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GitHub - deepspeedai/DeepSpeed: DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

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class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div></span> </span> </button> </div> <a class="mr-lg-3 color-fg-inherit flex-order-2 js-prevent-focus-on-mobile-nav" href="/" aria-label="Homepage" data-analytics-event="{&quot;category&quot;:&quot;Marketing nav&quot;,&quot;action&quot;:&quot;click to go to homepage&quot;,&quot;label&quot;:&quot;ref_page:Marketing;ref_cta:Logomark;ref_loc:Header&quot;}"> <svg height="32" aria-hidden="true" viewBox="0 0 24 24" version="1.1" width="32" data-view-component="true" class="octicon octicon-mark-github"> <path 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%2Fdeepspeedai%2FDeepSpeed" 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="{&quot;event_type&quot;:&quot;authentication.click&quot;,&quot;payload&quot;:{&quot;location_in_page&quot;:&quot;site header menu&quot;,&quot;repository_id&quot;:null,&quot;auth_type&quot;:&quot;SIGN_UP&quot;,&quot;originating_url&quot;:&quot;https://github.com/deepspeedai/DeepSpeed&quot;,&quot;user_id&quot;:null}}" data-hydro-click-hmac="4932882d08d76b7f2d848ef5d33cb43f9fdb5ad6e9f9b8b8920195bb10d31001" data-analytics-event="{&quot;category&quot;:&quot;Marketing nav&quot;,&quot;action&quot;:&quot;click to Sign in&quot;,&quot;label&quot;:&quot;ref_page:Marketing;ref_cta:Sign in;ref_loc:Header&quot;}" > 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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;github_copilot&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;github_copilot_link_product_navbar&quot;}" 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 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;security&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;security_link_product_navbar&quot;}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" 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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;actions&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;actions_link_product_navbar&quot;}" 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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;codespaces&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;codespaces_link_product_navbar&quot;}" 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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;issues&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;issues_link_product_navbar&quot;}" 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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;code_review&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;code_review_link_product_navbar&quot;}" 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 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;discussions&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;discussions_link_product_navbar&quot;}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Discussions</div> Collaborate outside of code </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" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;code_search&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;code_search_link_product_navbar&quot;}" href="https://github.com/features/code-search"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" 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" aria-labelledby="product-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;all_features&quot;,&quot;context&quot;:&quot;product&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;all_features_link_product_navbar&quot;}" href="https://github.com/features"> All features </a></li> <li> <a 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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> </ul> </div> </div> </div> </li> <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"> Solutions <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 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 pb-lg-3 mb-3 mb-lg-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-company-size-heading">By company size</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-company-size-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;enterprises&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;enterprises_link_solutions_navbar&quot;}" href="https://github.com/enterprise"> Enterprises </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;small_and_medium_teams&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;small_and_medium_teams_link_solutions_navbar&quot;}" href="https://github.com/team"> Small and medium teams </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;startups&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;startups_link_solutions_navbar&quot;}" href="https://github.com/enterprise/startups"> Startups </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;nonprofits&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;nonprofits_link_solutions_navbar&quot;}" href="/solutions/industry/nonprofits"> Nonprofits </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-use-case-heading">By use case</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-use-case-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;devsecops&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;devsecops_link_solutions_navbar&quot;}" href="/solutions/use-case/devsecops"> DevSecOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;devops&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;devops_link_solutions_navbar&quot;}" href="/solutions/use-case/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;ci_cd&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;ci_cd_link_solutions_navbar&quot;}" href="/solutions/use-case/ci-cd"> CI/CD </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;view_all_use_cases&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;view_all_use_cases_link_solutions_navbar&quot;}" href="/solutions/use-case"> View all use cases </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"> <span class="d-block h4 color-fg-default my-1" id="solutions-by-industry-heading">By industry</span> <ul class="list-style-none f5" aria-labelledby="solutions-by-industry-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;healthcare&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;healthcare_link_solutions_navbar&quot;}" href="/solutions/industry/healthcare"> Healthcare </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;financial_services&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;financial_services_link_solutions_navbar&quot;}" href="/solutions/industry/financial-services"> Financial services </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;manufacturing&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;manufacturing_link_solutions_navbar&quot;}" href="/solutions/industry/manufacturing"> Manufacturing </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;government&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;government_link_solutions_navbar&quot;}" href="/solutions/industry/government"> Government </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;view_all_industries&quot;,&quot;context&quot;:&quot;solutions&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;view_all_industries_link_solutions_navbar&quot;}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </div> </div> </li> <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"> Resources <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"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul class="list-style-none f5" aria-labelledby="resources-topics-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;ai&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;ai_link_resources_navbar&quot;}" href="/resources/articles/ai"> AI </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;devops&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;devops_link_resources_navbar&quot;}" href="/resources/articles/devops"> DevOps </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;security&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;security_link_resources_navbar&quot;}" href="/resources/articles/security"> Security </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;software_development&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;software_development_link_resources_navbar&quot;}" href="/resources/articles/software-development"> Software Development </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;view_all&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;view_all_link_resources_navbar&quot;}" href="/resources/articles"> View all </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="resources-explore-heading">Explore</span> <ul class="list-style-none f5" aria-labelledby="resources-explore-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;learning_pathways&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;learning_pathways_link_resources_navbar&quot;}" href="https://resources.github.com/learn/pathways"> Learning Pathways <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 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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;events_amp_webinars&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;events_amp_webinars_link_resources_navbar&quot;}" href="https://resources.github.com"> Events &amp; Webinars <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 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" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;ebooks_amp_whitepapers&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;ebooks_amp_whitepapers_link_resources_navbar&quot;}" href="https://github.com/resources/whitepapers"> Ebooks &amp; Whitepapers </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;customer_stories&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;customer_stories_link_resources_navbar&quot;}" href="https://github.com/customer-stories"> Customer Stories </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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;partners&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;partners_link_resources_navbar&quot;}" href="https://partner.github.com"> Partners <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 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" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;executive_insights&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;executive_insights_link_resources_navbar&quot;}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <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"> Open Source <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 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <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" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;github_sponsors&quot;,&quot;context&quot;:&quot;open_source&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;github_sponsors_link_open_source_navbar&quot;}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <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" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;the_readme_project&quot;,&quot;context&quot;:&quot;open_source&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;the_readme_project_link_open_source_navbar&quot;}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;topics&quot;,&quot;context&quot;:&quot;open_source&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;topics_link_open_source_navbar&quot;}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;trending&quot;,&quot;context&quot;:&quot;open_source&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;trending_link_open_source_navbar&quot;}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;collections&quot;,&quot;context&quot;:&quot;open_source&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;collections_link_open_source_navbar&quot;}" href="https://github.com/collections"> Collections </a></li> </ul> </div> </div> </div> </li> <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"> Enterprise <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 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <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" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;enterprise_platform&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;enterprise_platform_link_enterprise_navbar&quot;}" href="/enterprise"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-stack color-fg-subtle mr-3"> <path d="M11.063 1.456a1.749 1.749 0 0 1 1.874 0l8.383 5.316a1.751 1.751 0 0 1 0 2.956l-8.383 5.316a1.749 1.749 0 0 1-1.874 0L2.68 9.728a1.751 1.751 0 0 1 0-2.956Zm1.071 1.267a.25.25 0 0 0-.268 0L3.483 8.039a.25.25 0 0 0 0 .422l8.383 5.316a.25.25 0 0 0 .268 0l8.383-5.316a.25.25 0 0 0 0-.422Z"></path><path d="M1.867 12.324a.75.75 0 0 1 1.035-.232l8.964 5.685a.25.25 0 0 0 .268 0l8.964-5.685a.75.75 0 0 1 .804 1.267l-8.965 5.685a1.749 1.749 0 0 1-1.874 0l-8.965-5.685a.75.75 0 0 1-.231-1.035Z"></path><path d="M1.867 16.324a.75.75 0 0 1 1.035-.232l8.964 5.685a.25.25 0 0 0 .268 0l8.964-5.685a.75.75 0 0 1 .804 1.267l-8.965 5.685a1.749 1.749 0 0 1-1.874 0l-8.965-5.685a.75.75 0 0 1-.231-1.035Z"></path> </svg> <div> <div class="color-fg-default h4">Enterprise platform</div> AI-powered developer platform </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="enterprise-available-add-ons-heading">Available add-ons</span> <ul class="list-style-none f5" aria-labelledby="enterprise-available-add-ons-heading"> <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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;advanced_security&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;advanced_security_link_enterprise_navbar&quot;}" href="https://github.com/enterprise/advanced-security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" 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">Advanced Security</div> Enterprise-grade security features </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="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;copilot_for_business&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;copilot_for_business_link_enterprise_navbar&quot;}" href="/features/copilot/copilot-business"> <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 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 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2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Premium Support</div> Enterprise-grade 24/7 support </div> </a></li> </ul> </div> </div> </div> </li> <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"> <a class="HeaderMenu-link no-underline 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100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/DeepSpeed_light.svg#gh-light-mode-only\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/DeepSpeed_light.svg#gh-light-mode-only\" width=\"400px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/DeepSpeed_dark_transparent.svg#gh-dark-mode-only\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/DeepSpeed_dark_transparent.svg#gh-dark-mode-only\" width=\"400px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLatest News\u003c/h2\u003e\u003ca id=\"user-content-latest-news\" class=\"anchor\" aria-label=\"Permalink: Latest News\" href=\"#latest-news\"\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\"\u003e\u003cb\u003e \u003cspan\u003e DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales; \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat\"\u003elearn how\u003c/a\u003e\u003c/span\u003e.\u003c/b\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e[2025/03] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/huggingface-tp/README.md\"\u003eDeepSpeed-AutoTP: Automatic Tensor Parallel Training of Hugging Face models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/12] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/ulysses-offload/README.md\"\u003eUlysses-Offload: Democratizing Long Context LLM Training \u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/12] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-domino/README.md\"\u003eDeepSpeed-Domino: Communication-Free LLM Training Engine\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2024/08] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/README.md\"\u003eDeepSpeed on Windows\u003c/a\u003e [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/japanese/README.md\"\u003e日本語\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/chinese/README.md\"\u003e中文\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[2024/08] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-gds/README.md\"\u003eDeepNVMe: Improving DL Applications through I/O Optimizations\u003c/a\u003e [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-gds/japanese/README.md\"\u003e日本語\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[2024/07] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-ucp/README.md\"\u003eDeepSpeed Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training\u003c/a\u003e [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-ucp/chinese/README.md\"\u003e中文\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-ucp/japanese/README.md\"\u003e日本語\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[2024/03] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024\"\u003eDeepSpeed-FP6:The power of FP6-Centric Serving for Large Language Models\u003c/a\u003e [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README.md\"\u003eEnglish\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README-Chinese.md\"\u003e中文\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[2024/01] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19\"\u003eDeepSpeed-FastGen: Introducing Mixtral, Phi-2, and Falcon support with major performance and feature enhancements.\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2023/11] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/intel-inference\"\u003eLlama 2 Inference on 4th Gen Intel® Xeon® Scalable Processor with DeepSpeed\u003c/a\u003e \u003ca href=\"https://www.intel.com/content/www/us/en/developer/articles/technical/xllama-2-on-xeon-scalable-processor-with-deepspeed.html\" rel=\"nofollow\"\u003e[Intel version]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2023/11] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-offloadpp\"\u003eDeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e[2023/11] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen\"\u003eDeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference\u003c/a\u003e [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen\"\u003eEnglish\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen/chinese/README.md\"\u003e中文\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen/japanese/README.md\"\u003e日本語\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[2023/10] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md\"\u003eDeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs\u003c/a\u003e [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md\"\u003eEnglish\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md\"\u003e中文\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md\"\u003e日本語\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e[2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [\u003ca href=\"https://www.deepspeed.ai/deepspeed4science/\" rel=\"nofollow\"\u003eTutorials\u003c/a\u003e] [\u003ca href=\"https://arxiv.org/abs/2310.04610\" rel=\"nofollow\"\u003eWhite paper\u003c/a\u003e] [\u003ca href=\"https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/\" rel=\"nofollow\"\u003eBlog\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md\"\u003e中文\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md\"\u003e日本語\u003c/a\u003e]\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cdetails\u003e\n \u003csummary\u003eMore news\u003c/summary\u003e\n \u003cul dir=\"auto\"\u003e\n \u003cli\u003e[2023/08] \u003ca href=\"https://github.com/deepspeedai/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md\"\u003eDeepSpeed ZeRO-Inference: 20x faster inference through weight quantization and KV cache offloading\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e[2023/08] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat/ds-chat-release-8-31/README.md\"\u003eDeepSpeed-Chat: Llama/Llama-2 system support, efficiency boost, and training stability improvements\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003e[2023/08] \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-ulysses\"\u003eDeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models\u003c/a\u003e [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-ulysses/chinese/README.md\"\u003e中文\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-ulysses/japanese/README.md\"\u003e日本語\u003c/a\u003e]\u003c/li\u003e\n \u003cli\u003e[2023/06] \u003ca href=\"https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/\" rel=\"nofollow\"\u003eZeRO++: A leap in speed for LLM and chat model training with 4X less communication\u003c/a\u003e [\u003ca href=\"https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/\" rel=\"nofollow\"\u003eEnglish\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/zeropp/chinese/README.md\"\u003e中文\u003c/a\u003e] [\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/zeropp/japanese/README.md\"\u003e日本語\u003c/a\u003e]\u003c/li\u003e\n \u003c/ul\u003e\n\u003c/details\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eExtreme Speed and Scale for DL Training and Inference\u003c/h1\u003e\u003ca id=\"user-content-extreme-speed-and-scale-for-dl-training-and-inference\" class=\"anchor\" aria-label=\"Permalink: Extreme Speed and Scale for DL Training and Inference\" href=\"#extreme-speed-and-scale-for-dl-training-and-inference\"\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\"\u003e\u003cem\u003e\u003cstrong\u003e\u003ca href=\"https://www.deepspeed.ai/\" rel=\"nofollow\"\u003eDeepSpeed\u003c/a\u003e enabled the world's most powerful language models (at the time of this writing) such as \u003ca href=\"https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/\" rel=\"nofollow\"\u003eMT-530B\u003c/a\u003e and \u003ca href=\"https://huggingface.co/blog/bloom-megatron-deepspeed\" rel=\"nofollow\"\u003eBLOOM\u003c/a\u003e\u003c/strong\u003e\u003c/em\u003e. It is an easy-to-use deep learning optimization software suite that powers unprecedented scale and speed for both training and inference. With DeepSpeed you can:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eTrain/Inference dense or sparse models with billions or trillions of parameters\u003c/li\u003e\n\u003cli\u003eAchieve excellent system throughput and efficiently scale to thousands of GPUs\u003c/li\u003e\n\u003cli\u003eTrain/Inference on resource constrained GPU systems\u003c/li\u003e\n\u003cli\u003eAchieve unprecedented low latency and high throughput for inference\u003c/li\u003e\n\u003cli\u003eAchieve extreme compression for an unparalleled inference latency and model size reduction with low costs\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepSpeed's four innovation pillars\u003c/h1\u003e\u003ca id=\"user-content-deepspeeds-four-innovation-pillars\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed's four innovation pillars\" href=\"#deepspeeds-four-innovation-pillars\"\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\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/DeepSpeed-pillars.png\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/DeepSpeed-pillars.png\" width=\"800px\" 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\"\u003eDeepSpeed-Training\u003c/h2\u003e\u003ca id=\"user-content-deepspeed-training\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed-Training\" href=\"#deepspeed-training\"\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\"\u003eDeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc. fall under the training pillar. Learn more: \u003ca href=\"https://www.deepspeed.ai/training/\" rel=\"nofollow\"\u003eDeepSpeed-Training\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepSpeed-Inference\u003c/h2\u003e\u003ca id=\"user-content-deepspeed-inference\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed-Inference\" href=\"#deepspeed-inference\"\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\"\u003eDeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, throughput and cost reduction. This systematic composition of system technologies for inference falls under the inference pillar. Learn more: \u003ca href=\"https://www.deepspeed.ai/inference\" rel=\"nofollow\"\u003eDeepSpeed-Inference\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepSpeed-Compression\u003c/h2\u003e\u003ca id=\"user-content-deepspeed-compression\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed-Compression\" href=\"#deepspeed-compression\"\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\"\u003eTo further increase the inference efficiency, DeepSpeed offers easy-to-use and flexible-to-compose compression techniques for researchers and practitioners to compress their models while delivering faster speed, smaller model size, and significantly reduced compression cost. Moreover, SoTA innovations on compression like ZeroQuant and XTC are included under the compression pillar. Learn more: \u003ca href=\"https://www.deepspeed.ai/compression\" rel=\"nofollow\"\u003eDeepSpeed-Compression\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepSpeed4Science\u003c/h2\u003e\u003ca id=\"user-content-deepspeed4science\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed4Science\" href=\"#deepspeed4science\"\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\"\u003eIn line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called \u003cem\u003eDeepSpeed4Science\u003c/em\u003e, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. Learn more: \u003ca href=\"https://www.deepspeed.ai/deepspeed4science/\" rel=\"nofollow\"\u003etutorials\u003c/a\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepSpeed Software Suite\u003c/h1\u003e\u003ca id=\"user-content-deepspeed-software-suite\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed Software Suite\" href=\"#deepspeed-software-suite\"\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\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepSpeed Library\u003c/h2\u003e\u003ca id=\"user-content-deepspeed-library\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed Library\" href=\"#deepspeed-library\"\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\"\u003eThe \u003ca href=\"https://github.com/deepspeedai/deepspeed\"\u003eDeepSpeed\u003c/a\u003e library (this repository) implements and packages the innovations and technologies in DeepSpeed Training, Inference and Compression Pillars into a single easy-to-use, open-sourced repository. It allows for easy composition of multitude of features within a single training, inference or compression pipeline. The DeepSpeed Library is heavily adopted by the DL community, and has been used to enable some of the most powerful models (see \u003ca href=\"#deepspeed-adoption\"\u003eDeepSpeed Adoption\u003c/a\u003e).\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Implementations for Inference (MII)\u003c/h2\u003e\u003ca id=\"user-content-model-implementations-for-inference-mii\" class=\"anchor\" aria-label=\"Permalink: Model Implementations for Inference (MII)\" href=\"#model-implementations-for-inference-mii\"\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\"\u003e\u003ca href=\"https://github.com/deepspeedai/deepspeed-mii\"\u003eModel Implementations for Inference (MII)\u003c/a\u003e is an open-sourced repository for making low-latency and high-throughput inference accessible to all data scientists by alleviating the need to apply complex system optimization techniques themselves. Out-of-box, MII offers support for thousands of widely used DL models, optimized using DeepSpeed-Inference, that can be deployed with a few lines of code, while achieving significant latency reduction compared to their vanilla open-sourced versions.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepSpeed on Azure\u003c/h2\u003e\u003ca id=\"user-content-deepspeed-on-azure\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed on Azure\" href=\"#deepspeed-on-azure\"\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\"\u003eDeepSpeed users are diverse and have access to different environments. We recommend to try DeepSpeed on Azure as it is the simplest and easiest method. The recommended method to try DeepSpeed on Azure is through AzureML \u003ca href=\"https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/workflows/train/deepspeed\"\u003erecipes\u003c/a\u003e. The job submission and data preparation scripts have been made available \u003ca href=\"https://github.com/deepspeedai/Megatron-DeepSpeed/tree/main/examples_deepspeed/azureml\"\u003ehere\u003c/a\u003e. For more details on how to use DeepSpeed on Azure, please follow the \u003ca href=\"https://www.deepspeed.ai/tutorials/azure/\" rel=\"nofollow\"\u003eAzure tutorial\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeepSpeed Adoption\u003c/h1\u003e\u003ca id=\"user-content-deepspeed-adoption\" class=\"anchor\" aria-label=\"Permalink: DeepSpeed Adoption\" href=\"#deepspeed-adoption\"\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\"\u003eDeepSpeed was an important part of Microsoft’s\n\u003ca href=\"https://www.microsoft.com/en-us/research/project/ai-at-scale/\" rel=\"nofollow\"\u003eAI at Scale\u003c/a\u003e\ninitiative to enable next-generation AI capabilities at scale, where you can find more\ninformation \u003ca href=\"https://innovation.microsoft.com/en-us/exploring-ai-at-scale\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eDeepSpeed has been used to train many different large-scale models, below is a list of several examples that we are aware of (if you'd like to include your model please submit a PR):\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/\" rel=\"nofollow\"\u003eMegatron-Turing NLG (530B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/61138924626a6981ee09caf6_jurassic_tech_paper.pdf\" rel=\"nofollow\"\u003eJurassic-1 (178B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://huggingface.co/blog/bloom-megatron-deepspeed\" rel=\"nofollow\"\u003eBLOOM (176B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/THUDM/GLM-130B\"\u003eGLM (130B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.biorxiv.org/content/10.1101/2023.07.05.547496v2\" rel=\"nofollow\"\u003exTrimoPGLM (100B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/yandex/YaLM-100B\"\u003eYaLM (100B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/EleutherAI/gpt-neox\"\u003eGPT-NeoX (20B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.amazon.science/blog/20b-parameter-alexa-model-sets-new-marks-in-few-shot-learning\" rel=\"nofollow\"\u003eAlexaTM (20B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/\" rel=\"nofollow\"\u003eTuring NLG (17B)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/2204.06644.pdf\" rel=\"nofollow\"\u003eMETRO-LM (5.4B)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eDeepSpeed has been integrated with several different popular open-source DL frameworks such as:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003eDocumentation\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/transformers-light.png#gh-light-mode-only\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/transformers-light.png#gh-light-mode-only\" width=\"250px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/transformers-dark.png#gh-dark-mode-only\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/transformers-dark.png#gh-dark-mode-only\" width=\"250px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/docs/transformers/deepspeed\" rel=\"nofollow\"\u003eTransformers with DeepSpeed\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/accelerate-light.png#gh-light-mode-only\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/accelerate-light.png#gh-light-mode-only\" width=\"250px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/accelerate-dark.png#gh-dark-mode-only\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/accelerate-dark.png#gh-dark-mode-only\" width=\"250px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://huggingface.co/docs/accelerate/usage_guides/deepspeed\" rel=\"nofollow\"\u003eAccelerate with DeepSpeed\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/lightning-light.svg#gh-light-mode-only\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/lightning-light.svg#gh-light-mode-only\" width=\"200px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/lightning-dark.svg#gh-dark-mode-only\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/lightning-dark.svg#gh-dark-mode-only\" width=\"200px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://lightning.ai/docs/pytorch/stable/advanced/model_parallel.html#deepspeed\" rel=\"nofollow\"\u003eLightning with DeepSpeed\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/mosaicml.svg\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/mosaicml.svg\" width=\"200px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.mosaicml.com/projects/composer/en/latest/trainer/using_the_trainer.html?highlight=deepspeed#deepspeed-integration\" rel=\"nofollow\"\u003eMosaicML with DeepSpeed\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/images/determined.svg\"\u003e\u003cimg src=\"/deepspeedai/DeepSpeed/raw/master/docs/assets/images/determined.svg\" width=\"225px\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.determined.ai/latest/training/apis-howto/deepspeed/overview.html\" rel=\"nofollow\"\u003eDetermined with DeepSpeed\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg\"\u003e\u003cimg src=\"https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg\" width=\"150\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://mmengine.readthedocs.io/en/latest/common_usage/large_model_training.html#deepspeed\" rel=\"nofollow\"\u003eMMEngine with DeepSpeed\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBuild Pipeline Status\u003c/h1\u003e\u003ca id=\"user-content-build-pipeline-status\" class=\"anchor\" aria-label=\"Permalink: Build Pipeline Status\" href=\"#build-pipeline-status\"\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\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003cth\u003eStatus\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-p40.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-p40.yml/badge.svg?branch=master\" alt=\"nv-torch110-p40\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-v100.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-v100.yml/badge.svg?branch=master\" alt=\"nv-torch110-v100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-latest-v100.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-latest-v100.yml/badge.svg?branch=master\" alt=\"nv-torch-latest-v100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-h100.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-h100.yml/badge.svg?branch=master\" alt=\"nv-h100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-inference.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-inference.yml/badge.svg?branch=master\" alt=\"nv-inference\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-nightly.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-nightly.yml/badge.svg?branch=master\" alt=\"nv-nightly\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eAMD\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/amd-mi200.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/amd-mi200.yml/badge.svg?branch=master\" alt=\"amd-mi200\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCPU\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-torch-latest.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-torch-latest.yml/badge.svg?branch=master\" alt=\"torch-latest-cpu\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-inference.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-inference.yml/badge.svg?branch=master\" alt=\"cpu-inference\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIntel Gaudi\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/hpu-gaudi2.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/hpu-gaudi2.yml/badge.svg?branch=master\" alt=\"hpu-gaudi2\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIntel XPU\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/xpu-max1100.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/xpu-max1100.yml/badge.svg?branch=master\" alt=\"xpu-max1100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePyTorch Nightly\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml/badge.svg?branch=master\" alt=\"nv-torch-nightly-v100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIntegrations\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-transformers-v100.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-transformers-v100.yml/badge.svg?branch=master\" alt=\"nv-transformers-v100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-lightning-v100.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-lightning-v100.yml/badge.svg?branch=master\" alt=\"nv-lightning-v100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-accelerate-v100.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-accelerate-v100.yml/badge.svg?branch=master\" alt=\"nv-accelerate-v100\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-mii.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-mii.yml/badge.svg?branch=master\" alt=\"nv-mii\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master\" alt=\"nv-ds-chat\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-sd.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-sd.yml/badge.svg\" alt=\"nv-sd\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMisc\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/formatting.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/formatting.yml/badge.svg?branch=master\" alt=\"Formatting\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/pages/pages-build-deployment\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/pages/pages-build-deployment/badge.svg\" alt=\"pages-build-deployment\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \u003ca href=\"https://deepspeed.readthedocs.io/en/latest/?badge=latest\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/20accbec2bcbca6196130c0b5bd0e4bd5f8d8a630cbf6d11be31d7c938396da2/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6465657073706565642f62616467652f3f76657273696f6e3d6c6174657374\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.org/projects/deepspeed/badge/?version=latest\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/python.yml\"\u003e\u003cimg src=\"https://github.com/deepspeedai/DeepSpeed/actions/workflows/python.yml/badge.svg?branch=master\" alt=\"python\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eHuawei Ascend NPU\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml\"\u003e\u003cimg src=\"https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml/badge.svg?branch=main\" alt=\"Huawei Ascend NPU\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstallation\u003c/h1\u003e\u003ca id=\"user-content-installation\" class=\"anchor\" aria-label=\"Permalink: Installation\" href=\"#installation\"\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\"\u003eThe quickest way to get started with DeepSpeed is via pip, this will install\nthe latest release of DeepSpeed which is not tied to specific PyTorch or CUDA\nversions. DeepSpeed includes several C++/CUDA extensions that we commonly refer\nto as our 'ops'. By default, all of these extensions/ops will be built\njust-in-time (JIT) using \u003ca href=\"https://pytorch.org/docs/stable/cpp_extension.html\" rel=\"nofollow\"\u003etorch's JIT C++ extension loader that relies on\nninja\u003c/a\u003e to build and\ndynamically link them at runtime.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://pytorch.org/\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e must be installed \u003cem\u003ebefore\u003c/em\u003e installing DeepSpeed.\u003c/li\u003e\n\u003cli\u003eFor full feature support we recommend a version of PyTorch that is \u0026gt;= 1.9 and ideally the latest PyTorch stable release.\u003c/li\u003e\n\u003cli\u003eA CUDA or ROCm compiler such as \u003ca href=\"https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#introduction\" rel=\"nofollow\"\u003envcc\u003c/a\u003e or \u003ca href=\"https://github.com/ROCm-Developer-Tools/HIPCC\"\u003ehipcc\u003c/a\u003e used to compile C++/CUDA/HIP extensions.\u003c/li\u003e\n\u003cli\u003eSpecific GPUs we develop and test against are listed below, this doesn't mean your GPU will not work if it doesn't fall into this category it's just DeepSpeed is most well tested on the following:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eNVIDIA: Pascal, Volta, Ampere, and Hopper architectures\u003c/li\u003e\n\u003cli\u003eAMD: MI100 and MI200\u003c/li\u003e\n\u003c/ul\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\"\u003eContributed HW support\u003c/h2\u003e\u003ca id=\"user-content-contributed-hw-support\" class=\"anchor\" aria-label=\"Permalink: Contributed HW support\" href=\"#contributed-hw-support\"\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\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDeepSpeed now support various HW accelerators.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eContributor\u003c/th\u003e\n\u003cth\u003eHardware\u003c/th\u003e\n\u003cth\u003eAccelerator Name\u003c/th\u003e\n\u003cth\u003eContributor validated\u003c/th\u003e\n\u003cth\u003eUpstream validated\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eHuawei\u003c/td\u003e\n\u003ctd\u003eHuawei Ascend NPU\u003c/td\u003e\n\u003ctd\u003enpu\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003eNo\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIntel\u003c/td\u003e\n\u003ctd\u003eIntel(R) Gaudi(R) 2 AI accelerator\u003c/td\u003e\n\u003ctd\u003ehpu\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIntel\u003c/td\u003e\n\u003ctd\u003eIntel(R) Xeon(R) Processors\u003c/td\u003e\n\u003ctd\u003ecpu\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eIntel\u003c/td\u003e\n\u003ctd\u003eIntel(R) Data Center GPU Max series\u003c/td\u003e\n\u003ctd\u003expu\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTecorigin\u003c/td\u003e\n\u003ctd\u003eScalable Data Analytics Accelerator\u003c/td\u003e\n\u003ctd\u003esdaa\u003c/td\u003e\n\u003ctd\u003eYes\u003c/td\u003e\n\u003ctd\u003eNo\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePyPI\u003c/h2\u003e\u003ca id=\"user-content-pypi\" class=\"anchor\" aria-label=\"Permalink: PyPI\" href=\"#pypi\"\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 regularly push releases to \u003ca href=\"https://pypi.org/project/deepspeed/\" rel=\"nofollow\"\u003ePyPI\u003c/a\u003e and encourage users to install from there in most cases.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install deepspeed\"\u003e\u003cpre\u003epip install deepspeed\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAfter installation, you can validate your install and see which extensions/ops\nyour machine is compatible with via the DeepSpeed environment report.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"ds_report\"\u003e\u003cpre\u003eds_report\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you would like to pre-install any of the DeepSpeed extensions/ops (instead\nof JIT compiling) or install pre-compiled ops via PyPI please see our \u003ca href=\"https://www.deepspeed.ai/tutorials/advanced-install/\" rel=\"nofollow\"\u003eadvanced\ninstallation instructions\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWindows\u003c/h2\u003e\u003ca id=\"user-content-windows\" class=\"anchor\" aria-label=\"Permalink: Windows\" href=\"#windows\"\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\"\u003eMany DeepSpeed features are supported on Windows for both training and inference. You can read more about this in the original blog post \u003ca href=\"https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/README.md\"\u003ehere\u003c/a\u003e. Among features that are currently not supported are async io (AIO) and GDS (which does not support Windows).\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eInstall PyTorch, such as pytorch 2.3+cu121.\u003c/li\u003e\n\u003cli\u003eInstall Visual C++ build tools, such as VS2022 C++ x64/x86 build tools.\u003c/li\u003e\n\u003cli\u003eLaunch Cmd console with Administrator permissions for creating required symlink folders and ensure MSVC tools are added to your PATH or launch the Developer Command Prompt for Visual Studio 2022 with administrator permissions.\u003c/li\u003e\n\u003cli\u003eRun \u003ccode\u003ebuild_win.bat\u003c/code\u003e to build wheel in \u003ccode\u003edist\u003c/code\u003e folder.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFeatures\u003c/h1\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\"\u003ePlease checkout \u003ca href=\"https://www.deepspeed.ai/training\" rel=\"nofollow\"\u003eDeepSpeed-Training\u003c/a\u003e, \u003ca href=\"https://www.deepspeed.ai/inference\" rel=\"nofollow\"\u003eDeepSpeed-Inference\u003c/a\u003e and \u003ca href=\"https://www.deepspeed.ai/compression\" rel=\"nofollow\"\u003eDeepSpeed-Compression\u003c/a\u003e pages for full set of features offered along each of these three pillars.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFurther Reading\u003c/h1\u003e\u003ca id=\"user-content-further-reading\" class=\"anchor\" aria-label=\"Permalink: Further Reading\" href=\"#further-reading\"\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\"\u003eAll DeepSpeed documentation, tutorials, and blogs can be found on our website: \u003ca href=\"https://www.deepspeed.ai/\" rel=\"nofollow\"\u003edeepspeed.ai\u003c/a\u003e\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://www.deepspeed.ai/getting-started/\" rel=\"nofollow\"\u003eGetting Started\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eFirst steps with DeepSpeed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://www.deepspeed.ai/docs/config-json/\" rel=\"nofollow\"\u003eDeepSpeed JSON Configuration\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eConfiguring DeepSpeed\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://deepspeed.readthedocs.io/en/latest/\" rel=\"nofollow\"\u003eAPI Documentation\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eGenerated DeepSpeed API documentation\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://www.deepspeed.ai/tutorials/\" rel=\"nofollow\"\u003eTutorials\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eTutorials\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ca href=\"https://www.deepspeed.ai/posts/\" rel=\"nofollow\"\u003eBlogs\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003eBlogs\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributing\u003c/h1\u003e\u003ca id=\"user-content-contributing\" class=\"anchor\" aria-label=\"Permalink: Contributing\" href=\"#contributing\"\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\"\u003eDeepSpeed welcomes your contributions! Please see our\n\u003ca href=\"/deepspeedai/DeepSpeed/blob/master/CONTRIBUTING.md\"\u003econtributing\u003c/a\u003e guide for more details on formatting, testing,\netc.\u003cbr\u003e\nThanks so much to all of our amazing contributors!\u003c/p\u003e\n\u003ca href=\"https://github.com/deepspeedai/DeepSpeed/graphs/contributors\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/4dc25610b42ca1390f8eef15b69b6389dd12f048ff60a73cf2807f04b5cba4a6/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d6d6963726f736f66742f44656570537065656426723d\" width=\"800px\" data-canonical-src=\"https://contrib.rocks/image?repo=microsoft/DeepSpeed\u0026amp;r=\" style=\"max-width: 100%;\"\u003e\n\u003c/a\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContributor License Agreement\u003c/h2\u003e\u003ca id=\"user-content-contributor-license-agreement\" class=\"anchor\" aria-label=\"Permalink: Contributor License Agreement\" href=\"#contributor-license-agreement\"\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\"\u003eThis project welcomes contributions and suggestions. Most contributions require you to\nagree to a Contributor License Agreement (CLA) declaring that you have the right to, and\nactually do, grant us the rights to use your contribution. For details, visit\n\u003ca href=\"https://cla.opensource.microsoft.com\" rel=\"nofollow\"\u003ehttps://cla.opensource.microsoft.com\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen you submit a pull request, a CLA bot will automatically determine whether you need\nto provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply\nfollow the instructions provided by the bot. You will only need to do this once across\nall repos using our CLA.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCode of Conduct\u003c/h2\u003e\u003ca id=\"user-content-code-of-conduct\" class=\"anchor\" aria-label=\"Permalink: Code of Conduct\" href=\"#code-of-conduct\"\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\"\u003eThis project has adopted the \u003ca href=\"https://opensource.microsoft.com/codeofconduct/\" rel=\"nofollow\"\u003eMicrosoft Open Source Code of\nConduct\u003c/a\u003e. For more information see the\n\u003ca href=\"https://opensource.microsoft.com/codeofconduct/faq/\" rel=\"nofollow\"\u003eCode of Conduct FAQ\u003c/a\u003e or contact\n\u003ca href=\"mailto:opencode@microsoft.com\"\u003eopencode@microsoft.com\u003c/a\u003e with any additional questions or comments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePublications\u003c/h1\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\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\u003col dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSamyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He. (2019) ZeRO: memory optimizations toward training trillion parameter models. \u003ca href=\"https://arxiv.org/abs/1910.02054\" rel=\"nofollow\"\u003earXiv:1910.02054\u003c/a\u003e and \u003ca href=\"https://dl.acm.org/doi/10.5555/3433701.3433727\" rel=\"nofollow\"\u003eIn Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20)\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eJeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. (2020) DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters. \u003ca href=\"https://dl.acm.org/doi/10.1145/3394486.3406703\" rel=\"nofollow\"\u003eIn Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \u0026amp; Data Mining (KDD '20, Tutorial)\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eMinjia Zhang, Yuxiong He. (2020) Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. \u003ca href=\"https://arxiv.org/abs/2010.13369\" rel=\"nofollow\"\u003earXiv:2010.13369\u003c/a\u003e and \u003ca href=\"https://proceedings.neurips.cc/paper/2020/hash/a1140a3d0df1c81e24ae954d935e8926-Abstract.html\" rel=\"nofollow\"\u003eNeurIPS 2020\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eJie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He. (2021) ZeRO-Offload: Democratizing Billion-Scale Model Training. \u003ca href=\"https://arxiv.org/abs/2101.06840\" rel=\"nofollow\"\u003earXiv:2101.06840\u003c/a\u003e and \u003ca href=\"https://www.usenix.org/conference/atc21/presentation/ren-jie\" rel=\"nofollow\"\u003eUSENIX ATC 2021\u003c/a\u003e. \u003ca href=\"https://arxiv.org/abs/2101.06840\" rel=\"nofollow\"\u003e[paper]\u003c/a\u003e \u003ca href=\"https://www.usenix.org/system/files/atc21_slides_ren-jie.pdf\" rel=\"nofollow\"\u003e[slides]\u003c/a\u003e \u003ca href=\"https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eHanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He. (2021) 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. \u003ca href=\"https://arxiv.org/abs/2102.02888\" rel=\"nofollow\"\u003earXiv:2102.02888\u003c/a\u003e and \u003ca href=\"http://proceedings.mlr.press/v139/tang21a.html\" rel=\"nofollow\"\u003eICML 2021\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSamyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He. (2021) ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. \u003ca href=\"https://arxiv.org/abs/2104.07857\" rel=\"nofollow\"\u003earXiv:2104.07857\u003c/a\u003e and \u003ca href=\"https://dl.acm.org/doi/abs/10.1145/3458817.3476205\" rel=\"nofollow\"\u003eSC 2021\u003c/a\u003e. \u003ca href=\"https://arxiv.org/abs/2104.07857\" rel=\"nofollow\"\u003e[paper]\u003c/a\u003e \u003ca href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/files/SC21-ZeRO-Infinity.pdf\"\u003e[slides]\u003c/a\u003e \u003ca href=\"https://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eConglong Li, Ammar Ahmad Awan, Hanlin Tang, Samyam Rajbhandari, Yuxiong He. (2021) 1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed. \u003ca href=\"https://arxiv.org/abs/2104.06069\" rel=\"nofollow\"\u003earXiv:2104.06069\u003c/a\u003e and \u003ca href=\"https://hipc.org/advance-program/\" rel=\"nofollow\"\u003eHiPC 2022\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eConglong Li, Minjia Zhang, Yuxiong He. (2021) The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models. \u003ca href=\"https://arxiv.org/abs/2108.06084\" rel=\"nofollow\"\u003earXiv:2108.06084\u003c/a\u003e and \u003ca href=\"https://openreview.net/forum?id=JpZ5du_Kdh\" rel=\"nofollow\"\u003eNeurIPS 2022\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eYucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He. (2022) Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. \u003ca href=\"https://arxiv.org/abs/2202.06009\" rel=\"nofollow\"\u003earXiv:2202.06009\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSamyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He. (2022) DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale \u003ca href=\"https://arxiv.org/abs/2201.05596\" rel=\"nofollow\"\u003earXiv:2201.05596\u003c/a\u003e and \u003ca href=\"https://proceedings.mlr.press/v162/rajbhandari22a.html\" rel=\"nofollow\"\u003eICML 2022\u003c/a\u003e. \u003ca href=\"https://arxiv.org/abs/2201.05596\" rel=\"nofollow\"\u003e[pdf]\u003c/a\u003e \u003ca href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/files/ICML-5mins.pdf\"\u003e[slides]\u003c/a\u003e \u003ca href=\"https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eShaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, Bryan Catanzaro. (2022) Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model \u003ca href=\"https://arxiv.org/abs/2201.11990\" rel=\"nofollow\"\u003earXiv:2201.11990\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eXiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He. (2022) Extreme Compression for Pre-trained Transformers Made Simple and Efficient. \u003ca href=\"https://arxiv.org/abs/2206.01859\" rel=\"nofollow\"\u003earXiv:2206.01859\u003c/a\u003e and \u003ca href=\"https://openreview.net/forum?id=xNeAhc2CNAl\" rel=\"nofollow\"\u003eNeurIPS 2022\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eZhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He. (2022) ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. \u003ca href=\"https://arxiv.org/abs/2206.01861\" rel=\"nofollow\"\u003earXiv:2206.01861\u003c/a\u003e and \u003ca href=\"https://openreview.net/forum?id=f-fVCElZ-G1\" rel=\"nofollow\"\u003eNeurIPS 2022\u003c/a\u003e \u003ca href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf\"\u003e[slides]\u003c/a\u003e \u003ca href=\"https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eReza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He. (2022) DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. \u003ca href=\"https://arxiv.org/abs/2207.00032\" rel=\"nofollow\"\u003earXiv:2207.00032\u003c/a\u003e and \u003ca href=\"https://dl.acm.org/doi/abs/10.5555/3571885.3571946\" rel=\"nofollow\"\u003eSC 2022\u003c/a\u003e. \u003ca href=\"https://arxiv.org/abs/2207.00032\" rel=\"nofollow\"\u003e[paper]\u003c/a\u003e \u003ca href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/files/sc22-ds-inference.pdf\"\u003e[slides]\u003c/a\u003e \u003ca href=\"https://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eZhewei Yao, Xiaoxia Wu, Conglong Li, Connor Holmes, Minjia Zhang, Cheng Li, Yuxiong He. (2022) Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers. \u003ca href=\"https://arxiv.org/abs/2211.11586\" rel=\"nofollow\"\u003earXiv:2211.11586\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eConglong Li, Zhewei Yao, Xiaoxia Wu, Minjia Zhang, Yuxiong He. (2022) DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. \u003ca href=\"https://arxiv.org/abs/2212.03597\" rel=\"nofollow\"\u003earXiv:2212.03597\u003c/a\u003e \u003ca href=\"https://neurips2023-enlsp.github.io/\" rel=\"nofollow\"\u003eENLSP2023 Workshop at NeurIPS2023\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eXiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi, Zhewei Yao, Yuxiong He. (2023) Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases. \u003ca href=\"https://arxiv.org/abs/2301.12017\" rel=\"nofollow\"\u003earXiv:2301.12017\u003c/a\u003e and \u003ca href=\"https://icml.cc/Conferences/2023\" rel=\"nofollow\"\u003eICML2023\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSyed Zawad, Cheng Li, Zhewei Yao, Elton Zheng, Yuxiong He, Feng Yan. (2023) DySR: Adaptive Super-Resolution via Algorithm and System Co-design. \u003ca href=\"https://openreview.net/forum?id=Pgtn4l6eKjv\" rel=\"nofollow\"\u003eICLR:2023\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSheng Shen, Zhewei Yao, Chunyuan Li, Trevor Darrell, Kurt Keutzer, Yuxiong He. (2023) Scaling Vision-Language Models with Sparse Mixture of Experts. \u003ca href=\"https://arxiv.org/abs/2303.07226\" rel=\"nofollow\"\u003earXiv:2303.07226\u003c/a\u003e and \u003ca href=\"https://2023.emnlp.org/\" rel=\"nofollow\"\u003eFinding at EMNLP2023\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eQuentin Anthony, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, Aamir Shafi, Mustafa Abduljabbar, Hari Subramoni, Dhabaleswar Panda. (2023) MCR-DL: Mix-and-Match Communication Runtime for Deep Learning \u003ca href=\"https://arxiv.org/abs/2303.08374\" rel=\"nofollow\"\u003earXiv:2303.08374\u003c/a\u003e and will appear at IPDPS 2023.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSiddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He, Abhinav Bhatele. (2023) A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training \u003ca href=\"https://arxiv.org/abs/2303.06318\" rel=\"nofollow\"\u003earXiv:2303.06318\u003c/a\u003e and \u003ca href=\"https://dl.acm.org/doi/10.1145/3577193.3593704\" rel=\"nofollow\"\u003eICS 2023\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eGuanhua Wang, Heyang Qin, Sam Ade Jacobs, Xiaoxia Wu, Connor Holmes, Zhewei Yao, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He. (2023) ZeRO++: Extremely Efficient Collective Communication for Giant Model Training \u003ca href=\"https://arxiv.org/abs/2306.10209\" rel=\"nofollow\"\u003earXiv:2306.10209\u003c/a\u003e and \u003ca href=\"http://mlforsystems.org/\" rel=\"nofollow\"\u003eML for Sys Workshop at NeurIPS2023\u003c/a\u003e \u003ca href=\"https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eZhewei Yao, Xiaoxia Wu, Cheng Li, Stephen Youn, Yuxiong He. (2023) ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation \u003ca href=\"https://arxiv.org/abs/2303.08302\" rel=\"nofollow\"\u003earXiv:2303.08302\u003c/a\u003e and \u003ca href=\"https://neurips2023-enlsp.github.io/\" rel=\"nofollow\"\u003eENLSP2023 Workshop at NeurIPS2023\u003c/a\u003e \u003ca href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf\"\u003e[slides]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003ePareesa Ameneh Golnari, Zhewei Yao, Yuxiong He. (2023) Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important? \u003ca href=\"https://arxiv.org/abs/2305.09847\" rel=\"nofollow\"\u003earXiv:2305.09847\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eZhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He. (2023) DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales \u003ca href=\"https://arxiv.org/abs/2308.01320\" rel=\"nofollow\"\u003earXiv:2308.01320\u003c/a\u003e.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eXiaoxia Wu, Zhewei Yao, Yuxiong He. (2023) ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats \u003ca href=\"https://arxiv.org/abs/2307.09782\" rel=\"nofollow\"\u003earXiv:2307.09782\u003c/a\u003e and \u003ca href=\"https://neurips2023-enlsp.github.io/\" rel=\"nofollow\"\u003eENLSP2023 Workshop at NeurIPS2023\u003c/a\u003e \u003ca href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf\"\u003e[slides]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eZhewei Yao, Xiaoxia Wu, Conglong Li, Minjia Zhang, Heyang Qin, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He. (2023) DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention \u003ca href=\"https://arxiv.org/pdf/2309.14327.pdf\" rel=\"nofollow\"\u003earXiv:2309.14327\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eShuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, et al. (2023) DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies \u003ca href=\"https://arxiv.org/abs/2310.04610\" rel=\"nofollow\"\u003earXiv:2310.04610\u003c/a\u003e \u003ca href=\"https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/\" rel=\"nofollow\"\u003e[blog]\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eZhewei Yao, Reza Yazdani Aminabadi, Stephen Youn, Xiaoxia Wu, Elton Zheng, Yuxiong He. (2023) ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers \u003ca href=\"https://arxiv.org/abs/2310.17723\" rel=\"nofollow\"\u003earXiv:2310.17723\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eXiaoxia Wu, Haojun Xia, Stephen Youn, Zhen Zheng, Shiyang Chen, Arash Bakhtiari, Michael Wyatt, Reza Yazdani Aminabadi, Yuxiong He, Olatunji Ruwase, Leon Song, Zhewei Yao (2023) ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks \u003ca href=\"https://arxiv.org/abs/2312.08583\" rel=\"nofollow\"\u003earXiv:2312.08583\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eHaojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song. (2024) FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design \u003ca href=\"https://arxiv.org/abs/2401.14112\" rel=\"nofollow\"\u003earXiv:2401.14112\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Reza Yazdani Aminadabi, Shuaiwen Leon Song, Samyam Rajbhandari, Yuxiong He. (2024) \u003ca href=\"https://dl.acm.org/doi/10.1145/3662158.3662806\" rel=\"nofollow\"\u003eSystem Optimizations for Enabling Training of Extreme Long Sequence Transformer Models\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eXinyu Lian, Sam Ade Jacobs, Lev Kurilenko, Masahiro Tanaka, Stas Bekman, Olatunji Ruwase, Minjia Zhang. (2024) Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training \u003ca href=\"https://arxiv.org/abs/2406.18820\" rel=\"nofollow\"\u003earXiv:2406.18820\u003c/a\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVideos\u003c/h1\u003e\u003ca id=\"user-content-videos\" class=\"anchor\" aria-label=\"Permalink: Videos\" href=\"#videos\"\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\u003col dir=\"auto\"\u003e\n\u003cli\u003eDeepSpeed KDD 2020 Tutorial\n\u003col dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/watch?v=CaseqC45DNc\u0026amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0\u0026amp;index=29\" rel=\"nofollow\"\u003eOverview\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/watch?v=y4_bCiAsIAk\u0026amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0\u0026amp;index=28\" rel=\"nofollow\"\u003eZeRO + large model training\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/watch?v=9V-ZbP92drg\u0026amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0\u0026amp;index=27\" rel=\"nofollow\"\u003e17B T-NLG 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href=\"/deepspeedai/DeepSpeed/blob/master/docs/assets/files/presentation-mlops.pdf\"\u003e[slides]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eCommunity Tutorials\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/watch?v=pDGI668pNg0\" rel=\"nofollow\"\u003eDeepSpeed: All the tricks to scale to gigantic models (Mark Saroufim)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/watch?v=tC01FRB0M7w\" rel=\"nofollow\"\u003eTuring-NLG, DeepSpeed and the ZeRO optimizer (Yannic Kilcher)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.youtube.com/watch?v=hc0u4avAkuM\" rel=\"nofollow\"\u003eUltimate Guide To Scaling ML Models (The AI Epiphany)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"Latest News","anchor":"latest-news","htmlText":"Latest News"},{"level":1,"text":"Extreme Speed and Scale for DL Training and Inference","anchor":"extreme-speed-and-scale-for-dl-training-and-inference","htmlText":"Extreme Speed and Scale for DL Training and Inference"},{"level":1,"text":"DeepSpeed's four innovation pillars","anchor":"deepspeeds-four-innovation-pillars","htmlText":"DeepSpeed's four innovation pillars"},{"level":2,"text":"DeepSpeed-Training","anchor":"deepspeed-training","htmlText":"DeepSpeed-Training"},{"level":2,"text":"DeepSpeed-Inference","anchor":"deepspeed-inference","htmlText":"DeepSpeed-Inference"},{"level":2,"text":"DeepSpeed-Compression","anchor":"deepspeed-compression","htmlText":"DeepSpeed-Compression"},{"level":2,"text":"DeepSpeed4Science","anchor":"deepspeed4science","htmlText":"DeepSpeed4Science"},{"level":1,"text":"DeepSpeed Software Suite","anchor":"deepspeed-software-suite","htmlText":"DeepSpeed Software Suite"},{"level":2,"text":"DeepSpeed Library","anchor":"deepspeed-library","htmlText":"DeepSpeed Library"},{"level":2,"text":"Model Implementations for Inference (MII)","anchor":"model-implementations-for-inference-mii","htmlText":"Model Implementations for Inference (MII)"},{"level":2,"text":"DeepSpeed on Azure","anchor":"deepspeed-on-azure","htmlText":"DeepSpeed on Azure"},{"level":1,"text":"DeepSpeed Adoption","anchor":"deepspeed-adoption","htmlText":"DeepSpeed Adoption"},{"level":1,"text":"Build Pipeline Status","anchor":"build-pipeline-status","htmlText":"Build Pipeline Status"},{"level":1,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Requirements","anchor":"requirements","htmlText":"Requirements"},{"level":2,"text":"Contributed HW support","anchor":"contributed-hw-support","htmlText":"Contributed HW 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href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/DeepSpeed_dark_transparent.svg#gh-dark-mode-only"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/DeepSpeed_dark_transparent.svg#gh-dark-mode-only" width="400px" style="max-width: 100%;"></a> </div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Latest News</h2><a id="user-content-latest-news" class="anchor" aria-label="Permalink: Latest News" href="#latest-news"><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 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Communication-Free LLM Training Engine</a></li> <li>[2024/08] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/README.md">DeepSpeed on Windows</a> [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/japanese/README.md">日本語</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/chinese/README.md">中文</a>]</li> <li>[2024/08] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-gds/README.md">DeepNVMe: Improving DL Applications through I/O Optimizations</a> [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-gds/japanese/README.md">日本語</a>]</li> <li>[2024/07] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-ucp/README.md">DeepSpeed Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training</a> [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-ucp/chinese/README.md">中文</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-ucp/japanese/README.md">日本語</a>]</li> <li>[2024/03] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024">DeepSpeed-FP6:The power of FP6-Centric Serving for Large Language Models</a> [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README.md">English</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fp6/03-05-2024/README-Chinese.md">中文</a>]</li> <li>[2024/01] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen/2024-01-19">DeepSpeed-FastGen: Introducing Mixtral, Phi-2, and Falcon support with major performance and feature enhancements.</a></li> <li>[2023/11] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/intel-inference">Llama 2 Inference on 4th Gen Intel® Xeon® Scalable Processor with DeepSpeed</a> <a href="https://www.intel.com/content/www/us/en/developer/articles/technical/xllama-2-on-xeon-scalable-processor-with-deepspeed.html" rel="nofollow">[Intel version]</a></li> <li>[2023/11] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-offloadpp">DeepSpeed ZeRO-Offload++: 6x Higher Training Throughput via Collaborative CPU/GPU Twin-Flow</a></li> <li>[2023/11] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen">DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference</a> [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen">English</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen/chinese/README.md">中文</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-fastgen/japanese/README.md">日本語</a>]</li> <li>[2023/10] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md">DeepSpeed-VisualChat: Improve Your Chat Experience with Multi-Round Multi-Image Inputs</a> [<a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-visualchat/10-03-2023/README.md">English</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Chinese.md">中文</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-visualchat/10-03-2023/README-Japanese.md">日本語</a>]</li> <li>[2023/09] Announcing the DeepSpeed4Science Initiative: Enabling large-scale scientific discovery through sophisticated AI system technologies [<a href="https://www.deepspeed.ai/deepspeed4science/" rel="nofollow">Tutorials</a>] [<a href="https://arxiv.org/abs/2310.04610" rel="nofollow">White paper</a>] [<a href="https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/" rel="nofollow">Blog</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed4science/chinese/README.md">中文</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed4science/japanese/README.md">日本語</a>]</li> </ul> <details> <summary>More news</summary> <ul dir="auto"> <li>[2023/08] <a href="https://github.com/deepspeedai/DeepSpeedExamples/blob/master/inference/huggingface/zero_inference/README.md">DeepSpeed ZeRO-Inference: 20x faster inference through weight quantization and KV cache offloading</a></li> <li>[2023/08] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-chat/ds-chat-release-8-31/README.md">DeepSpeed-Chat: Llama/Llama-2 system support, efficiency boost, and training stability improvements</a></li> <li>[2023/08] <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/deepspeed-ulysses">DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models</a> [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-ulysses/chinese/README.md">中文</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/deepspeed-ulysses/japanese/README.md">日本語</a>]</li> <li>[2023/06] <a href="https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/" rel="nofollow">ZeRO++: A leap in speed for LLM and chat model training with 4X less communication</a> [<a href="https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/" rel="nofollow">English</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/zeropp/chinese/README.md">中文</a>] [<a href="https://github.com/deepspeedai/DeepSpeed/blob/master/blogs/zeropp/japanese/README.md">日本語</a>]</li> </ul> </details> <hr> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Extreme Speed and Scale for DL Training and Inference</h1><a id="user-content-extreme-speed-and-scale-for-dl-training-and-inference" class="anchor" aria-label="Permalink: Extreme Speed and Scale for DL Training and Inference" href="#extreme-speed-and-scale-for-dl-training-and-inference"><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"><em><strong><a href="https://www.deepspeed.ai/" rel="nofollow">DeepSpeed</a> enabled the world's most powerful language models (at the time of this writing) such as <a href="https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/" rel="nofollow">MT-530B</a> and <a href="https://huggingface.co/blog/bloom-megatron-deepspeed" rel="nofollow">BLOOM</a></strong></em>. It is an easy-to-use deep learning optimization software suite that powers unprecedented scale and speed for both training and inference. With DeepSpeed you can:</p> <ul dir="auto"> <li>Train/Inference dense or sparse models with billions or trillions of parameters</li> <li>Achieve excellent system throughput and efficiently scale to thousands of GPUs</li> <li>Train/Inference on resource constrained GPU systems</li> <li>Achieve unprecedented low latency and high throughput for inference</li> <li>Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs</li> </ul> <hr> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">DeepSpeed's four innovation pillars</h1><a id="user-content-deepspeeds-four-innovation-pillars" class="anchor" aria-label="Permalink: DeepSpeed's four innovation pillars" href="#deepspeeds-four-innovation-pillars"><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"><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/DeepSpeed-pillars.png"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/DeepSpeed-pillars.png" width="800px" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">DeepSpeed-Training</h2><a id="user-content-deepspeed-training" class="anchor" aria-label="Permalink: DeepSpeed-Training" href="#deepspeed-training"><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">DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc. fall under the training pillar. Learn more: <a href="https://www.deepspeed.ai/training/" rel="nofollow">DeepSpeed-Training</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">DeepSpeed-Inference</h2><a id="user-content-deepspeed-inference" class="anchor" aria-label="Permalink: DeepSpeed-Inference" href="#deepspeed-inference"><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">DeepSpeed brings together innovations in parallelism technology such as tensor, pipeline, expert and ZeRO-parallelism, and combines them with high performance custom inference kernels, communication optimizations and heterogeneous memory technologies to enable inference at an unprecedented scale, while achieving unparalleled latency, throughput and cost reduction. This systematic composition of system technologies for inference falls under the inference pillar. Learn more: <a href="https://www.deepspeed.ai/inference" rel="nofollow">DeepSpeed-Inference</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">DeepSpeed-Compression</h2><a id="user-content-deepspeed-compression" class="anchor" aria-label="Permalink: DeepSpeed-Compression" href="#deepspeed-compression"><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">To further increase the inference efficiency, DeepSpeed offers easy-to-use and flexible-to-compose compression techniques for researchers and practitioners to compress their models while delivering faster speed, smaller model size, and significantly reduced compression cost. Moreover, SoTA innovations on compression like ZeroQuant and XTC are included under the compression pillar. Learn more: <a href="https://www.deepspeed.ai/compression" rel="nofollow">DeepSpeed-Compression</a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">DeepSpeed4Science</h2><a id="user-content-deepspeed4science" class="anchor" aria-label="Permalink: DeepSpeed4Science" href="#deepspeed4science"><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">In line with Microsoft's mission to solve humanity's most pressing challenges, the DeepSpeed team at Microsoft is responding to this opportunity by launching a new initiative called <em>DeepSpeed4Science</em>, aiming to build unique capabilities through AI system technology innovations to help domain experts to unlock today's biggest science mysteries. Learn more: <a href="https://www.deepspeed.ai/deepspeed4science/" rel="nofollow">tutorials</a></p> <hr> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">DeepSpeed Software Suite</h1><a id="user-content-deepspeed-software-suite" class="anchor" aria-label="Permalink: DeepSpeed Software Suite" href="#deepspeed-software-suite"><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> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">DeepSpeed Library</h2><a id="user-content-deepspeed-library" class="anchor" aria-label="Permalink: DeepSpeed Library" href="#deepspeed-library"><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">The <a href="https://github.com/deepspeedai/deepspeed">DeepSpeed</a> library (this repository) implements and packages the innovations and technologies in DeepSpeed Training, Inference and Compression Pillars into a single easy-to-use, open-sourced repository. It allows for easy composition of multitude of features within a single training, inference or compression pipeline. The DeepSpeed Library is heavily adopted by the DL community, and has been used to enable some of the most powerful models (see <a href="#deepspeed-adoption">DeepSpeed Adoption</a>).</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Model Implementations for Inference (MII)</h2><a id="user-content-model-implementations-for-inference-mii" class="anchor" aria-label="Permalink: Model Implementations for Inference (MII)" href="#model-implementations-for-inference-mii"><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"><a href="https://github.com/deepspeedai/deepspeed-mii">Model Implementations for Inference (MII)</a> is an open-sourced repository for making low-latency and high-throughput inference accessible to all data scientists by alleviating the need to apply complex system optimization techniques themselves. Out-of-box, MII offers support for thousands of widely used DL models, optimized using DeepSpeed-Inference, that can be deployed with a few lines of code, while achieving significant latency reduction compared to their vanilla open-sourced versions.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">DeepSpeed on Azure</h2><a id="user-content-deepspeed-on-azure" class="anchor" aria-label="Permalink: DeepSpeed on Azure" href="#deepspeed-on-azure"><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">DeepSpeed users are diverse and have access to different environments. We recommend to try DeepSpeed on Azure as it is the simplest and easiest method. The recommended method to try DeepSpeed on Azure is through AzureML <a href="https://github.com/Azure/azureml-examples/tree/main/v1/python-sdk/workflows/train/deepspeed">recipes</a>. The job submission and data preparation scripts have been made available <a href="https://github.com/deepspeedai/Megatron-DeepSpeed/tree/main/examples_deepspeed/azureml">here</a>. For more details on how to use DeepSpeed on Azure, please follow the <a href="https://www.deepspeed.ai/tutorials/azure/" rel="nofollow">Azure tutorial</a>.</p> <hr> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">DeepSpeed Adoption</h1><a id="user-content-deepspeed-adoption" class="anchor" aria-label="Permalink: DeepSpeed Adoption" href="#deepspeed-adoption"><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">DeepSpeed was an important part of Microsoft’s <a href="https://www.microsoft.com/en-us/research/project/ai-at-scale/" rel="nofollow">AI at Scale</a> initiative to enable next-generation AI capabilities at scale, where you can find more information <a href="https://innovation.microsoft.com/en-us/exploring-ai-at-scale" rel="nofollow">here</a>.</p> <p dir="auto">DeepSpeed has been used to train many different large-scale models, below is a list of several examples that we are aware of (if you'd like to include your model please submit a PR):</p> <ul dir="auto"> <li><a href="https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/" rel="nofollow">Megatron-Turing NLG (530B)</a></li> <li><a href="https://uploads-ssl.webflow.com/60fd4503684b466578c0d307/61138924626a6981ee09caf6_jurassic_tech_paper.pdf" rel="nofollow">Jurassic-1 (178B)</a></li> <li><a href="https://huggingface.co/blog/bloom-megatron-deepspeed" rel="nofollow">BLOOM (176B)</a></li> <li><a href="https://github.com/THUDM/GLM-130B">GLM (130B)</a></li> <li><a href="https://www.biorxiv.org/content/10.1101/2023.07.05.547496v2" rel="nofollow">xTrimoPGLM (100B)</a></li> <li><a href="https://github.com/yandex/YaLM-100B">YaLM (100B)</a></li> <li><a href="https://github.com/EleutherAI/gpt-neox">GPT-NeoX (20B)</a></li> <li><a href="https://www.amazon.science/blog/20b-parameter-alexa-model-sets-new-marks-in-few-shot-learning" rel="nofollow">AlexaTM (20B)</a></li> <li><a href="https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/" rel="nofollow">Turing NLG (17B)</a></li> <li><a href="https://arxiv.org/pdf/2204.06644.pdf" rel="nofollow">METRO-LM (5.4B)</a></li> </ul> <p dir="auto">DeepSpeed has been integrated with several different popular open-source DL frameworks such as:</p> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th>Documentation</th> </tr> </thead> <tbody> <tr> <td><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/transformers-light.png#gh-light-mode-only"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/transformers-light.png#gh-light-mode-only" width="250px" style="max-width: 100%;"></a><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/transformers-dark.png#gh-dark-mode-only"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/transformers-dark.png#gh-dark-mode-only" width="250px" style="max-width: 100%;"></a></td> <td><a href="https://huggingface.co/docs/transformers/deepspeed" rel="nofollow">Transformers with DeepSpeed</a></td> </tr> <tr> <td><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/accelerate-light.png#gh-light-mode-only"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/accelerate-light.png#gh-light-mode-only" width="250px" style="max-width: 100%;"></a><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/accelerate-dark.png#gh-dark-mode-only"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/accelerate-dark.png#gh-dark-mode-only" width="250px" style="max-width: 100%;"></a></td> <td><a href="https://huggingface.co/docs/accelerate/usage_guides/deepspeed" rel="nofollow">Accelerate with DeepSpeed</a></td> </tr> <tr> <td><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/lightning-light.svg#gh-light-mode-only"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/lightning-light.svg#gh-light-mode-only" width="200px" style="max-width: 100%;"></a><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/lightning-dark.svg#gh-dark-mode-only"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/lightning-dark.svg#gh-dark-mode-only" width="200px" style="max-width: 100%;"></a></td> <td><a href="https://lightning.ai/docs/pytorch/stable/advanced/model_parallel.html#deepspeed" rel="nofollow">Lightning with DeepSpeed</a></td> </tr> <tr> <td><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/mosaicml.svg"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/mosaicml.svg" width="200px" style="max-width: 100%;"></a></td> <td><a href="https://docs.mosaicml.com/projects/composer/en/latest/trainer/using_the_trainer.html?highlight=deepspeed#deepspeed-integration" rel="nofollow">MosaicML with DeepSpeed</a></td> </tr> <tr> <td><a target="_blank" rel="noopener noreferrer" href="/deepspeedai/DeepSpeed/blob/master/docs/assets/images/determined.svg"><img src="/deepspeedai/DeepSpeed/raw/master/docs/assets/images/determined.svg" width="225px" style="max-width: 100%;"></a></td> <td><a href="https://docs.determined.ai/latest/training/apis-howto/deepspeed/overview.html" rel="nofollow">Determined with DeepSpeed</a></td> </tr> <tr> <td><a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg"><img src="https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg" width="150" style="max-width: 100%;"></a></td> <td><a href="https://mmengine.readthedocs.io/en/latest/common_usage/large_model_training.html#deepspeed" rel="nofollow">MMEngine with DeepSpeed</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <hr> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Build Pipeline Status</h1><a id="user-content-build-pipeline-status" class="anchor" aria-label="Permalink: Build Pipeline Status" href="#build-pipeline-status"><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> <markdown-accessiblity-table><table> <thead> <tr> <th>Description</th> <th>Status</th> </tr> </thead> <tbody> <tr> <td>NVIDIA</td> <td><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-p40.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-p40.yml/badge.svg?branch=master" alt="nv-torch110-p40" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-v100.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch110-v100.yml/badge.svg?branch=master" alt="nv-torch110-v100" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-latest-v100.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-latest-v100.yml/badge.svg?branch=master" alt="nv-torch-latest-v100" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-h100.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-h100.yml/badge.svg?branch=master" alt="nv-h100" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-inference.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-inference.yml/badge.svg?branch=master" alt="nv-inference" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-nightly.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-nightly.yml/badge.svg?branch=master" alt="nv-nightly" style="max-width: 100%;"></a></td> </tr> <tr> <td>AMD</td> <td><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/amd-mi200.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/amd-mi200.yml/badge.svg?branch=master" alt="amd-mi200" style="max-width: 100%;"></a></td> </tr> <tr> <td>CPU</td> <td><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-torch-latest.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-torch-latest.yml/badge.svg?branch=master" alt="torch-latest-cpu" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-inference.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/cpu-inference.yml/badge.svg?branch=master" alt="cpu-inference" style="max-width: 100%;"></a></td> </tr> <tr> <td>Intel Gaudi</td> <td><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/hpu-gaudi2.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/hpu-gaudi2.yml/badge.svg?branch=master" alt="hpu-gaudi2" style="max-width: 100%;"></a></td> </tr> <tr> <td>Intel XPU</td> <td><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/xpu-max1100.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/xpu-max1100.yml/badge.svg?branch=master" alt="xpu-max1100" style="max-width: 100%;"></a></td> </tr> <tr> <td>PyTorch Nightly</td> <td><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-torch-nightly-v100.yml/badge.svg?branch=master" alt="nv-torch-nightly-v100" style="max-width: 100%;"></a></td> </tr> <tr> <td>Integrations</td> <td><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-transformers-v100.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-transformers-v100.yml/badge.svg?branch=master" alt="nv-transformers-v100" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-lightning-v100.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-lightning-v100.yml/badge.svg?branch=master" alt="nv-lightning-v100" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-accelerate-v100.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-accelerate-v100.yml/badge.svg?branch=master" alt="nv-accelerate-v100" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-mii.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-mii.yml/badge.svg?branch=master" alt="nv-mii" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-ds-chat.yml/badge.svg?branch=master" alt="nv-ds-chat" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-sd.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/nv-sd.yml/badge.svg" alt="nv-sd" style="max-width: 100%;"></a></td> </tr> <tr> <td>Misc</td> <td><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/formatting.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/formatting.yml/badge.svg?branch=master" alt="Formatting" style="max-width: 100%;"></a> <a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/pages/pages-build-deployment"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/pages/pages-build-deployment/badge.svg" alt="pages-build-deployment" style="max-width: 100%;"></a> <a href="https://deepspeed.readthedocs.io/en/latest/?badge=latest" rel="nofollow"><img src="https://camo.githubusercontent.com/20accbec2bcbca6196130c0b5bd0e4bd5f8d8a630cbf6d11be31d7c938396da2/68747470733a2f2f72656164746865646f63732e6f72672f70726f6a656374732f6465657073706565642f62616467652f3f76657273696f6e3d6c6174657374" alt="Documentation Status" data-canonical-src="https://readthedocs.org/projects/deepspeed/badge/?version=latest" style="max-width: 100%;"></a><a href="https://github.com/deepspeedai/DeepSpeed/actions/workflows/python.yml"><img src="https://github.com/deepspeedai/DeepSpeed/actions/workflows/python.yml/badge.svg?branch=master" alt="python" style="max-width: 100%;"></a></td> </tr> <tr> <td>Huawei Ascend NPU</td> <td><a href="https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml"><img src="https://github.com/Ascend/Ascend-CI/actions/workflows/deepspeed.yaml/badge.svg?branch=main" alt="Huawei Ascend NPU" style="max-width: 100%;"></a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Installation</h1><a id="user-content-installation" class="anchor" aria-label="Permalink: Installation" href="#installation"><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">The quickest way to get started with DeepSpeed is via pip, this will install the latest release of DeepSpeed which is not tied to specific PyTorch or CUDA versions. DeepSpeed includes several C++/CUDA extensions that we commonly refer to as our 'ops'. By default, all of these extensions/ops will be built just-in-time (JIT) using <a href="https://pytorch.org/docs/stable/cpp_extension.html" rel="nofollow">torch's JIT C++ extension loader that relies on ninja</a> to build and dynamically link them at runtime.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Requirements</h2><a id="user-content-requirements" class="anchor" aria-label="Permalink: Requirements" href="#requirements"><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> <ul dir="auto"> <li><a href="https://pytorch.org/" rel="nofollow">PyTorch</a> must be installed <em>before</em> installing DeepSpeed.</li> <li>For full feature support we recommend a version of PyTorch that is &gt;= 1.9 and ideally the latest PyTorch stable release.</li> <li>A CUDA or ROCm compiler such as <a href="https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#introduction" rel="nofollow">nvcc</a> or <a href="https://github.com/ROCm-Developer-Tools/HIPCC">hipcc</a> used to compile C++/CUDA/HIP extensions.</li> <li>Specific GPUs we develop and test against are listed below, this doesn't mean your GPU will not work if it doesn't fall into this category it's just DeepSpeed is most well tested on the following: <ul dir="auto"> <li>NVIDIA: Pascal, Volta, Ampere, and Hopper architectures</li> <li>AMD: MI100 and MI200</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributed HW support</h2><a id="user-content-contributed-hw-support" class="anchor" aria-label="Permalink: Contributed HW support" href="#contributed-hw-support"><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> <ul dir="auto"> <li>DeepSpeed now support various HW accelerators.</li> </ul> <markdown-accessiblity-table><table> <thead> <tr> <th>Contributor</th> <th>Hardware</th> <th>Accelerator Name</th> <th>Contributor validated</th> <th>Upstream validated</th> </tr> </thead> <tbody> <tr> <td>Huawei</td> <td>Huawei Ascend NPU</td> <td>npu</td> <td>Yes</td> <td>No</td> </tr> <tr> <td>Intel</td> <td>Intel(R) Gaudi(R) 2 AI accelerator</td> <td>hpu</td> <td>Yes</td> <td>Yes</td> </tr> <tr> <td>Intel</td> <td>Intel(R) Xeon(R) Processors</td> <td>cpu</td> <td>Yes</td> <td>Yes</td> </tr> <tr> <td>Intel</td> <td>Intel(R) Data Center GPU Max series</td> <td>xpu</td> <td>Yes</td> <td>Yes</td> </tr> <tr> <td>Tecorigin</td> <td>Scalable Data Analytics Accelerator</td> <td>sdaa</td> <td>Yes</td> <td>No</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">PyPI</h2><a id="user-content-pypi" class="anchor" aria-label="Permalink: PyPI" href="#pypi"><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 regularly push releases to <a href="https://pypi.org/project/deepspeed/" rel="nofollow">PyPI</a> and encourage users to install from there in most cases.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install deepspeed"><pre>pip install deepspeed</pre></div> <p dir="auto">After installation, you can validate your install and see which extensions/ops your machine is compatible with via the DeepSpeed environment report.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="ds_report"><pre>ds_report</pre></div> <p dir="auto">If you would like to pre-install any of the DeepSpeed extensions/ops (instead of JIT compiling) or install pre-compiled ops via PyPI please see our <a href="https://www.deepspeed.ai/tutorials/advanced-install/" rel="nofollow">advanced installation instructions</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Windows</h2><a id="user-content-windows" class="anchor" aria-label="Permalink: Windows" href="#windows"><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">Many DeepSpeed features are supported on Windows for both training and inference. You can read more about this in the original blog post <a href="https://github.com/deepspeedai/DeepSpeed/tree/master/blogs/windows/08-2024/README.md">here</a>. Among features that are currently not supported are async io (AIO) and GDS (which does not support Windows).</p> <ol dir="auto"> <li>Install PyTorch, such as pytorch 2.3+cu121.</li> <li>Install Visual C++ build tools, such as VS2022 C++ x64/x86 build tools.</li> <li>Launch Cmd console with Administrator permissions for creating required symlink folders and ensure MSVC tools are added to your PATH or launch the Developer Command Prompt for Visual Studio 2022 with administrator permissions.</li> <li>Run <code>build_win.bat</code> to build wheel in <code>dist</code> folder.</li> </ol> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Features</h1><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">Please checkout <a href="https://www.deepspeed.ai/training" rel="nofollow">DeepSpeed-Training</a>, <a href="https://www.deepspeed.ai/inference" rel="nofollow">DeepSpeed-Inference</a> and <a href="https://www.deepspeed.ai/compression" rel="nofollow">DeepSpeed-Compression</a> pages for full set of features offered along each of these three pillars.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Further Reading</h1><a id="user-content-further-reading" class="anchor" aria-label="Permalink: Further Reading" href="#further-reading"><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">All DeepSpeed documentation, tutorials, and blogs can be found on our website: <a href="https://www.deepspeed.ai/" rel="nofollow">deepspeed.ai</a></p> <markdown-accessiblity-table><table> <thead> <tr> <th></th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><a href="https://www.deepspeed.ai/getting-started/" rel="nofollow">Getting Started</a></td> <td>First steps with DeepSpeed</td> </tr> <tr> <td><a href="https://www.deepspeed.ai/docs/config-json/" rel="nofollow">DeepSpeed JSON Configuration</a></td> <td>Configuring DeepSpeed</td> </tr> <tr> <td><a href="https://deepspeed.readthedocs.io/en/latest/" rel="nofollow">API Documentation</a></td> <td>Generated DeepSpeed API documentation</td> </tr> <tr> <td><a href="https://www.deepspeed.ai/tutorials/" rel="nofollow">Tutorials</a></td> <td>Tutorials</td> </tr> <tr> <td><a href="https://www.deepspeed.ai/posts/" rel="nofollow">Blogs</a></td> <td>Blogs</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Contributing</h1><a id="user-content-contributing" class="anchor" aria-label="Permalink: Contributing" href="#contributing"><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">DeepSpeed welcomes your contributions! Please see our <a href="/deepspeedai/DeepSpeed/blob/master/CONTRIBUTING.md">contributing</a> guide for more details on formatting, testing, etc.<br> Thanks so much to all of our amazing contributors!</p> <a href="https://github.com/deepspeedai/DeepSpeed/graphs/contributors"> <img src="https://camo.githubusercontent.com/4dc25610b42ca1390f8eef15b69b6389dd12f048ff60a73cf2807f04b5cba4a6/68747470733a2f2f636f6e747269622e726f636b732f696d6167653f7265706f3d6d6963726f736f66742f44656570537065656426723d" width="800px" data-canonical-src="https://contrib.rocks/image?repo=microsoft/DeepSpeed&amp;r=" style="max-width: 100%;"> </a> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contributor License Agreement</h2><a id="user-content-contributor-license-agreement" class="anchor" aria-label="Permalink: Contributor License Agreement" href="#contributor-license-agreement"><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">This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit <a href="https://cla.opensource.microsoft.com" rel="nofollow">https://cla.opensource.microsoft.com</a>.</p> <p dir="auto">When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Code of Conduct</h2><a id="user-content-code-of-conduct" class="anchor" aria-label="Permalink: Code of Conduct" href="#code-of-conduct"><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">This project has adopted the <a href="https://opensource.microsoft.com/codeofconduct/" rel="nofollow">Microsoft Open Source Code of Conduct</a>. For more information see the <a href="https://opensource.microsoft.com/codeofconduct/faq/" rel="nofollow">Code of Conduct FAQ</a> or contact <a href="mailto:opencode@microsoft.com">opencode@microsoft.com</a> with any additional questions or comments.</p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Publications</h1><a id="user-content-publications" class="anchor" aria-label="Permalink: Publications" href="#publications"><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> <ol dir="auto"> <li> <p dir="auto">Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He. (2019) ZeRO: memory optimizations toward training trillion parameter models. <a href="https://arxiv.org/abs/1910.02054" rel="nofollow">arXiv:1910.02054</a> and <a href="https://dl.acm.org/doi/10.5555/3433701.3433727" rel="nofollow">In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '20)</a>.</p> </li> <li> <p dir="auto">Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. (2020) DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters. <a href="https://dl.acm.org/doi/10.1145/3394486.3406703" rel="nofollow">In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining (KDD '20, Tutorial)</a>.</p> </li> <li> <p dir="auto">Minjia Zhang, Yuxiong He. (2020) Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping. <a href="https://arxiv.org/abs/2010.13369" rel="nofollow">arXiv:2010.13369</a> and <a href="https://proceedings.neurips.cc/paper/2020/hash/a1140a3d0df1c81e24ae954d935e8926-Abstract.html" rel="nofollow">NeurIPS 2020</a>.</p> </li> <li> <p dir="auto">Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He. (2021) ZeRO-Offload: Democratizing Billion-Scale Model Training. <a href="https://arxiv.org/abs/2101.06840" rel="nofollow">arXiv:2101.06840</a> and <a href="https://www.usenix.org/conference/atc21/presentation/ren-jie" rel="nofollow">USENIX ATC 2021</a>. <a href="https://arxiv.org/abs/2101.06840" rel="nofollow">[paper]</a> <a href="https://www.usenix.org/system/files/atc21_slides_ren-jie.pdf" rel="nofollow">[slides]</a> <a href="https://www.microsoft.com/en-us/research/blog/deepspeed-extreme-scale-model-training-for-everyone/" rel="nofollow">[blog]</a></p> </li> <li> <p dir="auto">Hanlin Tang, Shaoduo Gan, Ammar Ahmad Awan, Samyam Rajbhandari, Conglong Li, Xiangru Lian, Ji Liu, Ce Zhang, Yuxiong He. (2021) 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. <a href="https://arxiv.org/abs/2102.02888" rel="nofollow">arXiv:2102.02888</a> and <a href="http://proceedings.mlr.press/v139/tang21a.html" rel="nofollow">ICML 2021</a>.</p> </li> <li> <p dir="auto">Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, Yuxiong He. (2021) ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning. <a href="https://arxiv.org/abs/2104.07857" rel="nofollow">arXiv:2104.07857</a> and <a href="https://dl.acm.org/doi/abs/10.1145/3458817.3476205" rel="nofollow">SC 2021</a>. <a href="https://arxiv.org/abs/2104.07857" rel="nofollow">[paper]</a> <a href="/deepspeedai/DeepSpeed/blob/master/docs/assets/files/SC21-ZeRO-Infinity.pdf">[slides]</a> <a href="https://www.microsoft.com/en-us/research/blog/zero-infinity-and-deepspeed-unlocking-unprecedented-model-scale-for-deep-learning-training/" rel="nofollow">[blog]</a></p> </li> <li> <p dir="auto">Conglong Li, Ammar Ahmad Awan, Hanlin Tang, Samyam Rajbhandari, Yuxiong He. (2021) 1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed. <a href="https://arxiv.org/abs/2104.06069" rel="nofollow">arXiv:2104.06069</a> and <a href="https://hipc.org/advance-program/" rel="nofollow">HiPC 2022</a>.</p> </li> <li> <p dir="auto">Conglong Li, Minjia Zhang, Yuxiong He. (2021) The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models. <a href="https://arxiv.org/abs/2108.06084" rel="nofollow">arXiv:2108.06084</a> and <a href="https://openreview.net/forum?id=JpZ5du_Kdh" rel="nofollow">NeurIPS 2022</a>.</p> </li> <li> <p dir="auto">Yucheng Lu, Conglong Li, Minjia Zhang, Christopher De Sa, Yuxiong He. (2022) Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. <a href="https://arxiv.org/abs/2202.06009" rel="nofollow">arXiv:2202.06009</a>.</p> </li> <li> <p dir="auto">Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He. (2022) DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale <a href="https://arxiv.org/abs/2201.05596" rel="nofollow">arXiv:2201.05596</a> and <a href="https://proceedings.mlr.press/v162/rajbhandari22a.html" rel="nofollow">ICML 2022</a>. <a href="https://arxiv.org/abs/2201.05596" rel="nofollow">[pdf]</a> <a href="/deepspeedai/DeepSpeed/blob/master/docs/assets/files/ICML-5mins.pdf">[slides]</a> <a href="https://www.microsoft.com/en-us/research/blog/deepspeed-advancing-moe-inference-and-training-to-power-next-generation-ai-scale/" rel="nofollow">[blog]</a></p> </li> <li> <p dir="auto">Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, Bryan Catanzaro. (2022) Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model <a href="https://arxiv.org/abs/2201.11990" rel="nofollow">arXiv:2201.11990</a>.</p> </li> <li> <p dir="auto">Xiaoxia Wu, Zhewei Yao, Minjia Zhang, Conglong Li, Yuxiong He. (2022) Extreme Compression for Pre-trained Transformers Made Simple and Efficient. <a href="https://arxiv.org/abs/2206.01859" rel="nofollow">arXiv:2206.01859</a> and <a href="https://openreview.net/forum?id=xNeAhc2CNAl" rel="nofollow">NeurIPS 2022</a>.</p> </li> <li> <p dir="auto">Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, Xiaoxia Wu, Conglong Li, Yuxiong He. (2022) ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. <a href="https://arxiv.org/abs/2206.01861" rel="nofollow">arXiv:2206.01861</a> and <a href="https://openreview.net/forum?id=f-fVCElZ-G1" rel="nofollow">NeurIPS 2022</a> <a href="/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf">[slides]</a> <a href="https://www.microsoft.com/en-us/research/blog/deepspeed-compression-a-composable-library-for-extreme-compression-and-zero-cost-quantization/" rel="nofollow">[blog]</a></p> </li> <li> <p dir="auto">Reza Yazdani Aminabadi, Samyam Rajbhandari, Minjia Zhang, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Jeff Rasley, Shaden Smith, Olatunji Ruwase, Yuxiong He. (2022) DeepSpeed Inference: Enabling Efficient Inference of Transformer Models at Unprecedented Scale. <a href="https://arxiv.org/abs/2207.00032" rel="nofollow">arXiv:2207.00032</a> and <a href="https://dl.acm.org/doi/abs/10.5555/3571885.3571946" rel="nofollow">SC 2022</a>. <a href="https://arxiv.org/abs/2207.00032" rel="nofollow">[paper]</a> <a href="/deepspeedai/DeepSpeed/blob/master/docs/assets/files/sc22-ds-inference.pdf">[slides]</a> <a href="https://www.microsoft.com/en-us/research/blog/deepspeed-accelerating-large-scale-model-inference-and-training-via-system-optimizations-and-compression/" rel="nofollow">[blog]</a></p> </li> <li> <p dir="auto">Zhewei Yao, Xiaoxia Wu, Conglong Li, Connor Holmes, Minjia Zhang, Cheng Li, Yuxiong He. (2022) Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers. <a href="https://arxiv.org/abs/2211.11586" rel="nofollow">arXiv:2211.11586</a>.</p> </li> <li> <p dir="auto">Conglong Li, Zhewei Yao, Xiaoxia Wu, Minjia Zhang, Yuxiong He. (2022) DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. <a href="https://arxiv.org/abs/2212.03597" rel="nofollow">arXiv:2212.03597</a> <a href="https://neurips2023-enlsp.github.io/" rel="nofollow">ENLSP2023 Workshop at NeurIPS2023</a></p> </li> <li> <p dir="auto">Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi, Zhewei Yao, Yuxiong He. (2023) Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases. <a href="https://arxiv.org/abs/2301.12017" rel="nofollow">arXiv:2301.12017</a> and <a href="https://icml.cc/Conferences/2023" rel="nofollow">ICML2023</a>.</p> </li> <li> <p dir="auto">Syed Zawad, Cheng Li, Zhewei Yao, Elton Zheng, Yuxiong He, Feng Yan. (2023) DySR: Adaptive Super-Resolution via Algorithm and System Co-design. <a href="https://openreview.net/forum?id=Pgtn4l6eKjv" rel="nofollow">ICLR:2023</a>.</p> </li> <li> <p dir="auto">Sheng Shen, Zhewei Yao, Chunyuan Li, Trevor Darrell, Kurt Keutzer, Yuxiong He. (2023) Scaling Vision-Language Models with Sparse Mixture of Experts. <a href="https://arxiv.org/abs/2303.07226" rel="nofollow">arXiv:2303.07226</a> and <a href="https://2023.emnlp.org/" rel="nofollow">Finding at EMNLP2023</a>.</p> </li> <li> <p dir="auto">Quentin Anthony, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He, Aamir Shafi, Mustafa Abduljabbar, Hari Subramoni, Dhabaleswar Panda. (2023) MCR-DL: Mix-and-Match Communication Runtime for Deep Learning <a href="https://arxiv.org/abs/2303.08374" rel="nofollow">arXiv:2303.08374</a> and will appear at IPDPS 2023.</p> </li> <li> <p dir="auto">Siddharth Singh, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He, Abhinav Bhatele. (2023) A Hybrid Tensor-Expert-Data Parallelism Approach to Optimize Mixture-of-Experts Training <a href="https://arxiv.org/abs/2303.06318" rel="nofollow">arXiv:2303.06318</a> and <a href="https://dl.acm.org/doi/10.1145/3577193.3593704" rel="nofollow">ICS 2023</a>.</p> </li> <li> <p dir="auto">Guanhua Wang, Heyang Qin, Sam Ade Jacobs, Xiaoxia Wu, Connor Holmes, Zhewei Yao, Samyam Rajbhandari, Olatunji Ruwase, Feng Yan, Lei Yang, Yuxiong He. (2023) ZeRO++: Extremely Efficient Collective Communication for Giant Model Training <a href="https://arxiv.org/abs/2306.10209" rel="nofollow">arXiv:2306.10209</a> and <a href="http://mlforsystems.org/" rel="nofollow">ML for Sys Workshop at NeurIPS2023</a> <a href="https://www.microsoft.com/en-us/research/blog/deepspeed-zero-a-leap-in-speed-for-llm-and-chat-model-training-with-4x-less-communication/" rel="nofollow">[blog]</a></p> </li> <li> <p dir="auto">Zhewei Yao, Xiaoxia Wu, Cheng Li, Stephen Youn, Yuxiong He. (2023) ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation <a href="https://arxiv.org/abs/2303.08302" rel="nofollow">arXiv:2303.08302</a> and <a href="https://neurips2023-enlsp.github.io/" rel="nofollow">ENLSP2023 Workshop at NeurIPS2023</a> <a href="/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf">[slides]</a></p> </li> <li> <p dir="auto">Pareesa Ameneh Golnari, Zhewei Yao, Yuxiong He. (2023) Selective Guidance: Are All the Denoising Steps of Guided Diffusion Important? <a href="https://arxiv.org/abs/2305.09847" rel="nofollow">arXiv:2305.09847</a></p> </li> <li> <p dir="auto">Zhewei Yao, Reza Yazdani Aminabadi, Olatunji Ruwase, Samyam Rajbhandari, Xiaoxia Wu, Ammar Ahmad Awan, Jeff Rasley, Minjia Zhang, Conglong Li, Connor Holmes, Zhongzhu Zhou, Michael Wyatt, Molly Smith, Lev Kurilenko, Heyang Qin, Masahiro Tanaka, Shuai Che, Shuaiwen Leon Song, Yuxiong He. (2023) DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales <a href="https://arxiv.org/abs/2308.01320" rel="nofollow">arXiv:2308.01320</a>.</p> </li> <li> <p dir="auto">Xiaoxia Wu, Zhewei Yao, Yuxiong He. (2023) ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats <a href="https://arxiv.org/abs/2307.09782" rel="nofollow">arXiv:2307.09782</a> and <a href="https://neurips2023-enlsp.github.io/" rel="nofollow">ENLSP2023 Workshop at NeurIPS2023</a> <a href="/deepspeedai/DeepSpeed/blob/master/docs/assets/files/zeroquant_series.pdf">[slides]</a></p> </li> <li> <p dir="auto">Zhewei Yao, Xiaoxia Wu, Conglong Li, Minjia Zhang, Heyang Qin, Olatunji Ruwase, Ammar Ahmad Awan, Samyam Rajbhandari, Yuxiong He. (2023) DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention <a href="https://arxiv.org/pdf/2309.14327.pdf" rel="nofollow">arXiv:2309.14327</a></p> </li> <li> <p dir="auto">Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, et al. (2023) DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies <a href="https://arxiv.org/abs/2310.04610" rel="nofollow">arXiv:2310.04610</a> <a href="https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/" rel="nofollow">[blog]</a></p> </li> <li> <p dir="auto">Zhewei Yao, Reza Yazdani Aminabadi, Stephen Youn, Xiaoxia Wu, Elton Zheng, Yuxiong He. (2023) ZeroQuant-HERO: Hardware-Enhanced Robust Optimized Post-Training Quantization Framework for W8A8 Transformers <a href="https://arxiv.org/abs/2310.17723" rel="nofollow">arXiv:2310.17723</a></p> </li> <li> <p dir="auto">Xiaoxia Wu, Haojun Xia, Stephen Youn, Zhen Zheng, Shiyang Chen, Arash Bakhtiari, Michael Wyatt, Reza Yazdani Aminabadi, Yuxiong He, Olatunji Ruwase, Leon Song, Zhewei Yao (2023) ZeroQuant(4+2): Redefining LLMs Quantization with a New FP6-Centric Strategy for Diverse Generative Tasks <a href="https://arxiv.org/abs/2312.08583" rel="nofollow">arXiv:2312.08583</a></p> </li> <li> <p dir="auto">Haojun Xia, Zhen Zheng, Xiaoxia Wu, Shiyang Chen, Zhewei Yao, Stephen Youn, Arash Bakhtiari, Michael Wyatt, Donglin Zhuang, Zhongzhu Zhou, Olatunji Ruwase, Yuxiong He, Shuaiwen Leon Song. (2024) FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design <a href="https://arxiv.org/abs/2401.14112" rel="nofollow">arXiv:2401.14112</a></p> </li> <li> <p dir="auto">Sam Ade Jacobs, Masahiro Tanaka, Chengming Zhang, Minjia Zhang, Reza Yazdani Aminadabi, Shuaiwen Leon Song, Samyam Rajbhandari, Yuxiong He. (2024) <a href="https://dl.acm.org/doi/10.1145/3662158.3662806" rel="nofollow">System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models</a></p> </li> <li> <p dir="auto">Xinyu Lian, Sam Ade Jacobs, Lev Kurilenko, Masahiro Tanaka, Stas Bekman, Olatunji Ruwase, Minjia Zhang. (2024) Universal Checkpointing: Efficient and Flexible Checkpointing for Large Scale Distributed Training <a href="https://arxiv.org/abs/2406.18820" rel="nofollow">arXiv:2406.18820</a></p> </li> </ol> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Videos</h1><a id="user-content-videos" class="anchor" aria-label="Permalink: Videos" href="#videos"><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> <ol dir="auto"> <li>DeepSpeed KDD 2020 Tutorial <ol dir="auto"> <li><a href="https://www.youtube.com/watch?v=CaseqC45DNc&amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&amp;index=29" rel="nofollow">Overview</a></li> <li><a href="https://www.youtube.com/watch?v=y4_bCiAsIAk&amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&amp;index=28" rel="nofollow">ZeRO + large model training</a></li> <li><a href="https://www.youtube.com/watch?v=9V-ZbP92drg&amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&amp;index=27" rel="nofollow">17B T-NLG demo</a></li> <li><a href="https://www.youtube.com/watch?v=o1K-ZG9F6u0&amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&amp;index=26" rel="nofollow">Fastest BERT training + RScan tuning</a></li> <li>DeepSpeed hands on deep dive: <a href="https://www.youtube.com/watch?v=_NOk-mBwDYg&amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&amp;index=92" rel="nofollow">part 1</a>, <a href="https://www.youtube.com/watch?v=sG6_c4VXLww&amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&amp;index=94" rel="nofollow">part 2</a>, <a href="https://www.youtube.com/watch?v=k9yPkBTayos&amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&amp;index=93" rel="nofollow">part 3</a></li> <li><a href="https://www.youtube.com/watch?v=nsHu6vEgPew&amp;list=PLa85ZdUjfWS21mgibJ2vCvLziprjpKoW0&amp;index=24" rel="nofollow">FAQ</a></li> </ol> </li> <li>Microsoft Research Webinar <ul dir="auto"> <li>Registration is free and all videos are available on-demand.</li> <li><a href="https://note.microsoft.com/MSR-Webinar-DeepSpeed-Registration-On-Demand.html" rel="nofollow">ZeRO &amp; Fastest BERT: Increasing the scale and speed of deep learning training in DeepSpeed</a>.</li> </ul> </li> <li><a href="https://youtu.be/yBVXR8G8Bg8" rel="nofollow">DeepSpeed on AzureML</a></li> <li><a href="https://www.youtube.com/watch?v=cntxC3g22oU" rel="nofollow">Large Model Training and Inference with DeepSpeed // Samyam Rajbhandari // LLMs in Prod Conference</a> <a href="/deepspeedai/DeepSpeed/blob/master/docs/assets/files/presentation-mlops.pdf">[slides]</a></li> <li>Community Tutorials <ul dir="auto"> <li><a href="https://www.youtube.com/watch?v=pDGI668pNg0" rel="nofollow">DeepSpeed: All the tricks to scale to gigantic models (Mark Saroufim)</a></li> <li><a href="https://www.youtube.com/watch?v=tC01FRB0M7w" rel="nofollow">Turing-NLG, DeepSpeed and the ZeRO optimizer (Yannic Kilcher)</a></li> <li><a href="https://www.youtube.com/watch?v=hc0u4avAkuM" rel="nofollow">Ultimate Guide To Scaling ML Models (The AI Epiphany)</a></li> </ul> </li> </ol> </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="ZsDi/Jc2ROyMsviDuQ+8/lg/QNRxsG8pBoVmeHD9HzZuUb1RXf8JRWfeepB/MfHpT7a1PWteq7nqiI6U30wfEQ==" /> </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"> DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective. </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <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> <span class="flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a title="https://www.deepspeed.ai/" role="link" target="_blank" rel="noopener noreferrer nofollow" class="text-bold" href="https://www.deepspeed.ai/">www.deepspeed.ai/</a> </span> </div> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/machine-learning" title="Topic: machine-learning" data-view-component="true" class="topic-tag topic-tag-link"> machine-learning </a> <a href="/topics/compression" title="Topic: compression" data-view-component="true" class="topic-tag topic-tag-link"> compression </a> <a href="/topics/deep-learning" title="Topic: deep-learning" data-view-component="true" class="topic-tag topic-tag-link"> deep-learning </a> <a href="/topics/gpu" title="Topic: gpu" data-view-component="true" class="topic-tag topic-tag-link"> gpu </a> <a href="/topics/inference" title="Topic: inference" data-view-component="true" class="topic-tag topic-tag-link"> inference </a> <a href="/topics/pytorch" title="Topic: pytorch" data-view-component="true" class="topic-tag topic-tag-link"> pytorch </a> <a href="/topics/zero" title="Topic: zero" data-view-component="true" class="topic-tag topic-tag-link"> zero </a> <a href="/topics/data-parallelism" title="Topic: data-parallelism" data-view-component="true" class="topic-tag topic-tag-link"> data-parallelism </a> <a href="/topics/model-parallelism" title="Topic: model-parallelism" data-view-component="true" class="topic-tag topic-tag-link"> model-parallelism </a> <a href="/topics/mixture-of-experts" title="Topic: mixture-of-experts" data-view-component="true" class="topic-tag topic-tag-link"> mixture-of-experts </a> <a href="/topics/pipeline-parallelism" title="Topic: pipeline-parallelism" data-view-component="true" class="topic-tag topic-tag-link"> pipeline-parallelism </a> <a href="/topics/billion-parameters" title="Topic: billion-parameters" data-view-component="true" class="topic-tag topic-tag-link"> billion-parameters </a> <a href="/topics/trillion-parameters" title="Topic: trillion-parameters" data-view-component="true" class="topic-tag topic-tag-link"> trillion-parameters </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{&quot;category&quot;:&quot;Repository Overview&quot;,&quot;action&quot;:&quot;click&quot;,&quot;label&quot;:&quot;location:sidebar;file:readme&quot;}" href="#readme-ov-file"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-book mr-2"> <path d="M0 1.75A.75.75 0 0 1 .75 1h4.253c1.227 0 2.317.59 3 1.501A3.743 3.743 0 0 1 11.006 1h4.245a.75.75 0 0 1 .75.75v10.5a.75.75 0 0 1-.75.75h-4.507a2.25 2.25 0 0 0-1.591.659l-.622.621a.75.75 0 0 1-1.06 0l-.622-.621A2.25 2.25 0 0 0 5.258 13H.75a.75.75 0 0 1-.75-.75Zm7.251 10.324.004-5.073-.002-2.253A2.25 2.25 0 0 0 5.003 2.5H1.5v9h3.757a3.75 3.75 0 0 1 1.994.574ZM8.755 4.75l-.004 7.322a3.752 3.752 0 0 1 1.992-.572H14.5v-9h-3.495a2.25 2.25 0 0 0-2.25 2.25Z"></path> </svg> Readme </a> </div> <h3 class="sr-only">License</h3> <div class="mt-2"> <a href="#Apache-2.0-1-ov-file" class="Link--muted" data-analytics-event="{&quot;category&quot;:&quot;Repository Overview&quot;,&quot;action&quot;:&quot;click&quot;,&quot;label&quot;:&quot;location:sidebar;file:license&quot;}" > <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-law mr-2"> <path d="M8.75.75V2h.985c.304 0 .603.08.867.231l1.29.736c.038.022.08.033.124.033h2.234a.75.75 0 0 1 0 1.5h-.427l2.111 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.006.005-.01.01-.045.04c-.21.176-.441.327-.686.45C14.556 10.78 13.88 11 13 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L12.178 4.5h-.162c-.305 0-.604-.079-.868-.231l-1.29-.736a.245.245 0 0 0-.124-.033H8.75V13h2.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1 0-1.5h2.5V3.5h-.984a.245.245 0 0 0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path> </svg> Apache-2.0 license </a> </div> <h3 class="sr-only">Code of conduct</h3> <div class="mt-2"> <a href="#coc-ov-file" class="Link--muted" data-analytics-event="{&quot;category&quot;:&quot;Repository Overview&quot;,&quot;action&quot;:&quot;click&quot;,&quot;label&quot;:&quot;location:sidebar;file:code of conduct&quot;}" > <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-code-of-conduct mr-2"> <path d="M8.048 2.241c.964-.709 2.079-1.238 3.325-1.241a4.616 4.616 0 0 1 3.282 1.355c.41.408.757.86.996 1.428.238.568.348 1.206.347 1.968 0 2.193-1.505 4.254-3.081 5.862-1.496 1.526-3.213 2.796-4.249 3.563l-.22.163a.749.749 0 0 1-.895 0l-.221-.163c-1.036-.767-2.753-2.037-4.249-3.563C1.51 10.008.007 7.952.002 5.762a4.614 4.614 0 0 1 1.353-3.407C3.123.585 6.223.537 8.048 2.24Zm-1.153.983c-1.25-1.033-3.321-.967-4.48.191a3.115 3.115 0 0 0-.913 2.335c0 1.556 1.109 3.24 2.652 4.813C5.463 11.898 6.96 13.032 8 13.805c.353-.262.758-.565 1.191-.905l-1.326-1.223a.75.75 0 0 1 1.018-1.102l1.48 1.366c.328-.281.659-.577.984-.887L9.99 9.802a.75.75 0 1 1 1.019-1.103l1.384 1.28c.295-.329.566-.661.81-.995L12.92 8.7l-1.167-1.168c-.674-.671-1.78-.664-2.474.03-.268.269-.538.537-.802.797-.893.882-2.319.843-3.185-.032-.346-.35-.693-.697-1.043-1.047a.75.75 0 0 1-.04-1.016c.162-.191.336-.401.52-.623.62-.748 1.356-1.637 2.166-2.417Zm7.112 4.442c.313-.65.491-1.293.491-1.916v-.001c0-.614-.088-1.045-.23-1.385-.143-.339-.357-.633-.673-.949a3.111 3.111 0 0 0-2.218-.915c-1.092.003-2.165.627-3.226 1.602-.823.755-1.554 1.637-2.228 2.45l-.127.154.562.566a.755.755 0 0 0 1.066.02l.794-.79c1.258-1.258 3.312-1.31 4.594-.032.396.394.792.791 1.173 1.173Z"></path> </svg> Code of conduct </a> </div> <h3 class="sr-only">Security policy</h3> <div class="mt-2"> <a href="#security-ov-file" class="Link--muted" data-analytics-event="{&quot;category&quot;:&quot;Repository Overview&quot;,&quot;action&quot;:&quot;click&quot;,&quot;label&quot;:&quot;location:sidebar;file:security policy&quot;}" > <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-law mr-2"> <path d="M8.75.75V2h.985c.304 0 .603.08.867.231l1.29.736c.038.022.08.033.124.033h2.234a.75.75 0 0 1 0 1.5h-.427l2.111 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.006.005-.01.01-.045.04c-.21.176-.441.327-.686.45C14.556 10.78 13.88 11 13 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L12.178 4.5h-.162c-.305 0-.604-.079-.868-.231l-1.29-.736a.245.245 0 0 0-.124-.033H8.75V13h2.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1 0-1.5h2.5V3.5h-.984a.245.245 0 0 0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path> </svg> Security policy </a> </div> <include-fragment src="/deepspeedai/DeepSpeed/hovercards/citation/sidebar_partial?tree_name=master"> </include-fragment> <div class="mt-2"> <a href="/deepspeedai/DeepSpeed/activity" data-view-component="true" class="Link Link--muted"><svg text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-pulse mr-2"> <path d="M6 2c.306 0 .582.187.696.471L10 10.731l1.304-3.26A.751.751 0 0 1 12 7h3.25a.75.75 0 0 1 0 1.5h-2.742l-1.812 4.528a.751.751 0 0 1-1.392 0L6 4.77 4.696 8.03A.75.75 0 0 1 4 8.5H.75a.75.75 0 0 1 0-1.5h2.742l1.812-4.529A.751.751 0 0 1 6 2Z"></path> </svg> <span class="color-fg-muted">Activity</span></a> </div> <div class="mt-2"> <a href="/deepspeedai/DeepSpeed/custom-properties" data-view-component="true" class="Link Link--muted"><svg text="gray" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-note mr-2"> <path d="M0 3.75C0 2.784.784 2 1.75 2h12.5c.966 0 1.75.784 1.75 1.75v8.5A1.75 1.75 0 0 1 14.25 14H1.75A1.75 1.75 0 0 1 0 12.25Zm1.75-.25a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h12.5a.25.25 0 0 0 .25-.25v-8.5a.25.25 0 0 0-.25-.25ZM3.5 6.25a.75.75 0 0 1 .75-.75h7a.75.75 0 0 1 0 1.5h-7a.75.75 0 0 1-.75-.75Zm.75 2.25h4a.75.75 0 0 1 0 1.5h-4a.75.75 0 0 1 0-1.5Z"></path> </svg> <span class="color-fg-muted">Custom properties</span></a> </div> <h3 class="sr-only">Stars</h3> <div class="mt-2"> <a href="/deepspeedai/DeepSpeed/stargazers" 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-star mr-2"> <path d="M8 .25a.75.75 0 0 1 .673.418l1.882 3.815 4.21.612a.75.75 0 0 1 .416 1.279l-3.046 2.97.719 4.192a.751.751 0 0 1-1.088.791L8 12.347l-3.766 1.98a.75.75 0 0 1-1.088-.79l.72-4.194L.818 6.374a.75.75 0 0 1 .416-1.28l4.21-.611L7.327.668A.75.75 0 0 1 8 .25Zm0 2.445L6.615 5.5a.75.75 0 0 1-.564.41l-3.097.45 2.24 2.184a.75.75 0 0 1 .216.664l-.528 3.084 2.769-1.456a.75.75 0 0 1 .698 0l2.77 1.456-.53-3.084a.75.75 0 0 1 .216-.664l2.24-2.183-3.096-.45a.75.75 0 0 1-.564-.41L8 2.694Z"></path> </svg> <strong>37.6k</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/deepspeedai/DeepSpeed/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>345</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/deepspeedai/DeepSpeed/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>4.3k</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2Fdeepspeedai%2FDeepSpeed&amp;report=deepspeedai+%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="/deepspeedai/DeepSpeed/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="87" data-view-component="true" class="Counter">87</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="/deepspeedai/DeepSpeed/releases/tag/v0.16.4"> <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;">v0.16.4 Patch 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="2025-02-20T15:25:19Z" class="no-wrap">Feb 20, 2025</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="/deepspeedai/DeepSpeed/releases" data-view-component="true" class="Link">+ 86 releases</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/deepspeedai/packages?repo_name=DeepSpeed" 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" > <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/deepspeedai/DeepSpeed/network/dependents" data-view-component="true" class="Link--primary no-underline Link">Used by <span title="11,725" data-view-component="true" class="Counter">11.7k</span></a> </h2> <a class="d-flex flex-items-center" href="/deepspeedai/DeepSpeed/network/dependents"> <ul class="hx_flex-avatar-stack list-style-none min-width-0"> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/74886629?s=64&amp;v=4" width="32" height="32" alt="@b09902097" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/66865882?s=64&amp;v=4" width="32" height="32" alt="@liuff19" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/132433471?s=64&amp;v=4" width="32" height="32" alt="@JasonYeh0111" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/127215412?s=64&amp;v=4" width="32" height="32" alt="@ytding1999" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/143054484?s=64&amp;v=4" width="32" height="32" alt="@AlisonDexter" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/55176896?s=64&amp;v=4" width="32" height="32" alt="@steven-ccq" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/199700115?s=64&amp;v=4" width="32" height="32" alt="@noahwei682" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar" src="https://avatars.githubusercontent.com/u/161336901?s=64&amp;v=4" width="32" height="32" alt="@MTXAI" /> </li> </ul> <span class="px-2 text-bold text-small no-wrap"> + 11,717 </span> </a> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/deepspeedai/DeepSpeed/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Contributors <span title="407" data-view-component="true" class="Counter ml-1">407</span></a></h2> <ul class="list-style-none d-flex flex-wrap mb-n2"> <li class="mb-2 mr-2" > <a href="https://github.com/jeffra" class="" data-hovercard-type="user" data-hovercard-url="/users/jeffra/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/645595?s=64&amp;v=4" alt="@jeffra" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/tjruwase" class="" data-hovercard-type="user" data-hovercard-url="/users/tjruwase/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/4271600?s=64&amp;v=4" alt="@tjruwase" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/loadams" class="" data-hovercard-type="user" data-hovercard-url="/users/loadams/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/114770087?s=64&amp;v=4" alt="@loadams" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/mrwyattii" class="" data-hovercard-type="user" data-hovercard-url="/users/mrwyattii/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/18311180?s=64&amp;v=4" alt="@mrwyattii" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/ShadenSmith" class="" data-hovercard-type="user" data-hovercard-url="/users/ShadenSmith/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/620322?s=64&amp;v=4" alt="@ShadenSmith" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/RezaYazdaniAminabadi" class="" data-hovercard-type="user" data-hovercard-url="/users/RezaYazdaniAminabadi/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/44502768?s=64&amp;v=4" alt="@RezaYazdaniAminabadi" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> <li class="mb-2 mr-2" > <a href="https://github.com/stas00" class="" data-hovercard-type="user" data-hovercard-url="/users/stas00/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img 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