KungFu" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Packages <span title="0" hidden="hidden" data-view-component="true" class="Counter ml-1">0</span></a></h2> <div class="text-small color-fg-muted" > No packages published <br> </div> </div> </div> <div class="BorderGrid-row" hidden> <div class="BorderGrid-cell"> <include-fragment src="/lsds/KungFu/used_by_list" accept="text/fragment+html"> </include-fragment> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/lsds/KungFu/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Contributors <span title="7" data-view-component="true" class="Counter ml-1">7</span></a></h2> <include-fragment src="/lsds/KungFu/contributors_list?count=7&current_repository=KungFu&items_to_show=7" aria-busy="true" aria-label="Loading contributors"> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> <li class="mb-2 "> <div class="Skeleton avatar avatar-user mr-2" style="width:32px;height:32px;"></div> </li> </ul> </include-fragment> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#00ADD8 !important;;width: 35.7%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3572A5 !important;;width: 32.7%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#f34b7d !important;;width: 25.7%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#89e051 !important;;width: 3.4%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#DA3434 !important;;width: 1.9%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#555555 !important;;width: 0.6%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/lsds/KungFu/search?l=go" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#00ADD8;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Go</span> <span>35.7%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/lsds/KungFu/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>32.7%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/lsds/KungFu/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C++</span> <span>25.7%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/lsds/KungFu/search?l=shell" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#89e051;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" 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id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>