Nemo with PyTorch Lightning

PyTorch Lightning is a light weight PyTorch wrapper that reduces the engineering boilerplate and resources required to implement state-of-the-art AI. Organizing PyTorch code with Lightning, enables seamless training on multiple-GPUs, TPUs, CPUs as well as the use of difficult to implement best practices such as model sharding, 16-bit precision and more. PyTorch Lightning support an extremly robust ecosystem of Machine Learning and Deep Learning projects from 1st Party Repos such as Flash to 3rd party frameworks such as Nvidia NeMo. NVIDIA NeMo is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech synthesis (TTS). The primary objective of NeMo is to help researchers from industry and academia to reuse prior work (code and pretrained models and make it easier to create new conversational AI models.

Run the Jupyter notebook:

Download the jupyter notebook from top right section of this page and run the cells of the notebook to install required libraries, download the models and train the model.

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Organizing PyTorch code with Lightning, enables seamless training on multiple-GPUs, TPUs, CPUs as well as the use of difficult to implement best practices such as model sharding, 16-bit precision and more. PyTorch Lightning support an extremly robust ecosystem of Machine Learning and Deep Learning projects from 1st Party Repos such as Flash to 3rd party frameworks such as Nvidia NeMo. NVIDIA NeMo is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech synthesis (TTS). The primary objective of NeMo is to help researchers from industry and academia to reuse prior work (code and pretrained models and make it easier to create new conversational AI models.\n\n## Run the Jupyter notebook:\nDownload the jupyter notebook from top right section of this page and run the cells of the notebook to install required libraries, download the models and train the model.","displayName","Nemo with Pytorch Lightning",["D",1680632499660],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/resources/nemo_pytorchlightning","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/Grid%20Lightning%20Thumbnail.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[266,267,268],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_235":278,"_270":279,"_271":280,"_273":86},{"_235":276,"_270":276,"_271":277,"_273":86},{"_235":269,"_270":269,"_271":272,"_273":86},"PyTorch","value","filter",{"_274":275,"_270":269},"category","field","framework","NeMo",{"_274":275,"_270":276},"DL","soln_dl",{"_274":281,"_270":279},"technology",{"_151":1260,"_153":1261},"ngcApps",[1213],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1194":1195,"_1196":1197,"_1198":1199,"_1200":1201,"_1202":293,"_108":317,"_1203":1204,"_1205":59,"_1206":89,"_1207":89,"_1208":1209,"_1210":89,"_1211":1212},"amplitude",{"_1191":1192,"_830":-7,"_1193":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1185":1186,"_1187":1188,"_1189":1190},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1070":127,"_1071":1072,"_1073":1074,"_1075":1076},"announcements",[],"consentState",{"_355":1060,"_1061":89,"_1062":34,"_1063":34,"_1064":1065},"consentConfig",{"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348,"_349":350,"_351":352,"_353":354,"_355":356},"showContactAdmin",1785219799317,[],[],[331,332,333,334,335,336],{"_48":62,"_50":72,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":67,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":146,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":146,"_54":-5,"_56":-5,"_58":59},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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