Vyasa Analytics

Vyasa Analytics provides highly scalable deep learning software and analytics. We enable organizations to ask complex questions across large scale integrated biomedical data sets, thereby gaining critical insights to make better decisions.

Vyasa Product Suite

The Vyasa Product Suite includes Layar, Axon, Retina and Synapse, each of which is specifically designed for cutting-edge data analytics use cases. From data integration and search in Layar to deep learning image analytics with Retina to intuitive dynamic knowledge graph creation with Axon, experience the next generation of A.I. powered analytics capabilities with the Vyasa Product Suite.

What's included in the Vyasa Product Suite?

Layar - Layar is a secure, highly scalable, data fabric platform built specifically for enterprise analytics. Layar can be added to existing enterprise data architectures to augment analytics capabilities or can operate as a stand-alone data fabric for text, image, and data stream integration and analytics.

Axon - A knowledge graph application that enables derivation of dynamically generated knowledge graphs directly from integrated data and documents sources integrated in a Layar data fabric.

Retina - Retina is an image analytics application that offers a wide range of deep learning image-related tasks, including management, annotation and deep learning analytics on images.

Synapse - Synapse provides Smart Table Technology that directly connects a user's spreadsheet content to the analytical capabilities of Layar Data Fabrics.

System Requirements

Hardware

Please ensure your system meets the following hardware requirements:

128 GB RAM
16 CPUs
500GB SSD disk
Four V100 or later generation GPUs

Software

Operating system:

Ubuntu 18
Ubuntu 20
DGX OS 5

Additional Software:

Ensure you have a functioning Helm v3 cluster (example set up guide).
Ensure you have a functioning NVIDIA Docker installation. The following command should show the available GPUs:

sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n

Installation

Increase the operating system max_map_count:

/etc/sysctl.d/99-vyasa.conf\nsysctl -p /etc/sysctl.d/99-vyasa.conf\n\">

echo \"vm.max_map_count=262144\" > /etc/sysctl.d/99-vyasa.conf\nsysctl -p /etc/sysctl.d/99-vyasa.conf\n

Install prerequisites to provide Ingress, Kubernetes CNI, and a default storage provisioner. Skip these steps if you have independently provided these prerequisites.

/usr/bin/kubectl apply -f https://docs.projectcalico.org/manifests/tigera-operator.yaml\n/usr/bin/kubectl apply -f https://vyasa-static-assets.s3.amazonaws.com/layar/custom-resources.yaml\n/usr/bin/kubectl apply -f https://vyasa-static-assets.s3.amazonaws.com/layar/nginx-ingress.yaml\n/usr/bin/kubectl apply -f https://vyasa-static-assets.s3.amazonaws.com/layar/local-storage-provisioner.yml\n/usr/bin/kubectl patch storageclass 'local-path' -p '{\"metadata\":{\"annotations\":{\"storageclass.kubernetes.io/is-default-class\":\"true\"}}}'\n/usr/bin/kubectl taint nodes --all node-role.kubernetes.io/master:NoSchedule-\n

Download and install the helm chart using the fetch command at the top of the page. Change MY_APP_URL to the DNS name or IP address of the system. This is the location you will browse to once Layar is started in order to use the software. Change X.Y.Z below to match the version used in the fetch commmand.

helm install layar ./layar-X.Y.Z.tgz --set APPURL=MY_APP_URL\n

Once launched you can monitor progress of stack startup by running:

kubectl get pods | grep vyasa\n

Once all pods have entered the Running state you should be able to visit the APPURL page defined above and get the Layar registration screen. It may take an additional few minutes for the screen to load even though the pods are all in the Running state. Be sure to specify http:// when connecting and not https://. SSL enablement is described further in the documentation.

Technical Support & Documentation

A README for the Vyasa Product Suite Helm Chart can be found here. Documentation on API endpoints is located here. An example application using these endpoints is available here.

For additional tutorials on the various applications and features, support documentation can be found in the Vyasa Knowledge Base.

License Information

By using the software you agree to fully comply with the terms and conditions of the SLA (Software License Agreement) located here.

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We enable organizations to ask complex questions across large scale integrated biomedical data sets, thereby gaining critical insights to make better decisions.\n\n## Vyasa Product Suite\n\nThe Vyasa Product Suite includes **Layar**, **Axon**, **Retina** and **Synapse**, each of which is specifically designed for cutting-edge data analytics use cases. From data integration and search in Layar to deep learning image analytics with Retina to intuitive dynamic knowledge graph creation with Axon, experience the next generation of A.I. powered analytics capabilities with the Vyasa Product Suite.\n\n## What's included in the Vyasa Product Suite? \n\n**Layar** - Layar is a secure, highly scalable, data fabric platform built specifically for enterprise analytics. Layar can be added to existing enterprise data architectures to augment analytics capabilities or can operate as a stand-alone data fabric for text, image, and data stream integration and analytics.\n\n**Axon** - A knowledge graph application that enables derivation of dynamically generated knowledge graphs directly from integrated data and documents sources integrated in a Layar data fabric.\n\n**Retina** - Retina is an image analytics application that offers a wide range of deep learning image-related tasks, including management, annotation and deep learning analytics on images.\n\n**Synapse** - Synapse provides Smart Table Technology that directly connects a user's spreadsheet content to the analytical capabilities of Layar Data Fabrics.\n\n## System Requirements\n### Hardware\nPlease ensure your system meets the following hardware requirements:\n- 128 GB RAM\n- 16 CPUs\n- 500GB SSD disk\n- Four V100 or later generation GPUs\n\n### Software\nOperating system:\n- Ubuntu 18 \n- Ubuntu 20 \n- DGX OS 5\n\nAdditional Software:\n- Ensure you have a functioning Helm v3 cluster ([example set up guide](http://help.vyasa.com/en/articles/5149454-example-installation)). \n- Ensure you have a functioning [NVIDIA Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) installation. The following command should show the available GPUs:\n\n```\nsudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi\n```\n\n## Installation\n\n1. Increase the operating system `max_map_count`:\n\n```\necho \"vm.max_map_count=262144\" > /etc/sysctl.d/99-vyasa.conf\nsysctl -p /etc/sysctl.d/99-vyasa.conf\n```\n\n2. Install prerequisites to provide Ingress, Kubernetes CNI, and a default storage provisioner. Skip these steps if you have independently provided these prerequisites.\n\n```\n/usr/bin/kubectl apply -f https://docs.projectcalico.org/manifests/tigera-operator.yaml\n/usr/bin/kubectl apply -f https://vyasa-static-assets.s3.amazonaws.com/layar/custom-resources.yaml\n/usr/bin/kubectl apply -f https://vyasa-static-assets.s3.amazonaws.com/layar/nginx-ingress.yaml\n/usr/bin/kubectl apply -f https://vyasa-static-assets.s3.amazonaws.com/layar/local-storage-provisioner.yml\n/usr/bin/kubectl patch storageclass 'local-path' -p '{\"metadata\":{\"annotations\":{\"storageclass.kubernetes.io/is-default-class\":\"true\"}}}'\n/usr/bin/kubectl taint nodes --all node-role.kubernetes.io/master:NoSchedule-\n```\n\n3. Download and install the helm chart using the fetch command at the top of the page. Change `MY_APP_URL` to the DNS name or IP address of the system. This is the location you will browse to once Layar is started in order to use the software. Change `X.Y.Z` below to match the version used in the fetch commmand.\n\n```\nhelm install layar ./layar-X.Y.Z.tgz --set APPURL=MY_APP_URL\n```\n\nOnce launched you can monitor progress of stack startup by running:\n\n```\nkubectl get pods | grep vyasa\n```\n\nOnce all pods have entered the `Running` state you should be able to visit the `APPURL` page defined above and get the Layar registration screen. It may take an additional few minutes for the screen to load even though the pods are all in the `Running` state. Be sure to specify `http://` when connecting and not `https://`. SSL enablement is described further in the documentation.\n\n## Technical Support & Documentation\nA README for the Vyasa Product Suite Helm Chart can be found here. Documentation on API endpoints is located here. An example application using these endpoints is available here.\n\nFor additional tutorials on the various applications and features, support documentation can be found in the Vyasa Knowledge Base.\n\n## License Information\nBy using the software you agree to fully comply with the terms and conditions of the SLA (Software License Agreement) located here.","displayName","Vyasa Product Suite",["D",1680648061040],"publisher","Vyasa Analytics","latestVersion","collectionCounts","imageUrl","/api/image/partners/vyasa/helm-charts/layar","rawImageUrl","https://raw.githubusercontent.com/kbojo/images/master/ISV-OSS-Non-Nvidia-Publishing-Vyasa.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[251,252,253,254,255,256,257,258,259,260,261],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_220":295,"_263":295,"_264":296,"_266":267},{"_220":292,"_263":293,"_264":294,"_266":267},{"_220":289,"_263":290,"_264":291,"_266":267},{"_220":287,"_263":287,"_264":288,"_266":267},{"_220":285,"_263":285,"_264":286,"_266":267},{"_220":283,"_263":283,"_264":284,"_266":267},{"_220":279,"_263":280,"_264":281,"_266":267},{"_220":275,"_263":276,"_264":277,"_266":267},{"_220":271,"_263":272,"_264":273,"_266":267},{"_220":269,"_263":269,"_264":270,"_266":267},{"_220":262,"_263":262,"_264":265,"_266":267},"Text Analytics","value","filter",{"_268":267,"_263":262},"category","label","field","Ontologies",{"_268":267,"_263":269},"Named Entity Recognition","uscs_named_entity_recognition",{"_268":274,"_263":272},"application","Life Sciences","indus_life_sciences",{"_268":278,"_263":276},"industry","Kubernetes Infrastructure","soln_kubernetes_infrastructure",{"_268":282,"_263":280},"technology","Knowledge Graphs",{"_268":267,"_263":283},"Image Analytics",{"_268":267,"_263":285},"Helm 3.0",{"_268":267,"_263":287},"Healthcare","indus_healthcare",{"_268":278,"_263":290},"DL","soln_dl",{"_268":282,"_263":293},"Biomedical Literature",{"_268":267,"_263":295},{"_127":1473,"_129":1474},"ngcApps",[1426],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1407":1408,"_1409":1410,"_1411":1412,"_1413":1414,"_1415":308,"_158":332,"_1416":1417,"_1418":60,"_1419":106,"_1420":106,"_1421":1422,"_1423":106,"_1424":1425},"amplitude",{"_1404":1405,"_1005":-7,"_1406":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1398":1399,"_1400":1401,"_1402":1403},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1280":412,"_1281":1282,"_1283":1284,"_1285":1286},"announcements",[],"consentState",{"_369":1270,"_1271":106,"_1272":35,"_1273":35,"_1274":1275},"consentConfig",{"_351":352,"_353":354,"_355":356,"_357":358,"_359":360,"_361":362,"_363":364,"_365":366,"_367":368,"_369":370},"showContactAdmin",1784343300049,[],[],[346,347,348,349,350],{"_49":64,"_51":74,"_53":66,"_55":67,"_57":68,"_59":60},{"_49":64,"_51":72,"_53":122,"_55":-5,"_57":-5,"_59":60},{"_49":64,"_51":69,"_53":122,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":54,"_55":56,"_57":16,"_59":60},{"_49":50,"_51":52,"_53":54,"_55":56,"_57":58,"_59":60},"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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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.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. 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The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","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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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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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