Empower AI Transformation and Advance AI For Everyone 4Paradigm Open Source Community

[deeper_spacer mobile=”80″]

[deeper_text text_width=”750px” text_margin_bottom=”40px” text_font_size=”28px” text_line_height=”34px”]We blend design, engineering, and analytics expertise to help you build the future[/deeper_text]

[deeper_spacer desktop=”75″ mobile=”55″ smobile=”35″]

[deeper_spacer desktop=”270″ mobile=”270″ smobile=”270″]

[deeper_carouselbox gap=”30″ fullaside=”true” center=”center” groupcell=”true” class=”evenodd show-arrow-active” column2=”3″][deeper_contentbox padding=”50px 45px 60px” mobile_padding=”50px 45px 60px” margin=”0 0 35px” mobile_margin=”0 0 35px” background_color=”#ffffff” border_color=”#e7e7e7″ rounded=”5px” shadow=”inset 0 0 0 1px #e7e7e7″ translatex=”0″ translatey=”0″ show_arrow=”yes” arrow_url=”url:https%3A%2F%2Fgithub.com%2F4paradigm%2Fpafka” class=”position-relative”][deeper_iconbox icon_align=”align-center” icon_display=”icon-image” icon_image=”2391″ heading=”Pafka” heading_font_weight=”” desc_font_weight=”” desc_font_size=”18px” desc_line_height=”27px” heading_top_margin=”25px” heading_bottom_margin=”18px” arrow_url=”url:%23|||”]Pafka equips Kafka with Intel® Optane™ Persistent Memory (PMem) support, which relies on the native pmdk libraries. Pafka can achieve 7.5 GB/s write throughput and 10 GB/s read throughput in terms of single-server performance.[/deeper_iconbox][/deeper_contentbox][deeper_contentbox padding=”50px 45px 60px” mobile_padding=”50px 45px 60px” margin=”0 0 35px” mobile_margin=”0 0 35px” background_color=”#ffffff” rounded=”5px” shadow=”inset 0 0 0 1px #e7e7e7″ translatex=”0″ translatey=”0″ show_arrow=”yes” arrow_url=”url:https%3A%2F%2Fgithub.com%2F4paradigm%2Ffedb”][deeper_iconbox icon_align=”align-center” icon_display=”icon-image” icon_image=”2392″ heading=”FEDB” heading_font_weight=”” desc_font_weight=”” desc_font_size=”18px” desc_line_height=”27px” heading_top_margin=”25px” heading_bottom_margin=”18px” arrow_url=”url:%23|||”]FEDB is a NewSQL optimised for realtime inference and decisioning application.[/deeper_iconbox][/deeper_contentbox][deeper_contentbox padding=”50px 45px 60px” mobile_padding=”50px 45px 60px” margin=”0 0 35px” mobile_margin=”0 0 35px” background_color=”#ffffff” border_color=”#e7e7e7″ rounded=”5px” shadow=”inset 0 0 0 1px #e7e7e7″ translatex=”0″ translatey=”0″ show_arrow=”yes” arrow_url=”url:https%3A%2F%2Fgithub.com%2F4paradigm%2FNativeSpark|target:_blank|rel:nofollow”][deeper_iconbox icon_align=”align-center” icon_display=”icon-image” icon_image=”2394″ heading=”SparkFE” heading_font_weight=”” desc_font_weight=”” desc_font_size=”18px” desc_line_height=”27px” heading_top_margin=”25px” heading_bottom_margin=”18px” arrow_url=”url:%23|||”]SparkFE provides data scientists with end-to-end high-performance batch real-time feature engineering processing capabilities based on Spark, allowing data scientists to independently and efficiently complete feature enginnering delivery.[/deeper_iconbox][/deeper_contentbox][/deeper_carouselbox]

[deeper_carouselbox gap=”30″ fullaside=”true” center=”center” groupcell=”true” class=”evenodd show-arrow-active” column2=”3″][deeper_contentbox padding=”50px 45px 60px” mobile_padding=”50px 45px 60px” margin=”0 0 35px” mobile_margin=”0 0 35px” background_color=”#ffffff” border_color=”#e7e7e7″ rounded=”5px” shadow=”inset 0 0 0 1px #e7e7e7″ translatex=”0″ translatey=”0″ class=”position-relative”][deeper_iconbox icon_align=”align-center” icon_display=”icon-image” icon_image=”2395″ heading=”Computing Container Platform” heading_font_weight=”” desc_font_weight=”” desc_font_size=”18px” desc_line_height=”27px” heading_top_margin=”25px” heading_bottom_margin=”18px” arrow_url=”url:%23|||”]Computing Container Platform is a platform that based on kubernetes clusters. Here you would easily get different kinds of instances and applications.[/deeper_iconbox][/deeper_contentbox][deeper_contentbox padding=”50px 45px 60px” mobile_padding=”50px 45px 60px” margin=”0 0 35px” mobile_margin=”0 0 35px” background_color=”#ffffff” rounded=”5px” shadow=”inset 0 0 0 1px #e7e7e7″ translatex=”0″ translatey=”0″ show_arrow=”yes” arrow_url=”url:https%3A%2F%2Fgithub.com%2F4paradigm%2Fk8s-device-plugin|target:_blank|rel:nofollow”][deeper_iconbox icon_align=”align-center” icon_display=”icon-image” icon_image=”2399″ heading=”vGPU K8s Device Plugin” heading_font_weight=”” desc_font_weight=”” desc_font_size=”18px” desc_line_height=”27px” heading_top_margin=”25px” heading_bottom_margin=”18px” arrow_url=”url:%23|||”]The vGPU device plugin is based on NVIDIA device plugin(NVIDIA/k8s-device-plugin). It splits the physical GPU, and limits the memory and computing unit, thereby simulating multiple small vGPU cards. In the k8s cluster, different containers can safely share the same physical GPU.[/deeper_iconbox][/deeper_contentbox][deeper_contentbox padding=”50px 45px 60px” mobile_padding=”50px 45px 60px” margin=”0 0 35px” mobile_margin=”0 0 35px” background_color=”#ffffff” border_color=”#e7e7e7″ rounded=”5px” shadow=”inset 0 0 0 1px #e7e7e7″ translatex=”0″ translatey=”0″ show_arrow=”yes” arrow_url=”url:https%3A%2F%2Fgithub.com%2F4paradigm%2Fpmemstore|title:Github|target:_blank|rel:nofollow”][deeper_iconbox icon_align=”align-center” icon_display=”icon-image” heading=”PmemStore” heading_font_weight=”” desc_font_weight=”” desc_font_size=”18px” desc_line_height=”27px” heading_top_margin=”25px” heading_bottom_margin=”18px” arrow_url=”url:%23|||”]PmemStore is a utility library that provides rapid key-value-like access on state-of-art persistent memory such as Intel DC Persistent Memory Module(PMEM). We will provide several PMEM-aware data structures, such as persistent skiplist as the core engine.[/deeper_iconbox][/deeper_contentbox][/deeper_carouselbox]

[deeper_spacer desktop=”120″ mobile=”100″ smobile=”80″]

[deeper_spacer]

[deeper_spacer desktop=”50″ mobile=”0″ smobile=”0″]

[deeper_contentbox padding=”0 30px 0 0″ translatex=”0″ translatey=”0″ animation=”yes”]

[deeper_text text_align=”align-left” text_font_size=”28px” text_line_height=”34px”]We’d love to hear from you. Please leave you message and we will contact with you later.[/deeper_text]

[deeper_contactform form_id=”2327″]

[/deeper_contentbox]

[deeper_spacer desktop=”50″ mobile=”30″ smobile=”30″]

[deeper_contentbox d_height=”0 0 050px” padding=”0 0 030px” translatex=”0″ translatey=”0″ animation=”yes”][deeper_fancyimage image=”2134″ effect=”reveal” rounded=”20px”][/deeper_contentbox]

[deeper_spacer]