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GitHub - NVIDIA/apex: A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

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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 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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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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" 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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;white_papers_ebooks_webinars&quot;,&quot;context&quot;:&quot;resources&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;white_papers_ebooks_webinars_link_resources_navbar&quot;}" href="https://resources.github.com"> White papers, Ebooks, 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;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" 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<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 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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> 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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;github_copilot&quot;,&quot;context&quot;:&quot;enterprise&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;github_copilot_link_enterprise_navbar&quot;}" href="/features/copilot#enterprise"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-copilot 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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> Enterprise-grade AI 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" 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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 px-0 px-lg-2 py-3 py-lg-2 d-block d-lg-inline-block" data-analytics-event="{&quot;location&quot;:&quot;navbar&quot;,&quot;action&quot;:&quot;pricing&quot;,&quot;context&quot;:&quot;global&quot;,&quot;tag&quot;:&quot;link&quot;,&quot;label&quot;:&quot;pricing_link_global_navbar&quot;}" href="https://github.com/pricing">Pricing</a> </li> </ul> </nav> <div class="d-flex flex-column flex-lg-row width-full flex-justify-end flex-lg-items-center 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id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\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 repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch.\nSome of the code here will be included in upstream Pytorch eventually.\nThe intent of Apex is to make up-to-date utilities available to users as quickly as possible.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFull API Documentation: \u003ca href=\"https://nvidia.github.io/apex\" rel=\"nofollow\"\u003ehttps://nvidia.github.io/apex\u003c/a\u003e\u003c/h2\u003e\u003ca id=\"user-content-full-api-documentation-httpsnvidiagithubioapex\" class=\"anchor\" aria-label=\"Permalink: Full API Documentation: https://nvidia.github.io/apex\" href=\"#full-api-documentation-httpsnvidiagithubioapex\"\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\"\u003e\u003ca href=\"https://github.com/mcarilli/mixed_precision_references/tree/master/GTC_2019\"\u003eGTC 2019\u003c/a\u003e and \u003ca href=\"https://github.com/mcarilli/mixed_precision_references/tree/master/Pytorch_Devcon_2019\"\u003ePytorch DevCon 2019\u003c/a\u003e Slides\u003c/h2\u003e\u003ca id=\"user-content-gtc-2019-and-pytorch-devcon-2019-slides\" class=\"anchor\" aria-label=\"Permalink: GTC 2019 and Pytorch DevCon 2019 Slides\" href=\"#gtc-2019-and-pytorch-devcon-2019-slides\"\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\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContents\u003c/h1\u003e\u003ca id=\"user-content-contents\" class=\"anchor\" aria-label=\"Permalink: Contents\" href=\"#contents\"\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\"\u003e1. Amp: Automatic Mixed Precision\u003c/h2\u003e\u003ca id=\"user-content-1-amp--automatic-mixed-precision\" class=\"anchor\" aria-label=\"Permalink: 1. Amp: Automatic Mixed Precision\" href=\"#1-amp--automatic-mixed-precision\"\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\u003cstrong\u003eDeprecated. Use \u003ca href=\"https://pytorch.org/docs/stable/amp.html\" rel=\"nofollow\"\u003ePyTorch AMP\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eapex.amp\u003c/code\u003e is a tool to enable mixed precision training by changing only 3 lines of your script.\nUsers can easily experiment with different pure and mixed precision training modes by supplying\ndifferent flags to \u003ccode\u003eamp.initialize\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://info.nvidia.com/webinar-mixed-precision-with-pytorch-reg-page.html\" rel=\"nofollow\"\u003eWebinar introducing Amp\u003c/a\u003e\n(The flag \u003ccode\u003ecast_batchnorm\u003c/code\u003e has been renamed to \u003ccode\u003ekeep_batchnorm_fp32\u003c/code\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://nvidia.github.io/apex/amp.html\" rel=\"nofollow\"\u003eAPI Documentation\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/NVIDIA/apex/tree/master/examples/imagenet\"\u003eComprehensive Imagenet example\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/NVIDIA/apex/tree/master/examples/dcgan\"\u003eDCGAN example coming soon...\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://nvidia.github.io/apex/amp.html#transition-guide-for-old-api-users\" rel=\"nofollow\"\u003eMoving to the new Amp API\u003c/a\u003e (for users of the deprecated \"Amp\" and \"FP16_Optimizer\" APIs)\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2. Distributed Training\u003c/h2\u003e\u003ca id=\"user-content-2-distributed-training\" class=\"anchor\" aria-label=\"Permalink: 2. Distributed Training\" href=\"#2-distributed-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\"\u003e\u003cstrong\u003e\u003ccode\u003eapex.parallel.DistributedDataParallel\u003c/code\u003e is deprecated. Use \u003ca href=\"https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html?highlight=distributeddataparallel#torch.nn.parallel.DistributedDataParallel\" rel=\"nofollow\"\u003e\u003ccode\u003etorch.nn.parallel.DistributedDataParallel\u003c/code\u003e\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eapex.parallel.DistributedDataParallel\u003c/code\u003e is a module wrapper, similar to\n\u003ccode\u003etorch.nn.parallel.DistributedDataParallel\u003c/code\u003e. It enables convenient multiprocess distributed training,\noptimized for NVIDIA's NCCL communication library.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://nvidia.github.io/apex/parallel.html\" rel=\"nofollow\"\u003eAPI Documentation\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/NVIDIA/apex/tree/master/apex/parallel\"\u003ePython Source\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/NVIDIA/apex/tree/master/examples/simple/distributed\"\u003eExample/Walkthrough\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ca href=\"https://github.com/NVIDIA/apex/tree/master/examples/imagenet\"\u003eImagenet example\u003c/a\u003e\nshows use of \u003ccode\u003eapex.parallel.DistributedDataParallel\u003c/code\u003e along with \u003ccode\u003eapex.amp\u003c/code\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSynchronized Batch Normalization\u003c/h3\u003e\u003ca id=\"user-content-synchronized-batch-normalization\" class=\"anchor\" aria-label=\"Permalink: Synchronized Batch Normalization\" href=\"#synchronized-batch-normalization\"\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\u003cstrong\u003eDeprecated. Use \u003ca href=\"https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html\" rel=\"nofollow\"\u003e\u003ccode\u003etorch.nn.SyncBatchNorm\u003c/code\u003e\u003c/a\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eapex.parallel.SyncBatchNorm\u003c/code\u003e extends \u003ccode\u003etorch.nn.modules.batchnorm._BatchNorm\u003c/code\u003e to\nsupport synchronized BN.\nIt allreduces stats across processes during multiprocess (DistributedDataParallel) training.\nSynchronous BN has been used in cases where only a small\nlocal minibatch can fit on each GPU.\nAllreduced stats increase the effective batch size for the BN layer to the\nglobal batch size across all processes (which, technically, is the correct\nformulation).\nSynchronous BN has been observed to improve converged accuracy in some of our research models.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCheckpointing\u003c/h3\u003e\u003ca id=\"user-content-checkpointing\" class=\"anchor\" aria-label=\"Permalink: Checkpointing\" href=\"#checkpointing\"\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 properly save and load your \u003ccode\u003eamp\u003c/code\u003e training, we introduce the \u003ccode\u003eamp.state_dict()\u003c/code\u003e, which contains all \u003ccode\u003eloss_scalers\u003c/code\u003e and their corresponding unskipped steps,\nas well as \u003ccode\u003eamp.load_state_dict()\u003c/code\u003e to restore these attributes.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn order to get bitwise accuracy, we recommend the following workflow:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Initialization\nopt_level = 'O1'\nmodel, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)\n\n# Train your model\n...\nwith amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n...\n\n# Save checkpoint\ncheckpoint = {\n 'model': model.state_dict(),\n 'optimizer': optimizer.state_dict(),\n 'amp': amp.state_dict()\n}\ntorch.save(checkpoint, 'amp_checkpoint.pt')\n...\n\n# Restore\nmodel = ...\noptimizer = ...\ncheckpoint = torch.load('amp_checkpoint.pt')\n\nmodel, optimizer = amp.initialize(model, optimizer, opt_level=opt_level)\nmodel.load_state_dict(checkpoint['model'])\noptimizer.load_state_dict(checkpoint['optimizer'])\namp.load_state_dict(checkpoint['amp'])\n\n# Continue training\n...\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Initialization\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eopt_level\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e'O1'\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eamp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003einitialize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eopt_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eopt_level\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# Train your model\u003c/span\u003e\n...\n\u003cspan class=\"pl-k\"\u003ewith\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eamp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003escale_loss\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e) \u003cspan class=\"pl-k\"\u003eas\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003escaled_loss\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003escaled_loss\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebackward\u003c/span\u003e()\n...\n\n\u003cspan class=\"pl-c\"\u003e# Save checkpoint\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003echeckpoint\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e {\n \u003cspan class=\"pl-s\"\u003e'model'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003estate_dict\u003c/span\u003e(),\n \u003cspan class=\"pl-s\"\u003e'optimizer'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003estate_dict\u003c/span\u003e(),\n \u003cspan class=\"pl-s\"\u003e'amp'\u003c/span\u003e: \u003cspan class=\"pl-s1\"\u003eamp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003estate_dict\u003c/span\u003e()\n}\n\u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esave\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003echeckpoint\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'amp_checkpoint.pt'\u003c/span\u003e)\n...\n\n\u003cspan class=\"pl-c\"\u003e# Restore\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ...\n\u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ...\n\u003cspan class=\"pl-s1\"\u003echeckpoint\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eload\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'amp_checkpoint.pt'\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eamp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003einitialize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eopt_level\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eopt_level\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eload_state_dict\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003echeckpoint\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'model'\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eload_state_dict\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003echeckpoint\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'optimizer'\u003c/span\u003e])\n\u003cspan class=\"pl-s1\"\u003eamp\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eload_state_dict\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003echeckpoint\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'amp'\u003c/span\u003e])\n\n\u003cspan class=\"pl-c\"\u003e# Continue training\u003c/span\u003e\n...\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote that we recommend restoring the model using the same \u003ccode\u003eopt_level\u003c/code\u003e. Also note that we recommend calling the \u003ccode\u003eload_state_dict\u003c/code\u003e methods after \u003ccode\u003eamp.initialize\u003c/code\u003e.\u003c/p\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\"\u003eEach \u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib\"\u003e\u003ccode\u003eapex.contrib\u003c/code\u003e\u003c/a\u003e module requires one or more install options other than \u003ccode\u003e--cpp_ext\u003c/code\u003e and \u003ccode\u003e--cuda_ext\u003c/code\u003e.\nNote that contrib modules do not necessarily support stable PyTorch releases.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContainers\u003c/h2\u003e\u003ca id=\"user-content-containers\" class=\"anchor\" aria-label=\"Permalink: Containers\" href=\"#containers\"\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\"\u003eNVIDIA PyTorch Containers are available on NGC: \u003ca href=\"https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch\" rel=\"nofollow\"\u003ehttps://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch\u003c/a\u003e.\nThe containers come with all the custom extensions available at the moment.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSee \u003ca href=\"https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html\" rel=\"nofollow\"\u003ethe NGC documentation\u003c/a\u003e for details such as:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ehow to pull a container\u003c/li\u003e\n\u003cli\u003ehow to run a pulled container\u003c/li\u003e\n\u003cli\u003erelease notes\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFrom Source\u003c/h2\u003e\u003ca id=\"user-content-from-source\" class=\"anchor\" aria-label=\"Permalink: From Source\" href=\"#from-source\"\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 install Apex from source, we recommend using the nightly Pytorch obtainable from \u003ca href=\"https://github.com/pytorch/pytorch\"\u003ehttps://github.com/pytorch/pytorch\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe latest stable release obtainable from \u003ca href=\"https://pytorch.org\" rel=\"nofollow\"\u003ehttps://pytorch.org\u003c/a\u003e should also work.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe recommend installing \u003ca href=\"https://ninja-build.org/\" rel=\"nofollow\"\u003e\u003ccode\u003eNinja\u003c/code\u003e\u003c/a\u003e to make compilation faster.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLinux\u003c/h3\u003e\u003ca id=\"user-content-linux\" class=\"anchor\" aria-label=\"Permalink: Linux\" href=\"#linux\"\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\"\u003eFor performance and full functionality, we recommend installing Apex with\nCUDA and C++ extensions via\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/NVIDIA/apex\ncd apex\n# if pip \u0026gt;= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... \npip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings \u0026quot;--build-option=--cpp_ext\u0026quot; --config-settings \u0026quot;--build-option=--cuda_ext\u0026quot; ./\n# otherwise\npip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option=\u0026quot;--cpp_ext\u0026quot; --global-option=\u0026quot;--cuda_ext\u0026quot; ./\"\u003e\u003cpre\u003egit clone https://github.com/NVIDIA/apex\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e apex\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e if pip \u0026gt;= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... \u003c/span\u003e\npip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--build-option=--cpp_ext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --config-settings \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--build-option=--cuda_ext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ./\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e otherwise\u003c/span\u003e\npip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--cpp_ext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e --global-option=\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e--cuda_ext\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e ./\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAPEX also supports a Python-only build via\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install -v --disable-pip-version-check --no-build-isolation --no-cache-dir ./\"\u003e\u003cpre\u003epip install -v --disable-pip-version-check --no-build-isolation --no-cache-dir ./\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA Python-only build omits:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFused kernels required to use \u003ccode\u003eapex.optimizers.FusedAdam\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eFused kernels required to use \u003ccode\u003eapex.normalization.FusedLayerNorm\u003c/code\u003e and \u003ccode\u003eapex.normalization.FusedRMSNorm\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eFused kernels that improve the performance and numerical stability of \u003ccode\u003eapex.parallel.SyncBatchNorm\u003c/code\u003e.\u003c/li\u003e\n\u003cli\u003eFused kernels that improve the performance of \u003ccode\u003eapex.parallel.DistributedDataParallel\u003c/code\u003e and \u003ccode\u003eapex.amp\u003c/code\u003e.\n\u003ccode\u003eDistributedDataParallel\u003c/code\u003e, \u003ccode\u003eamp\u003c/code\u003e, and \u003ccode\u003eSyncBatchNorm\u003c/code\u003e will still be usable, but they may be slower.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e[Experimental] Windows\u003c/h3\u003e\u003ca id=\"user-content-experimental-windows\" class=\"anchor\" aria-label=\"Permalink: [Experimental] Windows\" href=\"#experimental-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\"\u003e\u003ccode\u003epip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings \"--build-option=--cpp_ext\" --config-settings \"--build-option=--cuda_ext\" .\u003c/code\u003e may work if you were able to build Pytorch from source\non your system. A Python-only build via \u003ccode\u003epip install -v --no-cache-dir .\u003c/code\u003e is more likely to work.\u003cbr\u003e\nIf you installed Pytorch in a Conda environment, make sure to install Apex in that same environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCustom C++/CUDA Extensions and Install Options\u003c/h2\u003e\u003ca id=\"user-content-custom-ccuda-extensions-and-install-options\" class=\"anchor\" aria-label=\"Permalink: Custom C++/CUDA Extensions and Install Options\" href=\"#custom-ccuda-extensions-and-install-options\"\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\"\u003eIf a requirement of a module is not met, then it will not be built.\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eModule Name\u003c/th\u003e\n\u003cth\u003eInstall Option\u003c/th\u003e\n\u003cth\u003eMisc\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eapex_C\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cpp_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003eamp_C\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cuda_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003esyncbn\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cuda_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efused_layer_norm_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cuda_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/normalization\"\u003e\u003ccode\u003eapex.normalization\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003emlp_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cuda_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003escaled_upper_triang_masked_softmax_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cuda_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003egeneric_scaled_masked_softmax_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cuda_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003escaled_masked_softmax_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cuda_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efused_weight_gradient_mlp_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cuda_ext\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRequires CUDA\u0026gt;=11\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epermutation_search_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--permutation_search\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/sparsity\"\u003e\u003ccode\u003eapex.contrib.sparsity\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ebnp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--bnp\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/groupbn\"\u003e\u003ccode\u003eapex.contrib.groupbn\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003exentropy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--xentropy\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/xentropy\"\u003e\u003ccode\u003eapex.contrib.xentropy\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efocal_loss_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--focal_loss\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/focal_loss\"\u003e\u003ccode\u003eapex.contrib.focal_loss\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efused_index_mul_2d\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--index_mul_2d\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/index_mul_2d\"\u003e\u003ccode\u003eapex.contrib.index_mul_2d\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efused_adam_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--deprecated_fused_adam\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/optimizers\"\u003e\u003ccode\u003eapex.contrib.optimizers\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efused_lamb_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--deprecated_fused_lamb\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/optimizers\"\u003e\u003ccode\u003eapex.contrib.optimizers\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efast_layer_norm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--fast_layer_norm\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/layer_norm\"\u003e\u003ccode\u003eapex.contrib.layer_norm\u003c/code\u003e\u003c/a\u003e. different from \u003ccode\u003efused_layer_norm\u003c/code\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efmhalib\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--fmha\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/fmha\"\u003e\u003ccode\u003eapex.contrib.fmha\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efast_multihead_attn\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--fast_multihead_attn\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/multihead_attn\"\u003e\u003ccode\u003eapex.contrib.multihead_attn\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003etransducer_joint_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--transducer\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/transducer\"\u003e\u003ccode\u003eapex.contrib.transducer\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003etransducer_loss_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--transducer\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/transducer\"\u003e\u003ccode\u003eapex.contrib.transducer\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ecudnn_gbn_lib\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--cudnn_gbn\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRequires cuDNN\u0026gt;=8.5, \u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/cudnn_gbn\"\u003e\u003ccode\u003eapex.contrib.cudnn_gbn\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003epeer_memory_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--peer_memory\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/peer_memory\"\u003e\u003ccode\u003eapex.contrib.peer_memory\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003enccl_p2p_cuda\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--nccl_p2p\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRequires NCCL \u0026gt;= 2.10, \u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/nccl_p2p\"\u003e\u003ccode\u003eapex.contrib.nccl_p2p\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efast_bottleneck\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--fast_bottleneck\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRequires \u003ccode\u003epeer_memory_cuda\u003c/code\u003e and \u003ccode\u003enccl_p2p_cuda\u003c/code\u003e, \u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/bottleneck\"\u003e\u003ccode\u003eapex.contrib.bottleneck\u003c/code\u003e\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003efused_conv_bias_relu\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ccode\u003e--fused_conv_bias_relu\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eRequires cuDNN\u0026gt;=8.4, \u003ca href=\"/NVIDIA/apex/blob/master/apex/contrib/conv_bias_relu\"\u003e\u003ccode\u003eapex.contrib.conv_bias_relu\u003c/code\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\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Introduction","anchor":"introduction","htmlText":"Introduction"},{"level":2,"text":"Full API Documentation: https://nvidia.github.io/apex","anchor":"full-api-documentation-httpsnvidiagithubioapex","htmlText":"Full API Documentation: https://nvidia.github.io/apex"},{"level":2,"text":"GTC 2019 and Pytorch DevCon 2019 Slides","anchor":"gtc-2019-and-pytorch-devcon-2019-slides","htmlText":"GTC 2019 and Pytorch DevCon 2019 Slides"},{"level":1,"text":"Contents","anchor":"contents","htmlText":"Contents"},{"level":2,"text":"1. Amp: Automatic Mixed Precision","anchor":"1-amp--automatic-mixed-precision","htmlText":"1. Amp: Automatic Mixed Precision"},{"level":2,"text":"2. Distributed Training","anchor":"2-distributed-training","htmlText":"2. Distributed Training"},{"level":3,"text":"Synchronized Batch Normalization","anchor":"synchronized-batch-normalization","htmlText":"Synchronized Batch Normalization"},{"level":3,"text":"Checkpointing","anchor":"checkpointing","htmlText":"Checkpointing"},{"level":1,"text":"Installation","anchor":"installation","htmlText":"Installation"},{"level":2,"text":"Containers","anchor":"containers","htmlText":"Containers"},{"level":2,"text":"From Source","anchor":"from-source","htmlText":"From Source"},{"level":3,"text":"Linux","anchor":"linux","htmlText":"Linux"},{"level":3,"text":"[Experimental] Windows","anchor":"experimental-windows","htmlText":"[Experimental] Windows"},{"level":2,"text":"Custom C++/CUDA Extensions and Install Options","anchor":"custom-ccuda-extensions-and-install-options","htmlText":"Custom C++/CUDA Extensions and Install Options"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fnvidia%2Fapex"}},{"displayName":"LICENSE","repoName":"apex","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"BSD-3-Clause","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fnvidia%2Fapex"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-9f8a877aa99f.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-96e76d5fdb2c.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"accessible_code_button":true}}}}</script> <div data-target="react-partial.reactRoot"><style 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itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Introduction</h1><a id="user-content-introduction" class="anchor" aria-label="Permalink: Introduction" href="#introduction"><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 repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch. Some of the code here will be included in upstream Pytorch eventually. The intent of Apex is to make up-to-date utilities available to users as quickly as possible.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Full API Documentation: <a href="https://nvidia.github.io/apex" rel="nofollow">https://nvidia.github.io/apex</a></h2><a id="user-content-full-api-documentation-httpsnvidiagithubioapex" class="anchor" aria-label="Permalink: Full API Documentation: https://nvidia.github.io/apex" href="#full-api-documentation-httpsnvidiagithubioapex"><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"><a href="https://github.com/mcarilli/mixed_precision_references/tree/master/GTC_2019">GTC 2019</a> and <a href="https://github.com/mcarilli/mixed_precision_references/tree/master/Pytorch_Devcon_2019">Pytorch DevCon 2019</a> Slides</h2><a id="user-content-gtc-2019-and-pytorch-devcon-2019-slides" class="anchor" aria-label="Permalink: GTC 2019 and Pytorch DevCon 2019 Slides" href="#gtc-2019-and-pytorch-devcon-2019-slides"><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"><h1 tabindex="-1" class="heading-element" dir="auto">Contents</h1><a id="user-content-contents" class="anchor" aria-label="Permalink: Contents" href="#contents"><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">1. Amp: Automatic Mixed Precision</h2><a id="user-content-1-amp--automatic-mixed-precision" class="anchor" aria-label="Permalink: 1. Amp: Automatic Mixed Precision" href="#1-amp--automatic-mixed-precision"><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"><strong>Deprecated. Use <a href="https://pytorch.org/docs/stable/amp.html" rel="nofollow">PyTorch AMP</a></strong></p> <p dir="auto"><code>apex.amp</code> is a tool to enable mixed precision training by changing only 3 lines of your script. Users can easily experiment with different pure and mixed precision training modes by supplying different flags to <code>amp.initialize</code>.</p> <p dir="auto"><a href="https://info.nvidia.com/webinar-mixed-precision-with-pytorch-reg-page.html" rel="nofollow">Webinar introducing Amp</a> (The flag <code>cast_batchnorm</code> has been renamed to <code>keep_batchnorm_fp32</code>).</p> <p dir="auto"><a href="https://nvidia.github.io/apex/amp.html" rel="nofollow">API Documentation</a></p> <p dir="auto"><a href="https://github.com/NVIDIA/apex/tree/master/examples/imagenet">Comprehensive Imagenet example</a></p> <p dir="auto"><a href="https://github.com/NVIDIA/apex/tree/master/examples/dcgan">DCGAN example coming soon...</a></p> <p dir="auto"><a href="https://nvidia.github.io/apex/amp.html#transition-guide-for-old-api-users" rel="nofollow">Moving to the new Amp API</a> (for users of the deprecated "Amp" and "FP16_Optimizer" APIs)</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2. Distributed Training</h2><a id="user-content-2-distributed-training" class="anchor" aria-label="Permalink: 2. Distributed Training" href="#2-distributed-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"><strong><code>apex.parallel.DistributedDataParallel</code> is deprecated. Use <a href="https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html?highlight=distributeddataparallel#torch.nn.parallel.DistributedDataParallel" rel="nofollow"><code>torch.nn.parallel.DistributedDataParallel</code></a></strong></p> <p dir="auto"><code>apex.parallel.DistributedDataParallel</code> is a module wrapper, similar to <code>torch.nn.parallel.DistributedDataParallel</code>. It enables convenient multiprocess distributed training, optimized for NVIDIA's NCCL communication library.</p> <p dir="auto"><a href="https://nvidia.github.io/apex/parallel.html" rel="nofollow">API Documentation</a></p> <p dir="auto"><a href="https://github.com/NVIDIA/apex/tree/master/apex/parallel">Python Source</a></p> <p dir="auto"><a href="https://github.com/NVIDIA/apex/tree/master/examples/simple/distributed">Example/Walkthrough</a></p> <p dir="auto">The <a href="https://github.com/NVIDIA/apex/tree/master/examples/imagenet">Imagenet example</a> shows use of <code>apex.parallel.DistributedDataParallel</code> along with <code>apex.amp</code>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Synchronized Batch Normalization</h3><a id="user-content-synchronized-batch-normalization" class="anchor" aria-label="Permalink: Synchronized Batch Normalization" href="#synchronized-batch-normalization"><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"><strong>Deprecated. Use <a href="https://pytorch.org/docs/stable/generated/torch.nn.SyncBatchNorm.html" rel="nofollow"><code>torch.nn.SyncBatchNorm</code></a></strong></p> <p dir="auto"><code>apex.parallel.SyncBatchNorm</code> extends <code>torch.nn.modules.batchnorm._BatchNorm</code> to support synchronized BN. It allreduces stats across processes during multiprocess (DistributedDataParallel) training. Synchronous BN has been used in cases where only a small local minibatch can fit on each GPU. Allreduced stats increase the effective batch size for the BN layer to the global batch size across all processes (which, technically, is the correct formulation). Synchronous BN has been observed to improve converged accuracy in some of our research models.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Checkpointing</h3><a id="user-content-checkpointing" class="anchor" aria-label="Permalink: Checkpointing" href="#checkpointing"><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 properly save and load your <code>amp</code> training, we introduce the <code>amp.state_dict()</code>, which contains all <code>loss_scalers</code> and their corresponding unskipped steps, as well as <code>amp.load_state_dict()</code> to restore these attributes.</p> <p dir="auto">In order to get bitwise accuracy, we recommend the following workflow:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Initialization opt_level = 'O1' model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level) # Train your model ... with amp.scale_loss(loss, optimizer) as scaled_loss: scaled_loss.backward() ... # Save checkpoint checkpoint = { 'model': model.state_dict(), 'optimizer': optimizer.state_dict(), 'amp': amp.state_dict() } torch.save(checkpoint, 'amp_checkpoint.pt') ... # Restore model = ... optimizer = ... checkpoint = torch.load('amp_checkpoint.pt') model, optimizer = amp.initialize(model, optimizer, opt_level=opt_level) model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer']) amp.load_state_dict(checkpoint['amp']) # Continue training ..."><pre><span class="pl-c"># Initialization</span> <span class="pl-s1">opt_level</span> <span class="pl-c1">=</span> <span class="pl-s">'O1'</span> <span class="pl-s1">model</span>, <span class="pl-s1">optimizer</span> <span class="pl-c1">=</span> <span class="pl-s1">amp</span>.<span class="pl-c1">initialize</span>(<span class="pl-s1">model</span>, <span class="pl-s1">optimizer</span>, <span class="pl-s1">opt_level</span><span class="pl-c1">=</span><span class="pl-s1">opt_level</span>) <span class="pl-c"># Train your model</span> ... <span class="pl-k">with</span> <span class="pl-s1">amp</span>.<span class="pl-c1">scale_loss</span>(<span class="pl-s1">loss</span>, <span class="pl-s1">optimizer</span>) <span class="pl-k">as</span> <span class="pl-s1">scaled_loss</span>: <span class="pl-s1">scaled_loss</span>.<span class="pl-c1">backward</span>() ... <span class="pl-c"># Save checkpoint</span> <span class="pl-s1">checkpoint</span> <span class="pl-c1">=</span> { <span class="pl-s">'model'</span>: <span class="pl-s1">model</span>.<span class="pl-c1">state_dict</span>(), <span class="pl-s">'optimizer'</span>: <span class="pl-s1">optimizer</span>.<span class="pl-c1">state_dict</span>(), <span class="pl-s">'amp'</span>: <span class="pl-s1">amp</span>.<span class="pl-c1">state_dict</span>() } <span class="pl-s1">torch</span>.<span class="pl-c1">save</span>(<span class="pl-s1">checkpoint</span>, <span class="pl-s">'amp_checkpoint.pt'</span>) ... <span class="pl-c"># Restore</span> <span class="pl-s1">model</span> <span class="pl-c1">=</span> ... <span class="pl-s1">optimizer</span> <span class="pl-c1">=</span> ... <span class="pl-s1">checkpoint</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-c1">load</span>(<span class="pl-s">'amp_checkpoint.pt'</span>) <span class="pl-s1">model</span>, <span class="pl-s1">optimizer</span> <span class="pl-c1">=</span> <span class="pl-s1">amp</span>.<span class="pl-c1">initialize</span>(<span class="pl-s1">model</span>, <span class="pl-s1">optimizer</span>, <span class="pl-s1">opt_level</span><span class="pl-c1">=</span><span class="pl-s1">opt_level</span>) <span class="pl-s1">model</span>.<span class="pl-c1">load_state_dict</span>(<span class="pl-s1">checkpoint</span>[<span class="pl-s">'model'</span>]) <span class="pl-s1">optimizer</span>.<span class="pl-c1">load_state_dict</span>(<span class="pl-s1">checkpoint</span>[<span class="pl-s">'optimizer'</span>]) <span class="pl-s1">amp</span>.<span class="pl-c1">load_state_dict</span>(<span class="pl-s1">checkpoint</span>[<span class="pl-s">'amp'</span>]) <span class="pl-c"># Continue training</span> ...</pre></div> <p dir="auto">Note that we recommend restoring the model using the same <code>opt_level</code>. Also note that we recommend calling the <code>load_state_dict</code> methods after <code>amp.initialize</code>.</p> <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">Each <a href="/NVIDIA/apex/blob/master/apex/contrib"><code>apex.contrib</code></a> module requires one or more install options other than <code>--cpp_ext</code> and <code>--cuda_ext</code>. Note that contrib modules do not necessarily support stable PyTorch releases.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Containers</h2><a id="user-content-containers" class="anchor" aria-label="Permalink: Containers" href="#containers"><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">NVIDIA PyTorch Containers are available on NGC: <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch" rel="nofollow">https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch</a>. The containers come with all the custom extensions available at the moment.</p> <p dir="auto">See <a href="https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html" rel="nofollow">the NGC documentation</a> for details such as:</p> <ul dir="auto"> <li>how to pull a container</li> <li>how to run a pulled container</li> <li>release notes</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">From Source</h2><a id="user-content-from-source" class="anchor" aria-label="Permalink: From Source" href="#from-source"><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 install Apex from source, we recommend using the nightly Pytorch obtainable from <a href="https://github.com/pytorch/pytorch">https://github.com/pytorch/pytorch</a>.</p> <p dir="auto">The latest stable release obtainable from <a href="https://pytorch.org" rel="nofollow">https://pytorch.org</a> should also work.</p> <p dir="auto">We recommend installing <a href="https://ninja-build.org/" rel="nofollow"><code>Ninja</code></a> to make compilation faster.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Linux</h3><a id="user-content-linux" class="anchor" aria-label="Permalink: Linux" href="#linux"><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">For performance and full functionality, we recommend installing Apex with CUDA and C++ extensions via</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/NVIDIA/apex cd apex # if pip &gt;= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings &quot;--build-option=--cpp_ext&quot; --config-settings &quot;--build-option=--cuda_ext&quot; ./ # otherwise pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option=&quot;--cpp_ext&quot; --global-option=&quot;--cuda_ext&quot; ./"><pre>git clone https://github.com/NVIDIA/apex <span class="pl-c1">cd</span> apex <span class="pl-c"><span class="pl-c">#</span> if pip &gt;= 23.1 (ref: https://pip.pypa.io/en/stable/news/#v23-1) which supports multiple `--config-settings` with the same key... </span> pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings <span class="pl-s"><span class="pl-pds">"</span>--build-option=--cpp_ext<span class="pl-pds">"</span></span> --config-settings <span class="pl-s"><span class="pl-pds">"</span>--build-option=--cuda_ext<span class="pl-pds">"</span></span> ./ <span class="pl-c"><span class="pl-c">#</span> otherwise</span> pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --global-option=<span class="pl-s"><span class="pl-pds">"</span>--cpp_ext<span class="pl-pds">"</span></span> --global-option=<span class="pl-s"><span class="pl-pds">"</span>--cuda_ext<span class="pl-pds">"</span></span> ./</pre></div> <p dir="auto">APEX also supports a Python-only build via</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install -v --disable-pip-version-check --no-build-isolation --no-cache-dir ./"><pre>pip install -v --disable-pip-version-check --no-build-isolation --no-cache-dir ./</pre></div> <p dir="auto">A Python-only build omits:</p> <ul dir="auto"> <li>Fused kernels required to use <code>apex.optimizers.FusedAdam</code>.</li> <li>Fused kernels required to use <code>apex.normalization.FusedLayerNorm</code> and <code>apex.normalization.FusedRMSNorm</code>.</li> <li>Fused kernels that improve the performance and numerical stability of <code>apex.parallel.SyncBatchNorm</code>.</li> <li>Fused kernels that improve the performance of <code>apex.parallel.DistributedDataParallel</code> and <code>apex.amp</code>. <code>DistributedDataParallel</code>, <code>amp</code>, and <code>SyncBatchNorm</code> will still be usable, but they may be slower.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">[Experimental] Windows</h3><a id="user-content-experimental-windows" class="anchor" aria-label="Permalink: [Experimental] Windows" href="#experimental-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"><code>pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" .</code> may work if you were able to build Pytorch from source on your system. A Python-only build via <code>pip install -v --no-cache-dir .</code> is more likely to work.<br> If you installed Pytorch in a Conda environment, make sure to install Apex in that same environment.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Custom C++/CUDA Extensions and Install Options</h2><a id="user-content-custom-ccuda-extensions-and-install-options" class="anchor" aria-label="Permalink: Custom C++/CUDA Extensions and Install Options" href="#custom-ccuda-extensions-and-install-options"><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">If a requirement of a module is not met, then it will not be built.</p> <markdown-accessiblity-table><table> <thead> <tr> <th>Module Name</th> <th>Install Option</th> <th>Misc</th> </tr> </thead> <tbody> <tr> <td><code>apex_C</code></td> <td><code>--cpp_ext</code></td> <td></td> </tr> <tr> <td><code>amp_C</code></td> <td><code>--cuda_ext</code></td> <td></td> </tr> <tr> <td><code>syncbn</code></td> <td><code>--cuda_ext</code></td> <td></td> </tr> <tr> <td><code>fused_layer_norm_cuda</code></td> <td><code>--cuda_ext</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/normalization"><code>apex.normalization</code></a></td> </tr> <tr> <td><code>mlp_cuda</code></td> <td><code>--cuda_ext</code></td> <td></td> </tr> <tr> <td><code>scaled_upper_triang_masked_softmax_cuda</code></td> <td><code>--cuda_ext</code></td> <td></td> </tr> <tr> <td><code>generic_scaled_masked_softmax_cuda</code></td> <td><code>--cuda_ext</code></td> <td></td> </tr> <tr> <td><code>scaled_masked_softmax_cuda</code></td> <td><code>--cuda_ext</code></td> <td></td> </tr> <tr> <td><code>fused_weight_gradient_mlp_cuda</code></td> <td><code>--cuda_ext</code></td> <td>Requires CUDA&gt;=11</td> </tr> <tr> <td><code>permutation_search_cuda</code></td> <td><code>--permutation_search</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/sparsity"><code>apex.contrib.sparsity</code></a></td> </tr> <tr> <td><code>bnp</code></td> <td><code>--bnp</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/groupbn"><code>apex.contrib.groupbn</code></a></td> </tr> <tr> <td><code>xentropy</code></td> <td><code>--xentropy</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/xentropy"><code>apex.contrib.xentropy</code></a></td> </tr> <tr> <td><code>focal_loss_cuda</code></td> <td><code>--focal_loss</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/focal_loss"><code>apex.contrib.focal_loss</code></a></td> </tr> <tr> <td><code>fused_index_mul_2d</code></td> <td><code>--index_mul_2d</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/index_mul_2d"><code>apex.contrib.index_mul_2d</code></a></td> </tr> <tr> <td><code>fused_adam_cuda</code></td> <td><code>--deprecated_fused_adam</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/optimizers"><code>apex.contrib.optimizers</code></a></td> </tr> <tr> <td><code>fused_lamb_cuda</code></td> <td><code>--deprecated_fused_lamb</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/optimizers"><code>apex.contrib.optimizers</code></a></td> </tr> <tr> <td><code>fast_layer_norm</code></td> <td><code>--fast_layer_norm</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/layer_norm"><code>apex.contrib.layer_norm</code></a>. different from <code>fused_layer_norm</code></td> </tr> <tr> <td><code>fmhalib</code></td> <td><code>--fmha</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/fmha"><code>apex.contrib.fmha</code></a></td> </tr> <tr> <td><code>fast_multihead_attn</code></td> <td><code>--fast_multihead_attn</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/multihead_attn"><code>apex.contrib.multihead_attn</code></a></td> </tr> <tr> <td><code>transducer_joint_cuda</code></td> <td><code>--transducer</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/transducer"><code>apex.contrib.transducer</code></a></td> </tr> <tr> <td><code>transducer_loss_cuda</code></td> <td><code>--transducer</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/transducer"><code>apex.contrib.transducer</code></a></td> </tr> <tr> <td><code>cudnn_gbn_lib</code></td> <td><code>--cudnn_gbn</code></td> <td>Requires cuDNN&gt;=8.5, <a href="/NVIDIA/apex/blob/master/apex/contrib/cudnn_gbn"><code>apex.contrib.cudnn_gbn</code></a></td> </tr> <tr> <td><code>peer_memory_cuda</code></td> <td><code>--peer_memory</code></td> <td><a href="/NVIDIA/apex/blob/master/apex/contrib/peer_memory"><code>apex.contrib.peer_memory</code></a></td> </tr> <tr> <td><code>nccl_p2p_cuda</code></td> <td><code>--nccl_p2p</code></td> <td>Requires NCCL &gt;= 2.10, <a href="/NVIDIA/apex/blob/master/apex/contrib/nccl_p2p"><code>apex.contrib.nccl_p2p</code></a></td> </tr> <tr> <td><code>fast_bottleneck</code></td> <td><code>--fast_bottleneck</code></td> <td>Requires <code>peer_memory_cuda</code> and <code>nccl_p2p_cuda</code>, <a href="/NVIDIA/apex/blob/master/apex/contrib/bottleneck"><code>apex.contrib.bottleneck</code></a></td> </tr> <tr> <td><code>fused_conv_bias_relu</code></td> <td><code>--fused_conv_bias_relu</code></td> <td>Requires cuDNN&gt;=8.4, <a href="/NVIDIA/apex/blob/master/apex/contrib/conv_bias_relu"><code>apex.contrib.conv_bias_relu</code></a></td> </tr> </tbody> </table></markdown-accessiblity-table> </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="V350oDZqSz4AcslkIwHF8T4LX6Ew/5XqMw2Uehea+x+aEeLQCMrbPq04RRTLewJ+H0sbT6YNZFQS1ptfUY7aBg==" /> </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"> A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch </p> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{&quot;category&quot;:&quot;Repository 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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>8.5k</strong> stars</a> </div> <h3 class="sr-only">Watchers</h3> <div class="mt-2"> <a href="/NVIDIA/apex/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>99</strong> watching</a> </div> <h3 class="sr-only">Forks</h3> <div class="mt-2"> <a href="/NVIDIA/apex/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>1.4k</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FNVIDIA%2Fapex&amp;report=NVIDIA+%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="/NVIDIA/apex/releases" data-view-component="true" class="Link--primary no-underline Link">Releases</a></h2> <a class="Link--primary no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/NVIDIA/apex/tags"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag"> <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> <span class="text-bold">6</span> <span class="color-fg-muted">tags</span> </a> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/orgs/NVIDIA/packages?repo_name=apex" 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="/NVIDIA/apex/network/dependents" data-view-component="true" class="Link--primary no-underline Link">Used by <span title="2,954" data-view-component="true" class="Counter">3k</span></a> </h2> <a class="d-flex flex-items-center" href="/NVIDIA/apex/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/71096630?s=64&amp;v=4" width="32" height="32" alt="@matheus-rech" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar" src="https://avatars.githubusercontent.com/u/69273984?s=64&amp;v=4" width="32" height="32" alt="@zhu-xlab" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar" src="https://avatars.githubusercontent.com/u/94522163?s=64&amp;v=4" width="32" height="32" alt="@OpenGVLab" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/39010092?s=64&amp;v=4" width="32" height="32" alt="@weiwei1206" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/198337277?s=64&amp;v=4" width="32" height="32" alt="@sep-attack-demo" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar" src="https://avatars.githubusercontent.com/u/118160626?s=64&amp;v=4" width="32" height="32" alt="@baaivision" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/182361448?s=64&amp;v=4" width="32" height="32" alt="@SaulBefore" /> </li> <li class="hx_flex-avatar-stack-item"> <img class="avatar avatar-user" src="https://avatars.githubusercontent.com/u/146257084?s=64&amp;v=4" width="32" height="32" alt="@AkshatGupta15" /> </li> </ul> <span class="px-2 text-bold text-small no-wrap"> + 2,946 </span> </a> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3"> <a href="/NVIDIA/apex/graphs/contributors" data-view-component="true" class="Link--primary no-underline Link d-flex flex-items-center">Contributors <span title="127" data-view-component="true" class="Counter ml-1">127</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/thorjohnsen" class="" data-hovercard-type="user" data-hovercard-url="/users/thorjohnsen/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/41591019?s=64&amp;v=4" alt="@thorjohnsen" 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/crcrpar" class="" data-hovercard-type="user" data-hovercard-url="/users/crcrpar/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/16191443?s=64&amp;v=4" alt="@crcrpar" 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/mcarilli" class="" data-hovercard-type="user" data-hovercard-url="/users/mcarilli/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/7799218?s=64&amp;v=4" alt="@mcarilli" 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/kexinyu" class="" data-hovercard-type="user" data-hovercard-url="/users/kexinyu/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/10904167?s=64&amp;v=4" alt="@kexinyu" 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/csarofeen" class="" data-hovercard-type="user" data-hovercard-url="/users/csarofeen/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/22205833?s=64&amp;v=4" alt="@csarofeen" 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/timmoon10" class="" data-hovercard-type="user" data-hovercard-url="/users/timmoon10/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/4406448?s=64&amp;v=4" alt="@timmoon10" 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/FDecaYed" class="" data-hovercard-type="user" data-hovercard-url="/users/FDecaYed/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/17164548?s=64&amp;v=4" alt="@FDecaYed" 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/eqy" class="" data-hovercard-type="user" data-hovercard-url="/users/eqy/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/2239616?s=64&amp;v=4" alt="@eqy" 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/jjsjann123" class="" data-hovercard-type="user" data-hovercard-url="/users/jjsjann123/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/3709243?s=64&amp;v=4" alt="@jjsjann123" 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/ptrblck" class="" data-hovercard-type="user" data-hovercard-url="/users/ptrblck/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/11662379?s=64&amp;v=4" alt="@ptrblck" 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/carlc-nv" class="" data-hovercard-type="user" data-hovercard-url="/users/carlc-nv/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/29262414?s=64&amp;v=4" alt="@carlc-nv" 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/Aidyn-A" class="" data-hovercard-type="user" data-hovercard-url="/users/Aidyn-A/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/31858918?s=64&amp;v=4" alt="@Aidyn-A" 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/seryilmaz" class="" data-hovercard-type="user" data-hovercard-url="/users/seryilmaz/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/24972237?s=64&amp;v=4" alt="@seryilmaz" 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/alpha0422" class="" data-hovercard-type="user" data-hovercard-url="/users/alpha0422/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" > <img src="https://avatars.githubusercontent.com/u/4337967?s=64&amp;v=4" alt="@alpha0422" size="32" height="32" width="32" data-view-component="true" class="avatar circle" /> </a> </li> </ul> <div data-view-component="true" class="mt-3"> <a text="small" href="/NVIDIA/apex/graphs/contributors" data-view-component="true" class="Link--inTextBlock Link">+ 113 contributors</a></div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3">Languages</h2> <div class="mb-2"> <span data-view-component="true" class="Progress"> <span style="background-color:#3572A5 !important;;width: 49.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#3A4E3A !important;;width: 26.4%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#f34b7d !important;;width: 23.4%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> <span style="background-color:#ededed !important;;width: 1.1%;" itemprop="keywords" data-view-component="true" class="Progress-item color-bg-success-emphasis"></span> </span></div> <ul class="list-style-none"> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/apex/search?l=python" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3572A5;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Python</span> <span>49.1%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/apex/search?l=cuda" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#3A4E3A;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Cuda</span> <span>26.4%</span> </a> </li> <li class="d-inline"> <a class="d-inline-flex flex-items-center flex-nowrap Link--secondary no-underline text-small mr-3" href="/NVIDIA/apex/search?l=c%2B%2B" data-ga-click="Repository, language stats search click, location:repo overview"> <svg style="color:#f34b7d;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">C++</span> <span>23.4%</span> </a> </li> <li class="d-inline"> <span class="d-inline-flex flex-items-center flex-nowrap text-small mr-3"> <svg style="color:#ededed;" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-dot-fill mr-2"> <path d="M8 4a4 4 0 1 1 0 8 4 4 0 0 1 0-8Z"></path> </svg> <span class="color-fg-default text-bold mr-1">Other</span> <span>1.1%</span> </span> </li> </ul> </div> </div> </div> </div> </div></div> </div> </div> </turbo-frame> </main> </div> </div> <footer class="footer pt-8 pb-6 f6 color-fg-muted p-responsive" role="contentinfo" > <h2 class='sr-only'>Footer</h2> <div class="d-flex flex-justify-center flex-items-center flex-column-reverse flex-lg-row flex-wrap flex-lg-nowrap"> <div class="d-flex flex-items-center flex-shrink-0 mx-2"> <a aria-label="Homepage" title="GitHub" class="footer-octicon mr-2" href="https://github.com"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" 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