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17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 12 1Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2Fclearml%2Fclearml-agent" class="HeaderMenu-link HeaderMenu-button d-inline-flex d-lg-none flex-order-1 f5 no-underline border color-border-default rounded-2 px-2 py-1 color-fg-inherit js-prevent-focus-on-mobile-nav" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/clearml/clearml-agent","user_id":null}}" data-hydro-click-hmac="9cbf89fdd6d422c2dbfd4c04769cbec23bef8c67ec7c6dec9a0c35ab76e807ee" data-analytics-event="{"category":"Marketing nav","action":"click to Sign in","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > Sign in </a> </div> </div> <div class="HeaderMenu js-header-menu height-fit position-lg-relative d-lg-flex flex-column flex-auto top-0"> <div class="HeaderMenu-wrapper d-flex flex-column flex-self-start flex-lg-row flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down 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data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"security","context":"product","tag":"link","label":"security_link_product_navbar"}" href="https://github.com/features/security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">Security</div> Find and fix vulnerabilities </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any 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0-.25-.25Z"></path><path d="M10 17.75a.75.75 0 0 1 .75-.75h6.5a.75.75 0 0 1 0 1.5h-6.5a.75.75 0 0 1-.75-.75Zm-4 0a.75.75 0 0 1 .75-.75h.5a.75.75 0 0 1 0 1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 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1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Discussions</div> Collaborate outside of code </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"code_search","context":"product","tag":"link","label":"code_search_link_product_navbar"}" href="https://github.com/features/code-search"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-code-square color-fg-subtle mr-3"> <path d="M10.3 8.24a.75.75 0 0 1-.04 1.06L7.352 12l2.908 2.7a.75.75 0 1 1-1.02 1.1l-3.5-3.25a.75.75 0 0 1 0-1.1l3.5-3.25a.75.75 0 0 1 1.06.04Zm3.44 1.06a.75.75 0 1 1 1.02-1.1l3.5 3.25a.75.75 0 0 1 0 1.1l-3.5 3.25a.75.75 0 1 1-1.02-1.1l2.908-2.7-2.908-2.7Z"></path><path d="M2 3.75C2 2.784 2.784 2 3.75 2h16.5c.966 0 1.75.784 1.75 1.75v16.5A1.75 1.75 0 0 1 20.25 22H3.75A1.75 1.75 0 0 1 2 20.25Zm1.75-.25a.25.25 0 0 0-.25.25v16.5c0 .138.112.25.25.25h16.5a.25.25 0 0 0 .25-.25V3.75a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Search</div> Find more, search less </div> </a></li> </ul> </div> </div> <div class="HeaderMenu-column px-lg-4"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0 border-bottom-0"> <span class="d-block h4 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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> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 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Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" 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 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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="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"trending","context":"open_source","tag":"link","label":"trending_link_open_source_navbar"}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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id="validation-ee8af794-9cf2-4b5b-822b-ab7bb5c57679" hidden="hidden"> <span class="FormControl-inlineValidation--visual"> <svg aria-hidden="true" height="12" viewBox="0 0 12 12" version="1.1" width="12" data-view-component="true" class="octicon octicon-alert-fill"> <path d="M4.855.708c.5-.896 1.79-.896 2.29 0l4.675 8.351a1.312 1.312 0 0 1-1.146 1.954H1.33A1.313 1.313 0 0 1 .183 9.058ZM7 7V3H5v4Zm-1 3a1 1 0 1 0 0-2 1 1 0 0 0 0 2Z"></path> </svg> </span> <span></span> </div> </div> <div data-target="query-builder.screenReaderFeedback" aria-live="polite" aria-atomic="true" class="sr-only"></div> </query-builder></form> <div class="d-flex flex-row color-fg-muted px-3 text-small color-bg-default search-feedback-prompt"> <a target="_blank" href="https://docs.github.com/search-github/github-code-search/understanding-github-code-search-syntax" data-view-component="true" class="Link color-fg-accent text-normal ml-2">Search syntax tips</a> <div class="d-flex flex-1"></div> </div> </div> </div> </div> </modal-dialog></div> </div> <div data-action="click:qbsearch-input#retract" class="dark-backdrop position-fixed" hidden data-target="qbsearch-input.darkBackdrop"></div> <div class="color-fg-default"> <dialog-helper> <dialog data-target="qbsearch-input.feedbackDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg></button> </div> </div> </div> <scrollable-region data-labelled-by="custom-scopes-dialog-title"> <div data-view-component="true" class="Overlay-body"> <div data-target="custom-scopes.customScopesModalDialogFlash"></div> <div hidden class="create-custom-scope-form" data-target="custom-scopes.createCustomScopeForm"> <!-- '"` --><!-- </textarea></xmp> --></option></form><form id="custom-scopes-dialog-form" data-turbo="false" action="/search/custom_scopes" accept-charset="UTF-8" method="post"><input type="hidden" data-csrf="true" name="authenticity_token" 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dir=\"auto\"\u003e\u003ccode\u003e🌟 ClearML is open-source - Leave a star to support the project! 🌟\u003c/code\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eClearML-Agent\u003c/h3\u003e\u003ca id=\"user-content-clearml-agent\" class=\"anchor\" aria-label=\"Permalink: ClearML-Agent\" href=\"#clearml-agent\"\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\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003cem\u003eFormerly known as Trains Agent\u003c/em\u003e\u003c/h4\u003e\u003ca id=\"user-content-formerly-known-as-trains-agent\" class=\"anchor\" aria-label=\"Permalink: Formerly known as Trains Agent\" href=\"#formerly-known-as-trains-agent\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eRun jobs (experiments) on any local or cloud based resource\u003c/li\u003e\n\u003cli\u003eImplement optimized resource utilization policies\u003c/li\u003e\n\u003cli\u003eDeploy execution environments with either virtualenv or fully docker containerized with zero effort\u003c/li\u003e\n\u003cli\u003eLaunch-and-Forget service containers\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://clear.ml/docs/latest/docs/guides/services/aws_autoscaler\" rel=\"nofollow\"\u003eCloud autoscaling\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://clear.ml/docs/latest/docs/guides/services/cleanup_service\" rel=\"nofollow\"\u003eCustomizable cleanup\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eAdvanced \u003ca href=\"https://clear.ml/docs/latest/docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline\" rel=\"nofollow\"\u003epipeline building and execution\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eIt is a zero configuration fire-and-forget execution agent, providing a full ML/DL cluster solution.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eFull Automation in 5 steps\u003c/strong\u003e\u003c/p\u003e\n\u003col dir=\"auto\"\u003e\n\u003cli\u003eClearML Server \u003ca href=\"https://github.com/clearml/clearml-server\"\u003eself-hosted\u003c/a\u003e\nor \u003ca href=\"https://app.clear.ml\" rel=\"nofollow\"\u003efree tier hosting\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ccode\u003epip install clearml-agent\u003c/code\u003e (\u003ca href=\"#installing-the-clearml-agent\"\u003einstall\u003c/a\u003e the ClearML Agent on any GPU machine:\non-premises / cloud / ...)\u003c/li\u003e\n\u003cli\u003eCreate a \u003ca href=\"https://clear.ml/docs/latest/docs/apps/clearml_task\" rel=\"nofollow\"\u003ejob\u003c/a\u003e or\nadd \u003ca href=\"https://github.com/clearml/clearml\"\u003eClearML\u003c/a\u003e to your code with just 2 lines of code\u003c/li\u003e\n\u003cli\u003eChange the \u003ca href=\"#using-the-clearml-agent\"\u003eparameters\u003c/a\u003e in the UI \u0026amp; schedule for \u003ca href=\"#using-the-clearml-agent\"\u003eexecution\u003c/a\u003e (or\nautomate with an \u003ca href=\"#automl-and-orchestration-pipelines-\"\u003eAutoML pipeline\u003c/a\u003e)\u003c/li\u003e\n\u003cli\u003e📉 📈 👀 🍺\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp dir=\"auto\"\u003e\"All the Deep/Machine-Learning DevOps your research needs, and then some... Because ain't nobody got time for that\"\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eTry ClearML now\u003c/strong\u003e \u003ca href=\"https://github.com/clearml/clearml-server\"\u003eSelf Hosted\u003c/a\u003e\nor \u003ca href=\"https://app.clear.ml\" rel=\"nofollow\"\u003eFree tier Hosting\u003c/a\u003e\n\u003ca href=\"https://app.clear.ml\" rel=\"nofollow\"\u003e\u003cimg src=\"https://github.com/clearml/clearml-agent/raw/master/docs/screenshots.gif?raw=true\" width=\"100%\" data-animated-image=\"\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSimple, Flexible Experiment Orchestration\u003c/h3\u003e\u003ca id=\"user-content-simple-flexible-experiment-orchestration\" class=\"anchor\" aria-label=\"Permalink: Simple, Flexible Experiment Orchestration\" href=\"#simple-flexible-experiment-orchestration\"\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\u003eThe ClearML Agent was built to address the DL/ML R\u0026amp;D DevOps needs:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eEasily add \u0026amp; remove machines from the cluster\u003c/li\u003e\n\u003cli\u003eReuse machines without the need for any dedicated containers or images\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eCombine GPU resources across any cloud and on-prem\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNo need for yaml / json / template configuration of any kind\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUser friendly UI\u003c/strong\u003e\u003c/li\u003e\n\u003cli\u003eManageable resource allocation that can be used by researchers and engineers\u003c/li\u003e\n\u003cli\u003eFlexible and controllable scheduler with priority support\u003c/li\u003e\n\u003cli\u003eAutomatic instance spinning in the cloud\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eUsing the ClearML Agent, you can now set up a dynamic cluster with *epsilon DevOps\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e*epsilon - Because we are 📐 and nothing is really zero work\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKubernetes Integration (Optional)\u003c/h3\u003e\u003ca id=\"user-content-kubernetes-integration-optional\" class=\"anchor\" aria-label=\"Permalink: Kubernetes Integration (Optional)\" href=\"#kubernetes-integration-optional\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe think Kubernetes is awesome, but it is not a must to get started with remote execution agents and cluster management.\nWe designed \u003ccode\u003eclearml-agent\u003c/code\u003e so you can run both bare-metal and on top of Kubernetes, in any combination that fits your environment.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can find the Dockerfiles in the \u003ca href=\"/clearml/clearml-agent/blob/master/docker\"\u003edocker folder\u003c/a\u003e and the helm Chart in \u003ca href=\"https://github.com/clearml/clearml-helm-charts\"\u003ehttps://github.com/clearml/clearml-helm-charts\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBenefits of integrating existing Kubernetes cluster with ClearML\u003c/h4\u003e\u003ca id=\"user-content-benefits-of-integrating-existing-kubernetes-cluster-with-clearml\" class=\"anchor\" aria-label=\"Permalink: Benefits of integrating existing Kubernetes cluster with ClearML\" href=\"#benefits-of-integrating-existing-kubernetes-cluster-with-clearml\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eClearML-Agent adds the missing scheduling capabilities to your Kubernetes cluster\u003c/li\u003e\n\u003cli\u003eUsers do not need to have direct Kubernetes access!\u003c/li\u003e\n\u003cli\u003eEasy learning curve with UI and CLI requiring no DevOps knowledge from end users\u003c/li\u003e\n\u003cli\u003eUnlike other solutions, ClearML-Agents work in tandem with other customers of your Kubernetes cluster\u003c/li\u003e\n\u003cli\u003eAllows for more flexible automation from code, building pipelines and visibility\u003c/li\u003e\n\u003cli\u003eA programmatic interface for easy CI/CD workflows, enabling GitOps to trigger jobs inside your cluster\u003c/li\u003e\n\u003cli\u003eSeamless integration with the ClearML ML/DL/GenAI experiment manager\u003c/li\u003e\n\u003cli\u003eWeb UI for customization, scheduling \u0026amp; prioritization of jobs\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEnterprise Features\u003c/strong\u003e: RBAC, vault, multi-tenancy, scheduler, quota management, fractional GPU support\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eRun the agent in Kubernetes Glue mode an map ClearML jobs directly to K8s jobs:\u003c/strong\u003e\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUse the \u003ca href=\"https://github.com/clearml/clearml-helm-charts/tree/main/charts/clearml-agent\"\u003eClearML Agent Helm Chart\u003c/a\u003e to spin an agent pod acting as a controller\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eOr run the \u003ca href=\"https://github.com/clearml/clearml-agent/blob/master/examples/k8s_glue_example.py\"\u003eclearml-k8s glue\u003c/a\u003e on\na Kubernetes cpu node\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe clearml-k8s glue pulls jobs from the ClearML job execution queue and prepares a Kubernetes job (based on provided\nyaml template)\u003c/li\u003e\n\u003cli\u003eInside each pod the clearml-agent will install the job (experiment) environment and spin and monitor the\nexperiment's process, fully visible in the clearml UI\u003c/li\u003e\n\u003cli\u003eBenefits: Kubernetes full view of all running jobs in the system\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eEnterprise Features\u003c/strong\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFull scheduler features added on Top of Kubernetes, with quota/over-quota management, priorities and order.\u003c/li\u003e\n\u003cli\u003eFractional GPU support, allowing multiple isolated containers sharing the same GPU with memory/compute limit per container\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSLURM (Optional)\u003c/h3\u003e\u003ca id=\"user-content-slurm-optional\" class=\"anchor\" aria-label=\"Permalink: SLURM (Optional)\" href=\"#slurm-optional\"\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\"\u003eYes! Slurm integration is available, check the \u003ca href=\"https://clear.ml/docs/latest/docs/clearml_agent/#slurm\" rel=\"nofollow\"\u003edocumentation\u003c/a\u003e for further details\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUsing the ClearML Agent\u003c/h3\u003e\u003ca id=\"user-content-using-the-clearml-agent\" class=\"anchor\" aria-label=\"Permalink: Using the ClearML Agent\" href=\"#using-the-clearml-agent\"\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\u003eFull scale HPC with a click of a button\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe ClearML Agent is a job scheduler that listens on job queue(s), pulls jobs, sets the job environments, executes the\njob and monitors its progress.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAny 'Draft' experiment can be scheduled for execution by a ClearML agent.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eA previously run experiment can be put into 'Draft' state by either of two methods:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUsing the \u003cstrong\u003e'Reset'\u003c/strong\u003e action from the experiment right-click context menu in the ClearML UI - This will clear any\nresults and artifacts the previous run had created.\u003c/li\u003e\n\u003cli\u003eUsing the \u003cstrong\u003e'Clone'\u003c/strong\u003e action from the experiment right-click context menu in the ClearML UI - This will create a new\n'Draft' experiment with the same configuration as the original experiment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eAn experiment is scheduled for execution using the \u003cstrong\u003e'Enqueue'\u003c/strong\u003e action from the experiment right-click context menu in\nthe ClearML UI and selecting the execution queue.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSee \u003ca href=\"#from-scratch\"\u003ecreating an experiment and enqueuing it for execution\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eOnce an experiment is enqueued, it will be picked up and executed by a ClearML Agent monitoring this queue.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe ClearML UI Workers \u0026amp; Queues page provides ongoing execution information:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eWorkers Tab: Monitor you cluster\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eReview available resources\u003c/li\u003e\n\u003cli\u003eMonitor machines statistics (CPU / GPU / Disk / Network)\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eQueues Tab:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eControl the scheduling order of jobs\u003c/li\u003e\n\u003cli\u003eCancel or abort job execution\u003c/li\u003e\n\u003cli\u003eMove jobs between execution queues\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhat The ClearML Agent Actually Does\u003c/h4\u003e\u003ca id=\"user-content-what-the-clearml-agent-actually-does\" class=\"anchor\" aria-label=\"Permalink: What The ClearML Agent Actually Does\" href=\"#what-the-clearml-agent-actually-does\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe ClearML Agent executes experiments using the following process:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCreate a new virtual environment (or launch the selected docker image)\u003c/li\u003e\n\u003cli\u003eClone the code into the virtual-environment (or inside the docker)\u003c/li\u003e\n\u003cli\u003eInstall python packages based on the package requirements listed for the experiment\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSpecial note for PyTorch: The ClearML Agent will automatically select the torch packages based on the CUDA_VERSION\nenvironment variable of the machine\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eExecute the code, while monitoring the process\u003c/li\u003e\n\u003cli\u003eLog all stdout/stderr in the ClearML UI, including the cloning and installation process, for easy debugging\u003c/li\u003e\n\u003cli\u003eMonitor the execution and allow you to manually abort the job using the ClearML UI (or, in the unfortunate case of a\ncode crash, catch the error and signal the experiment has failed)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSystem Design \u0026amp; Flow\u003c/h4\u003e\u003ca id=\"user-content-system-design--flow\" class=\"anchor\" aria-label=\"Permalink: System Design \u0026amp; Flow\" href=\"#system-design--flow\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/clearml/clearml-agent/blob/master/docs/clearml_architecture.png\"\u003e\u003cimg src=\"https://github.com/clearml/clearml-agent/raw/master/docs/clearml_architecture.png\" width=\"100%\" alt=\"clearml-architecture\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstalling the ClearML Agent\u003c/h4\u003e\u003ca id=\"user-content-installing-the-clearml-agent\" class=\"anchor\" aria-label=\"Permalink: Installing the ClearML Agent\" href=\"#installing-the-clearml-agent\"\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=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install clearml-agent\"\u003e\u003cpre\u003epip install clearml-agent\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eClearML Agent Usage Examples\u003c/h4\u003e\u003ca id=\"user-content-clearml-agent-usage-examples\" class=\"anchor\" aria-label=\"Permalink: ClearML Agent Usage Examples\" href=\"#clearml-agent-usage-examples\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFull Interface and capabilities are available with\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent --help\nclearml-agent daemon --help\"\u003e\u003cpre\u003eclearml-agent --help\nclearml-agent daemon --help\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConfiguring the ClearML Agent\u003c/h4\u003e\u003ca id=\"user-content-configuring-the-clearml-agent\" class=\"anchor\" aria-label=\"Permalink: Configuring the ClearML Agent\" href=\"#configuring-the-clearml-agent\"\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=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent init\"\u003e\u003cpre\u003eclearml-agent init\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote: The ClearML Agent uses a cache folder to cache pip packages, apt packages and cloned repositories. The default\nClearML Agent cache folder is \u003ccode\u003e~/.clearml\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSee full details in your configuration file at \u003ccode\u003e~/clearml.conf\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote: The \u003cstrong\u003eClearML Agent\u003c/strong\u003e extends the \u003cstrong\u003eClearML\u003c/strong\u003e configuration file \u003ccode\u003e~/clearml.conf\u003c/code\u003e.\nThey are designed to share the same configuration file, see example \u003ca href=\"/clearml/clearml-agent/blob/master/docs/clearml.conf\"\u003ehere\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRunning the ClearML Agent\u003c/h4\u003e\u003ca id=\"user-content-running-the-clearml-agent\" class=\"anchor\" aria-label=\"Permalink: Running the ClearML Agent\" href=\"#running-the-clearml-agent\"\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 debug and experimentation, start the ClearML agent in \u003ccode\u003eforeground\u003c/code\u003e mode, where all the output is printed to screen:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --queue default --foreground\"\u003e\u003cpre\u003eclearml-agent daemon --queue default --foreground\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor actual service mode, all the stdout will be stored automatically into a temporary file (no need to pipe).\nNotice: with \u003ccode\u003e--detached\u003c/code\u003e flag, the \u003cem\u003eclearml-agent\u003c/em\u003e will be running in the background\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --detached --queue default\"\u003e\u003cpre\u003eclearml-agent daemon --detached --queue default\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eGPU allocation is controlled via the standard OS environment \u003ccode\u003eNVIDIA_VISIBLE_DEVICES\u003c/code\u003e or \u003ccode\u003e--gpus\u003c/code\u003e flag (or disabled\nwith \u003ccode\u003e--cpu-only\u003c/code\u003e).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf no flag is set, and \u003ccode\u003eNVIDIA_VISIBLE_DEVICES\u003c/code\u003e variable doesn't exist, all GPUs will be allocated for\nthe \u003ccode\u003eclearml-agent\u003c/code\u003e. \u003cbr\u003e\nIf \u003ccode\u003e--cpu-only\u003c/code\u003e flag is set, or \u003ccode\u003eNVIDIA_VISIBLE_DEVICES=\"none\"\u003c/code\u003e, no gpu will be allocated for\nthe \u003ccode\u003eclearml-agent\u003c/code\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eExample: spin two agents, one per GPU on the same machine:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNotice: with \u003ccode\u003e--detached\u003c/code\u003e flag, the \u003cem\u003eclearml-agent\u003c/em\u003e will run in the background\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --detached --gpus 0 --queue default\nclearml-agent daemon --detached --gpus 1 --queue default\"\u003e\u003cpre\u003eclearml-agent daemon --detached --gpus 0 --queue default\nclearml-agent daemon --detached --gpus 1 --queue default\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample: spin two agents, pulling from dedicated \u003ccode\u003edual_gpu\u003c/code\u003e queue, two GPUs per agent\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu\nclearml-agent daemon --detached --gpus 2,3 --queue dual_gpu\"\u003e\u003cpre\u003eclearml-agent daemon --detached --gpus 0,1 --queue dual_gpu\nclearml-agent daemon --detached --gpus 2,3 --queue dual_gpu\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStarting the ClearML Agent in docker mode\u003c/h5\u003e\u003ca id=\"user-content-starting-the-clearml-agent-in-docker-mode\" class=\"anchor\" aria-label=\"Permalink: Starting the ClearML Agent in docker mode\" href=\"#starting-the-clearml-agent-in-docker-mode\"\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 debug and experimentation, start the ClearML agent in \u003ccode\u003eforeground\u003c/code\u003e mode, where all the output is printed to screen\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --queue default --docker --foreground\"\u003e\u003cpre\u003eclearml-agent daemon --queue default --docker --foreground\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor actual service mode, all the stdout will be stored automatically into a file (no need to pipe).\nNotice: with \u003ccode\u003e--detached\u003c/code\u003e flag, the \u003cem\u003eclearml-agent\u003c/em\u003e will run in the background\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --detached --queue default --docker\"\u003e\u003cpre\u003eclearml-agent daemon --detached --queue default --docker\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample: spin two agents, one per gpu on the same machine, with default \u003ccode\u003envidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\u003c/code\u003e\ndocker:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\nclearml-agent daemon --detached --gpus 1 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\"\u003e\u003cpre\u003eclearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\nclearml-agent daemon --detached --gpus 1 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eExample: spin two agents, pulling from dedicated \u003ccode\u003edual_gpu\u003c/code\u003e queue, two GPUs per agent, with default\n\u003ccode\u003envidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\u003c/code\u003e docker:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\nclearml-agent daemon --detached --gpus 2,3 --queue dual_gpu --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\"\u003e\u003cpre\u003eclearml-agent daemon --detached --gpus 0,1 --queue dual_gpu --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\nclearml-agent daemon --detached --gpus 2,3 --queue dual_gpu --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStarting the ClearML Agent - Priority Queues\u003c/h5\u003e\u003ca id=\"user-content-starting-the-clearml-agent---priority-queues\" class=\"anchor\" aria-label=\"Permalink: Starting the ClearML Agent - Priority Queues\" href=\"#starting-the-clearml-agent---priority-queues\"\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\"\u003ePriority Queues are also supported, example use case:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHigh priority queue: \u003ccode\u003eimportant_jobs\u003c/code\u003e, low priority queue: \u003ccode\u003edefault\u003c/code\u003e\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --queue important_jobs default\"\u003e\u003cpre\u003eclearml-agent daemon --queue important_jobs default\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003cstrong\u003eClearML Agent\u003c/strong\u003e will first try to pull jobs from the \u003ccode\u003eimportant_jobs\u003c/code\u003e queue, and only if it is empty, the agent\nwill try to pull from the \u003ccode\u003edefault\u003c/code\u003e queue.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAdding queues, managing job order within a queue, and moving jobs between queues, is available using the Web UI, see\nexample on our \u003ca href=\"https://app.clear.ml/workers-and-queues/queues\" rel=\"nofollow\"\u003efree server\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch5 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eStopping the ClearML Agent\u003c/h5\u003e\u003ca id=\"user-content-stopping-the-clearml-agent\" class=\"anchor\" aria-label=\"Permalink: Stopping the ClearML Agent\" href=\"#stopping-the-clearml-agent\"\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 stop a \u003cstrong\u003eClearML Agent\u003c/strong\u003e running in the background, run the same command line used to start the agent with \u003ccode\u003e--stop\u003c/code\u003e\nappended. For example, to stop the first of the above shown same machine, single gpu agents:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 --stop\"\u003e\u003cpre\u003eclearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 --stop\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow do I create an experiment on the ClearML Server? \u003ca name=\"user-content-from-scratch\"\u003e\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-how-do-i-create-an-experiment-on-the-clearml-server-\" class=\"anchor\" aria-label=\"Permalink: How do I create an experiment on the ClearML Server? \" href=\"#how-do-i-create-an-experiment-on-the-clearml-server-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIntegrate \u003ca href=\"https://github.com/clearml/clearml\"\u003eClearML\u003c/a\u003e with your code\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eExecute the code on your machine (Manually / PyCharm / Jupyter Notebook)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eAs your code is running, \u003cstrong\u003eClearML\u003c/strong\u003e creates an experiment logging all the necessary execution information:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eGit repository link and commit ID (or an entire jupyter notebook)\u003c/li\u003e\n\u003cli\u003eGit diff (we’re not saying you never commit and push, but still...)\u003c/li\u003e\n\u003cli\u003ePython packages used by your code (including specific versions used)\u003c/li\u003e\n\u003cli\u003eHyperparameters\u003c/li\u003e\n\u003cli\u003eInput artifacts\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eYou now have a 'template' of your experiment with everything required for automated execution\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIn the ClearML UI, right-click on the experiment and select 'clone'. A copy of your experiment will be created.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eYou now have a new draft experiment cloned from your original experiment, feel free to edit it\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eChange the hyperparameters\u003c/li\u003e\n\u003cli\u003eSwitch to the latest code base of the repository\u003c/li\u003e\n\u003cli\u003eUpdate package versions\u003c/li\u003e\n\u003cli\u003eSelect a specific docker image to run in (see docker execution mode section)\u003c/li\u003e\n\u003cli\u003eOr simply change nothing to run the same experiment again...\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eSchedule the newly created experiment for execution: right-click the experiment and select 'enqueue'\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eClearML-Agent Services Mode \u003ca name=\"user-content-services\"\u003e\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-clearml-agent-services-mode-\" class=\"anchor\" aria-label=\"Permalink: ClearML-Agent Services Mode \" href=\"#clearml-agent-services-mode-\"\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\"\u003eClearML-Agent Services is a special mode of ClearML-Agent that provides the ability to launch long-lasting jobs that\npreviously had to be executed on local / dedicated machines. It allows a single agent to launch multiple dockers (Tasks)\nfor different use cases:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAuto-scaler service (spinning instances when the need arises and the budget allows)\u003c/li\u003e\n\u003cli\u003eControllers (Implementing pipelines and more sophisticated DevOps logic)\u003c/li\u003e\n\u003cli\u003eOptimizer (such as Hyperparameter Optimization or sweeping)\u003c/li\u003e\n\u003cli\u003eApplication (such as interactive Bokeh apps for increased data transparency)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eClearML-Agent Services mode will spin \u003cstrong\u003eany\u003c/strong\u003e task enqueued into the specified queue. Every task launched by\nClearML-Agent Services will be registered as a new node in the system, providing tracking and transparency capabilities.\nCurrently, clearml-agent in services-mode supports CPU only configuration. ClearML-Agent services mode can be launched\nalongside GPU agents.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"clearml-agent daemon --services-mode --detached --queue services --create-queue --docker ubuntu:18.04 --cpu-only\"\u003e\u003cpre\u003eclearml-agent daemon --services-mode --detached --queue services --create-queue --docker ubuntu:18.04 --cpu-only\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: It is the user's responsibility to make sure the proper tasks are pushed into the specified queue.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAutoML and Orchestration Pipelines \u003ca name=\"user-content-automl-pipes\"\u003e\u003c/a\u003e\u003c/h3\u003e\u003ca id=\"user-content-automl-and-orchestration-pipelines-\" class=\"anchor\" aria-label=\"Permalink: AutoML and Orchestration Pipelines \" href=\"#automl-and-orchestration-pipelines-\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe ClearML Agent can also be used to implement AutoML orchestration and Experiment Pipelines in conjunction with the\nClearML package.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eSample AutoML \u0026amp; Orchestration examples can be found in the\nClearML \u003ca href=\"https://github.com/clearml/clearml/tree/master/examples/automation\"\u003eexample/automation\u003c/a\u003e folder.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAutoML examples:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/clearml/clearml/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py\"\u003eToy Keras training experiment\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIn order to create an experiment-template in the system, this code must be executed once manually\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/clearml/clearml/blob/master/examples/automation/manual_random_param_search_example.py\"\u003eRandom Search over the above Keras experiment-template\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThis example will create multiple copies of the Keras experiment-template, with different hyperparameter\ncombinations\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eExperiment Pipeline examples:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/clearml/clearml/blob/master/examples/automation/task_piping_example.py\"\u003eFirst step experiment\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThis example will \"process data\", and once done, will launch a copy of the 'second step' experiment-template\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/clearml/clearml/blob/master/examples/automation/toy_base_task.py\"\u003eSecond step experiment\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIn order to create an experiment-template in the system, this code must be executed once manually\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicense\u003c/h3\u003e\u003ca id=\"user-content-license\" class=\"anchor\" aria-label=\"Permalink: License\" href=\"#license\"\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\"\u003eApache License, Version 2.0 (see the \u003ca href=\"https://www.apache.org/licenses/LICENSE-2.0.html\" rel=\"nofollow\"\u003eLICENSE\u003c/a\u003e for more information)\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":3,"text":"ClearML-Agent","anchor":"clearml-agent","htmlText":"ClearML-Agent"},{"level":4,"text":"Formerly known as Trains Agent","anchor":"formerly-known-as-trains-agent","htmlText":"Formerly known as Trains Agent"},{"level":3,"text":"Simple, Flexible Experiment Orchestration","anchor":"simple-flexible-experiment-orchestration","htmlText":"Simple, Flexible Experiment Orchestration"},{"level":3,"text":"Kubernetes Integration (Optional)","anchor":"kubernetes-integration-optional","htmlText":"Kubernetes Integration (Optional)"},{"level":4,"text":"Benefits of integrating existing Kubernetes cluster with ClearML","anchor":"benefits-of-integrating-existing-kubernetes-cluster-with-clearml","htmlText":"Benefits of integrating existing Kubernetes cluster with ClearML"},{"level":3,"text":"SLURM (Optional)","anchor":"slurm-optional","htmlText":"SLURM (Optional)"},{"level":3,"text":"Using the ClearML Agent","anchor":"using-the-clearml-agent","htmlText":"Using the ClearML Agent"},{"level":4,"text":"What The ClearML Agent Actually Does","anchor":"what-the-clearml-agent-actually-does","htmlText":"What The ClearML Agent Actually Does"},{"level":4,"text":"System Design \u0026 Flow","anchor":"system-design--flow","htmlText":"System Design \u0026amp; Flow"},{"level":4,"text":"Installing the ClearML Agent","anchor":"installing-the-clearml-agent","htmlText":"Installing the ClearML Agent"},{"level":4,"text":"ClearML Agent Usage Examples","anchor":"clearml-agent-usage-examples","htmlText":"ClearML Agent Usage Examples"},{"level":4,"text":"Configuring the ClearML Agent","anchor":"configuring-the-clearml-agent","htmlText":"Configuring the ClearML Agent"},{"level":4,"text":"Running the ClearML Agent","anchor":"running-the-clearml-agent","htmlText":"Running the ClearML Agent"},{"level":5,"text":"Starting the ClearML Agent in docker mode","anchor":"starting-the-clearml-agent-in-docker-mode","htmlText":"Starting the ClearML Agent in docker mode"},{"level":5,"text":"Starting the ClearML Agent - Priority Queues","anchor":"starting-the-clearml-agent---priority-queues","htmlText":"Starting the ClearML Agent - Priority Queues"},{"level":5,"text":"Stopping the ClearML Agent","anchor":"stopping-the-clearml-agent","htmlText":"Stopping the ClearML Agent"},{"level":3,"text":"How do I create an experiment on the ClearML Server? ","anchor":"how-do-i-create-an-experiment-on-the-clearml-server-","htmlText":"How do I create an experiment on the ClearML Server? 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<div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ClearML-Agent</h3><a id="user-content-clearml-agent" class="anchor" aria-label="Permalink: ClearML-Agent" href="#clearml-agent"><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"><h4 tabindex="-1" class="heading-element" dir="auto"><em>Formerly known as Trains Agent</em></h4><a id="user-content-formerly-known-as-trains-agent" class="anchor" aria-label="Permalink: Formerly known as Trains Agent" href="#formerly-known-as-trains-agent"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Run jobs (experiments) on any local or cloud based resource</li> <li>Implement optimized resource utilization policies</li> <li>Deploy execution environments with either virtualenv or fully docker containerized with zero effort</li> <li>Launch-and-Forget service containers</li> <li><a href="https://clear.ml/docs/latest/docs/guides/services/aws_autoscaler" rel="nofollow">Cloud autoscaling</a></li> <li><a href="https://clear.ml/docs/latest/docs/guides/services/cleanup_service" rel="nofollow">Customizable cleanup</a></li> <li>Advanced <a href="https://clear.ml/docs/latest/docs/guides/frameworks/pytorch/notebooks/table/tabular_training_pipeline" rel="nofollow">pipeline building and execution</a></li> </ul> <p dir="auto">It is a zero configuration fire-and-forget execution agent, providing a full ML/DL cluster solution.</p> <p dir="auto"><strong>Full Automation in 5 steps</strong></p> <ol dir="auto"> <li>ClearML Server <a href="https://github.com/clearml/clearml-server">self-hosted</a> or <a href="https://app.clear.ml" rel="nofollow">free tier hosting</a></li> <li><code>pip install clearml-agent</code> (<a href="#installing-the-clearml-agent">install</a> the ClearML Agent on any GPU machine: on-premises / cloud / ...)</li> <li>Create a <a href="https://clear.ml/docs/latest/docs/apps/clearml_task" rel="nofollow">job</a> or add <a href="https://github.com/clearml/clearml">ClearML</a> to your code with just 2 lines of code</li> <li>Change the <a href="#using-the-clearml-agent">parameters</a> in the UI & schedule for <a href="#using-the-clearml-agent">execution</a> (or automate with an <a href="#automl-and-orchestration-pipelines-">AutoML pipeline</a>)</li> <li>📉 📈 👀 🍺</li> </ol> <p dir="auto">"All the Deep/Machine-Learning DevOps your research needs, and then some... Because ain't nobody got time for that"</p> <p dir="auto"><strong>Try ClearML now</strong> <a href="https://github.com/clearml/clearml-server">Self Hosted</a> or <a href="https://app.clear.ml" rel="nofollow">Free tier Hosting</a> <a href="https://app.clear.ml" rel="nofollow"><img src="https://github.com/clearml/clearml-agent/raw/master/docs/screenshots.gif?raw=true" width="100%" data-animated-image="" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Simple, Flexible Experiment Orchestration</h3><a id="user-content-simple-flexible-experiment-orchestration" class="anchor" aria-label="Permalink: Simple, Flexible Experiment Orchestration" href="#simple-flexible-experiment-orchestration"><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>The ClearML Agent was built to address the DL/ML R&D DevOps needs:</strong></p> <ul dir="auto"> <li>Easily add & remove machines from the cluster</li> <li>Reuse machines without the need for any dedicated containers or images</li> <li><strong>Combine GPU resources across any cloud and on-prem</strong></li> <li><strong>No need for yaml / json / template configuration of any kind</strong></li> <li><strong>User friendly UI</strong></li> <li>Manageable resource allocation that can be used by researchers and engineers</li> <li>Flexible and controllable scheduler with priority support</li> <li>Automatic instance spinning in the cloud</li> </ul> <p dir="auto"><strong>Using the ClearML Agent, you can now set up a dynamic cluster with *epsilon DevOps</strong></p> <p dir="auto">*epsilon - Because we are 📐 and nothing is really zero work</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Kubernetes Integration (Optional)</h3><a id="user-content-kubernetes-integration-optional" class="anchor" aria-label="Permalink: Kubernetes Integration (Optional)" href="#kubernetes-integration-optional"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We think Kubernetes is awesome, but it is not a must to get started with remote execution agents and cluster management. We designed <code>clearml-agent</code> so you can run both bare-metal and on top of Kubernetes, in any combination that fits your environment.</p> <p dir="auto">You can find the Dockerfiles in the <a href="/clearml/clearml-agent/blob/master/docker">docker folder</a> and the helm Chart in <a href="https://github.com/clearml/clearml-helm-charts">https://github.com/clearml/clearml-helm-charts</a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Benefits of integrating existing Kubernetes cluster with ClearML</h4><a id="user-content-benefits-of-integrating-existing-kubernetes-cluster-with-clearml" class="anchor" aria-label="Permalink: Benefits of integrating existing Kubernetes cluster with ClearML" href="#benefits-of-integrating-existing-kubernetes-cluster-with-clearml"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>ClearML-Agent adds the missing scheduling capabilities to your Kubernetes cluster</li> <li>Users do not need to have direct Kubernetes access!</li> <li>Easy learning curve with UI and CLI requiring no DevOps knowledge from end users</li> <li>Unlike other solutions, ClearML-Agents work in tandem with other customers of your Kubernetes cluster</li> <li>Allows for more flexible automation from code, building pipelines and visibility</li> <li>A programmatic interface for easy CI/CD workflows, enabling GitOps to trigger jobs inside your cluster</li> <li>Seamless integration with the ClearML ML/DL/GenAI experiment manager</li> <li>Web UI for customization, scheduling & prioritization of jobs</li> <li><strong>Enterprise Features</strong>: RBAC, vault, multi-tenancy, scheduler, quota management, fractional GPU support</li> </ul> <p dir="auto"><strong>Run the agent in Kubernetes Glue mode an map ClearML jobs directly to K8s jobs:</strong></p> <ul dir="auto"> <li>Use the <a href="https://github.com/clearml/clearml-helm-charts/tree/main/charts/clearml-agent">ClearML Agent Helm Chart</a> to spin an agent pod acting as a controller <ul dir="auto"> <li>Or run the <a href="https://github.com/clearml/clearml-agent/blob/master/examples/k8s_glue_example.py">clearml-k8s glue</a> on a Kubernetes cpu node</li> </ul> </li> <li>The clearml-k8s glue pulls jobs from the ClearML job execution queue and prepares a Kubernetes job (based on provided yaml template)</li> <li>Inside each pod the clearml-agent will install the job (experiment) environment and spin and monitor the experiment's process, fully visible in the clearml UI</li> <li>Benefits: Kubernetes full view of all running jobs in the system</li> <li><strong>Enterprise Features</strong> <ul dir="auto"> <li>Full scheduler features added on Top of Kubernetes, with quota/over-quota management, priorities and order.</li> <li>Fractional GPU support, allowing multiple isolated containers sharing the same GPU with memory/compute limit per container</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">SLURM (Optional)</h3><a id="user-content-slurm-optional" class="anchor" aria-label="Permalink: SLURM (Optional)" href="#slurm-optional"><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">Yes! Slurm integration is available, check the <a href="https://clear.ml/docs/latest/docs/clearml_agent/#slurm" rel="nofollow">documentation</a> for further details</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Using the ClearML Agent</h3><a id="user-content-using-the-clearml-agent" class="anchor" aria-label="Permalink: Using the ClearML Agent" href="#using-the-clearml-agent"><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>Full scale HPC with a click of a button</strong></p> <p dir="auto">The ClearML Agent is a job scheduler that listens on job queue(s), pulls jobs, sets the job environments, executes the job and monitors its progress.</p> <p dir="auto">Any 'Draft' experiment can be scheduled for execution by a ClearML agent.</p> <p dir="auto">A previously run experiment can be put into 'Draft' state by either of two methods:</p> <ul dir="auto"> <li>Using the <strong>'Reset'</strong> action from the experiment right-click context menu in the ClearML UI - This will clear any results and artifacts the previous run had created.</li> <li>Using the <strong>'Clone'</strong> action from the experiment right-click context menu in the ClearML UI - This will create a new 'Draft' experiment with the same configuration as the original experiment.</li> </ul> <p dir="auto">An experiment is scheduled for execution using the <strong>'Enqueue'</strong> action from the experiment right-click context menu in the ClearML UI and selecting the execution queue.</p> <p dir="auto">See <a href="#from-scratch">creating an experiment and enqueuing it for execution</a>.</p> <p dir="auto">Once an experiment is enqueued, it will be picked up and executed by a ClearML Agent monitoring this queue.</p> <p dir="auto">The ClearML UI Workers & Queues page provides ongoing execution information:</p> <ul dir="auto"> <li>Workers Tab: Monitor you cluster <ul dir="auto"> <li>Review available resources</li> <li>Monitor machines statistics (CPU / GPU / Disk / Network)</li> </ul> </li> <li>Queues Tab: <ul dir="auto"> <li>Control the scheduling order of jobs</li> <li>Cancel or abort job execution</li> <li>Move jobs between execution queues</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">What The ClearML Agent Actually Does</h4><a id="user-content-what-the-clearml-agent-actually-does" class="anchor" aria-label="Permalink: What The ClearML Agent Actually Does" href="#what-the-clearml-agent-actually-does"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The ClearML Agent executes experiments using the following process:</p> <ul dir="auto"> <li>Create a new virtual environment (or launch the selected docker image)</li> <li>Clone the code into the virtual-environment (or inside the docker)</li> <li>Install python packages based on the package requirements listed for the experiment <ul dir="auto"> <li>Special note for PyTorch: The ClearML Agent will automatically select the torch packages based on the CUDA_VERSION environment variable of the machine</li> </ul> </li> <li>Execute the code, while monitoring the process</li> <li>Log all stdout/stderr in the ClearML UI, including the cloning and installation process, for easy debugging</li> <li>Monitor the execution and allow you to manually abort the job using the ClearML UI (or, in the unfortunate case of a code crash, catch the error and signal the experiment has failed)</li> </ul> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">System Design & Flow</h4><a id="user-content-system-design--flow" class="anchor" aria-label="Permalink: System Design & Flow" href="#system-design--flow"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/clearml/clearml-agent/blob/master/docs/clearml_architecture.png"><img src="https://github.com/clearml/clearml-agent/raw/master/docs/clearml_architecture.png" width="100%" alt="clearml-architecture" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Installing the ClearML Agent</h4><a id="user-content-installing-the-clearml-agent" class="anchor" aria-label="Permalink: Installing the ClearML Agent" href="#installing-the-clearml-agent"><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="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install clearml-agent"><pre>pip install clearml-agent</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">ClearML Agent Usage Examples</h4><a id="user-content-clearml-agent-usage-examples" class="anchor" aria-label="Permalink: ClearML Agent Usage Examples" href="#clearml-agent-usage-examples"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Full Interface and capabilities are available with</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent --help clearml-agent daemon --help"><pre>clearml-agent --help clearml-agent daemon --help</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Configuring the ClearML Agent</h4><a id="user-content-configuring-the-clearml-agent" class="anchor" aria-label="Permalink: Configuring the ClearML Agent" href="#configuring-the-clearml-agent"><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="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent init"><pre>clearml-agent init</pre></div> <p dir="auto">Note: The ClearML Agent uses a cache folder to cache pip packages, apt packages and cloned repositories. The default ClearML Agent cache folder is <code>~/.clearml</code>.</p> <p dir="auto">See full details in your configuration file at <code>~/clearml.conf</code>.</p> <p dir="auto">Note: The <strong>ClearML Agent</strong> extends the <strong>ClearML</strong> configuration file <code>~/clearml.conf</code>. They are designed to share the same configuration file, see example <a href="/clearml/clearml-agent/blob/master/docs/clearml.conf">here</a></p> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Running the ClearML Agent</h4><a id="user-content-running-the-clearml-agent" class="anchor" aria-label="Permalink: Running the ClearML Agent" href="#running-the-clearml-agent"><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 debug and experimentation, start the ClearML agent in <code>foreground</code> mode, where all the output is printed to screen:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --queue default --foreground"><pre>clearml-agent daemon --queue default --foreground</pre></div> <p dir="auto">For actual service mode, all the stdout will be stored automatically into a temporary file (no need to pipe). Notice: with <code>--detached</code> flag, the <em>clearml-agent</em> will be running in the background</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --detached --queue default"><pre>clearml-agent daemon --detached --queue default</pre></div> <p dir="auto">GPU allocation is controlled via the standard OS environment <code>NVIDIA_VISIBLE_DEVICES</code> or <code>--gpus</code> flag (or disabled with <code>--cpu-only</code>).</p> <p dir="auto">If no flag is set, and <code>NVIDIA_VISIBLE_DEVICES</code> variable doesn't exist, all GPUs will be allocated for the <code>clearml-agent</code>. <br> If <code>--cpu-only</code> flag is set, or <code>NVIDIA_VISIBLE_DEVICES="none"</code>, no gpu will be allocated for the <code>clearml-agent</code>.</p> <p dir="auto">Example: spin two agents, one per GPU on the same machine:</p> <p dir="auto">Notice: with <code>--detached</code> flag, the <em>clearml-agent</em> will run in the background</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --detached --gpus 0 --queue default clearml-agent daemon --detached --gpus 1 --queue default"><pre>clearml-agent daemon --detached --gpus 0 --queue default clearml-agent daemon --detached --gpus 1 --queue default</pre></div> <p dir="auto">Example: spin two agents, pulling from dedicated <code>dual_gpu</code> queue, two GPUs per agent</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu"><pre>clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu</pre></div> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Starting the ClearML Agent in docker mode</h5><a id="user-content-starting-the-clearml-agent-in-docker-mode" class="anchor" aria-label="Permalink: Starting the ClearML Agent in docker mode" href="#starting-the-clearml-agent-in-docker-mode"><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 debug and experimentation, start the ClearML agent in <code>foreground</code> mode, where all the output is printed to screen</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --queue default --docker --foreground"><pre>clearml-agent daemon --queue default --docker --foreground</pre></div> <p dir="auto">For actual service mode, all the stdout will be stored automatically into a file (no need to pipe). Notice: with <code>--detached</code> flag, the <em>clearml-agent</em> will run in the background</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --detached --queue default --docker"><pre>clearml-agent daemon --detached --queue default --docker</pre></div> <p dir="auto">Example: spin two agents, one per gpu on the same machine, with default <code>nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04</code> docker:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 clearml-agent daemon --detached --gpus 1 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04"><pre>clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 clearml-agent daemon --detached --gpus 1 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04</pre></div> <p dir="auto">Example: spin two agents, pulling from dedicated <code>dual_gpu</code> queue, two GPUs per agent, with default <code>nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04</code> docker:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04"><pre>clearml-agent daemon --detached --gpus 0,1 --queue dual_gpu --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 clearml-agent daemon --detached --gpus 2,3 --queue dual_gpu --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04</pre></div> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Starting the ClearML Agent - Priority Queues</h5><a id="user-content-starting-the-clearml-agent---priority-queues" class="anchor" aria-label="Permalink: Starting the ClearML Agent - Priority Queues" href="#starting-the-clearml-agent---priority-queues"><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">Priority Queues are also supported, example use case:</p> <p dir="auto">High priority queue: <code>important_jobs</code>, low priority queue: <code>default</code></p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --queue important_jobs default"><pre>clearml-agent daemon --queue important_jobs default</pre></div> <p dir="auto">The <strong>ClearML Agent</strong> will first try to pull jobs from the <code>important_jobs</code> queue, and only if it is empty, the agent will try to pull from the <code>default</code> queue.</p> <p dir="auto">Adding queues, managing job order within a queue, and moving jobs between queues, is available using the Web UI, see example on our <a href="https://app.clear.ml/workers-and-queues/queues" rel="nofollow">free server</a></p> <div class="markdown-heading" dir="auto"><h5 tabindex="-1" class="heading-element" dir="auto">Stopping the ClearML Agent</h5><a id="user-content-stopping-the-clearml-agent" class="anchor" aria-label="Permalink: Stopping the ClearML Agent" href="#stopping-the-clearml-agent"><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 stop a <strong>ClearML Agent</strong> running in the background, run the same command line used to start the agent with <code>--stop</code> appended. For example, to stop the first of the above shown same machine, single gpu agents:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 --stop"><pre>clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:11.0.3-cudnn8-runtime-ubuntu20.04 --stop</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How do I create an experiment on the ClearML Server? <a name="user-content-from-scratch"></a></h3><a id="user-content-how-do-i-create-an-experiment-on-the-clearml-server-" class="anchor" aria-label="Permalink: How do I create an experiment on the ClearML Server? " href="#how-do-i-create-an-experiment-on-the-clearml-server-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto">Integrate <a href="https://github.com/clearml/clearml">ClearML</a> with your code</p> </li> <li> <p dir="auto">Execute the code on your machine (Manually / PyCharm / Jupyter Notebook)</p> </li> <li> <p dir="auto">As your code is running, <strong>ClearML</strong> creates an experiment logging all the necessary execution information:</p> <ul dir="auto"> <li>Git repository link and commit ID (or an entire jupyter notebook)</li> <li>Git diff (we’re not saying you never commit and push, but still...)</li> <li>Python packages used by your code (including specific versions used)</li> <li>Hyperparameters</li> <li>Input artifacts</li> </ul> <p dir="auto">You now have a 'template' of your experiment with everything required for automated execution</p> </li> <li> <p dir="auto">In the ClearML UI, right-click on the experiment and select 'clone'. A copy of your experiment will be created.</p> </li> <li> <p dir="auto">You now have a new draft experiment cloned from your original experiment, feel free to edit it</p> <ul dir="auto"> <li>Change the hyperparameters</li> <li>Switch to the latest code base of the repository</li> <li>Update package versions</li> <li>Select a specific docker image to run in (see docker execution mode section)</li> <li>Or simply change nothing to run the same experiment again...</li> </ul> </li> <li> <p dir="auto">Schedule the newly created experiment for execution: right-click the experiment and select 'enqueue'</p> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">ClearML-Agent Services Mode <a name="user-content-services"></a></h3><a id="user-content-clearml-agent-services-mode-" class="anchor" aria-label="Permalink: ClearML-Agent Services Mode " href="#clearml-agent-services-mode-"><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">ClearML-Agent Services is a special mode of ClearML-Agent that provides the ability to launch long-lasting jobs that previously had to be executed on local / dedicated machines. It allows a single agent to launch multiple dockers (Tasks) for different use cases:</p> <ul dir="auto"> <li>Auto-scaler service (spinning instances when the need arises and the budget allows)</li> <li>Controllers (Implementing pipelines and more sophisticated DevOps logic)</li> <li>Optimizer (such as Hyperparameter Optimization or sweeping)</li> <li>Application (such as interactive Bokeh apps for increased data transparency)</li> </ul> <p dir="auto">ClearML-Agent Services mode will spin <strong>any</strong> task enqueued into the specified queue. Every task launched by ClearML-Agent Services will be registered as a new node in the system, providing tracking and transparency capabilities. Currently, clearml-agent in services-mode supports CPU only configuration. ClearML-Agent services mode can be launched alongside GPU agents.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="clearml-agent daemon --services-mode --detached --queue services --create-queue --docker ubuntu:18.04 --cpu-only"><pre>clearml-agent daemon --services-mode --detached --queue services --create-queue --docker ubuntu:18.04 --cpu-only</pre></div> <p dir="auto"><strong>Note</strong>: It is the user's responsibility to make sure the proper tasks are pushed into the specified queue.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">AutoML and Orchestration Pipelines <a name="user-content-automl-pipes"></a></h3><a id="user-content-automl-and-orchestration-pipelines-" class="anchor" aria-label="Permalink: AutoML and Orchestration Pipelines " href="#automl-and-orchestration-pipelines-"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The ClearML Agent can also be used to implement AutoML orchestration and Experiment Pipelines in conjunction with the ClearML package.</p> <p dir="auto">Sample AutoML & Orchestration examples can be found in the ClearML <a href="https://github.com/clearml/clearml/tree/master/examples/automation">example/automation</a> folder.</p> <p dir="auto">AutoML examples:</p> <ul dir="auto"> <li><a href="https://github.com/clearml/clearml/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py">Toy Keras training experiment</a> <ul dir="auto"> <li>In order to create an experiment-template in the system, this code must be executed once manually</li> </ul> </li> <li><a href="https://github.com/clearml/clearml/blob/master/examples/automation/manual_random_param_search_example.py">Random Search over the above Keras experiment-template</a> <ul dir="auto"> <li>This example will create multiple copies of the Keras experiment-template, with different hyperparameter combinations</li> </ul> </li> </ul> <p dir="auto">Experiment Pipeline examples:</p> <ul dir="auto"> <li><a href="https://github.com/clearml/clearml/blob/master/examples/automation/task_piping_example.py">First step experiment</a> <ul dir="auto"> <li>This example will "process data", and once done, will launch a copy of the 'second step' experiment-template</li> </ul> </li> <li><a href="https://github.com/clearml/clearml/blob/master/examples/automation/toy_base_task.py">Second step experiment</a> <ul dir="auto"> <li>In order to create an experiment-template in the system, this code must be executed once manually</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">License</h3><a id="user-content-license" class="anchor" aria-label="Permalink: License" href="#license"><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">Apache License, Version 2.0 (see the <a href="https://www.apache.org/licenses/LICENSE-2.0.html" rel="nofollow">LICENSE</a> for more information)</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="Yw+11dP6oqwE9rntLYT/NVG83SLsoBrnsCFmPY51A/lDlak/6gTQMXS+e0w1tEpXrfiuFjaI26wevpiB3/sNhg==" /> </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"> ClearML Agent - ML-Ops made easy. 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