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class="flex-1"> <button aria-label="Toggle navigation" aria-expanded="false" type="button" data-view-component="true" class="js-details-target js-nav-padding-recalculate js-header-menu-toggle Button--link Button--medium Button d-lg-none color-fg-inherit p-1"> <span class="Button-content"> <span class="Button-label"><div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div> <div class="HeaderMenu-toggle-bar rounded my-1"></div></span> </span> </button> </div> <a class="mr-lg-3 color-fg-inherit flex-order-2 js-prevent-focus-on-mobile-nav" href="/" aria-label="Homepage" data-analytics-event="{"category":"Marketing nav","action":"click to go to homepage","label":"ref_page:Marketing;ref_cta:Logomark;ref_loc:Header"}"> <svg height="32" aria-hidden="true" viewBox="0 0 24 24" version="1.1" width="32" data-view-component="true" class="octicon octicon-mark-github"> <path d="M12 1C5.9225 1 1 5.9225 1 12C1 16.8675 4.14875 20.9787 8.52125 22.4362C9.07125 22.5325 9.2775 22.2025 9.2775 21.9137C9.2775 21.6525 9.26375 20.7862 9.26375 19.865C6.5 20.3737 5.785 19.1912 5.565 18.5725C5.44125 18.2562 4.905 17.28 4.4375 17.0187C4.0525 16.8125 3.5025 16.3037 4.42375 16.29C5.29 16.2762 5.90875 17.0875 6.115 17.4175C7.105 19.0812 8.68625 18.6137 9.31875 18.325C9.415 17.61 9.70375 17.1287 10.02 16.8537C7.5725 16.5787 5.015 15.63 5.015 11.4225C5.015 10.2262 5.44125 9.23625 6.1425 8.46625C6.0325 8.19125 5.6475 7.06375 6.2525 5.55125C6.2525 5.55125 7.17375 5.2625 9.2775 6.67875C10.1575 6.43125 11.0925 6.3075 12.0275 6.3075C12.9625 6.3075 13.8975 6.43125 14.7775 6.67875C16.8813 5.24875 17.8025 5.55125 17.8025 5.55125C18.4075 7.06375 18.0225 8.19125 17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 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flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 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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":"github_advanced_security","context":"product","tag":"link","label":"github_advanced_security_link_product_navbar"}" href="https://github.com/security/advanced-security"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-shield-check color-fg-subtle mr-3"> <path d="M16.53 9.78a.75.75 0 0 0-1.06-1.06L11 13.19l-1.97-1.97a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l5-5Z"></path><path d="m12.54.637 8.25 2.675A1.75 1.75 0 0 1 22 4.976V10c0 6.19-3.771 10.704-9.401 12.83a1.704 1.704 0 0 1-1.198 0C5.77 20.705 2 16.19 2 10V4.976c0-.758.489-1.43 1.21-1.664L11.46.637a1.748 1.748 0 0 1 1.08 0Zm-.617 1.426-8.25 2.676a.249.249 0 0 0-.173.237V10c0 5.46 3.28 9.483 8.43 11.426a.199.199 0 0 0 .14 0C17.22 19.483 20.5 15.461 20.5 10V4.976a.25.25 0 0 0-.173-.237l-8.25-2.676a.253.253 0 0 0-.154 0Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Advanced 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" 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1.5h-.5a.75.75 0 0 1-.75-.75Z"></path> </svg> <div> <div class="color-fg-default h4">Codespaces</div> Instant dev environments </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"issues","context":"product","tag":"link","label":"issues_link_product_navbar"}" href="https://github.com/features/issues"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-issue-opened color-fg-subtle mr-3"> <path d="M12 1c6.075 0 11 4.925 11 11s-4.925 11-11 11S1 18.075 1 12 5.925 1 12 1ZM2.5 12a9.5 9.5 0 0 0 9.5 9.5 9.5 9.5 0 0 0 9.5-9.5A9.5 9.5 0 0 0 12 2.5 9.5 9.5 0 0 0 2.5 12Zm9.5 2a2 2 0 1 1-.001-3.999A2 2 0 0 1 12 14Z"></path> </svg> <div> <div class="color-fg-default h4">Issues</div> Plan and track work </div> </a></li> <li> <a 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0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary Link--external" target="_blank" data-analytics-event="{"location":"navbar","action":"github_skills","context":"product","tag":"link","label":"github_skills_link_product_navbar"}" href="https://skills.github.com"> GitHub Skills <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 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alt=\"Code style: black\" data-canonical-src=\"https://img.shields.io/badge/code%20style-black-000000.svg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\u003cstrong\u003eNVIDIA NeMo Framework\u003c/strong\u003e\u003c/h1\u003e\u003ca id=\"user-content-nvidia-nemo-framework\" class=\"anchor\" aria-label=\"Permalink: NVIDIA NeMo Framework\" href=\"#nvidia-nemo-framework\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLatest News\u003c/h2\u003e\u003ca id=\"user-content-latest-news\" class=\"anchor\" aria-label=\"Permalink: Latest News\" href=\"#latest-news\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\n\u003cdetails open=\"\"\u003e\n \u003csummary\u003e\u003cb\u003ePretrain and finetune 🤗Hugging Face models via AutoModel\u003c/b\u003e\u003c/summary\u003e\n Nemo Framework's latest feature AutoModel enables broad support for 🤗Hugging Face models, with 25.02 focusing on \u003ca href=\"https://huggingface.co/transformers/v3.5.1/model_doc/auto.html#automodelforcausallm\" rel=\"nofollow\"\u003eAutoModelForCausalLM\u003c/a\u003e in the \u003ca href=\"https://huggingface.co/models?pipeline_tag=text-generation\u0026amp;sort=trending\" rel=\"nofollow\"\u003etext generation category\u003c/a\u003e. Future releases will enable support for more model families such as Vision Language Model.\n\u003c/details\u003e\n\u003cdetails open=\"\"\u003e\n \u003csummary\u003e\u003cb\u003eTraining on Blackwell using Nemo\u003c/b\u003e\u003c/summary\u003e\n NeMo Framework has added Blackwell support, with 25.02 focusing on functional parity for B200. More optimizations to come in the upcoming releases.\n\u003c/details\u003e\n\u003cdetails open=\"\"\u003e\n \u003csummary\u003e\u003cb\u003eNeMo Framework 2.0\u003c/b\u003e\u003c/summary\u003e\n We've released NeMo 2.0, an update on the NeMo Framework which prioritizes modularity and ease-of-use. Please refer to the \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/index.html\" rel=\"nofollow\"\u003eNeMo Framework User Guide\u003c/a\u003e to get started.\n\u003c/details\u003e\n\u003cdetails open=\"\"\u003e\n \u003csummary\u003e\u003cb\u003eNew Cosmos World Foundation Models Support\u003c/b\u003e\u003c/summary\u003e\n \u003cdetails\u003e \n \u003csummary\u003e \u003ca href=\"https://developer.nvidia.com/blog/advancing-physical-ai-with-nvidia-cosmos-world-foundation-model-platform\" rel=\"nofollow\"\u003eAdvancing Physical AI with NVIDIA Cosmos World Foundation Model Platform \u003c/a\u003e (2025-01-09) \n \u003c/summary\u003e \n The end-to-end NVIDIA Cosmos platform accelerates world model development for physical AI systems. Built on CUDA, Cosmos combines state-of-the-art world foundation models, video tokenizers, and AI-accelerated data processing pipelines. Developers can accelerate world model development by fine-tuning Cosmos world foundation models or building new ones from the ground up. These models create realistic synthetic videos of environments and interactions, providing a scalable foundation for training complex systems, from simulating humanoid robots performing advanced actions to developing end-to-end autonomous driving models. \n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/accelerate-custom-video-foundation-model-pipelines-with-new-nvidia-nemo-framework-capabilities/\" rel=\"nofollow\"\u003e\n Accelerate Custom Video Foundation Model Pipelines with New NVIDIA NeMo Framework Capabilities\n \u003c/a\u003e (2025-01-07)\n \u003c/summary\u003e\n The NeMo Framework now supports training and customizing the \u003ca href=\"https://github.com/NVIDIA/Cosmos\"\u003eNVIDIA Cosmos\u003c/a\u003e collection of world foundation models. Cosmos leverages advanced text-to-world generation techniques to create fluid, coherent video content from natural language prompts.\n \u003cbr\u003e\u003cbr\u003e\n You can also now accelerate your video processing step using the \u003ca href=\"https://developer.nvidia.com/nemo-curator-video-processing-early-access\" rel=\"nofollow\"\u003eNeMo Curator\u003c/a\u003e library, which provides optimized video processing and captioning features that can deliver up to 89x faster video processing when compared to an unoptimized CPU pipeline.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n\u003c/details\u003e\n\u003cdetails open=\"\"\u003e\n \u003csummary\u003e\u003cb\u003eLarge Language Models and Multimodal Models\u003c/b\u003e\u003c/summary\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/state-of-the-art-multimodal-generative-ai-model-development-with-nvidia-nemo/\" rel=\"nofollow\"\u003e\n State-of-the-Art Multimodal Generative AI Model Development with NVIDIA NeMo\n \u003c/a\u003e (2024-11-06)\n \u003c/summary\u003e\n NVIDIA recently announced significant enhancements to the NeMo platform, focusing on multimodal generative AI models. The update includes NeMo Curator and the Cosmos tokenizer, which streamline the data curation process and enhance the quality of visual data. These tools are designed to handle large-scale data efficiently, making it easier to develop high-quality AI models for various applications, including robotics and autonomous driving. The Cosmos tokenizers, in particular, efficiently map visual data into compact, semantic tokens, which is crucial for training large-scale generative models. The tokenizer is available now on the \u003ca href=\"http://github.com/NVIDIA/cosmos-tokenizer/NVIDIA/cosmos-tokenizer\"\u003eNVIDIA/cosmos-tokenizer\u003c/a\u003e GitHub repo and on \u003ca href=\"https://huggingface.co/nvidia/Cosmos-Tokenizer-CV8x8x8\" rel=\"nofollow\"\u003eHugging Face\u003c/a\u003e.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/llama/index.html#new-llama-3-1-support for more information/\" rel=\"nofollow\"\u003e\n New Llama 3.1 Support\n \u003c/a\u003e (2024-07-23)\n \u003c/summary\u003e\n The NeMo Framework now supports training and customizing the Llama 3.1 collection of LLMs from Meta.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://aws.amazon.com/blogs/machine-learning/accelerate-your-generative-ai-distributed-training-workloads-with-the-nvidia-nemo-framework-on-amazon-eks/\" rel=\"nofollow\"\u003e\n Accelerate your Generative AI Distributed Training Workloads with the NVIDIA NeMo Framework on Amazon EKS\n \u003c/a\u003e (2024-07-16)\n \u003c/summary\u003e\n NVIDIA NeMo Framework now runs distributed training workloads on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. For step-by-step instructions on creating an EKS cluster and running distributed training workloads with NeMo, see the GitHub repository \u003ca href=\"https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/2.nemo-launcher/EKS/\"\u003e here.\u003c/a\u003e\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/nvidia-nemo-accelerates-llm-innovation-with-hybrid-state-space-model-support/\" rel=\"nofollow\"\u003e\n NVIDIA NeMo Accelerates LLM Innovation with Hybrid State Space Model Support\n \u003c/a\u003e (2024/06/17)\n \u003c/summary\u003e\n NVIDIA NeMo and Megatron Core now support pre-training and fine-tuning of state space models (SSMs). NeMo also supports training models based on the Griffin architecture as described by Google DeepMind. \n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://huggingface.co/models?sort=trending\u0026amp;search=nvidia%2Fnemotron-4-340B\" rel=\"nofollow\"\u003e\n NVIDIA releases 340B base, instruct, and reward models pretrained on a total of 9T tokens.\n \u003c/a\u003e (2024-06-18)\n \u003c/summary\u003e\n See documentation and tutorials for SFT, PEFT, and PTQ with \n \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/nemotron/index.html\" rel=\"nofollow\"\u003e\n Nemotron 340B \n \u003c/a\u003e\n in the NeMo Framework User Guide.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/nvidia-sets-new-generative-ai-performance-and-scale-records-in-mlperf-training-v4-0/\" rel=\"nofollow\"\u003e\n NVIDIA sets new generative AI performance and scale records in MLPerf Training v4.0\n \u003c/a\u003e (2024/06/12)\n \u003c/summary\u003e\n Using NVIDIA NeMo Framework and NVIDIA Hopper GPUs NVIDIA was able to scale to 11,616 H100 GPUs and achieve near-linear performance scaling on LLM pretraining. \n NVIDIA also achieved the highest LLM fine-tuning performance and raised the bar for text-to-image training.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://cloud.google.com/blog/products/compute/gke-and-nvidia-nemo-framework-to-train-generative-ai-models\" rel=\"nofollow\"\u003e\n Accelerate your generative AI journey with NVIDIA NeMo Framework on GKE\n \u003c/a\u003e (2024/03/16)\n \u003c/summary\u003e\n An end-to-end walkthrough to train generative AI models on the Google Kubernetes Engine (GKE) using the NVIDIA NeMo Framework is available at \u003ca href=\"https://github.com/GoogleCloudPlatform/nvidia-nemo-on-gke\"\u003ehttps://github.com/GoogleCloudPlatform/nvidia-nemo-on-gke\u003c/a\u003e. \n The walkthrough includes detailed instructions on how to set up a Google Cloud Project and pre-train a GPT model using the NeMo Framework.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n\u003c/details\u003e\n\u003cdetails open=\"\"\u003e\n \u003csummary\u003e\u003cb\u003eSpeech Recognition\u003c/b\u003e\u003c/summary\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/accelerating-leaderboard-topping-asr-models-10x-with-nvidia-nemo/\" rel=\"nofollow\"\u003e\n Accelerating Leaderboard-Topping ASR Models 10x with NVIDIA NeMo\n \u003c/a\u003e (2024/09/24)\n \u003c/summary\u003e\n NVIDIA NeMo team released a number of inference optimizations for CTC, RNN-T, and TDT models that resulted in up to 10x inference speed-up. \n These models now exceed an inverse real-time factor (RTFx) of 2,000, with some reaching RTFx of even 6,000.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/new-standard-for-speech-recognition-and-translation-from-the-nvidia-nemo-canary-model/\" rel=\"nofollow\"\u003e\n New Standard for Speech Recognition and Translation from the NVIDIA NeMo Canary Model\n \u003c/a\u003e (2024/04/18)\n \u003c/summary\u003e\n The NeMo team just released Canary, a multilingual model that transcribes speech in English, Spanish, German, and French with punctuation and capitalization. \n Canary also provides bi-directional translation, between English and the three other supported languages.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/pushing-the-boundaries-of-speech-recognition-with-nemo-parakeet-asr-models/\" rel=\"nofollow\"\u003e\n Pushing the Boundaries of Speech Recognition with NVIDIA NeMo Parakeet ASR Models\n \u003c/a\u003e (2024/04/18)\n \u003c/summary\u003e\n NVIDIA NeMo, an end-to-end platform for the development of multimodal generative AI models at scale anywhere—on any cloud and on-premises—released the Parakeet family of automatic speech recognition (ASR) models. \n These state-of-the-art ASR models, developed in collaboration with Suno.ai, transcribe spoken English with exceptional accuracy.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/turbocharge-asr-accuracy-and-speed-with-nvidia-nemo-parakeet-tdt/\" rel=\"nofollow\"\u003e\n Turbocharge ASR Accuracy and Speed with NVIDIA NeMo Parakeet-TDT\n \u003c/a\u003e (2024/04/18)\n \u003c/summary\u003e\n NVIDIA NeMo, an end-to-end platform for developing multimodal generative AI models at scale anywhere—on any cloud and on-premises—recently released Parakeet-TDT. \n This new addition to the  NeMo ASR Parakeet model family boasts better accuracy and 64% greater speed over the previously best model, Parakeet-RNNT-1.1B.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n\u003c/details\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eIntroduction\u003c/h2\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNVIDIA NeMo Framework is a scalable and cloud-native generative AI\nframework built for researchers and PyTorch developers working on Large\nLanguage Models (LLMs), Multimodal Models (MMs), Automatic Speech\nRecognition (ASR), Text to Speech (TTS), and Computer Vision (CV)\ndomains. It is designed to help you efficiently create, customize, and\ndeploy new generative AI models by leveraging existing code and\npre-trained model checkpoints.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor technical documentation, please see the \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/index.html\" rel=\"nofollow\"\u003eNeMo Framework User\nGuide\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhat's New in NeMo 2.0\u003c/h2\u003e\u003ca id=\"user-content-whats-new-in-nemo-20\" class=\"anchor\" aria-label=\"Permalink: What's New in NeMo 2.0\" href=\"#whats-new-in-nemo-20\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNVIDIA NeMo 2.0 introduces several significant improvements over its predecessor, NeMo 1.0, enhancing flexibility, performance, and scalability.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003ePython-Based Configuration\u003c/strong\u003e - NeMo 2.0 transitions from YAML files to a Python-based configuration, providing more flexibility and control. This shift makes it easier to extend and customize configurations programmatically.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eModular Abstractions\u003c/strong\u003e - By adopting PyTorch Lightning’s modular abstractions, NeMo 2.0 simplifies adaptation and experimentation. This modular approach allows developers to more easily modify and experiment with different components of their models.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eScalability\u003c/strong\u003e - NeMo 2.0 seamlessly scaling large-scale experiments across thousands of GPUs using \u003ca href=\"https://github.com/NVIDIA/NeMo-Run\"\u003eNeMo-Run\u003c/a\u003e, a powerful tool designed to streamline the configuration, execution, and management of machine learning experiments across computing environments.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eOverall, these enhancements make NeMo 2.0 a powerful, scalable, and user-friendly framework for AI model development.\u003c/p\u003e\n\u003cdiv class=\"markdown-alert markdown-alert-important\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-report mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z\"\u003e\u003c/path\u003e\u003c/svg\u003eImportant\u003c/p\u003e\u003cp dir=\"auto\"\u003eNeMo 2.0 is currently supported by the LLM (large language model) and VLM (vision language model) collections.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGet Started with NeMo 2.0\u003c/h3\u003e\u003ca id=\"user-content-get-started-with-nemo-20\" class=\"anchor\" aria-label=\"Permalink: Get Started with NeMo 2.0\" href=\"#get-started-with-nemo-20\"\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\u003eRefer to the \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/quickstart.html\" rel=\"nofollow\"\u003eQuickstart\u003c/a\u003e for examples of using NeMo-Run to launch NeMo 2.0 experiments locally and on a slurm cluster.\u003c/li\u003e\n\u003cli\u003eFor more information about NeMo 2.0, see the \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/index.html\" rel=\"nofollow\"\u003eNeMo Framework User Guide\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/llm/recipes\"\u003eNeMo 2.0 Recipes\u003c/a\u003e contains additional examples of launching large-scale runs using NeMo 2.0 and NeMo-Run.\u003c/li\u003e\n\u003cli\u003eFor an in-depth exploration of the main features of NeMo 2.0, see the \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/features/index.html#feature-guide\" rel=\"nofollow\"\u003eFeature Guide\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eTo transition from NeMo 1.0 to 2.0, see the \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/migration/index.html#migration-guide\" rel=\"nofollow\"\u003eMigration Guide\u003c/a\u003e for step-by-step instructions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGet Started with Cosmos\u003c/h3\u003e\u003ca id=\"user-content-get-started-with-cosmos\" class=\"anchor\" aria-label=\"Permalink: Get Started with Cosmos\" href=\"#get-started-with-cosmos\"\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\"\u003eNeMo Curator and NeMo Framework support video curation and post-training of the Cosmos World Foundation Models, which are open and available on \u003ca href=\"https://catalog.ngc.nvidia.com/orgs/nvidia/teams/cosmos/collections/cosmos\" rel=\"nofollow\"\u003eNGC\u003c/a\u003e and \u003ca href=\"https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6\" rel=\"nofollow\"\u003eHugging Face\u003c/a\u003e. For more information on video datasets, refer to \u003ca href=\"https://developer.nvidia.com/nemo-curator\" rel=\"nofollow\"\u003eNeMo Curator\u003c/a\u003e. To post-train World Foundation Models using the NeMo Framework for your custom physical AI tasks, see the \u003ca href=\"https://github.com/NVIDIA/Cosmos/blob/main/cosmos1/models/diffusion/nemo/post_training/README.md\"\u003eCosmos Diffusion models\u003c/a\u003e and the \u003ca href=\"https://github.com/NVIDIA/Cosmos/blob/main/cosmos1/models/autoregressive/nemo/post_training/README.md\"\u003eCosmos Autoregressive models\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLLMs and MMs Training, Alignment, and Customization\u003c/h2\u003e\u003ca id=\"user-content-llms-and-mms-training-alignment-and-customization\" class=\"anchor\" aria-label=\"Permalink: LLMs and MMs Training, Alignment, and Customization\" href=\"#llms-and-mms-training-alignment-and-customization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAll NeMo models are trained with\n\u003ca href=\"https://github.com/Lightning-AI/lightning\"\u003eLightning\u003c/a\u003e. Training is\nautomatically scalable to 1000s of GPUs. You can check the performance benchmarks using the\nlatest NeMo Framework container \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/performance/performance_summary.html\" rel=\"nofollow\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen applicable, NeMo models leverage cutting-edge distributed training\ntechniques, incorporating \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/modeloverview.html\" rel=\"nofollow\"\u003eparallelism\nstrategies\u003c/a\u003e\nto enable efficient training of very large models. These techniques\ninclude Tensor Parallelism (TP), Pipeline Parallelism (PP), Fully\nSharded Data Parallelism (FSDP), Mixture-of-Experts (MoE), and Mixed\nPrecision Training with BFloat16 and FP8, as well as others.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNeMo Transformer-based LLMs and MMs utilize \u003ca href=\"https://github.com/NVIDIA/TransformerEngine\"\u003eNVIDIA Transformer\nEngine\u003c/a\u003e for FP8 training on\nNVIDIA Hopper GPUs, while leveraging \u003ca href=\"https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core\"\u003eNVIDIA Megatron\nCore\u003c/a\u003e for\nscaling Transformer model training.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNeMo LLMs can be aligned with state-of-the-art methods such as SteerLM,\nDirect Preference Optimization (DPO), and Reinforcement Learning from\nHuman Feedback (RLHF). See \u003ca href=\"https://github.com/NVIDIA/NeMo-Aligner\"\u003eNVIDIA NeMo\nAligner\u003c/a\u003e for more information.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn addition to supervised fine-tuning (SFT), NeMo also supports the\nlatest parameter efficient fine-tuning (PEFT) techniques such as LoRA,\nP-Tuning, Adapters, and IA3. Refer to the \u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/sft_peft/index.html\" rel=\"nofollow\"\u003eNeMo Framework User\nGuide\u003c/a\u003e\nfor the full list of supported models and techniques.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLLMs and MMs Deployment and Optimization\u003c/h2\u003e\u003ca id=\"user-content-llms-and-mms-deployment-and-optimization\" class=\"anchor\" aria-label=\"Permalink: LLMs and MMs Deployment and Optimization\" href=\"#llms-and-mms-deployment-and-optimization\"\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\"\u003eNeMo LLMs and MMs can be deployed and optimized with \u003ca href=\"https://developer.nvidia.com/nemo-microservices-early-access\" rel=\"nofollow\"\u003eNVIDIA NeMo\nMicroservices\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eSpeech AI\u003c/h2\u003e\u003ca id=\"user-content-speech-ai\" class=\"anchor\" aria-label=\"Permalink: Speech AI\" href=\"#speech-ai\"\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\"\u003eNeMo ASR and TTS models can be optimized for inference and deployed for\nproduction use cases with \u003ca href=\"https://developer.nvidia.com/riva\" rel=\"nofollow\"\u003eNVIDIA Riva\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNeMo Framework Launcher\u003c/h2\u003e\u003ca id=\"user-content-nemo-framework-launcher\" class=\"anchor\" aria-label=\"Permalink: NeMo Framework Launcher\" href=\"#nemo-framework-launcher\"\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-alert markdown-alert-important\" dir=\"auto\"\u003e\u003cp class=\"markdown-alert-title\" dir=\"auto\"\u003e\u003csvg class=\"octicon octicon-report mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z\"\u003e\u003c/path\u003e\u003c/svg\u003eImportant\u003c/p\u003e\u003cp dir=\"auto\"\u003eNeMo Framework Launcher is compatible with NeMo version 1.0 only. \u003ca href=\"https://github.com/NVIDIA/NeMo-Run\"\u003eNeMo-Run\u003c/a\u003e is recommended for launching experiments using NeMo 2.0.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"https://github.com/NVIDIA/NeMo-Megatron-Launcher\"\u003eNeMo Framework\nLauncher\u003c/a\u003e is a\ncloud-native tool that streamlines the NeMo Framework experience. It is\nused for launching end-to-end NeMo Framework training jobs on CSPs and\nSlurm clusters.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe NeMo Framework Launcher includes extensive recipes, scripts,\nutilities, and documentation for training NeMo LLMs. It also includes\nthe NeMo Framework \u003ca href=\"https://github.com/NVIDIA/NeMo-Megatron-Launcher#53-using-autoconfigurator-to-find-the-optimal-configuration\"\u003eAutoconfigurator\u003c/a\u003e,\nwhich is designed to find the optimal model parallel configuration for\ntraining on a specific cluster.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo get started quickly with the NeMo Framework Launcher, please see the\n\u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/index.html\" rel=\"nofollow\"\u003eNeMo Framework\nPlaybooks\u003c/a\u003e.\nThe NeMo Framework Launcher does not currently support ASR and TTS\ntraining, but it will soon.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGet Started with NeMo Framework\u003c/h2\u003e\u003ca id=\"user-content-get-started-with-nemo-framework\" class=\"anchor\" aria-label=\"Permalink: Get Started with NeMo Framework\" href=\"#get-started-with-nemo-framework\"\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\"\u003eGetting started with NeMo Framework is easy. State-of-the-art pretrained\nNeMo models are freely available on \u003ca href=\"https://huggingface.co/models?library=nemo\u0026amp;sort=downloads\u0026amp;search=nvidia\" rel=\"nofollow\"\u003eHugging Face\nHub\u003c/a\u003e\nand \u003ca href=\"https://catalog.ngc.nvidia.com/models?query=nemo\u0026amp;orderBy=weightPopularDESC\" rel=\"nofollow\"\u003eNVIDIA\nNGC\u003c/a\u003e.\nThese models can be used to generate text or images, transcribe audio,\nand synthesize speech in just a few lines of code.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe have extensive\n\u003ca href=\"https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/tutorials.html\" rel=\"nofollow\"\u003etutorials\u003c/a\u003e\nthat can be run on \u003ca href=\"https://colab.research.google.com\" rel=\"nofollow\"\u003eGoogle Colab\u003c/a\u003e or\nwith our \u003ca href=\"https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo\" rel=\"nofollow\"\u003eNGC NeMo Framework\nContainer\u003c/a\u003e.\nWe also have\n\u003ca href=\"https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/index.html\" rel=\"nofollow\"\u003eplaybooks\u003c/a\u003e\nfor users who want to train NeMo models with the NeMo Framework\nLauncher.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor advanced users who want to train NeMo models from scratch or\nfine-tune existing NeMo models, we have a full suite of \u003ca href=\"https://github.com/NVIDIA/NeMo/tree/main/examples\"\u003eexample\nscripts\u003c/a\u003e that support\nmulti-GPU/multi-node training.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eKey Features\u003c/h2\u003e\u003ca id=\"user-content-key-features\" class=\"anchor\" aria-label=\"Permalink: Key Features\" href=\"#key-features\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/NeMo/blob/main/nemo/collections/nlp/README.md\"\u003eLarge Language Models\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/NeMo/blob/main/nemo/collections/multimodal/README.md\"\u003eMultimodal\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/NeMo/blob/main/nemo/collections/asr/README.md\"\u003eAutomatic Speech Recognition\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/NeMo/blob/main/nemo/collections/tts/README.md\"\u003eText to Speech\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"/NVIDIA/NeMo/blob/main/nemo/collections/vision/README.md\"\u003eComputer Vision\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRequirements\u003c/h2\u003e\u003ca id=\"user-content-requirements\" class=\"anchor\" aria-label=\"Permalink: Requirements\" href=\"#requirements\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003ePython 3.10 or above\u003c/li\u003e\n\u003cli\u003ePytorch 2.5 or above\u003c/li\u003e\n\u003cli\u003eNVIDIA GPU (if you intend to do model training)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeveloper Documentation\u003c/h2\u003e\u003ca id=\"user-content-developer-documentation\" class=\"anchor\" aria-label=\"Permalink: Developer Documentation\" href=\"#developer-documentation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eVersion\u003c/th\u003e\n\u003cth\u003eStatus\u003c/th\u003e\n\u003cth\u003eDescription\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eLatest\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/e15da58ddd034a51255e31ee991eed8d10a62e92df02abcd24a2a039b1219e32/68747470733a2f2f72656164746865646f63732e636f6d2f70726f6a656374732f6e76696469612d6e656d6f2f62616467652f3f76657273696f6e3d6d61696e\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.com/projects/nvidia-nemo/badge/?version=main\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/\" rel=\"nofollow\"\u003eDocumentation of the latest (i.e. main) branch.\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eStable\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/\" rel=\"nofollow\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/2080dcf6855ff22d900294c5c4be9f008ed95762e767a22d02485286e3497002/68747470733a2f2f72656164746865646f63732e636f6d2f70726f6a656374732f6e76696469612d6e656d6f2f62616467652f3f76657273696f6e3d737461626c65\" alt=\"Documentation Status\" data-canonical-src=\"https://readthedocs.com/projects/nvidia-nemo/badge/?version=stable\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/td\u003e\n\u003ctd\u003e\u003ca href=\"https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/\" rel=\"nofollow\"\u003eDocumentation of the stable (i.e. most recent release)\u003c/a\u003e\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall NeMo Framework\u003c/h2\u003e\u003ca id=\"user-content-install-nemo-framework\" class=\"anchor\" aria-label=\"Permalink: Install NeMo Framework\" href=\"#install-nemo-framework\"\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 NeMo Framework can be installed in a variety of ways, depending on\nyour needs. Depending on the domain, you may find one of the following\ninstallation methods more suitable.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"#conda--pip\"\u003eConda / Pip\u003c/a\u003e: Install NeMo-Framework with native Pip into a virtual environment.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUsed to explore NeMo on any supported platform.\u003c/li\u003e\n\u003cli\u003eThis is the recommended method for ASR and TTS domains.\u003c/li\u003e\n\u003cli\u003eLimited feature-completeness for other domains.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ngc-pytorch-container\"\u003eNGC PyTorch container\u003c/a\u003e: Install NeMo-Framework from source with feature-completeness into a highly optimized container.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFor users that want to install from source in a highly optimized container.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"#ngc-nemo-container\"\u003eNGC NeMo container\u003c/a\u003e: Ready-to-go solution of NeMo-Framework\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFor users that seek highest performance.\u003c/li\u003e\n\u003cli\u003eContains all dependencies installed and tested for performance and convergence.\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\"\u003eSupport matrix\u003c/h3\u003e\u003ca id=\"user-content-support-matrix\" class=\"anchor\" aria-label=\"Permalink: Support matrix\" href=\"#support-matrix\"\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\"\u003eNeMo-Framework provides tiers of support based on OS / Platform and mode of installation. Please refer the following overview of support levels:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFully supported: Max performance and feature-completeness.\u003c/li\u003e\n\u003cli\u003eLimited supported: Used to explore NeMo.\u003c/li\u003e\n\u003cli\u003eNo support yet: In development.\u003c/li\u003e\n\u003cli\u003eDeprecated: Support has reached end of life.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003ePlease refer to the following table for current support levels:\u003c/p\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eOS / Platform\u003c/th\u003e\n\u003cth\u003eInstall from PyPi\u003c/th\u003e\n\u003cth\u003eSource into NGC container\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003elinux\u003c/code\u003e - \u003ccode\u003eamd64/x84_64\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLimited support\u003c/td\u003e\n\u003ctd\u003eFull support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003elinux\u003c/code\u003e - \u003ccode\u003earm64\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLimited support\u003c/td\u003e\n\u003ctd\u003eLimited support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edarwin\u003c/code\u003e - \u003ccode\u003eamd64/x64_64\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eDeprecated\u003c/td\u003e\n\u003ctd\u003eDeprecated\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003edarwin\u003c/code\u003e - \u003ccode\u003earm64\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eLimited support\u003c/td\u003e\n\u003ctd\u003eLimited support\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ewindows\u003c/code\u003e - \u003ccode\u003eamd64/x64_64\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eNo support yet\u003c/td\u003e\n\u003ctd\u003eNo support yet\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003ccode\u003ewindows\u003c/code\u003e - \u003ccode\u003earm64\u003c/code\u003e\u003c/td\u003e\n\u003ctd\u003eNo support yet\u003c/td\u003e\n\u003ctd\u003eNo support yet\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eConda / Pip\u003c/h3\u003e\u003ca id=\"user-content-conda--pip\" class=\"anchor\" aria-label=\"Permalink: Conda / Pip\" href=\"#conda--pip\"\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\"\u003eInstall NeMo in a fresh Conda environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda create --name nemo python==3.10.12\nconda activate nemo\"\u003e\u003cpre\u003econda create --name nemo python==3.10.12\nconda activate nemo\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePick the right version\u003c/h4\u003e\u003ca id=\"user-content-pick-the-right-version\" class=\"anchor\" aria-label=\"Permalink: Pick the right version\" href=\"#pick-the-right-version\"\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\"\u003eNeMo-Framework publishes pre-built wheels with each release.\nTo install nemo_toolkit from such a wheel, use the following installation method:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install \u0026quot;nemo_toolkit[all]\u0026quot;\"\u003e\u003cpre\u003epip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003enemo_toolkit[all]\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf a more specific version is desired, we recommend a Pip-VCS install. From \u003ca href=\"/NVIDIA/NeMo/blob/main/github.com/NVIDIA/NeMo\"\u003eNVIDIA/NeMo\u003c/a\u003e, fetch the commit, branch, or tag that you would like to install.\u003cbr\u003e\nTo install nemo_toolkit from this Git reference \u003ccode\u003e$REF\u003c/code\u003e, use the following installation method:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"git clone https://github.com/NVIDIA/NeMo\ncd NeMo\ngit checkout @${REF:-'main'}\npip install '.[all]'\"\u003e\u003cpre\u003egit clone https://github.com/NVIDIA/NeMo\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e NeMo\ngit checkout @\u003cspan class=\"pl-smi\"\u003e${REF\u003cspan class=\"pl-k\"\u003e:-\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003emain\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e}\u003c/span\u003e\npip install \u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e.[all]\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch4 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstall a specific Domain\u003c/h4\u003e\u003ca id=\"user-content-install-a-specific-domain\" class=\"anchor\" aria-label=\"Permalink: Install a specific Domain\" href=\"#install-a-specific-domain\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install a specific domain of NeMo, you must first install the\nnemo_toolkit using the instructions listed above. Then, you run the\nfollowing domain-specific commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install nemo_toolkit['all'] # or pip install \u0026quot;git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[all]\u0026quot;\npip install nemo_toolkit['asr'] # or pip install \u0026quot;git+https://github.com/NVIDIA/NeMo@$REF#egg=nemo_toolkit[asr]\u0026quot;\npip install nemo_toolkit['nlp'] # or pip install \u0026quot;git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[nlp]\u0026quot;\npip install nemo_toolkit['tts'] # or pip install \u0026quot;git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[tts]\u0026quot;\npip install nemo_toolkit['vision'] # or pip install \u0026quot;git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[vision]\u0026quot;\npip install nemo_toolkit['multimodal'] # or pip install \u0026quot;git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[multimodal]\u0026quot;\"\u003e\u003cpre\u003epip install nemo_toolkit[\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003eall\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or pip install \"git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[all]\"\u003c/span\u003e\npip install nemo_toolkit[\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003easr\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or pip install \"git+https://github.com/NVIDIA/NeMo@$REF#egg=nemo_toolkit[asr]\"\u003c/span\u003e\npip install nemo_toolkit[\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003enlp\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or pip install \"git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[nlp]\"\u003c/span\u003e\npip install nemo_toolkit[\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003etts\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or pip install \"git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[tts]\"\u003c/span\u003e\npip install nemo_toolkit[\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003evision\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or pip install \"git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[vision]\"\u003c/span\u003e\npip install nemo_toolkit[\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003emultimodal\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e] \u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e#\u003c/span\u003e or pip install \"git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[multimodal]\"\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNGC PyTorch container\u003c/h3\u003e\u003ca id=\"user-content-ngc-pytorch-container\" class=\"anchor\" aria-label=\"Permalink: NGC PyTorch container\" href=\"#ngc-pytorch-container\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cstrong\u003eNOTE: The following steps are supported beginning with 24.04 (NeMo-Toolkit 2.3.0)\u003c/strong\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWe recommended that you start with a base NVIDIA PyTorch container:\nnvcr.io/nvidia/pytorch:25.01-py3.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf starting with a base NVIDIA PyTorch container, you must first launch\nthe container:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker run \\\n --gpus all \\\n -it \\\n --rm \\\n --shm-size=16g \\\n --ulimit memlock=-1 \\\n --ulimit stack=67108864 \\\n nvcr.io/nvidia/pytorch:${NV_PYTORCH_TAG:-'nvcr.io/nvidia/pytorch:25.01-py3'}\"\u003e\u003cpre\u003edocker run \\\n --gpus all \\\n -it \\\n --rm \\\n --shm-size=16g \\\n --ulimit memlock=-1 \\\n --ulimit stack=67108864 \\\n nvcr.io/nvidia/pytorch:\u003cspan class=\"pl-smi\"\u003e${NV_PYTORCH_TAG\u003cspan class=\"pl-k\"\u003e:-\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003envcr.io/nvidia/pytorch:25.01-py3\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFrom \u003ca href=\"/NVIDIA/NeMo/blob/main/github.com/NVIDIA/NeMo\"\u003eNVIDIA/NeMo\u003c/a\u003e, fetch the commit/branch/tag that you want to install.\u003cbr\u003e\nTo install nemo_toolkit including all of its dependencies from this Git reference \u003ccode\u003e$REF\u003c/code\u003e, use the following installation method:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"cd /opt\ngit clone https://github.com/NVIDIA/NeMo\ncd NeMo\ngit checkout ${REF:-'main'}\nbash reinstall.sh --library all\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e /opt\ngit clone https://github.com/NVIDIA/NeMo\n\u003cspan class=\"pl-c1\"\u003ecd\u003c/span\u003e NeMo\ngit checkout \u003cspan class=\"pl-smi\"\u003e${REF\u003cspan class=\"pl-k\"\u003e:-\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003emain\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e}\u003c/span\u003e\nbash reinstall.sh --library all\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNGC NeMo container\u003c/h2\u003e\u003ca id=\"user-content-ngc-nemo-container\" class=\"anchor\" aria-label=\"Permalink: NGC NeMo container\" href=\"#ngc-nemo-container\"\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\"\u003eNeMo containers are launched concurrently with NeMo version updates.\nNeMo Framework now supports LLMs, MMs, ASR, and TTS in a single\nconsolidated Docker container. You can find additional information about\nreleased containers on the \u003ca href=\"https://github.com/NVIDIA/NeMo/releases\"\u003eNeMo releases\npage\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo use a pre-built container, run the following code:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"docker run \\\n --gpus all \\\n -it \\\n --rm \\\n --shm-size=16g \\\n --ulimit memlock=-1 \\\n --ulimit stack=67108864 \\\n nvcr.io/nvidia/pytorch:${NV_PYTORCH_TAG:-'nvcr.io/nvidia/nemo:25.02'}\"\u003e\u003cpre\u003edocker run \\\n --gpus all \\\n -it \\\n --rm \\\n --shm-size=16g \\\n --ulimit memlock=-1 \\\n --ulimit stack=67108864 \\\n nvcr.io/nvidia/pytorch:\u003cspan class=\"pl-smi\"\u003e${NV_PYTORCH_TAG\u003cspan class=\"pl-k\"\u003e:-\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003envcr.io/nvidia/nemo:25.02\u003cspan class=\"pl-pds\"\u003e'\u003c/span\u003e\u003c/span\u003e}\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFuture Work\u003c/h2\u003e\u003ca id=\"user-content-future-work\" class=\"anchor\" aria-label=\"Permalink: Future Work\" href=\"#future-work\"\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 NeMo Framework Launcher does not currently support ASR and TTS\ntraining, but it will soon.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDiscussions Board\u003c/h2\u003e\u003ca id=\"user-content-discussions-board\" class=\"anchor\" aria-label=\"Permalink: Discussions Board\" href=\"#discussions-board\"\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\"\u003eFAQ can be found on the NeMo \u003ca href=\"https://github.com/NVIDIA/NeMo/discussions\"\u003eDiscussions\nboard\u003c/a\u003e. You are welcome to\nask questions or start discussions on the board.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eContribute to NeMo\u003c/h2\u003e\u003ca id=\"user-content-contribute-to-nemo\" class=\"anchor\" aria-label=\"Permalink: Contribute to NeMo\" href=\"#contribute-to-nemo\"\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 welcome community contributions! Please refer to\n\u003ca href=\"https://github.com/NVIDIA/NeMo/blob/stable/CONTRIBUTING.md\"\u003eCONTRIBUTING.md\u003c/a\u003e\nfor the process.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePublications\u003c/h2\u003e\u003ca id=\"user-content-publications\" class=\"anchor\" aria-label=\"Permalink: Publications\" href=\"#publications\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe provide an ever-growing list of\n\u003ca href=\"https://nvidia.github.io/NeMo/publications/\" rel=\"nofollow\"\u003epublications\u003c/a\u003e that utilize\nthe NeMo Framework.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo contribute an article to the collection, please submit a pull request\nto the \u003ccode\u003egh-pages-src\u003c/code\u003e branch of this repository. For detailed\ninformation, please consult the README located at the \u003ca href=\"https://github.com/NVIDIA/NeMo/tree/gh-pages-src#readme\"\u003egh-pages-src\nbranch\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eBlogs\u003c/h2\u003e\u003ca id=\"user-content-blogs\" class=\"anchor\" aria-label=\"Permalink: Blogs\" href=\"#blogs\"\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\n\u003cdetails open=\"\"\u003e\n \u003csummary\u003e\u003cb\u003eLarge Language Models and Multimodal Models\u003c/b\u003e\u003c/summary\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://blogs.nvidia.com/blog/bria-builds-responsible-generative-ai-using-nemo-picasso/\" rel=\"nofollow\"\u003e\n Bria Builds Responsible Generative AI for Enterprises Using NVIDIA NeMo, Picasso\n \u003c/a\u003e (2024/03/06)\n \u003c/summary\u003e\n Bria, a Tel Aviv startup at the forefront of visual generative AI for enterprises now leverages the NVIDIA NeMo Framework. \n The Bria.ai platform uses reference implementations from the NeMo Multimodal collection, trained on NVIDIA Tensor Core GPUs, to enable high-throughput and low-latency image generation. \n Bria has also adopted NVIDIA Picasso, a foundry for visual generative AI models, to run inference.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/\" rel=\"nofollow\"\u003e\n New NVIDIA NeMo Framework Features and NVIDIA H200\n \u003c/a\u003e (2023/12/06)\n \u003c/summary\u003e\n NVIDIA NeMo Framework now includes several optimizations and enhancements, \n including: \n 1) Fully Sharded Data Parallelism (FSDP) to improve the efficiency of training large-scale AI models, \n 2) Mix of Experts (MoE)-based LLM architectures with expert parallelism for efficient LLM training at scale, \n 3) Reinforcement Learning from Human Feedback (RLHF) with TensorRT-LLM for inference stage acceleration, and \n 4) up to 4.2x speedups for Llama 2 pre-training on NVIDIA H200 Tensor Core GPUs.\n \u003cbr\u003e\u003cbr\u003e\n \u003ca href=\"https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://github.com/sbhavani/TransformerEngine/raw/main/docs/examples/H200-NeMo-performance.png\" alt=\"H200-NeMo-performance\" style=\"width: 600px; max-width: 100%;\"\u003e\u003c/a\u003e\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n \u003cdetails\u003e\n \u003csummary\u003e\n \u003ca href=\"https://blogs.nvidia.com/blog/nemo-amazon-titan/\" rel=\"nofollow\"\u003e\n NVIDIA now powers training for Amazon Titan Foundation models\n \u003c/a\u003e (2023/11/28)\n \u003c/summary\u003e\n NVIDIA NeMo Framework now empowers the Amazon Titan foundation models (FM) with efficient training of large language models (LLMs). \n The Titan FMs form the basis of Amazon’s generative AI service, Amazon Bedrock. \n The NeMo Framework provides a versatile framework for building, customizing, and running LLMs.\n \u003cbr\u003e\u003cbr\u003e\n \u003c/details\u003e\n\u003c/details\u003e\n\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLicenses\u003c/h2\u003e\u003ca id=\"user-content-licenses\" class=\"anchor\" aria-label=\"Permalink: Licenses\" href=\"#licenses\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/NVIDIA/NeMo?tab=Apache-2.0-1-ov-file#readme\"\u003eNeMo GitHub Apache 2.0\nlicense\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eNeMo is licensed under the \u003ca href=\"https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/\" rel=\"nofollow\"\u003eNVIDIA AI PRODUCT\nAGREEMENT\u003c/a\u003e.\nBy pulling and using the container, you accept the terms and\nconditions of this license.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"NVIDIA NeMo Framework","anchor":"nvidia-nemo-framework","htmlText":"NVIDIA NeMo Framework"},{"level":2,"text":"Latest News","anchor":"latest-news","htmlText":"Latest News"},{"level":2,"text":"Introduction","anchor":"introduction","htmlText":"Introduction"},{"level":2,"text":"What's New in NeMo 2.0","anchor":"whats-new-in-nemo-20","htmlText":"What's New in NeMo 2.0"},{"level":3,"text":"Get Started with NeMo 2.0","anchor":"get-started-with-nemo-20","htmlText":"Get Started with NeMo 2.0"},{"level":3,"text":"Get Started with Cosmos","anchor":"get-started-with-cosmos","htmlText":"Get Started with Cosmos"},{"level":2,"text":"LLMs and MMs Training, Alignment, and Customization","anchor":"llms-and-mms-training-alignment-and-customization","htmlText":"LLMs and MMs Training, Alignment, and Customization"},{"level":2,"text":"LLMs and MMs Deployment and Optimization","anchor":"llms-and-mms-deployment-and-optimization","htmlText":"LLMs and MMs Deployment and Optimization"},{"level":2,"text":"Speech AI","anchor":"speech-ai","htmlText":"Speech AI"},{"level":2,"text":"NeMo Framework Launcher","anchor":"nemo-framework-launcher","htmlText":"NeMo Framework Launcher"},{"level":2,"text":"Get Started with NeMo Framework","anchor":"get-started-with-nemo-framework","htmlText":"Get Started with NeMo Framework"},{"level":2,"text":"Key Features","anchor":"key-features","htmlText":"Key Features"},{"level":2,"text":"Requirements","anchor":"requirements","htmlText":"Requirements"},{"level":2,"text":"Developer Documentation","anchor":"developer-documentation","htmlText":"Developer Documentation"},{"level":2,"text":"Install NeMo Framework","anchor":"install-nemo-framework","htmlText":"Install NeMo Framework"},{"level":3,"text":"Support matrix","anchor":"support-matrix","htmlText":"Support matrix"},{"level":3,"text":"Conda / Pip","anchor":"conda--pip","htmlText":"Conda / Pip"},{"level":4,"text":"Pick the right version","anchor":"pick-the-right-version","htmlText":"Pick the right version"},{"level":4,"text":"Install a specific Domain","anchor":"install-a-specific-domain","htmlText":"Install a specific Domain"},{"level":3,"text":"NGC PyTorch container","anchor":"ngc-pytorch-container","htmlText":"NGC PyTorch container"},{"level":2,"text":"NGC NeMo 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data-canonical-src="https://static.pepy.tech/personalized-badge/nemo-toolkit?period=total&units=international_system&left_color=grey&right_color=brightgreen&left_text=downloads" style="max-width: 100%;"></a> <a href="https://github.com/psf/black"><img src="https://camo.githubusercontent.com/5bf9e9fa18966df7cb5fac7715bef6b72df15e01a6efa9d616c83f9fcb527fe2/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f636f64652532307374796c652d626c61636b2d3030303030302e737667" alt="Code style: black" data-canonical-src="https://img.shields.io/badge/code%20style-black-000000.svg" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto"><strong>NVIDIA NeMo Framework</strong></h1><a id="user-content-nvidia-nemo-framework" class="anchor" aria-label="Permalink: NVIDIA NeMo Framework" href="#nvidia-nemo-framework"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Latest News</h2><a id="user-content-latest-news" class="anchor" aria-label="Permalink: Latest News" href="#latest-news"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 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> <details open=""> <summary><b>Pretrain and finetune 🤗Hugging Face models via AutoModel</b></summary> Nemo Framework's latest feature AutoModel enables broad support for 🤗Hugging Face models, with 25.02 focusing on <a href="https://huggingface.co/transformers/v3.5.1/model_doc/auto.html#automodelforcausallm" rel="nofollow">AutoModelForCausalLM</a> in the <a href="https://huggingface.co/models?pipeline_tag=text-generation&sort=trending" rel="nofollow">text generation category</a>. Future releases will enable support for more model families such as Vision Language Model. </details> <details open=""> <summary><b>Training on Blackwell using Nemo</b></summary> NeMo Framework has added Blackwell support, with 25.02 focusing on functional parity for B200. More optimizations to come in the upcoming releases. </details> <details open=""> <summary><b>NeMo Framework 2.0</b></summary> We've released NeMo 2.0, an update on the NeMo Framework which prioritizes modularity and ease-of-use. Please refer to the <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/index.html" rel="nofollow">NeMo Framework User Guide</a> to get started. </details> <details open=""> <summary><b>New Cosmos World Foundation Models Support</b></summary> <details> <summary> <a href="https://developer.nvidia.com/blog/advancing-physical-ai-with-nvidia-cosmos-world-foundation-model-platform" rel="nofollow">Advancing Physical AI with NVIDIA Cosmos World Foundation Model Platform </a> (2025-01-09) </summary> The end-to-end NVIDIA Cosmos platform accelerates world model development for physical AI systems. Built on CUDA, Cosmos combines state-of-the-art world foundation models, video tokenizers, and AI-accelerated data processing pipelines. Developers can accelerate world model development by fine-tuning Cosmos world foundation models or building new ones from the ground up. These models create realistic synthetic videos of environments and interactions, providing a scalable foundation for training complex systems, from simulating humanoid robots performing advanced actions to developing end-to-end autonomous driving models. <br><br> </details> <details> <summary> <a href="https://developer.nvidia.com/blog/accelerate-custom-video-foundation-model-pipelines-with-new-nvidia-nemo-framework-capabilities/" rel="nofollow"> Accelerate Custom Video Foundation Model Pipelines with New NVIDIA NeMo Framework Capabilities </a> (2025-01-07) </summary> The NeMo Framework now supports training and customizing the <a href="https://github.com/NVIDIA/Cosmos">NVIDIA Cosmos</a> collection of world foundation models. Cosmos leverages advanced text-to-world generation techniques to create fluid, coherent video content from natural language prompts. <br><br> You can also now accelerate your video processing step using the <a href="https://developer.nvidia.com/nemo-curator-video-processing-early-access" rel="nofollow">NeMo Curator</a> library, which provides optimized video processing and captioning features that can deliver up to 89x faster video processing when compared to an unoptimized CPU pipeline. <br><br> </details> </details> <details open=""> <summary><b>Large Language Models and Multimodal Models</b></summary> <details> <summary> <a href="https://developer.nvidia.com/blog/state-of-the-art-multimodal-generative-ai-model-development-with-nvidia-nemo/" rel="nofollow"> State-of-the-Art Multimodal Generative AI Model Development with NVIDIA NeMo </a> (2024-11-06) </summary> NVIDIA recently announced significant enhancements to the NeMo platform, focusing on multimodal generative AI models. The update includes NeMo Curator and the Cosmos tokenizer, which streamline the data curation process and enhance the quality of visual data. These tools are designed to handle large-scale data efficiently, making it easier to develop high-quality AI models for various applications, including robotics and autonomous driving. The Cosmos tokenizers, in particular, efficiently map visual data into compact, semantic tokens, which is crucial for training large-scale generative models. The tokenizer is available now on the <a href="http://github.com/NVIDIA/cosmos-tokenizer/NVIDIA/cosmos-tokenizer">NVIDIA/cosmos-tokenizer</a> GitHub repo and on <a href="https://huggingface.co/nvidia/Cosmos-Tokenizer-CV8x8x8" rel="nofollow">Hugging Face</a>. <br><br> </details> <details> <summary> <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/llama/index.html#new-llama-3-1-support for more information/" rel="nofollow"> New Llama 3.1 Support </a> (2024-07-23) </summary> The NeMo Framework now supports training and customizing the Llama 3.1 collection of LLMs from Meta. <br><br> </details> <details> <summary> <a href="https://aws.amazon.com/blogs/machine-learning/accelerate-your-generative-ai-distributed-training-workloads-with-the-nvidia-nemo-framework-on-amazon-eks/" rel="nofollow"> Accelerate your Generative AI Distributed Training Workloads with the NVIDIA NeMo Framework on Amazon EKS </a> (2024-07-16) </summary> NVIDIA NeMo Framework now runs distributed training workloads on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. For step-by-step instructions on creating an EKS cluster and running distributed training workloads with NeMo, see the GitHub repository <a href="https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/2.nemo-launcher/EKS/"> here.</a> <br><br> </details> <details> <summary> <a href="https://developer.nvidia.com/blog/nvidia-nemo-accelerates-llm-innovation-with-hybrid-state-space-model-support/" rel="nofollow"> NVIDIA NeMo Accelerates LLM Innovation with Hybrid State Space Model Support </a> (2024/06/17) </summary> NVIDIA NeMo and Megatron Core now support pre-training and fine-tuning of state space models (SSMs). NeMo also supports training models based on the Griffin architecture as described by Google DeepMind. <br><br> </details> <details> <summary> <a href="https://huggingface.co/models?sort=trending&search=nvidia%2Fnemotron-4-340B" rel="nofollow"> NVIDIA releases 340B base, instruct, and reward models pretrained on a total of 9T tokens. </a> (2024-06-18) </summary> See documentation and tutorials for SFT, PEFT, and PTQ with <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/llms/nemotron/index.html" rel="nofollow"> Nemotron 340B </a> in the NeMo Framework User Guide. <br><br> </details> <details> <summary> <a href="https://developer.nvidia.com/blog/nvidia-sets-new-generative-ai-performance-and-scale-records-in-mlperf-training-v4-0/" rel="nofollow"> NVIDIA sets new generative AI performance and scale records in MLPerf Training v4.0 </a> (2024/06/12) </summary> Using NVIDIA NeMo Framework and NVIDIA Hopper GPUs NVIDIA was able to scale to 11,616 H100 GPUs and achieve near-linear performance scaling on LLM pretraining. NVIDIA also achieved the highest LLM fine-tuning performance and raised the bar for text-to-image training. <br><br> </details> <details> <summary> <a href="https://cloud.google.com/blog/products/compute/gke-and-nvidia-nemo-framework-to-train-generative-ai-models" rel="nofollow"> Accelerate your generative AI journey with NVIDIA NeMo Framework on GKE </a> (2024/03/16) </summary> An end-to-end walkthrough to train generative AI models on the Google Kubernetes Engine (GKE) using the NVIDIA NeMo Framework is available at <a href="https://github.com/GoogleCloudPlatform/nvidia-nemo-on-gke">https://github.com/GoogleCloudPlatform/nvidia-nemo-on-gke</a>. The walkthrough includes detailed instructions on how to set up a Google Cloud Project and pre-train a GPT model using the NeMo Framework. <br><br> </details> </details> <details open=""> <summary><b>Speech Recognition</b></summary> <details> <summary> <a href="https://developer.nvidia.com/blog/accelerating-leaderboard-topping-asr-models-10x-with-nvidia-nemo/" rel="nofollow"> Accelerating Leaderboard-Topping ASR Models 10x with NVIDIA NeMo </a> (2024/09/24) </summary> NVIDIA NeMo team released a number of inference optimizations for CTC, RNN-T, and TDT models that resulted in up to 10x inference speed-up. These models now exceed an inverse real-time factor (RTFx) of 2,000, with some reaching RTFx of even 6,000. <br><br> </details> <details> <summary> <a href="https://developer.nvidia.com/blog/new-standard-for-speech-recognition-and-translation-from-the-nvidia-nemo-canary-model/" rel="nofollow"> New Standard for Speech Recognition and Translation from the NVIDIA NeMo Canary Model </a> (2024/04/18) </summary> The NeMo team just released Canary, a multilingual model that transcribes speech in English, Spanish, German, and French with punctuation and capitalization. Canary also provides bi-directional translation, between English and the three other supported languages. <br><br> </details> <details> <summary> <a href="https://developer.nvidia.com/blog/pushing-the-boundaries-of-speech-recognition-with-nemo-parakeet-asr-models/" rel="nofollow"> Pushing the Boundaries of Speech Recognition with NVIDIA NeMo Parakeet ASR Models </a> (2024/04/18) </summary> NVIDIA NeMo, an end-to-end platform for the development of multimodal generative AI models at scale anywhere—on any cloud and on-premises—released the Parakeet family of automatic speech recognition (ASR) models. These state-of-the-art ASR models, developed in collaboration with Suno.ai, transcribe spoken English with exceptional accuracy. <br><br> </details> <details> <summary> <a href="https://developer.nvidia.com/blog/turbocharge-asr-accuracy-and-speed-with-nvidia-nemo-parakeet-tdt/" rel="nofollow"> Turbocharge ASR Accuracy and Speed with NVIDIA NeMo Parakeet-TDT </a> (2024/04/18) </summary> NVIDIA NeMo, an end-to-end platform for developing multimodal generative AI models at scale anywhere—on any cloud and on-premises—recently released Parakeet-TDT. This new addition to the  NeMo ASR Parakeet model family boasts better accuracy and 64% greater speed over the previously best model, Parakeet-RNNT-1.1B. <br><br> </details> </details> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Introduction</h2><a id="user-content-introduction" class="anchor" aria-label="Permalink: Introduction" href="#introduction"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NVIDIA NeMo Framework is a scalable and cloud-native generative AI framework built for researchers and PyTorch developers working on Large Language Models (LLMs), Multimodal Models (MMs), Automatic Speech Recognition (ASR), Text to Speech (TTS), and Computer Vision (CV) domains. It is designed to help you efficiently create, customize, and deploy new generative AI models by leveraging existing code and pre-trained model checkpoints.</p> <p dir="auto">For technical documentation, please see the <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/index.html" rel="nofollow">NeMo Framework User Guide</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">What's New in NeMo 2.0</h2><a id="user-content-whats-new-in-nemo-20" class="anchor" aria-label="Permalink: What's New in NeMo 2.0" href="#whats-new-in-nemo-20"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">NVIDIA NeMo 2.0 introduces several significant improvements over its predecessor, NeMo 1.0, enhancing flexibility, performance, and scalability.</p> <ul dir="auto"> <li> <p dir="auto"><strong>Python-Based Configuration</strong> - NeMo 2.0 transitions from YAML files to a Python-based configuration, providing more flexibility and control. This shift makes it easier to extend and customize configurations programmatically.</p> </li> <li> <p dir="auto"><strong>Modular Abstractions</strong> - By adopting PyTorch Lightning’s modular abstractions, NeMo 2.0 simplifies adaptation and experimentation. This modular approach allows developers to more easily modify and experiment with different components of their models.</p> </li> <li> <p dir="auto"><strong>Scalability</strong> - NeMo 2.0 seamlessly scaling large-scale experiments across thousands of GPUs using <a href="https://github.com/NVIDIA/NeMo-Run">NeMo-Run</a>, a powerful tool designed to streamline the configuration, execution, and management of machine learning experiments across computing environments.</p> </li> </ul> <p dir="auto">Overall, these enhancements make NeMo 2.0 a powerful, scalable, and user-friendly framework for AI model development.</p> <div class="markdown-alert markdown-alert-important" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-report mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path></svg>Important</p><p dir="auto">NeMo 2.0 is currently supported by the LLM (large language model) and VLM (vision language model) collections.</p> </div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Get Started with NeMo 2.0</h3><a id="user-content-get-started-with-nemo-20" class="anchor" aria-label="Permalink: Get Started with NeMo 2.0" href="#get-started-with-nemo-20"><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>Refer to the <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/quickstart.html" rel="nofollow">Quickstart</a> for examples of using NeMo-Run to launch NeMo 2.0 experiments locally and on a slurm cluster.</li> <li>For more information about NeMo 2.0, see the <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/index.html" rel="nofollow">NeMo Framework User Guide</a>.</li> <li><a href="https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/llm/recipes">NeMo 2.0 Recipes</a> contains additional examples of launching large-scale runs using NeMo 2.0 and NeMo-Run.</li> <li>For an in-depth exploration of the main features of NeMo 2.0, see the <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/features/index.html#feature-guide" rel="nofollow">Feature Guide</a>.</li> <li>To transition from NeMo 1.0 to 2.0, see the <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/nemo-2.0/migration/index.html#migration-guide" rel="nofollow">Migration Guide</a> for step-by-step instructions.</li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Get Started with Cosmos</h3><a id="user-content-get-started-with-cosmos" class="anchor" aria-label="Permalink: Get Started with Cosmos" href="#get-started-with-cosmos"><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">NeMo Curator and NeMo Framework support video curation and post-training of the Cosmos World Foundation Models, which are open and available on <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/cosmos/collections/cosmos" rel="nofollow">NGC</a> and <a href="https://huggingface.co/collections/nvidia/cosmos-6751e884dc10e013a0a0d8e6" rel="nofollow">Hugging Face</a>. For more information on video datasets, refer to <a href="https://developer.nvidia.com/nemo-curator" rel="nofollow">NeMo Curator</a>. To post-train World Foundation Models using the NeMo Framework for your custom physical AI tasks, see the <a href="https://github.com/NVIDIA/Cosmos/blob/main/cosmos1/models/diffusion/nemo/post_training/README.md">Cosmos Diffusion models</a> and the <a href="https://github.com/NVIDIA/Cosmos/blob/main/cosmos1/models/autoregressive/nemo/post_training/README.md">Cosmos Autoregressive models</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">LLMs and MMs Training, Alignment, and Customization</h2><a id="user-content-llms-and-mms-training-alignment-and-customization" class="anchor" aria-label="Permalink: LLMs and MMs Training, Alignment, and Customization" href="#llms-and-mms-training-alignment-and-customization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">All NeMo models are trained with <a href="https://github.com/Lightning-AI/lightning">Lightning</a>. Training is automatically scalable to 1000s of GPUs. You can check the performance benchmarks using the latest NeMo Framework container <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/performance/performance_summary.html" rel="nofollow">here</a>.</p> <p dir="auto">When applicable, NeMo models leverage cutting-edge distributed training techniques, incorporating <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/modeloverview.html" rel="nofollow">parallelism strategies</a> to enable efficient training of very large models. These techniques include Tensor Parallelism (TP), Pipeline Parallelism (PP), Fully Sharded Data Parallelism (FSDP), Mixture-of-Experts (MoE), and Mixed Precision Training with BFloat16 and FP8, as well as others.</p> <p dir="auto">NeMo Transformer-based LLMs and MMs utilize <a href="https://github.com/NVIDIA/TransformerEngine">NVIDIA Transformer Engine</a> for FP8 training on NVIDIA Hopper GPUs, while leveraging <a href="https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core">NVIDIA Megatron Core</a> for scaling Transformer model training.</p> <p dir="auto">NeMo LLMs can be aligned with state-of-the-art methods such as SteerLM, Direct Preference Optimization (DPO), and Reinforcement Learning from Human Feedback (RLHF). See <a href="https://github.com/NVIDIA/NeMo-Aligner">NVIDIA NeMo Aligner</a> for more information.</p> <p dir="auto">In addition to supervised fine-tuning (SFT), NeMo also supports the latest parameter efficient fine-tuning (PEFT) techniques such as LoRA, P-Tuning, Adapters, and IA3. Refer to the <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/sft_peft/index.html" rel="nofollow">NeMo Framework User Guide</a> for the full list of supported models and techniques.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">LLMs and MMs Deployment and Optimization</h2><a id="user-content-llms-and-mms-deployment-and-optimization" class="anchor" aria-label="Permalink: LLMs and MMs Deployment and Optimization" href="#llms-and-mms-deployment-and-optimization"><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">NeMo LLMs and MMs can be deployed and optimized with <a href="https://developer.nvidia.com/nemo-microservices-early-access" rel="nofollow">NVIDIA NeMo Microservices</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Speech AI</h2><a id="user-content-speech-ai" class="anchor" aria-label="Permalink: Speech AI" href="#speech-ai"><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">NeMo ASR and TTS models can be optimized for inference and deployed for production use cases with <a href="https://developer.nvidia.com/riva" rel="nofollow">NVIDIA Riva</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">NeMo Framework Launcher</h2><a id="user-content-nemo-framework-launcher" class="anchor" aria-label="Permalink: NeMo Framework Launcher" href="#nemo-framework-launcher"><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-alert markdown-alert-important" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-report mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .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-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path></svg>Important</p><p dir="auto">NeMo Framework Launcher is compatible with NeMo version 1.0 only. <a href="https://github.com/NVIDIA/NeMo-Run">NeMo-Run</a> is recommended for launching experiments using NeMo 2.0.</p> </div> <p dir="auto"><a href="https://github.com/NVIDIA/NeMo-Megatron-Launcher">NeMo Framework Launcher</a> is a cloud-native tool that streamlines the NeMo Framework experience. It is used for launching end-to-end NeMo Framework training jobs on CSPs and Slurm clusters.</p> <p dir="auto">The NeMo Framework Launcher includes extensive recipes, scripts, utilities, and documentation for training NeMo LLMs. It also includes the NeMo Framework <a href="https://github.com/NVIDIA/NeMo-Megatron-Launcher#53-using-autoconfigurator-to-find-the-optimal-configuration">Autoconfigurator</a>, which is designed to find the optimal model parallel configuration for training on a specific cluster.</p> <p dir="auto">To get started quickly with the NeMo Framework Launcher, please see the <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/index.html" rel="nofollow">NeMo Framework Playbooks</a>. The NeMo Framework Launcher does not currently support ASR and TTS training, but it will soon.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Get Started with NeMo Framework</h2><a id="user-content-get-started-with-nemo-framework" class="anchor" aria-label="Permalink: Get Started with NeMo Framework" href="#get-started-with-nemo-framework"><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">Getting started with NeMo Framework is easy. State-of-the-art pretrained NeMo models are freely available on <a href="https://huggingface.co/models?library=nemo&sort=downloads&search=nvidia" rel="nofollow">Hugging Face Hub</a> and <a href="https://catalog.ngc.nvidia.com/models?query=nemo&orderBy=weightPopularDESC" rel="nofollow">NVIDIA NGC</a>. These models can be used to generate text or images, transcribe audio, and synthesize speech in just a few lines of code.</p> <p dir="auto">We have extensive <a href="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/tutorials.html" rel="nofollow">tutorials</a> that can be run on <a href="https://colab.research.google.com" rel="nofollow">Google Colab</a> or with our <a href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo" rel="nofollow">NGC NeMo Framework Container</a>. We also have <a href="https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/index.html" rel="nofollow">playbooks</a> for users who want to train NeMo models with the NeMo Framework Launcher.</p> <p dir="auto">For advanced users who want to train NeMo models from scratch or fine-tune existing NeMo models, we have a full suite of <a href="https://github.com/NVIDIA/NeMo/tree/main/examples">example scripts</a> that support multi-GPU/multi-node training.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Key Features</h2><a id="user-content-key-features" class="anchor" aria-label="Permalink: Key Features" href="#key-features"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="/NVIDIA/NeMo/blob/main/nemo/collections/nlp/README.md">Large Language Models</a></li> <li><a href="/NVIDIA/NeMo/blob/main/nemo/collections/multimodal/README.md">Multimodal</a></li> <li><a href="/NVIDIA/NeMo/blob/main/nemo/collections/asr/README.md">Automatic Speech Recognition</a></li> <li><a href="/NVIDIA/NeMo/blob/main/nemo/collections/tts/README.md">Text to Speech</a></li> <li><a href="/NVIDIA/NeMo/blob/main/nemo/collections/vision/README.md">Computer Vision</a></li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Requirements</h2><a id="user-content-requirements" class="anchor" aria-label="Permalink: Requirements" href="#requirements"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Python 3.10 or above</li> <li>Pytorch 2.5 or above</li> <li>NVIDIA GPU (if you intend to do model training)</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Developer Documentation</h2><a id="user-content-developer-documentation" class="anchor" aria-label="Permalink: Developer Documentation" href="#developer-documentation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th>Version</th> <th>Status</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>Latest</td> <td><a href="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/" rel="nofollow"><img src="https://camo.githubusercontent.com/e15da58ddd034a51255e31ee991eed8d10a62e92df02abcd24a2a039b1219e32/68747470733a2f2f72656164746865646f63732e636f6d2f70726f6a656374732f6e76696469612d6e656d6f2f62616467652f3f76657273696f6e3d6d61696e" alt="Documentation Status" data-canonical-src="https://readthedocs.com/projects/nvidia-nemo/badge/?version=main" style="max-width: 100%;"></a></td> <td><a href="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/main/" rel="nofollow">Documentation of the latest (i.e. main) branch.</a></td> </tr> <tr> <td>Stable</td> <td><a href="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/" rel="nofollow"><img src="https://camo.githubusercontent.com/2080dcf6855ff22d900294c5c4be9f008ed95762e767a22d02485286e3497002/68747470733a2f2f72656164746865646f63732e636f6d2f70726f6a656374732f6e76696469612d6e656d6f2f62616467652f3f76657273696f6e3d737461626c65" alt="Documentation Status" data-canonical-src="https://readthedocs.com/projects/nvidia-nemo/badge/?version=stable" style="max-width: 100%;"></a></td> <td><a href="https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/" rel="nofollow">Documentation of the stable (i.e. most recent release)</a></td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Install NeMo Framework</h2><a id="user-content-install-nemo-framework" class="anchor" aria-label="Permalink: Install NeMo Framework" href="#install-nemo-framework"><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 NeMo Framework can be installed in a variety of ways, depending on your needs. Depending on the domain, you may find one of the following installation methods more suitable.</p> <ul dir="auto"> <li><a href="#conda--pip">Conda / Pip</a>: Install NeMo-Framework with native Pip into a virtual environment. <ul dir="auto"> <li>Used to explore NeMo on any supported platform.</li> <li>This is the recommended method for ASR and TTS domains.</li> <li>Limited feature-completeness for other domains.</li> </ul> </li> <li><a href="#ngc-pytorch-container">NGC PyTorch container</a>: Install NeMo-Framework from source with feature-completeness into a highly optimized container. <ul dir="auto"> <li>For users that want to install from source in a highly optimized container.</li> </ul> </li> <li><a href="#ngc-nemo-container">NGC NeMo container</a>: Ready-to-go solution of NeMo-Framework <ul dir="auto"> <li>For users that seek highest performance.</li> <li>Contains all dependencies installed and tested for performance and convergence.</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Support matrix</h3><a id="user-content-support-matrix" class="anchor" aria-label="Permalink: Support matrix" href="#support-matrix"><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">NeMo-Framework provides tiers of support based on OS / Platform and mode of installation. Please refer the following overview of support levels:</p> <ul dir="auto"> <li>Fully supported: Max performance and feature-completeness.</li> <li>Limited supported: Used to explore NeMo.</li> <li>No support yet: In development.</li> <li>Deprecated: Support has reached end of life.</li> </ul> <p dir="auto">Please refer to the following table for current support levels:</p> <markdown-accessiblity-table><table> <thead> <tr> <th>OS / Platform</th> <th>Install from PyPi</th> <th>Source into NGC container</th> </tr> </thead> <tbody> <tr> <td><code>linux</code> - <code>amd64/x84_64</code></td> <td>Limited support</td> <td>Full support</td> </tr> <tr> <td><code>linux</code> - <code>arm64</code></td> <td>Limited support</td> <td>Limited support</td> </tr> <tr> <td><code>darwin</code> - <code>amd64/x64_64</code></td> <td>Deprecated</td> <td>Deprecated</td> </tr> <tr> <td><code>darwin</code> - <code>arm64</code></td> <td>Limited support</td> <td>Limited support</td> </tr> <tr> <td><code>windows</code> - <code>amd64/x64_64</code></td> <td>No support yet</td> <td>No support yet</td> </tr> <tr> <td><code>windows</code> - <code>arm64</code></td> <td>No support yet</td> <td>No support yet</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Conda / Pip</h3><a id="user-content-conda--pip" class="anchor" aria-label="Permalink: Conda / Pip" href="#conda--pip"><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">Install NeMo in a fresh Conda environment:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda create --name nemo python==3.10.12 conda activate nemo"><pre>conda create --name nemo python==3.10.12 conda activate nemo</pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Pick the right version</h4><a id="user-content-pick-the-right-version" class="anchor" aria-label="Permalink: Pick the right version" href="#pick-the-right-version"><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">NeMo-Framework publishes pre-built wheels with each release. To install nemo_toolkit from such a wheel, use the following installation method:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install "nemo_toolkit[all]""><pre>pip install <span class="pl-s"><span class="pl-pds">"</span>nemo_toolkit[all]<span class="pl-pds">"</span></span></pre></div> <p dir="auto">If a more specific version is desired, we recommend a Pip-VCS install. From <a href="/NVIDIA/NeMo/blob/main/github.com/NVIDIA/NeMo">NVIDIA/NeMo</a>, fetch the commit, branch, or tag that you would like to install.<br> To install nemo_toolkit from this Git reference <code>$REF</code>, use the following installation method:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="git clone https://github.com/NVIDIA/NeMo cd NeMo git checkout @${REF:-'main'} pip install '.[all]'"><pre>git clone https://github.com/NVIDIA/NeMo <span class="pl-c1">cd</span> NeMo git checkout @<span class="pl-smi">${REF<span class="pl-k">:-</span><span class="pl-s"><span class="pl-pds">'</span>main<span class="pl-pds">'</span></span>}</span> pip install <span class="pl-s"><span class="pl-pds">'</span>.[all]<span class="pl-pds">'</span></span></pre></div> <div class="markdown-heading" dir="auto"><h4 tabindex="-1" class="heading-element" dir="auto">Install a specific Domain</h4><a id="user-content-install-a-specific-domain" class="anchor" aria-label="Permalink: Install a specific Domain" href="#install-a-specific-domain"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To install a specific domain of NeMo, you must first install the nemo_toolkit using the instructions listed above. Then, you run the following domain-specific commands:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install nemo_toolkit['all'] # or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[all]" pip install nemo_toolkit['asr'] # or pip install "git+https://github.com/NVIDIA/NeMo@$REF#egg=nemo_toolkit[asr]" pip install nemo_toolkit['nlp'] # or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[nlp]" pip install nemo_toolkit['tts'] # or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[tts]" pip install nemo_toolkit['vision'] # or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[vision]" pip install nemo_toolkit['multimodal'] # or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[multimodal]""><pre>pip install nemo_toolkit[<span class="pl-s"><span class="pl-pds">'</span>all<span class="pl-pds">'</span></span>] <span class="pl-c"><span class="pl-c">#</span> or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[all]"</span> pip install nemo_toolkit[<span class="pl-s"><span class="pl-pds">'</span>asr<span class="pl-pds">'</span></span>] <span class="pl-c"><span class="pl-c">#</span> or pip install "git+https://github.com/NVIDIA/NeMo@$REF#egg=nemo_toolkit[asr]"</span> pip install nemo_toolkit[<span class="pl-s"><span class="pl-pds">'</span>nlp<span class="pl-pds">'</span></span>] <span class="pl-c"><span class="pl-c">#</span> or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[nlp]"</span> pip install nemo_toolkit[<span class="pl-s"><span class="pl-pds">'</span>tts<span class="pl-pds">'</span></span>] <span class="pl-c"><span class="pl-c">#</span> or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[tts]"</span> pip install nemo_toolkit[<span class="pl-s"><span class="pl-pds">'</span>vision<span class="pl-pds">'</span></span>] <span class="pl-c"><span class="pl-c">#</span> or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[vision]"</span> pip install nemo_toolkit[<span class="pl-s"><span class="pl-pds">'</span>multimodal<span class="pl-pds">'</span></span>] <span class="pl-c"><span class="pl-c">#</span> or pip install "git+https://github.com/NVIDIA/NeMo@${REF:-'main'}#egg=nemo_toolkit[multimodal]"</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">NGC PyTorch container</h3><a id="user-content-ngc-pytorch-container" class="anchor" aria-label="Permalink: NGC PyTorch container" href="#ngc-pytorch-container"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><strong>NOTE: The following steps are supported beginning with 24.04 (NeMo-Toolkit 2.3.0)</strong></p> <p dir="auto">We recommended that you start with a base NVIDIA PyTorch container: nvcr.io/nvidia/pytorch:25.01-py3.</p> <p dir="auto">If starting with a base NVIDIA PyTorch container, you must first launch the container:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker run \ --gpus all \ -it \ --rm \ --shm-size=16g \ --ulimit memlock=-1 \ --ulimit stack=67108864 \ nvcr.io/nvidia/pytorch:${NV_PYTORCH_TAG:-'nvcr.io/nvidia/pytorch:25.01-py3'}"><pre>docker run \ --gpus all \ -it \ --rm \ --shm-size=16g \ --ulimit memlock=-1 \ --ulimit stack=67108864 \ nvcr.io/nvidia/pytorch:<span class="pl-smi">${NV_PYTORCH_TAG<span class="pl-k">:-</span><span class="pl-s"><span class="pl-pds">'</span>nvcr.io/nvidia/pytorch:25.01-py3<span class="pl-pds">'</span></span>}</span></pre></div> <p dir="auto">From <a href="/NVIDIA/NeMo/blob/main/github.com/NVIDIA/NeMo">NVIDIA/NeMo</a>, fetch the commit/branch/tag that you want to install.<br> To install nemo_toolkit including all of its dependencies from this Git reference <code>$REF</code>, use the following installation method:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="cd /opt git clone https://github.com/NVIDIA/NeMo cd NeMo git checkout ${REF:-'main'} bash reinstall.sh --library all"><pre><span class="pl-c1">cd</span> /opt git clone https://github.com/NVIDIA/NeMo <span class="pl-c1">cd</span> NeMo git checkout <span class="pl-smi">${REF<span class="pl-k">:-</span><span class="pl-s"><span class="pl-pds">'</span>main<span class="pl-pds">'</span></span>}</span> bash reinstall.sh --library all</pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">NGC NeMo container</h2><a id="user-content-ngc-nemo-container" class="anchor" aria-label="Permalink: NGC NeMo container" href="#ngc-nemo-container"><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">NeMo containers are launched concurrently with NeMo version updates. NeMo Framework now supports LLMs, MMs, ASR, and TTS in a single consolidated Docker container. You can find additional information about released containers on the <a href="https://github.com/NVIDIA/NeMo/releases">NeMo releases page</a>.</p> <p dir="auto">To use a pre-built container, run the following code:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="docker run \ --gpus all \ -it \ --rm \ --shm-size=16g \ --ulimit memlock=-1 \ --ulimit stack=67108864 \ nvcr.io/nvidia/pytorch:${NV_PYTORCH_TAG:-'nvcr.io/nvidia/nemo:25.02'}"><pre>docker run \ --gpus all \ -it \ --rm \ --shm-size=16g \ --ulimit memlock=-1 \ --ulimit stack=67108864 \ nvcr.io/nvidia/pytorch:<span class="pl-smi">${NV_PYTORCH_TAG<span class="pl-k">:-</span><span class="pl-s"><span class="pl-pds">'</span>nvcr.io/nvidia/nemo:25.02<span class="pl-pds">'</span></span>}</span></pre></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Future Work</h2><a id="user-content-future-work" class="anchor" aria-label="Permalink: Future Work" href="#future-work"><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 NeMo Framework Launcher does not currently support ASR and TTS training, but it will soon.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Discussions Board</h2><a id="user-content-discussions-board" class="anchor" aria-label="Permalink: Discussions Board" href="#discussions-board"><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">FAQ can be found on the NeMo <a href="https://github.com/NVIDIA/NeMo/discussions">Discussions board</a>. You are welcome to ask questions or start discussions on the board.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Contribute to NeMo</h2><a id="user-content-contribute-to-nemo" class="anchor" aria-label="Permalink: Contribute to NeMo" href="#contribute-to-nemo"><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 welcome community contributions! Please refer to <a href="https://github.com/NVIDIA/NeMo/blob/stable/CONTRIBUTING.md">CONTRIBUTING.md</a> for the process.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Publications</h2><a id="user-content-publications" class="anchor" aria-label="Permalink: Publications" href="#publications"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We provide an ever-growing list of <a href="https://nvidia.github.io/NeMo/publications/" rel="nofollow">publications</a> that utilize the NeMo Framework.</p> <p dir="auto">To contribute an article to the collection, please submit a pull request to the <code>gh-pages-src</code> branch of this repository. For detailed information, please consult the README located at the <a href="https://github.com/NVIDIA/NeMo/tree/gh-pages-src#readme">gh-pages-src branch</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Blogs</h2><a id="user-content-blogs" class="anchor" aria-label="Permalink: Blogs" href="#blogs"><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> <details open=""> <summary><b>Large Language Models and Multimodal Models</b></summary> <details> <summary> <a href="https://blogs.nvidia.com/blog/bria-builds-responsible-generative-ai-using-nemo-picasso/" rel="nofollow"> Bria Builds Responsible Generative AI for Enterprises Using NVIDIA NeMo, Picasso </a> (2024/03/06) </summary> Bria, a Tel Aviv startup at the forefront of visual generative AI for enterprises now leverages the NVIDIA NeMo Framework. The Bria.ai platform uses reference implementations from the NeMo Multimodal collection, trained on NVIDIA Tensor Core GPUs, to enable high-throughput and low-latency image generation. Bria has also adopted NVIDIA Picasso, a foundry for visual generative AI models, to run inference. <br><br> </details> <details> <summary> <a href="https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility/" rel="nofollow"> New NVIDIA NeMo Framework Features and NVIDIA H200 </a> (2023/12/06) </summary> NVIDIA NeMo Framework now includes several optimizations and enhancements, including: 1) Fully Sharded Data Parallelism (FSDP) to improve the efficiency of training large-scale AI models, 2) Mix of Experts (MoE)-based LLM architectures with expert parallelism for efficient LLM training at scale, 3) Reinforcement Learning from Human Feedback (RLHF) with TensorRT-LLM for inference stage acceleration, and 4) up to 4.2x speedups for Llama 2 pre-training on NVIDIA H200 Tensor Core GPUs. <br><br> <a href="https://developer.nvidia.com/blog/new-nvidia-nemo-framework-features-and-nvidia-h200-supercharge-llm-training-performance-and-versatility" rel="nofollow"> <img src="https://github.com/sbhavani/TransformerEngine/raw/main/docs/examples/H200-NeMo-performance.png" alt="H200-NeMo-performance" style="width: 600px; max-width: 100%;"></a> <br><br> </details> <details> <summary> <a href="https://blogs.nvidia.com/blog/nemo-amazon-titan/" rel="nofollow"> NVIDIA now powers training for Amazon Titan Foundation models </a> (2023/11/28) </summary> NVIDIA NeMo Framework now empowers the Amazon Titan foundation models (FM) with efficient training of large language models (LLMs). The Titan FMs form the basis of Amazon’s generative AI service, Amazon Bedrock. The NeMo Framework provides a versatile framework for building, customizing, and running LLMs. <br><br> </details> </details> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Licenses</h2><a id="user-content-licenses" class="anchor" aria-label="Permalink: Licenses" href="#licenses"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/NVIDIA/NeMo?tab=Apache-2.0-1-ov-file#readme">NeMo GitHub Apache 2.0 license</a></li> <li>NeMo is licensed under the <a href="https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/" rel="nofollow">NVIDIA AI PRODUCT AGREEMENT</a>. By pulling and using the container, you accept the terms and conditions of this license.</li> </ul> </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="3bIih96JaUe6Hya1rTamIc0NBTtuvZfUIM+K4BVKQyjTE2umLXbZ+YSj6e9x86d2NHqG5ESDKa1Fk+arl6iSVw==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech) </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path> </svg> <span class="flex-auto min-width-0 css-truncate css-truncate-target width-fit"> <a title="https://docs.nvidia.com/nemo-framework/user-guide/latest/overview.html" role="link" target="_blank" rel="noopener noreferrer nofollow" class="text-bold" href="https://docs.nvidia.com/nemo-framework/user-guide/latest/overview.html">docs.nvidia.com/nemo-framework/user-guide/latest/overview.html</a> </span> </div> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a href="/topics/machine-translation" title="Topic: machine-translation" data-view-component="true" class="topic-tag topic-tag-link"> machine-translation </a> <a href="/topics/tts" title="Topic: tts" data-view-component="true" class="topic-tag topic-tag-link"> tts </a> <a href="/topics/speech-synthesis" title="Topic: speech-synthesis" data-view-component="true" class="topic-tag topic-tag-link"> speech-synthesis </a> <a href="/topics/neural-networks" title="Topic: neural-networks" data-view-component="true" class="topic-tag topic-tag-link"> neural-networks </a> <a href="/topics/deeplearning" title="Topic: deeplearning" data-view-component="true" class="topic-tag topic-tag-link"> deeplearning </a> <a href="/topics/speaker-recognition" title="Topic: speaker-recognition" data-view-component="true" class="topic-tag topic-tag-link"> speaker-recognition </a> <a href="/topics/asr" title="Topic: asr" data-view-component="true" class="topic-tag topic-tag-link"> asr </a> <a href="/topics/multimodal" title="Topic: multimodal" data-view-component="true" class="topic-tag topic-tag-link"> multimodal </a> <a href="/topics/speech-translation" title="Topic: speech-translation" data-view-component="true" class="topic-tag topic-tag-link"> speech-translation </a> <a href="/topics/large-language-models" title="Topic: large-language-models" data-view-component="true" class="topic-tag topic-tag-link"> large-language-models </a> <a href="/topics/speaker-diariazation" title="Topic: speaker-diariazation" data-view-component="true" class="topic-tag topic-tag-link"> speaker-diariazation </a> <a href="/topics/generative-ai" title="Topic: generative-ai" data-view-component="true" class="topic-tag topic-tag-link"> generative-ai </a> </div> </div> <h3 class="sr-only">Resources</h3> <div class="mt-2"> <a class="Link--muted" data-analytics-event="{"category":"Repository 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1 1 1.5 0ZM5 3.25a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Zm6.75.75a.75.75 0 1 0 0-1.5.75.75 0 0 0 0 1.5Zm-3 8.75a.75.75 0 1 0-1.5 0 .75.75 0 0 0 1.5 0Z"></path> </svg> <strong>2.8k</strong> forks</a> </div> <div class="mt-2"> <a class="Link--muted" href="/contact/report-content?content_url=https%3A%2F%2Fgithub.com%2FNVIDIA%2FNeMo&report=NVIDIA+%28user%29"> Report repository </a> </div> </div> </div> </div> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <h2 class="h4 mb-3" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame"> <a href="/NVIDIA/NeMo/releases" data-view-component="true" class="Link--primary no-underline Link">Releases <span title="60" data-view-component="true" class="Counter">60</span></a></h2> <a class="Link--primary d-flex no-underline" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/NVIDIA/NeMo/releases/tag/v2.2.1"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-tag flex-shrink-0 mt-1 color-fg-success"> <path d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">NVIDIA Neural Modules 2.2.1</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2025-03-31T21:31:41Z" class="no-wrap">Mar 31, 2025</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" 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</button> You can’t perform that action at this time. </div> <template id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 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6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>