PhysicsNeMo Checkpoints: DLESyM-V1-ERA5

Description:

DLESyM-V1-ERA5 is an ensemble forecast model for global earth system modeling.\nThis model includes an atmosphere and ocean component, using atmospheric\nvariables as well as the sea-surface temperature on a HEALPix nside=64\n(approximately 1 degree) resolution grid. This model package includes several\nindividual trained checkpoints for the atmosphere and ocean components, which\ncan be used to improve model variability in ensembles. The model architecture\nis a U-Net with padding operations modified to support using the HEALPix grid.

This model is for research and development only.

License/Terms of Use:

Governing Terms: Use of this model is governed by the NVIDIA Community Model License.

Deployment Geography:

Global

Use Case:

Industry, academic, and government research teams interested in subseasonal-to-seasonal weather forecasting, and climate modeling.

Release Date:

NGC 05/12/2025

Reference:

Model Architecture:

Architecture Type: DLESyM uses two UNet architectures adapted to the HEALPix grid,\none for each of the atmosphere and ocean components.
\nNetwork Architecture: UNet

Input:

Input Type:

Tensor (9 surface and pressure-level variables)

Input Format: PyTorch Tensor
\nInput Parameters:

Six Dimensional (6D) (batch, lead time, variable, face, height, width)

Other Properties Related to Input:

Input latitude/longitude grid: 0.25 degree 721 x 1440, regridded to HEALPix nside=64\ngrid in \"XY\" format with a north origin and clockwise order\n
- See HEALPIX_PAD_XY in the earth2grid package\nfor specific details
\n
Input state weather variables: z500, tau300-700, z1000, t2m, tcwv, t850,\nz250, ws10m, sst\n
- tau300-700 (geopotential thickness) is defined as the difference between z300\nand z700 geopotential levels.
- ws10m (wind speed at 10m above surface) is defined as the square root of the sum\nof the squared zonal and meridional wind components, i.e. sqrt(u10m **2 + v10m **2).
\n

For variable name information, review the HRRR Lexicon at Earth2Studio.\nReview the config.yaml provided in the model package for information on the input lead\ntimes required by the model.

Output:

Output Type: Tensor (9 surface and pressure-level variables)
\nOutput Format: Pytorch Tensor
\nOutput Parameters: Six Dimensional (6D) (batch, lead time, variable, face, height,\nwidth)
\nOther Properties Related to Output:

Output latitude/longitude grid: 0.25 degree 721 x 1440, regridded to HEALPix nside=64\ngrid in \"XY\" format with a north origin and clockwise order.\n
- See HEALPIX_PAD_XY in the earth2grid package\nfor specific details
\n
Output state weather variables: z500, tau300-700, z1000, t2m, tcwv, t850,\nz250, ws10m, sst
\n
- tau300-700 (geopotential thickness) is defined as the difference between z300\nand z700 geopotential levels.
- ws10m (wind speed at 10m above surface) is defined as the square root of the sum\nof the squared zonal and meridional wind components, i.e. sqrt(u10m **2 + v10m **2).
\n

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems.\nBy leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA\nlibraries), the model achieves faster training and inference times compared to\nCPU-only solutions.

Software Integration

Runtime Engine: Pytorch
\nSupported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Turing

Supported Operating System:

Linux

Model Version:

Model Version: v1

Training, Testing, and Evaluation Datasets:

Total size (in number of data points): 110,960
\nTotal number of datasets: 1
\nDataset partition: training 90%, testing 5%, validation 5%

Training Dataset:

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period 1980-2015. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.

Testing Dataset:

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period 2016-2017. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.

Evaluation Dataset:

Link: ERA5

Data Collection Method by dataset

Automatic/Sensors

Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period 2018-2019. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.

Inference:

Acceleration Engine: Pytorch
\nTest Hardware:

A100
H100
L40S

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions",[196,197],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_179":28,"_180":-5,"_52":82,"_79":-5,"_83":181,"_85":182,"_183":81,"_92":93,"_88":89,"_184":-5,"_87":34,"_185":-5,"_186":34,"_187":-5,"_188":34,"_189":-5,"_190":34,"_191":-5,"_192":34,"_193":-5,"_194":34,"_90":195},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/earth-2/models/dlesym-v1-era5/-","version","-","imageReference","cliDownloadCommand","ngc registry model download-version nvidia/earth-2/dlesym-v1-era5:1.0.1","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/earth-2/models/dlesym-v1-era5/-/version-history","accessCheckResult",{"_178":89},"hasRelatedCollections","context",{"_174":175,"_176":177},[],[],[167,168,169,170,171,172,173],{"_48":63,"_50":74,"_52":65,"_54":66,"_56":75,"_58":59},{"_48":63,"_50":72,"_52":65,"_54":66,"_56":73,"_58":59},{"_48":63,"_50":70,"_52":65,"_54":66,"_56":71,"_58":59},{"_48":63,"_50":68,"_52":65,"_54":66,"_56":75,"_58":59},{"_48":63,"_50":64,"_52":65,"_54":66,"_56":67,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":62,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","earth-2","allowed","versionId","description",["D",1747154683262],["D",1747154688454],"totalSizeInBytes","malwareScanStatus","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_77":152,"_78":199,"_79":-5,"_88":89,"_87":34,"_90":198,"_186":34},{"_77":28,"_78":28,"_79":-5,"_88":89,"_87":34,"_90":198,"_186":34},[],"Latest (1.0.1)","nvidia/earth-2/dlesym-v1-era5","key","nvidia/earth-2/models/dlesym-v1-era5","resourceId","dlesym-v1-era5","shortDescription","DLESyM-V1-ERA5 is an ensemble forecast model for global earth system modeling, including atmosphere and ocean components. The model operates with 6 hour temporal resolution, forecasting 8 atmospheric variables and 1 oceanic variable.","# PhysicsNeMo Checkpoints: DLESyM-V1-ERA5\n\n## Description:\n\nDLESyM-V1-ERA5 is an ensemble forecast model for global earth system modeling.\nThis model includes an atmosphere and ocean component, using atmospheric\nvariables as well as the sea-surface temperature on a HEALPix nside=64\n(approximately 1 degree) resolution grid. This model package includes several\nindividual trained checkpoints for the atmosphere and ocean components, which\ncan be used to improve model variability in ensembles. The model architecture\nis a U-Net with padding operations modified to support using the HEALPix grid.\n\nThis model is for research and development only.\n\n### License/Terms of Use: \n**Governing Terms**: Use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).\n\n### Deployment Geography:\nGlobal\n\n### Use Case:\nIndustry, academic, and government research teams interested in subseasonal-to-seasonal weather forecasting, and climate modeling.\n\n### Release Date:\nNGC 05/12/2025\n\n## Reference:\n\n - [Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh](https://arxiv.org/abs/2311.06253)
\n - [A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate](https://arxiv.org/abs/2409.16247)
\n\n## Model Architecture: \n\n**Architecture Type:** DLESyM uses two UNet architectures adapted to the HEALPix grid,\none for each of the atmosphere and ocean components.
\n**Network Architecture:** UNet
\n\n## Input:\n\n**Input Type:**\n\n- Tensor (9 surface and pressure-level variables)\n\n**Input Format:** PyTorch Tensor
\n**Input Parameters:**\n\n- Six Dimensional (6D) (batch, lead time, variable, face, height, width)
\n\n**Other Properties Related to Input:** \n\n- Input latitude/longitude grid: 0.25 degree 721 x 1440, regridded to HEALPix nside=64\ngrid in \"XY\" format with a north origin and clockwise order\n - See `HEALPIX_PAD_XY` in the [earth2grid package](https://github.com/NVlabs/earth2grid)\n for specific details\n- Input state weather variables: `z500`, `tau300-700`, `z1000`, `t2m`, `tcwv`, `t850`,\n`z250`, `ws10m`, `sst`\n - `tau300-700` (geopotential thickness) is defined as the difference between `z300`\n and `z700` geopotential levels.\n - `ws10m` (wind speed at 10m above surface) is defined as the square root of the sum\n of the squared zonal and meridional wind components, i.e. `sqrt(u10m **2 + v10m **2)`.\n\nFor variable name information, review the `HRRR` Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio).\nReview the `config.yaml` provided in the model package for information on the input lead\ntimes required by the model.\n\n## Output:\n\n**Output Type:** Tensor (9 surface and pressure-level variables)
\n**Output Format:** Pytorch Tensor
\n**Output Parameters:** Six Dimensional (6D) (batch, lead time, variable, face, height,\nwidth)
\n**Other Properties Related to Output:**\n\n- Output latitude/longitude grid: 0.25 degree 721 x 1440, regridded to HEALPix nside=64\ngrid in \"XY\" format with a north origin and clockwise order.\n - See `HEALPIX_PAD_XY` in the [earth2grid package](https://github.com/NVlabs/earth2grid)\n for specific details\n- Output state weather variables: `z500`, `tau300-700`, `z1000`, `t2m`, `tcwv`, `t850`,\n`z250`, `ws10m`, `sst`
\n - `tau300-700` (geopotential thickness) is defined as the difference between `z300`\n and `z700` geopotential levels.\n - `ws10m` (wind speed at 10m above surface) is defined as the square root of the sum\n of the squared zonal and meridional wind components, i.e. `sqrt(u10m **2 + v10m **2)`.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems.\nBy leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA\nlibraries), the model achieves faster training and inference times compared to\nCPU-only solutions.\n\n## Software Integration\n\n**Runtime Engine:** Pytorch
\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n* NVIDIA Turing
\n\n\n**Supported Operating System:**\n* Linux
\n\n\n## Model Version: \n\n**Model Version:** v1
\n\n# Training, Testing, and Evaluation Datasets: \n\n**Total size (in number of data points):** 110,960
\n**Total number of datasets:** 1
\n**Dataset partition:** training 90%, testing 5%, validation 5%
\n\n## Training Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n**Data Collection Method by dataset**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset**
\n* Automatic/Sensors
\n\n**Properties:**\nERA5 data for the period 1980-2015. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.
\n\n\n## Testing Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n**Data Collection Method by dataset**
\n\n* Automatic/Sensors
\n\n**Labeling Method by dataset**
\n* Automatic/Sensors
\n\n**Properties:**\nERA5 data for the period 2016-2017. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.
\n\n## Evaluation Dataset:\n\n**Link:** [ERA5](https://cds.climate.copernicus.eu/)
\n\n**Data Collection Method by dataset**
\n* Automatic/Sensors
\n\n**Labeling Method by dataset**
\n* Automatic/Sensors
\n\n**Properties:**\nERA5 data for the period 2018-2019. ERA5 provides hourly estimates of various\natmospheric, land, and oceanic climate variables. The data covers the Earth on a 30km\ngrid and resolves the atmosphere at 137 levels. We re-grid to a healpix-64 grid that\ncorresponds to an approximate 1 degree lat/lon grid.
\n\n## Inference:\n\n**Acceleration Engine:** Pytorch
\n**Test Hardware:**\n* A100
\n* H100
\n* L40S
\n\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","PhysicsNeMo Checkpoints: DLESyM-V1-ERA5",["D",1747154688460],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/dlesym-v1-era5","rawImageUrl","https://www.nvidia.com/content/dam/en-zz/Solutions/about-nvidia/logo-and-brand/01-nvidia-logo-vert-500x200-2c50-p@2x.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[240,241,242,243,244,245,246,247,248],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_208":279,"_250":280,"_252":281,"_254":255},{"_208":277,"_250":277,"_252":278,"_254":255},{"_208":274,"_250":275,"_252":276,"_254":255},{"_208":271,"_250":272,"_252":273,"_254":255},{"_208":267,"_250":268,"_252":269,"_254":255},{"_208":265,"_250":265,"_252":266,"_254":255},{"_208":261,"_250":262,"_252":263,"_254":255},{"_208":258,"_250":258,"_252":259,"_254":255},{"_208":249,"_250":251,"_252":253,"_254":255},"Simulation and Modeling","value","uscs_simulation_and_modeling","filter",{"_256":257,"_250":251},"category","label","field","application","PyTorch",{"_256":260,"_250":258},"framework","PhysicsNeMo","pltfm_modulus",{"_256":264,"_250":262},"platform","NSPECT-MZ0N-6PSM",{"_256":255,"_250":265},"HPC / Supercomputing","indus_hpc_supercomputing",{"_256":270,"_250":268},"industry","High Performance Computing","uscs_high_performance_computing",{"_256":257,"_250":272},"Forecasting","uscs_forecasting",{"_256":257,"_250":275},"Earth-2",{"_256":255,"_250":277},"AI","soln_ai",{"_256":282,"_250":280},"technology",{"_120":1455,"_122":1456},"ngcApps",[1408],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1389":1390,"_1391":1392,"_1393":1394,"_1395":1396,"_1397":294,"_151":318,"_1398":1399,"_1400":59,"_1401":89,"_1402":89,"_1403":1404,"_1405":89,"_1406":1407},"amplitude",{"_1386":1387,"_990":-7,"_1388":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1380":1381,"_1382":1383,"_1384":1385},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1265":225,"_1266":1267,"_1268":1269,"_1270":1271},"announcements",[],"consentState",{"_356":1255,"_1256":89,"_1257":34,"_1258":34,"_1259":1260},"consentConfig",{"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351,"_352":353,"_354":355,"_356":357},"showContactAdmin",1784285593730,[],[],[332,333,334,335,336],{"_48":63,"_50":74,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":69,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":337,"_58":59},4,"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. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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This function remembers how far the user has scrolled on the current session across the website's sub-pages.","dmn_chk_-xxxxxxxxxxxx","com","Posthog Analytics","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_cs_id","blogs.nvidia.com","_session_id","resources.nvidia.com","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. It is used to persist the Hotjar User ID, unique to that site on the browser. 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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. 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. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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