PhysicsNeMo Checkpoints: AFNO_DX_FI-V1-ERA5 (ERA5 AFNO Forecast Interpolation Diagnostic)

Description:

This is a forecast interpolation diagnostic model checkpoint.\nThis deep learning model, based on the Adaptive Fourier Neural Operator framework\n(AFNO), interpolates a prognostic forecast model to a shorter time-step size\n(by default from 6 h to 1 h) and operates on 0.25 degree lat-lon equirectangular\ngrid with 73 variables.

This model interpolates the following variables surface variables:\n\"u10m\", \"v10m\", \"u100m\", \"v100m\", \"t2m\", \"sp\", \"msl\", \"tcwv\"\nand the variables u, v, t, z, q at pressure levels\n1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50.

For training recipes, see NVIDIA PhysicsNeMo. For inference, see NVIDIA Earth2Studio.

This model is ready for commercial use.

License/Terms of Use:

Use of this model is governed by the NVIDIA Community Model License. By pulling and using this model, you accept the terms and conditions of this license.

Reference(s):

Modulated Adaptive Fourier Neural Operators for Temporal Interpolation of Weather Forecasts

Model Architecture:

Architecture Type: This model uses an Adaptive Fourier Neural Operator (AFNO) architecture.
\nNetwork Architecture: AFNO

Input:

Input Type(s):

Tensor (73 atmospheric variables on a 0.25 degree grid.)
DateTime (NumPy Array)

Input Format(s): PyTorch Tensor / NumPy array
\nInput Parameters:

Five Dimensional (5D) (batch, time, variable, latitude, longitude)
DateTime (1D)

Other Properties Related to Input:

Input latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).
Input state weather variables: u10m, v10m, u100m, v100m, t2m, sp, msl tcwv, u1000, u925, u850, u700, u600, u500, u400, u300, u250, u200, u150, u100, u50, v1000, v925, v850, v700, v600, v500, v400, v300, v250, v200, v150, v100, v50, t1000, t925, t850, t700, t600, t500, t400, t300, t250, t200, t150, t100, t50, z1000, z925, z850, z700, z600, z500, z400, z300, z250, z200, z150, z100, z50, q1000, q925, q850, q700, q600, q500, q400, q300, q250, q200, q150, q100, q50

For lexicon information, review the ERA5 Lexicon at Earth2Studio. u, v, t, z and q refer to eastward and northward wind components, temperature, geopotential, and specific humidity (respectively).

Output:

Output Type(s):

Tensor (1-hourly interpolated forecasts of a 73-channel 6-hour forecast).
DateTime (NumPy Array)

Output Format: Pytorch Tensors
\nOutput Parameters: Five Dimensional (5D) (batch, time, variable, latitude, longitude)
\nOther Properties Related to Output:

Output latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).
Output state weather variables: u10m, v10m, u100m, v100m, t2m, sp, msl tcwv, u1000, u925, u850, u700, u600, u500, u400, u300, u250, u200, u150, u100, u50, v1000, v925, v850, v700, v600, v500, v400, v300, v250, v200, v150, v100, v50, t1000, t925, t850, t700, t600, t500, t400, t300, t250, t200, t150, t100, t50, z1000, z925, z850, z700, z600, z500, z400, z300, z250, z200, z150, z100, z50, q1000, q925, q850, q700, q600, q500, q400, q300, q250, q200, q150, q100, q50

Software Integration

Runtime Engine(s): PyTorch
\nSupported Hardware Microarchitecture Compatibility:

NVIDIA Ampere
NVIDIA Hopper
NVIDIA Turing

Supported Operating System(s):

Linux

Model Version(s):

Model Version: v1

Training, Testing, and Evaluation Datasets:

Training Dataset:

Link: ERA5

** Data Collection Method by dataset

Automatic/Sensors

** Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the period January 1980 - December 2016. 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.

For more information about ERA5 and a list of known and resolved issues, see the associated documentation and\nreference publication which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5\nreanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of\nregion and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable.\nReview ECMWF's publications for further information about the quality of its forecasts.

Testing Dataset:

Link: ERA5

** Data Collection Method by dataset

Automatic/Sensors

** Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the date range of January 2017 - December 2018. 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.

Evaluation Dataset:

Link: ERA5

** Data Collection Method by dataset

Automatic/Sensors

** Labeling Method by dataset

Automatic/Sensors

Properties:\nERA5 data for the date range of January 2019 - December 2020. 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.

Inference:

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.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here].

Please report security vulnerabilities or NVIDIA AI Concerns here.

","resourceVersions",[199,200],"resourceVersionsTotal","hasMoreVersions","versionMetadata",{"_181":28,"_182":-5,"_52":79,"_76":-5,"_80":183,"_82":184,"_185":78,"_89":90,"_85":86,"_186":-5,"_187":-5,"_84":34,"_188":-5,"_189":34,"_190":-5,"_191":34,"_192":-5,"_193":34,"_194":-5,"_195":34,"_196":-5,"_197":34,"_87":198},"versionNotFound","isLoggedIn","latestVersionPath","/orgs/nvidia/earth-2/models/afno_dx_fi-v1-era5/-","version","-","imageReference","registry","targetPath","cliDownloadCommand","ngc registry model download-version nvidia/earth-2/afno_dx_fi-v1-era5:v0.1.0","helmFetchCommand","viewAllVersionsPath","/orgs/nvidia/earth-2/models/afno_dx_fi-v1-era5/-/version-history","accessCheckResult",{"_180":86},"hasRelatedCollections","showNimMetadataTab","context",{"_176":177,"_178":179},1793855386305,[],[],[169,170,171,172,173,174,175],{"_48":62,"_50":72,"_52":64,"_54":65,"_56":71,"_58":59},{"_48":62,"_50":70,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":68,"_52":64,"_54":65,"_56":69,"_58":59},{"_48":62,"_50":67,"_52":64,"_54":65,"_56":66,"_58":59},{"_48":62,"_50":63,"_52":64,"_54":65,"_56":66,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":57,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},"orgName","nvidia","teamName","earth-2","allowed","versionId","description",["D",1743781788083],["D",1743781797560],"totalSizeInBytes","digest","malwareScanStatus","releaseNotes","hasReleaseNotes","setup","hasSetup","quickStartGuide","hasQuickStartGuide","advanced","hasAdvanced","performance","hasPerformance",[],{"_74":150,"_75":202,"_76":-5,"_85":86,"_84":34,"_87":201,"_189":34},{"_74":28,"_75":28,"_76":-5,"_85":86,"_84":34,"_87":201,"_189":34},[],"Latest (v0.1.0)","nvidia/earth-2/afno_dx_fi-v1-era5","key","nvidia/earth-2/models/afno_dx_fi-v1-era5","resourceId","afno_dx_fi-v1-era5","shortDescription","This deep learning model, based on the Adaptive Fourier Neural Operator framework (AFNO), interpolates a prognostic forecast model to a shorter time-step size (by default from 6 h to 1 h).","# PhysicsNeMo Checkpoints: AFNO_DX_FI-V1-ERA5 (ERA5 AFNO Forecast Interpolation Diagnostic)\n\n## Description:\n\nThis is a forecast interpolation diagnostic model checkpoint.\nThis deep learning model, based on the Adaptive Fourier Neural Operator framework \n(AFNO), interpolates a prognostic forecast model to a shorter time-step size \n(by default from 6 h to 1 h) and operates on 0.25 degree lat-lon equirectangular \ngrid with 73 variables.\n\nThis model interpolates the following variables surface variables:\n`\"u10m\", \"v10m\", \"u100m\", \"v100m\", \"t2m\", \"sp\", \"msl\", \"tcwv\"`\nand the variables `u, v, t, z, q` at pressure levels\n`1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 50`.\n\nFor training recipes, see [NVIDIA PhysicsNeMo](https://github.com/NVIDIA/PhysicsNeMo/tree/main/examples/weather/diagnostic). For inference, see [NVIDIA Earth2Studio](https://github.com/NVIDIA/earth2studio).\n\nThis model is ready for commercial use.\n\n### License/Terms of Use: \n\nUse of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). By pulling and using this model, you accept the terms and conditions of this license.
\n\n## Reference(s):\n\n[Modulated Adaptive Fourier Neural Operators for Temporal Interpolation of Weather Forecasts](https://arxiv.org/abs/2410.18904)
\n\n## Model Architecture: \n\n**Architecture Type:** This model uses an Adaptive Fourier Neural Operator (AFNO) architecture.
\n**Network Architecture:** AFNO
\n\n## Input:\n\n**Input Type(s):**\n\n- Tensor (73 atmospheric variables on a 0.25 degree grid.)
\n- DateTime (NumPy Array)
\n\n**Input Format(s):** PyTorch Tensor / NumPy array
\n**Input Parameters:**\n\n- Five Dimensional (5D) (batch, time, variable, latitude, longitude)
\n- DateTime (1D)
\n\n**Other Properties Related to Input:** \n\n- Input latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).\n- Input state weather variables: `u10m`, `v10m`, `u100m`, `v100m`, `t2m`, `sp`, `msl` `tcwv`, `u1000`, `u925`, `u850`, `u700`, `u600`, `u500`, `u400`, `u300`, `u250`, `u200`, `u150`, `u100`, `u50`, `v1000`, `v925`, `v850`, `v700`, `v600`, `v500`, `v400`, `v300`, `v250`, `v200`, `v150`, `v100`, `v50`, `t1000`, `t925`, `t850`, `t700`, `t600`, `t500`, `t400`, `t300`, `t250`, `t200`, `t150`, `t100`, `t50`, `z1000`, `z925`, `z850`, `z700`, `z600`, `z500`, `z400`, `z300`, `z250`, `z200`, `z150`, `z100`, `z50`, `q1000`, `q925`, `q850`, `q700`, `q600`, `q500`, `q400`, `q300`, `q250`, `q200`, `q150`, `q100`, `q50`\n\nFor lexicon information, review the `ERA5` Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/era5.py). `u`, `v`, `t`, `z` and `q` refer to eastward and northward wind components, temperature, geopotential, and specific humidity (respectively). \n\n## Output:\n\n**Output Type(s):** \n\n- Tensor (1-hourly interpolated forecasts of a 73-channel 6-hour forecast).
\n- DateTime (NumPy Array)
\n\n**Output Format:** Pytorch Tensors
\n**Output Parameters:** Five Dimensional (5D) (batch, time, variable, latitude, longitude)
\n**Other Properties Related to Output:**\n\n- Output latitude/longitude grid: 0.25 degree resolution regular latitude/longitude grid (720 x 1440 pixels).\n- Output state weather variables: `u10m`, `v10m`, `u100m`, `v100m`, `t2m`, `sp`, `msl` `tcwv`, `u1000`, `u925`, `u850`, `u700`, `u600`, `u500`, `u400`, `u300`, `u250`, `u200`, `u150`, `u100`, `u50`, `v1000`, `v925`, `v850`, `v700`, `v600`, `v500`, `v400`, `v300`, `v250`, `v200`, `v150`, `v100`, `v50`, `t1000`, `t925`, `t850`, `t700`, `t600`, `t500`, `t400`, `t300`, `t250`, `t200`, `t150`, `t100`, `t50`, `z1000`, `z925`, `z850`, `z700`, `z600`, `z500`, `z400`, `z300`, `z250`, `z200`, `z150`, `z100`, `z50`, `q1000`, `q925`, `q850`, `q700`, `q600`, `q500`, `q400`, `q300`, `q250`, `q200`, `q150`, `q100`, `q50`\n\nFor lexicon information, review the `ERA5` Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/era5.py). `u`, `v`, `t`, `z` and `q` refer to eastward and northward wind components, temperature, geopotential, and specific humidity (respectively).\n\n## Software Integration\n\n**Runtime Engine(s):** PyTorch
\n**Supported Hardware Microarchitecture Compatibility:**
\n* NVIDIA Ampere
\n* NVIDIA Hopper
\n* NVIDIA Turing
\n\n\n**Supported Operating System(s):**\n* Linux
\n\n\n## Model Version(s): \n\n**Model Version:** v1
\n\n# Training, Testing, and Evaluation Datasets: \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 January 1980 - December 2016. 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.
\n\nFor more information about ERA5 and a list of known and resolved issues, see the associated [documentation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation) and\n[reference publication](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803) which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5\nreanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of \nregion and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. \nReview [ECMWF's publications](https://www.ecmwf.int/en/forecasts/quality-our-forecasts) for further information about the quality of its forecasts.
\n\n## Testing 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 date range of January 2017 - December 2018. 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.
\n\nFor more information about ERA5 and a list of known and resolved issues, see the associated [documentation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation) and\n[reference publication](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803) which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5\nreanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of \nregion and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. \nReview [ECMWF's publications](https://www.ecmwf.int/en/forecasts/quality-our-forecasts) for further information about the quality of its forecasts.
\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 date range of January 2019 - December 2020. 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.
\n\nFor more information about ERA5 and a list of known and resolved issues, see the associated [documentation](https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation) and\n[reference publication](https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.3803) which provides more detailed information about the quality of the ERA5 reanalysis. The ERA5\nreanalysis, and therefore any model trained using it as a data source, is an imperfect representation of the state of global weather, with correlated errors as a function of \nregion and time. Furthermore, the limited spatial and vertical resolution of the dataset means that physical behavior near or below that resolution scale are not accurately resolvable. \nReview [ECMWF's publications](https://www.ecmwf.int/en/forecasts/quality-our-forecasts) for further information about the quality of its forecasts.
\n\n## Inference:\n\n**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\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards [Insert Link to Model Card++ here].\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).","displayName","PhysicsNeMo Checkpoints: AFNO_DX_FI-V1-ERA5",["D",1743781797564],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/afno_dx_fi-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","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------\nParticipation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | N/A\nMeasures taken to mitigate against unwanted bias: | N/A","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Application & Domain: | Weather, Energy Forecasting\nModel Type: | Adaptive Fourier Neural Operator (AFNO)\nIntended User: | Weather and energy scientists or practitioners accelerating predictions using AI.\nOutput: | Tensor (1-hourly interpolated forecasts of a 73-channel 6-hour forecast).\nDescribe how the model works: | AFNO decodes input variables into a latent space and learns a interpolating function.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | N/A\nTechnical Limitations: | The model may perform poorly for systems that are not similar to those in the training data, namely for rare weather phenomena or weather behavior outside of the 1980-2016 training dataset. There is no mechanism to enforce physical consistency for predictions.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | Root Mean Square Error (RMSE), Accuracy (ACC), and Bias (mean error).\nPotential Known Risks: | This model may inaccurately predict interpolations given technical limitations noted above.\nLicensing: | [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any personal data used? | Not Applicable (N/A)\nPersonal data used to create this model? | N/A\nHow often is dataset reviewed? | Before Release\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Weather, Energy Forecasting\nDescribe the life critical impact (if present). | Should not be used for life-critical use cases.\nUse Case Restrictions: | Abide by [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).\nModel and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.","featureLabels",[],"displayLabels",[247,248,249,250,251,252],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata","computedResourceType",{"_211":275,"_254":275,"_256":276,"_258":259},{"_211":271,"_254":272,"_256":273,"_258":259},{"_211":268,"_254":269,"_256":270,"_258":259},{"_211":266,"_254":266,"_256":267,"_258":259},{"_211":262,"_254":263,"_256":264,"_258":259},{"_211":253,"_254":255,"_256":257,"_258":259},"Simulation and Modeling","value","uscs_simulation_and_modeling","filter",{"_260":261,"_254":255},"category","label","field","application","PhysicsNeMo","pltfm_modulus",{"_260":265,"_254":263},"platform","NSPECT-2XYW-0G6K",{"_260":259,"_254":266},"Forecasting","uscs_forecasting",{"_260":261,"_254":269},"Energy","indus_energy",{"_260":274,"_254":272},"industry","Earth-2",{"_260":259,"_254":275},{"_118":1409,"_120":1410},"ngcApps",[1362],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1343":1344,"_1345":1346,"_1347":1348,"_1349":1350,"_1351":288,"_149":314,"_1352":1353,"_1354":59,"_1355":86,"_1356":86,"_1357":1358,"_1359":86,"_1360":1361},"amplitude",{"_1340":1341,"_948":-7,"_1342":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1334":1335,"_1336":1337,"_1338":1339},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793855386210,"1.30.0","defaultSearchSuggestions",{"_1218":393,"_1219":1220,"_1221":1222,"_1223":1224},"announcements",[],"consentState",{"_350":1208,"_1209":34,"_1210":34,"_1211":86,"_1212":1213},"consentConfig",{"_332":333,"_334":335,"_336":337,"_338":339,"_340":341,"_342":343,"_344":345,"_346":347,"_348":349,"_350":351},"showContactAdmin",[],[],[327,328,329,330,331],{"_48":62,"_50":72,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":68,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":113,"_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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These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. 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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.","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.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.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.\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.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","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","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.","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.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed 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.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. 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