batch_size: 1024 prompts training_tokens: 1,000,000 hook_layer: 5 hook_name: blocks.5.hook_mlp_out

The CacheAPI extension for CKAN allows administrators to clear the NGINX cache associated with their CKAN instance, either for specific URLs or for groups of URLs defined through a plugin interface. This functionality is useful for ensuring that users see the most up-to-date data after changes have been made to datasets or other CKAN resources, bypassing the NGINX caching layer. It aims to provide a mechanism to manage and invalidate cached content, ensuring data freshness. Key Features: URL-based Cache Clearing: Allows clearing the NGINX cache for a specific URL. This is useful when a single dataset or resource has been updated and the cache needs to be cleared immediately. Group-based Cache Clearing: Supports defining groups of URLs within CKAN plugins, enabling administrators to clear the cache for multiple related resources simultaneously. This is beneficial when updates affect multiple resources that should all be refreshed in the cache. ICache Interface: Provides an ICache interface that plugins can implement to define URL groups. This interface allows plugin developers to specify how URLs are grouped and how the cache should be cleared for each group. The getcaches method provides a dictionary of group names and URLs. Paster Command Integration: Offers a command-line interface (CLI) via Paster commands for clearing the cache. This allows administrators to automate the cache clearing process or perform it manually as needed. The paster cache clear command accepts either a URL or a group name as an argument. Proxy Cache Bypass Cookie Support: Supports integrating with a proxy cache bypass cookie in NGINX by letting users to configure the cookie name setting in the CKAN configuration file. By setting ckanext.cacheapi.httpxcookie_name = example where example is replaced with desired cookie name. This setting allows cache bypassing. Technical Integration: The CacheAPI extension integrates with CKAN through a plugin architecture, allowing extensions to implement the ICache interface and define their own URL groups for cache clearing. It also integrates with the CKAN command-line interface (CLI) via Paster commands, providing a way to trigger cache clearing operations. Configuration settings allow you to define a cookie-name for proxy cache bypass. Benefits & Impact:

This Land Cover-Land Use Tile Cache may be used for fast display in ArcGIS Online, ArcGIS Desktop, and other applications that can consume tile services.The statewide dataset contains a combination of land cover mapping from 2016 aerial imagery and land use derived from standardized assessor parcel information for Massachusetts. The data layer is the result of a cooperative project between MassGIS and the National Oceanic and Atmospheric Administration’s (NOAA) Office of Coastal Management (OCM). Funding was provided by the Mass. Executive Office of Energy and Environmental Affairs.

This land cover/land use dataset does not conform to the classification schemes or polygon delineation of previous land use data from MassGIS (1951-1999; 2005).In this hosted tile cache layer, all impervious polygons are symbolized by their generalized use code; all non-impervious land cover polygons are symbolized by their land cover category. The idea behind this method is to use both cover and use codes to provide a truer picture of how land is being used: parcel use codes may indicate allowed or assessed, not actual use; land cover alone (especially impervious) does not indicate actual use.

See the full datalayer description for more details.Also available are a Map Service and a Feature Service. They provide attribute query, although they will not display as quickly as the tile cache at smaller (zoomed out) scales.Add the Land Cover-Land Use Legend Map Service to an ArcGIS Online map along with this tile service to have a legend appear.

The LIO Topographic Data Cache is a collection of topographic data, that has been preprocessed for fast, seamless display at predefined scales. The topographic data includes constructed and natural features that make up Ontario’s landscape. The cache provides limited data from areas outside Ontario’s boundaries, such as the United States and adjacent provinces and territories. Technical information Two versions of the LIO Topographic Data Cache are available: 1. The traditional raster version is available for a variety of GIS applications and is updated annually. 2. The vector version is suitable for online web map applications as well as modern GIS software and is updated twice a year. Contributing data layers may have different maintenance and update cycles. Some cache layers have been processed in a way that makes it easier for them to be displayed in a mapping product. Other layers are unchanged from the authoritative data. The cartographic symbology used in the data cache is intentionally muted to allow users to showcase their data. The LIO Topographic Data Cache is created from many source datasets, which are described in the LIO Topographic Data Cache user guide. If you are interested in getting this authoritative data, you can download it from the Ontario GeoHub. For instructions on getting a copy of either version of the cache for use in mapping applications, visit the Ontario GeoHub.

TinyStories dataset first layer activations by Llama-3.2-1B Useful for accelerated training and testing of sparse autoencoders hooked onto the first layer Context size: 128 tokens, batch size: 4 prompts, limited to 100k input tokens For tokenized dataset before activation caching, see GulkoA/TinyStories-tokenized-Llama-3.2

The topographic data includes constructed and natural features that make up Ontario’s landscape.

The cache provides limited data from areas outside Ontario’s boundaries, such as the United States and adjacent provinces and territories.

Technical information Two versions of the LIO Topographic Data Cache are available:

The traditional raster version is available for a variety of GIS applications and is updated annually. The vector version is suitable for online web map applications as well as modern GIS software and is updated twice a year. Contributing data layers may have different maintenance and update cycles.

Some cache layers have been processed in a way that makes it easier for them to be displayed in a mapping product. Other layers are unchanged from the authoritative data.

The cartographic symbology used in the data cache is intentionally muted to allow users to showcase their data.The LIO Vector Topographic Data Cache is created from many source datasets as described in the LIO Topographic Data Cache user guide. If you are interested in obtaining this authoritative data, you can download it from the Ontario GeoHub.

Additional Documentation

LIO Topographic Data Cache - User Guide (DOCX)

LIO Vector Topographic Data Cache - Tile Layer

Status

On going: Data is continually being updated

Maintenance and Update Frequency

Biannually: data is updated twice each year

Contact

Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

This is a vector layer of Ile Cache island's base map. The GIS layer was originally created by the GIS section in the Ministry of Environment and Natural Resource and Transport (MENRT). The layer has been re-edited since then by the Centre for GIS.

The global SSD caching market has been valued at USD 21610 million in 2025 and is projected to grow at a CAGR of 8.6% during the forecast period (2025-2033). The growth of the market is primarily attributed to the increasing demand for high-performance storage solutions, the growing adoption of cloud computing, and the rising popularity of big data analytics. Solid-state drives (SSDs) offer significantly faster access times and data transfer rates compared to traditional hard disk drives (HDDs), making them ideal for use as a caching layer in storage systems. Some of the key trends in the SSD caching market include the growing adoption of write-back caching strategies, the emergence of NVMe-based SSDs, and the increasing use of SSD caching in cloud computing environments. Write-back caching strategies offer improved performance compared to write-through caching strategies by reducing the number of write operations to the underlying storage device. NVMe-based SSDs offer even higher performance than SATA-based SSDs, making them ideal for use in high-performance computing applications. Cloud computing environments are increasingly adopting SSD caching to improve the performance of storage systems and reduce the cost of storage.

The Land Information Ontario (LIO) Topographic Data Cache is a collection of topographic data built using a series of pre-drawn maps. The maps are compressed for fast, seamless display at predefined scales.

The topographic data includes constructed and natural features that make up Ontario’s landscape.

The cache provides limited data from areas outside Ontario’s boundaries, such as the United States and adjacent provinces and territories.

Technical information The cache is updated every year, but the contributing data layers may have different maintenance and update cycles.

Some cache layers have been processed in a way that makes it easier for them to be displayed in a mapping product.

Other layers are unchanged from the authoritative data.

The cartographic symbology used in the data cache is intentionally muted to allow users to showcase their data.

The LIO Topographic Data Cache is created from many source datasets as described in the LIO Topographic Data Cache user guide. If you are interested in obtaining this authoritative data, you can download it from Land Information Ontario.

Contact LIO Support at lio@ontario.ca to get a copy of the cache for use in mapping applications.

Additional Documentation

LIO Topographic Data Cache - User Guide (DOCX)

LIO Topographic Data Cache - layer file linking to web serviceLIO Web Service User Guide

Status

On going: Data is continually being updated

Maintenance and Update Frequency

Annually: Data is updated every year

Contact

Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

This cached map service provides access to the Kentucky Topographic Map Series (KyTopo) images in a seamless manner. The underlying data will be updated on a periodic basis. This Web Mercator-based service is intended for use in a web mapping framework.

This is a vector layer of Ile Cache island's beaches. The GIS layer was originally created by the GIS section in the Ministry of Environment and Natural Resource and Transport (MENRT). The layer has been re-edited since then by the Centre for GIS.

Network Cache Acceleration Service Market Outlook

The global network cache acceleration service market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 3.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.4% during the forecast period. This growth can be attributed to the ever-increasing demand for faster data access and retrieval in various industries such as BFSI, healthcare, IT and telecommunications, and media & entertainment.

One of the primary growth factors driving the network cache acceleration service market is the exponential increase in data generation across various sectors. With the advent of IoT, cloud computing, and big data analytics, organizations are generating and utilizing massive amounts of data. These organizations require efficient and rapid data access to maintain competitive advantages, making network cache acceleration services increasingly indispensable. The ability to speed up data retrieval processes significantly enhances operational efficiencies and overall user experience, which is crucial in today's data-driven world.

Another crucial growth driver is the rising adoption of edge computing. Edge computing brings data processing closer to the source of data generation, reducing latency and bandwidth usage. Network cache acceleration services play a vital role in edge computing by providing faster access to frequently used data, thereby enhancing the performance of edge devices and applications. As more industries integrate edge computing into their operations, the demand for network cache acceleration services is expected to grow substantially.

The increasing emphasis on digital transformation across various industries is also propelling the growth of the network cache acceleration service market. Enterprises are rapidly embracing digital technologies to improve operational efficiencies, enhance customer experience, and drive innovation. Network cache acceleration services enable these enterprises to access data quickly and efficiently, which is critical for the success of digital transformation initiatives. As more businesses embark on their digital transformation journeys, the demand for these services is likely to surge.

The integration of Proxy Server Service within network cache acceleration solutions is becoming increasingly important as organizations strive to enhance their data management capabilities. Proxy servers act as intermediaries between users and the internet, providing an additional layer of security and control over data traffic. By incorporating proxy server services, businesses can effectively manage and optimize data flow, ensuring that only authorized data is accessed and transmitted. This not only enhances data security but also improves the efficiency of cache acceleration processes by filtering unnecessary data and reducing latency. As data privacy concerns continue to rise, the demand for solutions that integrate proxy server services with cache acceleration is expected to grow, offering organizations a comprehensive approach to data management.

From a regional perspective, North America is anticipated to hold a significant share of the market due to the early adoption of advanced technologies and the presence of major market players in the region. The Asia-Pacific region is expected to witness the highest growth rate, driven by rapid industrialization, increasing IT infrastructure investments, and the growing adoption of cloud computing and IoT technologies. Europe and Latin America are also expected to contribute significantly to market growth, with steady adoption rates and increasing investments in digital technologies.

Component Analysis

The network cache acceleration service market is segmented into three primary components: hardware, software, and services. Each of these components plays a critical role in the overall functionality and efficiency of network cache acceleration solutions. The hardware segment includes specialized cache servers and devices that store and accelerate data retrieval processes. These hardware components are designed to handle high-speed data transfers and minimize latency, making them essential for organizations that require quick access to large volumes of data.

Software components in the network cache acceleration service market include various algorithms and protocols that manage data caching and retrieval processes. These software solutions are crucial

Cached activations at layer 5 for gpt2 using dataset apollo-research/roneneldan-TinyStories-tokenizer-gpt2 Useful for accelerated training and testing of sparse autoencoders context_window: 512 tokens total_tokens: 51,200,000 batch_size: 8 prompts (4096 tokens) layer_hook_name: blocks.5.hook_mlp_out

The geospatial data presented here as ArcGIS layers denote landcover/landuse classifications to support field sampling efforts that occurred within the Cache Creek Settling Basin (CCSB) from 2010-2019. Manual photointerpretation of a National Agriculture Imagery Program (NAIP) dataset collected in 2012 was used to characterize landcover/landuse categories (hereafter habitat classes). Initially 9 categories were assigned based on vegetation structure (Vegtype1). These were then parsed into two levels of habitat classes that were chosen for their representativeness and use for statistical analyses of field sampling. At the coarsest level (Landcover 1), five habitat classes were assigned: Agriculture, Riparian, Floodplain, Open Water, and Road. At the more refined level (Landcover 2), ten habitat classes were nested within these five categories. Agriculture was not further refined within Landcover 2, as little consistency was expected between years as fields rotated between corn, pumpkin, tomatoes, and other row crops. Riparian habitat, marked by large canopy trees (such as Populus fremontii (cottonwood)) neighboring stream channels, also was not further refined. Floodplain habitat was separated into two categories: Mixed NonWoody (which included both Mowed and Barren habitats) and Mixed Woody. This separation of the floodplain habitat class (Landcover1) into Woody and NonWoody was performed with a 100 m2 moving window analysis in ArcGIS, where habitats were designated as either ≥50% shrub or tree cover (Woody) or <50%, and thus dominated by herbaceous vegetation cover (NonWoody). Open Water habitat was refined to consider both agricultural Canal (created) and Stream (natural) habitats. Road habitat was refined to separate Levee Roads (which included both the drivable portion and the apron on either side) and Interior roads, which were less managed. The map was tested for errors of omission and commission on the initial 9 categories during November 2014. Random points (n=100) were predetermined, and a total of 80 were selected for field verification. Type 1 (false positive) and Type 2 (false negative) errors were assessed. The survey indicated several corrections necessary in the final version of the map. 1) We noted the presence of woody species in “NonWoody” habitats, especially Baccharus salicilifolia (mulefat). Habitats were thus classified as “Woody” only with ≥50% presence of canopy species (e.g. tamarisk, black willow) 2) Riparian sites were over-characterized, and thus constrained back to “near stream channels only”. Walnut (Juglans spp) and willow stands alongside fields and irrigation canals were changed to Mixed Woody Floodplain. Fine tuning the final habitat distributions was thus based on field reconnaissance, scalar needs for classifying field data (sediment, water, bird, and fish collections), and validation of data categories using species observations from scientist field notes. Calibration was made using point data from the random survey and scientist field notes, to remove all sources of error and reach accuracy of 100%. The coverage “CCSB_Habitat_2012” is provided as an ARCGIS shapefile based on a suite of 7 interconnected ARCGIS files coded with the suffixes: cpg, dbf, sbn, sbx, shp, shx, and prj. Each file provides a component of the coverage (such as database or projection) and all files are necessary to open the “CCSB_Habitat_2012.shp” file with full functionality. CCSB_Basin_Map.png represents the CCSB study area color coded by the four primary habitat types identified in this study.

The Mississippi Alluvial Plain (MAP) is one of the most important agricultural regions in the United States and underlies about 32,000 square miles of Missouri, Kentucky, Tennessee, Mississippi, Louisiana, and Arkansas. The MAP region supports a multibillion-dollar agricultural industry. The MAP is part of the Mississippi Embayment with several water-bearing units that make up the Mississippi Embayment Regional Aquifer System (MERAS). These water bearing units include the Mississippi River Valley Alluvial aquifer, Claiborne aquifers and Wilcox aquifers. The Grand Prairie area has been designated as a Critical Groundwater area because of decades of groundwater declines that resulted from past and current water use. The objective of the report associated with this data release is to document and describe the construction, calibration, and results of the inset groundwater-flow model developed for the Grand Prairie Critical Groundwater Area using the latest MODFLOW-6 code. The model derived boundary conditions from the parent MERAS 3 regional model to provide higher resolution simulations in the Grand Prairie focus area. The Grand Prairie model was spatially discretized into 500-meter x 500-meter orthogonal cells on a grid. The Grand Prairie model had 19 vertical layers, 245 rows, and 206 columns and simulated the Quaternary-age alluvial aquifer with 5-m constant thickness layers and increasing thickness layers for the Tertiary-age units below the alluvial aquifer. The Grand Prairie model included 148 stress periods with a simulation period from January 1, 1900 through December 31, 2018 where stress periods: April 1, 2007 through December 31, 2018 where monthly stress periods. Areal recharge was simulated by a soil-water-balance model of the MERAS and passed to the groundwater models. The model simulated agricultural, municipal, and thermoelectric pumping. The model simulated groundwater-surface water interactions and total streamflow by adding runoff from the soil-water-balance model. The model featured high-dimensional parameterization schemes for calibration using the PEST++ Iterative Ensemble Smoother. Mean absolute residuals for calibrated priority well observations were 2.71 meters for the Grand Prairie model. Mean horizontal hydraulic conductivity for the alluvial aquifer was about 63 meters per day for the Grand Prairie model. Calibrated areal recharge was 3.4 inches for the Grand Prairie model. Primary groundwater outflows represented in the model were from agricultural wells.

The LA SSD Caching Market is estimated to reach $XX million by 2033, exhibiting a CAGR of 8.10% during the forecast period. The market growth can be attributed to the increasing adoption of SSDs in enterprise and personal storage applications. SSDs offer several advantages over traditional hard disk drives (HDDs), such as faster boot times, improved application performance, and reduced power consumption. The key drivers of the market include the growing demand for high-performance computing, the increasing adoption of cloud computing, and the rising popularity of data-intensive applications. However, the market growth is restrained by the high cost of SSDs and the limited availability of NAND flash memory. The market is segmented into enterprise storage and personal storage applications. The enterprise storage segment is expected to hold a larger market share during the forecast period due to the increasing demand for SSDs in data centers and other enterprise environments. Recent developments include: March 2021 - A Solid State Drive (SSD) 670p has been released by Intel. The client SSD is a 144-layer quad-level cell-based device. The SSD can hold up to two terabytes of data in a single drive. According to Intel, SSDs can be used to supplement everyday computing needs as well as to assist extreme gaming. When compared to the previous generation Intel QLC 3D NAND SSD, the new SSD 670p is said to give improved performance, including a 2-times sequential read and a 20% endurance boost. To meet normal processing demands, the Intel SSD 670p has been optimized for low queue depth and mixed workloads., August 2020 - Intel launched SSD D7-P550/5600. The Intel" SSD D7-P5500 and Intel" SSD D7-P5600 Series is designed to improve IT efficiency and data security by providing optimum performance and capacity for all-TLC arrays. The Intel" SSD D7-P5500 and D7-P5600 include an all-new Intel PCle Gen4 controller and firmware that provides low latency, better administration capabilities, scalability, and crucial new NVMe features for Enterprise and Cloud settings.. Key drivers for this market are: Improvements Offered by SSDs Over Conventional HDDs. Potential restraints include: Slow Pace in Development of Applications Despite Heavy investments in R&D, Commplexities in Hardware Designing. Notable trends are: Enterprise Storage Expected to Hold Major Share.

Update information can be found within the layer’s attributes and in a table on the Utah Parcel Data webpage under Basic Parcels."Database containing parcel boundary, parcel identifier, parcel address, owner type, and county recorder contact information" - HB113. The intent of the bill was to not include any attributes that the counties rely on for data sales. If you want other attributes associated with the parcels you need to contact the county recorder.Users should be aware the owner type field 'OWN_TYPE' in the parcel polygons is a very generalized ownership type (Federal, Private, State, Tribal). It is populated with the value of the 'OWNER' field where the parcel's centroid intersects the CADASTRE.LandOwnership polygon layer.This dataset is a snapshot in time and may not be the most current. For the most current data contact the county recorder.

Geospatial data about Cache County, Utah Parcels. Export to CAD, GIS, PDF, CSV and access via API.

This tile service, hosted by MassGIS, features Lidar-derived elevation and shaded relief for the Commonwealth of Massachusetts.

The service uses statewide versions of the digital elevation model and shaded relief from the Lidar DEM and Shaded Relief imagery.MassGIS created the tile service in ArcGIS Pro, using the "Multiply" Darkening blending mode to "burn in" the shaded relief to the elevation layer. The elevation layer is symbolized with a custom color ramp. The shaded relief is displayed with 45% transparency.View the data along with an elevation image service in the Massachusetts Elevation Finder.

The LA SSD Caching market, exhibiting a robust CAGR of 8.10%, is poised for significant growth from 2025 to 2033. Driven by the increasing demand for high-performance computing and data storage solutions across enterprise and personal applications, this market is experiencing a transformation fueled by advancements in SSD technology. The enterprise storage segment is the major revenue contributor, propelled by the need for faster data access speeds and reduced latency in data centers and cloud environments. Growing adoption of cloud computing, big data analytics, and AI/ML applications further accelerates market expansion. However, the high initial investment cost associated with implementing LA SSD caching solutions and the potential for data loss in case of failures are key restraining factors. The market is segmented geographically, with North America and Europe currently holding significant market shares due to higher technological adoption rates and established infrastructure. Asia Pacific is expected to witness substantial growth in the coming years, fueled by rising digitalization and increasing investments in data centers across developing economies. Key players like Intel, Samsung, and Western Digital are driving innovation and competition through continuous advancements in SSD technology, capacity, and performance. The market is expected to see further consolidation as larger players acquire smaller firms to expand their market presence and product portfolios. The increasing focus on data security and reliability will also influence the development and adoption of advanced security features in LA SSD caching solutions, shaping the market's trajectory. The forecast period (2025-2033) presents lucrative opportunities for market players to capitalize on evolving technological advancements and growing customer demand. Strategic partnerships, technological collaborations, and robust marketing strategies will be crucial for success in this competitive market. The introduction of new, cost-effective solutions targeted at smaller businesses and individual consumers could significantly broaden the market's reach. Future growth will be shaped by the development of more efficient and power-saving SSD caching technologies, alongside enhanced data security and management capabilities. Furthermore, the emergence of new applications and industry verticals that require high-speed data access will further drive market expansion. The overall outlook for the LA SSD Caching market remains positive, with continuous growth anticipated throughout the forecast period. Recent developments include: March 2021 - A Solid State Drive (SSD) 670p has been released by Intel. The client SSD is a 144-layer quad-level cell-based device. The SSD can hold up to two terabytes of data in a single drive. According to Intel, SSDs can be used to supplement everyday computing needs as well as to assist extreme gaming. When compared to the previous generation Intel QLC 3D NAND SSD, the new SSD 670p is said to give improved performance, including a 2-times sequential read and a 20% endurance boost. To meet normal processing demands, the Intel SSD 670p has been optimized for low queue depth and mixed workloads., August 2020 - Intel launched SSD D7-P550/5600. The Intel" SSD D7-P5500 and Intel" SSD D7-P5600 Series is designed to improve IT efficiency and data security by providing optimum performance and capacity for all-TLC arrays. The Intel" SSD D7-P5500 and D7-P5600 include an all-new Intel PCle Gen4 controller and firmware that provides low latency, better administration capabilities, scalability, and crucial new NVMe features for Enterprise and Cloud settings.. Key drivers for this market are: Improvements Offered by SSDs Over Conventional HDDs. Potential restraints include: Improvements Offered by SSDs Over Conventional HDDs. Notable trends are: Enterprise Storage Expected to Hold Major Share.