PhysicsNeMo Checkpoints: AFNO_DX_SR-V1-ERA5 (ERA5 AFNO Solar Radiation Diagnostic)

Description:

This is a surface solar irradiance (SSI) diagnostic model checkpoint.\nThis deep learning model, based on the Adaptive Fourier Neural Operator framework\n(AFNO), predicts 6-hour accumulated surface solar irradiance given 24 atmospheric\nvariables plus invariants. For a given atmospheric state at time t, the model\npredicts accumulated SSI over (t-6h, t).

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):

Advancing Solar Irradiance Prediction with NVIDIA Earth-2
\nData-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale

Model Architecture:

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

Input:

Input Type(s):

Tensor (3 surface variables and 3 pressure-level variables at 7 pressure levels for a total of 24 total variables.)
DateTime (NumPy Array)

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

Four Dimensional (4D) (batch, 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: t2m, sp, tcwv, z50, z300, z500, z700, z850, z925, z1000, t50, t300, t500, t700, t850, t925, t1000, q50, q300, q500, q700, q850, q925, q1000

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

Output:

Output Type(s): Tensor (1 variable - 6-hour accumulated Surface Solar Irradiance)
\nOutput Format: Pytorch Tensors
\nOutput Parameters: Four Dimensional (4D) (batch, 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: ssi

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.

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\n\n## Reference(s):\n\n[Advancing Solar Irradiance Prediction with NVIDIA Earth-2](https://resources.nvidia.com/en-us-energy-surfaces/advancing-solar-irra)
\n[Data-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale](https://arxiv.org/abs/2411.08843)\n\n## Model Architecture: \n\n**Architecture Type:** This model uses an Adaptive Fourier Neural Operator architecture.
\n**Network Architecture:** AFNO
\n\n## Input:\n\n**Input Type(s):**\n\n- Tensor (3 surface variables and 3 pressure-level variables at 7 pressure levels for a total of 24 total variables.)\n- DateTime (NumPy Array)
\n\n**Input Format(s):** PyTorch Tensor / NumPy array
\n**Input Parameters:**\n\n- Four Dimensional (4D) (batch, 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: `t2m`, `sp`, `tcwv`, `z50`, `z300`, `z500`, `z700`, `z850`, `z925`, `z1000`, `t50`, `t300`, `t500`, `t700`, `t850`, `t925`, `t1000`, `q50`, `q300`, `q500`, `q700`, `q850`, `q925`, `q1000`\n\nFor lexicon information, review the `ERA5` Lexicon at [Earth2Studio](https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/era5.py). `t`, `z` and `q` refer to temperature, geopotential, and specific humidity (respectively). \n\n## Output:\n\n**Output Type(s):** Tensor (1 variable - 6-hour accumulated Surface Solar Irradiance)
\n**Output Format:** Pytorch Tensors
\n**Output Parameters:** Four Dimensional (4D) (batch, 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: `ssi`
\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_SR-V1-ERA5",["D",1748549043145],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/earth-2/models/afno_dx_sr-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 variable - 6-hour accumulated Surface Solar Irradiance).\nDescribe how the model works: | AFNO decodes input variables into a latent space and learns coupling with the output variable - surface solar irradiance.\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 surface solar irradiance 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. 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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536,537,538,539],[377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393],{"_394":501,"_207":501,"_178":396,"_367":502},{"_394":496,"_207":496,"_178":396,"_367":497},{"_394":492,"_207":492,"_178":396,"_367":493},{"_394":487,"_207":487,"_178":396,"_367":488},{"_394":482,"_207":482,"_178":396,"_367":483},{"_394":470,"_207":470,"_178":396,"_367":471},{"_394":458,"_207":458,"_178":396,"_367":459},{"_394":453,"_207":453,"_178":396,"_367":454},{"_394":442,"_207":442,"_178":396,"_367":443},{"_394":437,"_207":437,"_178":396,"_367":438},{"_394":432,"_207":432,"_178":396,"_367":433},{"_394":427,"_207":427,"_178":396,"_367":428},{"_394":422,"_207":422,"_178":396,"_367":423},{"_394":417,"_207":417,"_178":396,"_367":418},{"_394":412,"_207":412,"_178":396,"_367":413},{"_394":405,"_207":405,"_178":396,"_367":406},{"_394":395,"_207":395,"_178":396,"_367":397},"hostName","amazon-adsystem.com","",[398,399],{"_77":404,"_401":395,"_402":34,"_178":403},{"_77":400,"_401":395,"_402":34,"_178":403},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[407,408],{"_77":411,"_401":405,"_402":34,"_178":410},{"_77":409,"_401":405,"_402":34,"_178":410},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[434],{"_77":435,"_401":432,"_402":34,"_178":436},"sp","Testing","google.com",[439],{"_77":440,"_401":437,"_402":34,"_178":441},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[444,445,446,447],{"_77":452,"_401":442,"_402":34,"_178":449},{"_77":451,"_401":442,"_402":34,"_178":449},{"_77":450,"_401":442,"_402":34,"_178":449},{"_77":448,"_401":442,"_402":34,"_178":449},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[455],{"_77":456,"_401":453,"_402":34,"_178":457},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[460,461,462,463],{"_77":469,"_401":458,"_402":34,"_178":465},{"_77":467,"_401":458,"_402":34,"_178":468},{"_77":466,"_401":458,"_402":34,"_178":465},{"_77":464,"_401":458,"_402":34,"_178":465},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[472,473,474,475],{"_77":481,"_401":470,"_402":34,"_178":479},{"_77":480,"_401":470,"_402":34,"_178":479},{"_77":478,"_401":470,"_402":34,"_178":479},{"_77":476,"_401":470,"_402":34,"_178":477},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[484],{"_77":485,"_401":482,"_402":34,"_178":486},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[489],{"_77":490,"_401":487,"_402":34,"_178":491},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[494],{"_77":495,"_401":492,"_402":34,"_178":436},"R","6sc.co",[498],{"_77":499,"_401":496,"_402":34,"_178":500},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. 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. If you do not allow these cookies, then some or all of these services may not function properly.",4,[628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647,648],[593,594,595,596,597],{"_394":487,"_207":487,"_178":396,"_367":619},{"_394":453,"_207":453,"_178":396,"_367":614},{"_394":607,"_207":607,"_178":396,"_367":608},{"_394":603,"_207":603,"_178":396,"_367":604},{"_394":598,"_207":598,"_178":396,"_367":599},"nvcrm.my.site.com",[600],{"_77":601,"_401":598,"_402":34,"_178":602},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[605],{"_77":606,"_401":603,"_402":34,"_178":436},"edge","github.com",[609,610],{"_77":613,"_401":607,"_402":34,"_178":612},{"_77":611,"_401":607,"_402":34,"_178":612},"logged_in","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv.modal.cuda","This cookie name suggests it is related to a modal popup functionality on NVIDIA's website, possibly to remember user preferences or interactions with the modal.","readme_library","docs.api.nvidia.com","_im_ses.1006927","geo_locator","nv.modal.deepstream","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","_im_id.1006927","_hjUserAttributesHash","docs.nvidia.com","This cookie name is associated with Hotjar which is used to know when any user attribute has changed and needs to be updated.","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","nv.modal.cudnn","This cookie name suggests it is related to modal popup handling or user interface preferences on NVIDIA's website, specifically for the cuDNN library section.","readme_language","nv.modal.hpc","This cookie name suggests it is related to a modal pop-up on a website, possibly to control the display of a high-priority cookie notice or other important information to the user.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","_biz_uid","This cookie 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.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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