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octicon-report mr-2\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z\"\u003e\u003c/path\u003e\u003c/svg\u003eImportant\u003c/p\u003e\u003cp dir=\"auto\"\u003e\u003cem\u003e\u003cstrong\u003e\u003ccode\u003ebigdl-llm\u003c/code\u003e has now become \u003ccode\u003eipex-llm\u003c/code\u003e, and our future development will move to the \u003ca href=\"https://github.com/intel-analytics/ipex-llm\"\u003eIPEX-LLM\u003c/a\u003e project.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e \u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://camo.githubusercontent.com/df7fa1f3085ff259f05c26ad3f54eecb7743a032b3cb3a62922588ff09b6d67e/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f626967646c5f6c6f676f2e6a7067\"\u003e\u003cimg src=\"https://camo.githubusercontent.com/df7fa1f3085ff259f05c26ad3f54eecb7743a032b3cb3a62922588ff09b6d67e/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f626967646c5f6c6f676f2e6a7067\" height=\"140px\" data-canonical-src=\"https://llm-assets.readthedocs.io/en/latest/_images/bigdl_logo.jpg\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOverview\u003c/h2\u003e\u003ca id=\"user-content-overview\" class=\"anchor\" aria-label=\"Permalink: Overview\" href=\"#overview\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBigDL seamlessly scales your data analytics \u0026amp; AI applications from laptop to cloud, with the following libraries:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ccode\u003eLLM\u003c/code\u003e \u003cem\u003e\u003cstrong\u003e(deprecated - please use \u003ca href=\"https://github.com/intel-analytics/ipex-llm\"\u003eIPEX-LLM\u003c/a\u003e instead)\u003c/strong\u003e\u003c/em\u003e: Optimizaed large language model library for Intel CPU and GPU\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#orca\"\u003eOrca\u003c/a\u003e: Distributed Big Data \u0026amp; AI (TF \u0026amp; PyTorch) Pipeline on Spark and Ray\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#nano\"\u003eNano\u003c/a\u003e: Transparent Acceleration of Tensorflow \u0026amp; PyTorch Programs on Intel CPU/GPU\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#dllib\"\u003eDLlib\u003c/a\u003e: “Equivalent of Spark MLlib” for Deep Learning\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#chronos\"\u003eChronos\u003c/a\u003e: Scalable Time Series Analysis using AutoML\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#friesian\"\u003eFriesian\u003c/a\u003e: End-to-End Recommendation Systems\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003e\u003ca href=\"#ppml\"\u003ePPML\u003c/a\u003e: Secure Big Data and AI (with SGX/TDX Hardware Security)\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eFor more information, you may \u003ca href=\"https://bigdl.readthedocs.io/\" rel=\"nofollow\"\u003eread the docs\u003c/a\u003e.\u003c/p\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eChoosing the right BigDL library\u003c/h2\u003e\u003ca id=\"user-content-choosing-the-right-bigdl-library\" class=\"anchor\" aria-label=\"Permalink: Choosing the right BigDL library\" href=\"#choosing-the-right-bigdl-library\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003csection class=\"js-render-needs-enrichment render-needs-enrichment position-relative\" data-identity=\"f2abcb8f-af5a-426f-9f7a-1d61302a831c\" data-host=\"https://viewscreen.githubusercontent.com\" data-src=\"https://viewscreen.githubusercontent.com/markdown/mermaid?docs_host=https%3A%2F%2Fdocs.github.com\" data-type=\"mermaid\" aria-label=\"mermaid rendered output container\"\u003e\n \u003cdiv class=\"js-render-enrichment-target\" data-json=\"{\u0026quot;data\u0026quot;:\u0026quot;flowchart TD;\\n Feature1{{HW Secured Big Data \u0026amp;amp; AI?}};\\n Feature1-- No --\u0026amp;gt;Feature2{{Python vs. Scala/Java?}};\\n Feature1-- \\\u0026quot;Yes\\\u0026quot; --\u0026amp;gt;ReferPPML([\u0026amp;lt;em\u0026amp;gt;\u0026amp;lt;strong\u0026amp;gt;PPML\u0026amp;lt;/strong\u0026amp;gt;\u0026amp;lt;/em\u0026amp;gt;]);\\n Feature2-- Python --\u0026amp;gt;Feature3{{What type of application?}};\\n Feature2-- Scala/Java --\u0026amp;gt;ReferDLlib([\u0026amp;lt;em\u0026amp;gt;\u0026amp;lt;strong\u0026amp;gt;DLlib\u0026amp;lt;/strong\u0026amp;gt;\u0026amp;lt;/em\u0026amp;gt;]);\\n Feature3-- \\\u0026quot;Large Language Model\\\u0026quot; --\u0026amp;gt;ReferLLM([\u0026amp;lt;em\u0026amp;gt;\u0026amp;lt;strong\u0026amp;gt;LLM\u0026amp;lt;/strong\u0026amp;gt;\u0026amp;lt;/em\u0026amp;gt;]);\\n Feature3-- \\\u0026quot;Big Data + AI (TF/PyTorch)\\\u0026quot; --\u0026amp;gt;ReferOrca([\u0026amp;lt;em\u0026amp;gt;\u0026amp;lt;strong\u0026amp;gt;Orca\u0026amp;lt;/strong\u0026amp;gt;\u0026amp;lt;/em\u0026amp;gt;]);\\n Feature3-- Accelerate TensorFlow / PyTorch --\u0026amp;gt;ReferNano([\u0026amp;lt;em\u0026amp;gt;\u0026amp;lt;strong\u0026amp;gt;Nano\u0026amp;lt;/strong\u0026amp;gt;\u0026amp;lt;/em\u0026amp;gt;]);\\n Feature3-- DL for Spark MLlib --\u0026amp;gt;ReferDLlib2([\u0026amp;lt;em\u0026amp;gt;\u0026amp;lt;strong\u0026amp;gt;DLlib\u0026amp;lt;/strong\u0026amp;gt;\u0026amp;lt;/em\u0026amp;gt;]);\\n Feature3-- High Level App Framework --\u0026amp;gt;Feature4{{Domain?}};\\n Feature4-- Time Series --\u0026amp;gt;ReferChronos([\u0026amp;lt;em\u0026amp;gt;\u0026amp;lt;strong\u0026amp;gt;Chronos\u0026amp;lt;/strong\u0026amp;gt;\u0026amp;lt;/em\u0026amp;gt;]);\\n Feature4-- Recommender System --\u0026amp;gt;ReferFriesian([\u0026amp;lt;em\u0026amp;gt;\u0026amp;lt;strong\u0026amp;gt;Friesian\u0026amp;lt;/strong\u0026amp;gt;\u0026amp;lt;/em\u0026amp;gt;]);\\n \\n click ReferLLM \\\u0026quot;https://github.com/intel-analytics/ipex-llm\\\u0026quot;\\n click ReferNano \\\u0026quot;https://github.com/intel-analytics/BigDL-2.x#nano\\\u0026quot;\\n click ReferOrca \\\u0026quot;https://github.com/intel-analytics/BigDL-2.x#orca\\\u0026quot;\\n click ReferDLlib \\\u0026quot;https://github.com/intel-analytics/BigDL-2.x#dllib\\\u0026quot;\\n click ReferDLlib2 \\\u0026quot;https://github.com/intel-analytics/BigDL-2.x#dllib\\\u0026quot;\\n click ReferChronos \\\u0026quot;https://github.com/intel-analytics/BigDL-2.x#chronos\\\u0026quot;\\n click ReferFriesian \\\u0026quot;https://github.com/intel-analytics/BigDL-2.x#friesian\\\u0026quot;\\n click ReferPPML \\\u0026quot;https://github.com/intel-analytics/BigDL-2.x#ppml\\\u0026quot;\\n \\n classDef ReferStyle1 fill:#5099ce,stroke:#5099ce;\\n classDef Feature fill:#FFF,stroke:#08409c,stroke-width:1px;\\n class ReferLLM,ReferNano,ReferOrca,ReferDLlib,ReferDLlib2,ReferChronos,ReferFriesian,ReferPPML ReferStyle1;\\n class Feature1,Feature2,Feature3,Feature4,Feature5,Feature6,Feature7 Feature;\\n \\n\u0026quot;}\" data-plain=\"flowchart TD;\n Feature1{{HW Secured Big Data \u0026amp; AI?}};\n Feature1-- No --\u0026gt;Feature2{{Python vs. Scala/Java?}};\n Feature1-- \u0026quot;Yes\u0026quot; --\u0026gt;ReferPPML([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;PPML\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature2-- Python --\u0026gt;Feature3{{What type of application?}};\n Feature2-- Scala/Java --\u0026gt;ReferDLlib([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;DLlib\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- \u0026quot;Large Language Model\u0026quot; --\u0026gt;ReferLLM([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;LLM\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- \u0026quot;Big Data + AI (TF/PyTorch)\u0026quot; --\u0026gt;ReferOrca([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;Orca\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- Accelerate TensorFlow / PyTorch --\u0026gt;ReferNano([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;Nano\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- DL for Spark MLlib --\u0026gt;ReferDLlib2([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;DLlib\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- High Level App Framework --\u0026gt;Feature4{{Domain?}};\n Feature4-- Time Series --\u0026gt;ReferChronos([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;Chronos\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature4-- Recommender System --\u0026gt;ReferFriesian([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;Friesian\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n \n click ReferLLM \u0026quot;https://github.com/intel-analytics/ipex-llm\u0026quot;\n click ReferNano \u0026quot;https://github.com/intel-analytics/BigDL-2.x#nano\u0026quot;\n click ReferOrca \u0026quot;https://github.com/intel-analytics/BigDL-2.x#orca\u0026quot;\n click ReferDLlib \u0026quot;https://github.com/intel-analytics/BigDL-2.x#dllib\u0026quot;\n click ReferDLlib2 \u0026quot;https://github.com/intel-analytics/BigDL-2.x#dllib\u0026quot;\n click ReferChronos \u0026quot;https://github.com/intel-analytics/BigDL-2.x#chronos\u0026quot;\n click ReferFriesian \u0026quot;https://github.com/intel-analytics/BigDL-2.x#friesian\u0026quot;\n click ReferPPML \u0026quot;https://github.com/intel-analytics/BigDL-2.x#ppml\u0026quot;\n \n classDef ReferStyle1 fill:#5099ce,stroke:#5099ce;\n classDef Feature fill:#FFF,stroke:#08409c,stroke-width:1px;\n class ReferLLM,ReferNano,ReferOrca,ReferDLlib,ReferDLlib2,ReferChronos,ReferFriesian,ReferPPML ReferStyle1;\n class Feature1,Feature2,Feature3,Feature4,Feature5,Feature6,Feature7 Feature;\n \n\" dir=\"auto\"\u003e\n \u003cdiv class=\"render-plaintext-hidden\" dir=\"auto\"\u003e\n \u003cpre lang=\"mermaid\" aria-label=\"Raw mermaid code\"\u003eflowchart TD;\n Feature1{{HW Secured Big Data \u0026amp; AI?}};\n Feature1-- No --\u0026gt;Feature2{{Python vs. Scala/Java?}};\n Feature1-- \"Yes\" --\u0026gt;ReferPPML([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;PPML\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature2-- Python --\u0026gt;Feature3{{What type of application?}};\n Feature2-- Scala/Java --\u0026gt;ReferDLlib([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;DLlib\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- \"Large Language Model\" --\u0026gt;ReferLLM([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;LLM\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- \"Big Data + AI (TF/PyTorch)\" --\u0026gt;ReferOrca([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;Orca\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- Accelerate TensorFlow / PyTorch --\u0026gt;ReferNano([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;Nano\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- DL for Spark MLlib --\u0026gt;ReferDLlib2([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;DLlib\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature3-- High Level App Framework --\u0026gt;Feature4{{Domain?}};\n Feature4-- Time Series --\u0026gt;ReferChronos([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;Chronos\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n Feature4-- Recommender System --\u0026gt;ReferFriesian([\u0026lt;em\u0026gt;\u0026lt;strong\u0026gt;Friesian\u0026lt;/strong\u0026gt;\u0026lt;/em\u0026gt;]);\n \n click ReferLLM \"https://github.com/intel-analytics/ipex-llm\"\n click ReferNano \"https://github.com/intel-analytics/BigDL-2.x#nano\"\n click ReferOrca \"https://github.com/intel-analytics/BigDL-2.x#orca\"\n click ReferDLlib \"https://github.com/intel-analytics/BigDL-2.x#dllib\"\n click ReferDLlib2 \"https://github.com/intel-analytics/BigDL-2.x#dllib\"\n click ReferChronos \"https://github.com/intel-analytics/BigDL-2.x#chronos\"\n click ReferFriesian \"https://github.com/intel-analytics/BigDL-2.x#friesian\"\n click ReferPPML \"https://github.com/intel-analytics/BigDL-2.x#ppml\"\n \n classDef ReferStyle1 fill:#5099ce,stroke:#5099ce;\n classDef Feature fill:#FFF,stroke:#08409c,stroke-width:1px;\n class ReferLLM,ReferNano,ReferOrca,ReferDLlib,ReferDLlib2,ReferChronos,ReferFriesian,ReferPPML ReferStyle1;\n class Feature1,Feature2,Feature3,Feature4,Feature5,Feature6,Feature7 Feature;\n \n\u003c/pre\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cspan class=\"js-render-enrichment-loader d-flex flex-justify-center flex-items-center width-full\" style=\"min-height:100px\" role=\"presentation\"\u003e\n \u003cspan data-view-component=\"true\"\u003e\n \u003csvg style=\"box-sizing: content-box; color: var(--color-icon-primary);\" width=\"16\" height=\"16\" viewBox=\"0 0 16 16\" fill=\"none\" aria-hidden=\"true\" data-view-component=\"true\" class=\"octospinner mx-auto anim-rotate\"\u003e\n \u003ccircle cx=\"8\" cy=\"8\" r=\"7\" stroke=\"currentColor\" stroke-opacity=\"0.25\" stroke-width=\"2\" vector-effect=\"non-scaling-stroke\" fill=\"none\"\u003e\u003c/circle\u003e\n \u003cpath d=\"M15 8a7.002 7.002 0 00-7-7\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" vector-effect=\"non-scaling-stroke\"\u003e\u003c/path\u003e\n\u003c/svg\u003e \u003cspan class=\"sr-only\"\u003eLoading\u003c/span\u003e\n\u003c/span\u003e\n \u003c/span\u003e\n\u003c/section\u003e\n\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eInstalling\u003c/h2\u003e\u003ca id=\"user-content-installing\" class=\"anchor\" aria-label=\"Permalink: Installing\" href=\"#installing\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTo install BigDL, we recommend using \u003ca href=\"https://docs.conda.io/projects/conda/en/latest/user-guide/install/\" rel=\"nofollow\"\u003econda\u003c/a\u003e environment:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"conda create -n my_env \nconda activate my_env\npip install bigdl\"\u003e\u003cpre\u003econda create -n my_env \nconda activate my_env\npip install bigdl\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo install latest nightly build, use \u003ccode\u003epip install --pre --upgrade bigdl\u003c/code\u003e; see \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/UserGuide/python.html\" rel=\"nofollow\"\u003ePython\u003c/a\u003e and \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/UserGuide/scala.html\" rel=\"nofollow\"\u003eScala\u003c/a\u003e user guide for more details.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eTo install each individual library, such as Chronos, use \u003ccode\u003epip install bigdl-chronos\u003c/code\u003e; see the \u003ca href=\"https://bigdl.readthedocs.io/\" rel=\"nofollow\"\u003edocument website\u003c/a\u003e for more details.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eOrca\u003c/h3\u003e\u003ca id=\"user-content-orca\" class=\"anchor\" aria-label=\"Permalink: Orca\" href=\"#orca\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eThe \u003cem\u003eOrca\u003c/em\u003e library seamlessly scales out your single node \u003cstrong\u003eTensorFlow\u003c/strong\u003e, \u003cstrong\u003ePyTorch\u003c/strong\u003e or \u003cstrong\u003eOpenVINO\u003c/strong\u003e programs across large clusters (so as to process distributed Big Data).\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eShow Orca example\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003eYou can build end-to-end, distributed data processing \u0026amp; AI programs using \u003cem\u003eOrca\u003c/em\u003e in 4 simple steps:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop)\nfrom bigdl.orca import init_orca_context, OrcaContext\nsc = init_orca_context(cluster_mode=\u0026quot;k8s\u0026quot;, cores=4, memory=\u0026quot;10g\u0026quot;, num_nodes=2) \n\n# 2. Perform distribtued data processing (supporting Spark DataFrames,\n# TensorFlow Dataset, PyTorch DataLoader, Ray Dataset, Pandas, Pillow, etc.)\nspark = OrcaContext.get_spark_session()\ndf = spark.read.parquet(file_path)\ndf = df.withColumn('label', df.label-1)\n...\n\n# 3. Build deep learning models using standard framework APIs\n# (supporting TensorFlow, PyTorch, Keras, OpenVino, etc.)\nfrom tensorflow import keras\n...\nmodel = keras.models.Model(inputs=[user, item], outputs=predictions) \nmodel.compile(...)\n\n# 4. Use Orca Estimator for distributed training/inference\nfrom bigdl.orca.learn.tf.estimator import Estimator\nest = Estimator.from_keras(keras_model=model) \nest.fit(data=df,\n feature_cols=['user', 'item'],\n label_cols=['label'],\n ...)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eorca\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einit_orca_context\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eOrcaContext\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esc\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003einit_orca_context\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecluster_mode\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"k8s\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecores\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ememory\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"10g\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enum_nodes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e) \n\n\u003cspan class=\"pl-c\"\u003e# 2. Perform distribtued data processing (supporting Spark DataFrames,\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# TensorFlow Dataset, PyTorch DataLoader, Ray Dataset, Pandas, Pillow, etc.)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003espark\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eOrcaContext\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eget_spark_session\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espark\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eparquet\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efile_path\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ewithColumn\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003elabel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n...\n\n\u003cspan class=\"pl-c\"\u003e# 3. Build deep learning models using standard framework APIs\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e# (supporting TensorFlow, PyTorch, Keras, OpenVino, etc.)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etensorflow\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e\n...\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003emodels\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eModel\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003euser\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eitem\u003c/span\u003e], \u003cspan class=\"pl-s1\"\u003eoutputs\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003epredictions\u003c/span\u003e) \n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ecompile\u003c/span\u003e(...)\n\n\u003cspan class=\"pl-c\"\u003e# 4. Use Orca Estimator for distributed training/inference\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eorca\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elearn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003etf\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eestimator\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eEstimator\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eest\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eEstimator\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_keras\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ekeras_model\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e) \n\u003cspan class=\"pl-s1\"\u003eest\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003efeature_cols\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'user'\u003c/span\u003e, \u003cspan class=\"pl-s\"\u003e'item'\u003c/span\u003e],\n \u003cspan class=\"pl-s1\"\u003elabel_cols\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s\"\u003e'label'\u003c/span\u003e],\n ...)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e \n\u003cp dir=\"auto\"\u003e\u003cem\u003eSee Orca \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/orca.html\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e, as well as \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/tf2keras-quickstart.html\" rel=\"nofollow\"\u003eTensorFlow\u003c/a\u003e and \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/pytorch-quickstart.html\" rel=\"nofollow\"\u003ePyTorch\u003c/a\u003e quickstarts, for more details.\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIn addition, you can also run standard \u003cstrong\u003eRay\u003c/strong\u003e programs on Spark cluster using \u003cem\u003e\u003cstrong\u003eRayOnSpark\u003c/strong\u003e\u003c/em\u003e in Orca.\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eShow RayOnSpark example\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003eYou can not only run Ray program on Spark cluster, but also write Ray code inline with Spark code (so as to process the in-memory Spark RDDs or DataFrames) using \u003cem\u003eRayOnSpark\u003c/em\u003e in Orca.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop)\nfrom bigdl.orca import init_orca_context, OrcaContext\nsc = init_orca_context(cluster_mode=\u0026quot;yarn\u0026quot;, cores=4, memory=\u0026quot;10g\u0026quot;, num_nodes=2, init_ray_on_spark=True) \n\n# 2. Distribtued data processing using Spark\nspark = OrcaContext.get_spark_session()\ndf = spark.read.parquet(file_path).withColumn(...)\n\n# 3. Convert Spark DataFrame to Ray Dataset\nfrom bigdl.orca.data import spark_df_to_ray_dataset\ndataset = spark_df_to_ray_dataset(df)\n\n# 4. Use Ray to operate on Ray Datasets\nimport ray\n\n@ray.remote\ndef consume(data) -\u0026gt; int:\n num_batches = 0\n for batch in data.iter_batches(batch_size=10):\n num_batches += 1\n return num_batches\n\nprint(ray.get(consume.remote(dataset)))\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eorca\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einit_orca_context\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eOrcaContext\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esc\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003einit_orca_context\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ecluster_mode\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"yarn\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ecores\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ememory\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"10g\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enum_nodes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003einit_ray_on_spark\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e) \n\n\u003cspan class=\"pl-c\"\u003e# 2. Distribtued data processing using Spark\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003espark\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eOrcaContext\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eget_spark_session\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espark\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eparquet\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003efile_path\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003ewithColumn\u003c/span\u003e(...)\n\n\u003cspan class=\"pl-c\"\u003e# 3. Convert Spark DataFrame to Ray Dataset\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eorca\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espark_df_to_ray_dataset\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003espark_df_to_ray_dataset\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edf\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# 4. Use Ray to operate on Ray Datasets\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eray\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003e@\u003cspan class=\"pl-s1\"\u003eray\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eremote\u003c/span\u003e\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003econsume\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e-\u0026gt;\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eint\u003c/span\u003e:\n \u003cspan class=\"pl-s1\"\u003enum_batches\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebatch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eiter_batches\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ebatch_size\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003enum_batches\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e+=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003enum_batches\u003c/span\u003e\n\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eray\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eget\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003econsume\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eremote\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edataset\u003c/span\u003e)))\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e \n\u003cp dir=\"auto\"\u003e\u003cem\u003eSee RayOnSpark \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/ray.html\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e and \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/ray-quickstart.html\" rel=\"nofollow\"\u003equickstart\u003c/a\u003e for more details.\u003c/em\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNano\u003c/h3\u003e\u003ca id=\"user-content-nano\" class=\"anchor\" aria-label=\"Permalink: Nano\" href=\"#nano\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can transparently accelerate your TensorFlow or PyTorch programs on your laptop or server using \u003cem\u003eNano\u003c/em\u003e. With minimum code changes, \u003cem\u003eNano\u003c/em\u003e automatically applies modern CPU optimizations (e.g., SIMD, multiprocessing, low precision, etc.) to standard TensorFlow and PyTorch code, with up-to 10x speedup.\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eShow Nano inference example\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003eYou can automatically optimize a trained PyTorch model for inference or deployment using \u003cem\u003eNano\u003c/em\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"model = ResNet18().load_state_dict(...)\ntrain_dataloader = ...\nval_dataloader = ...\ndef accuracy (pred, target):\n ... \n\nfrom bigdl.nano.pytorch import InferenceOptimizer\noptimizer = InferenceOptimizer()\noptimizer.optimize(model,\n training_data=train_dataloader,\n validation_data=val_dataloader,\n metric=accuracy)\nnew_model, config = optimizer.get_best_model()\n\noptimizer.summary()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eResNet18\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003eload_state_dict\u003c/span\u003e(...)\n\u003cspan class=\"pl-s1\"\u003etrain_dataloader\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ...\n\u003cspan class=\"pl-s1\"\u003eval_dataloader\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ...\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eaccuracy\u003c/span\u003e (\u003cspan class=\"pl-s1\"\u003epred\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e):\n ... \n\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enano\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epytorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eInferenceOptimizer\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eInferenceOptimizer\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eoptimize\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etraining_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etrain_dataloader\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003evalidation_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eval_dataloader\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003emetric\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003eaccuracy\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003enew_model\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003econfig\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eget_best_model\u003c/span\u003e()\n\n\u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esummary\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe output of \u003ccode\u003eoptimizer.summary()\u003c/code\u003e will be something like:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\" -------------------------------- ---------------------- -------------- ----------------------\n| method | status | latency(ms) | metric value |\n -------------------------------- ---------------------- -------------- ----------------------\n| original | successful | 45.145 | 0.975 |\n| bf16 | successful | 27.549 | 0.975 |\n| static_int8 | successful | 11.339 | 0.975 |\n| jit_fp32_ipex | successful | 40.618 | 0.975* |\n| jit_fp32_ipex_channels_last | successful | 19.247 | 0.975* |\n| jit_bf16_ipex | successful | 10.149 | 0.975 |\n| jit_bf16_ipex_channels_last | successful | 9.782 | 0.975 |\n| openvino_fp32 | successful | 22.721 | 0.975* |\n| openvino_int8 | successful | 5.846 | 0.962 |\n| onnxruntime_fp32 | successful | 20.838 | 0.975* |\n| onnxruntime_int8_qlinear | successful | 7.123 | 0.981 |\n -------------------------------- ---------------------- -------------- ----------------------\n* means we assume the metric value of the traced model does not change, so we don't recompute metric value to save time.\nOptimization cost 60.8s in total.\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e -------------------------------- ---------------------- -------------- ----------------------\n| method | status | latency(ms) | metric value |\n -------------------------------- ---------------------- -------------- ----------------------\n| original | successful | 45.145 | 0.975 |\n| bf16 | successful | 27.549 | 0.975 |\n| static_int8 | successful | 11.339 | 0.975 |\n| jit_fp32_ipex | successful | 40.618 | 0.975* |\n| jit_fp32_ipex_channels_last | successful | 19.247 | 0.975* |\n| jit_bf16_ipex | successful | 10.149 | 0.975 |\n| jit_bf16_ipex_channels_last | successful | 9.782 | 0.975 |\n| openvino_fp32 | successful | 22.721 | 0.975* |\n| openvino_int8 | successful | 5.846 | 0.962 |\n| onnxruntime_fp32 | successful | 20.838 | 0.975* |\n| onnxruntime_int8_qlinear | successful | 7.123 | 0.981 |\n -------------------------------- ---------------------- -------------- ----------------------\n* means we assume the metric value of the traced model does not change, so we don't recompute metric value to save time.\nOptimization cost 60.8s in total.\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\u003csummary\u003eShow Nano Training example\u003c/summary\u003e\n\u003cbr\u003e\nYou may easily accelerate PyTorch training (e.g., IPEX, BF16, Multi-Instance Training, etc.) using Nano:\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"model = ResNet18()\noptimizer = torch.optim.SGD(...)\ntrain_loader = ...\nval_loader = ...\n\nfrom bigdl.nano.pytorch import TorchNano\n\n# Define your training loop inside `TorchNano.train`\nclass Trainer(TorchNano):\n\tdef train(self):\n\t# call `setup` to prepare for model, optimizer(s) and dataloader(s) for accelerated training\n\tmodel, optimizer, (train_loader, val_loader) = self.setup(model, optimizer,\n train_loader, val_loader)\n \n for epoch in range(num_epochs): \n model.train() \n for data, target in train_loader: \n optimizer.zero_grad() \n output = model(data) \n # replace the loss.backward() with self.backward(loss) \n loss = loss_fuc(output, target) \n self.backward(loss) \n optimizer.step() \n\n# Accelerated training (IPEX, BF16 and Multi-Instance Training)\nTrainer(use_ipex=True, precision='bf16', num_processes=2).train()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eResNet18\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etorch\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eoptim\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eSGD\u003c/span\u003e(...)\n\u003cspan class=\"pl-s1\"\u003etrain_loader\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ...\n\u003cspan class=\"pl-s1\"\u003eval_loader\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e ...\n\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enano\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003epytorch\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTorchNano\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# Define your training loop inside `TorchNano.train`\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eclass\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTrainer\u003c/span\u003e(\u003cspan class=\"pl-v\"\u003eTorchNano\u003c/span\u003e):\n\t\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003etrain\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e):\n\t\u003cspan class=\"pl-c\"\u003e# call `setup` to prepare for model, optimizer(s) and dataloader(s) for accelerated training\u003c/span\u003e\n\t\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e, (\u003cspan class=\"pl-s1\"\u003etrain_loader\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eval_loader\u003c/span\u003e) \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003esetup\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e,\n \u003cspan class=\"pl-s1\"\u003etrain_loader\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eval_loader\u003c/span\u003e)\n \n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eepoch\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enum_epochs\u003c/span\u003e): \n \u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etrain\u003c/span\u003e() \n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etrain_loader\u003c/span\u003e: \n \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ezero_grad\u003c/span\u003e() \n \u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003emodel\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e) \n \u003cspan class=\"pl-c\"\u003e# replace the loss.backward() with self.backward(loss) \u003c/span\u003e\n \u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eloss_fuc\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etarget\u003c/span\u003e) \n \u003cspan class=\"pl-s1\"\u003eself\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebackward\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eloss\u003c/span\u003e) \n \u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003estep\u003c/span\u003e() \n\n\u003cspan class=\"pl-c\"\u003e# Accelerated training (IPEX, BF16 and Multi-Instance Training)\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eTrainer\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003euse_ipex\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003eTrue\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eprecision\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'bf16'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003enum_processes\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003etrain\u003c/span\u003e()\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e \n\u003cp dir=\"auto\"\u003e\u003cem\u003eSee Nano \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/Nano/Overview/nano.html\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e and \u003ca href=\"https://github.com/intel-analytics/BigDL-2.x/tree/main/python/nano/tutorial\"\u003etutotial\u003c/a\u003e for more details.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDLlib\u003c/h3\u003e\u003ca id=\"user-content-dllib\" class=\"anchor\" aria-label=\"Permalink: DLlib\" href=\"#dllib\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWith \u003cem\u003eDLlib\u003c/em\u003e, you can write distributed deep learning applications as standard (\u003cstrong\u003eScala\u003c/strong\u003e or \u003cstrong\u003ePython\u003c/strong\u003e) Spark programs, using the same \u003cstrong\u003eSpark DataFrames\u003c/strong\u003e and \u003cstrong\u003eML Pipeline\u003c/strong\u003e APIs.\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eShow DLlib Scala example\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003eYou can build distributed deep learning applications for Spark using \u003cem\u003eDLlib\u003c/em\u003e Scala APIs in 3 simple steps:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-scala notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"// 1. Call `initNNContext` at the beginning of the code: \nimport com.intel.analytics.bigdl.dllib.NNContext\nval sc = NNContext.initNNContext()\n\n// 2. Define the deep learning model using Keras-style API in DLlib:\nimport com.intel.analytics.bigdl.dllib.keras.layers._\nimport com.intel.analytics.bigdl.dllib.keras.Model\nval input = Input[Float](inputShape = Shape(10)) \nval dense = Dense[Float](12).inputs(input) \nval output = Activation[Float](\u0026quot;softmax\u0026quot;).inputs(dense) \nval model = Model(input, output)\n\n// 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs\nimport org.apache.spark.sql.SparkSession\nimport org.apache.spark.ml.feature.MinMaxScaler\nimport org.apache.spark.ml.Pipeline\nimport com.intel.analytics.bigdl.dllib.nnframes.NNEstimator\nimport com.intel.analytics.bigdl.dllib.nn.CrossEntropyCriterion\nimport com.intel.analytics.bigdl.dllib.optim.Adam\nval spark = SparkSession.builder().getOrCreate()\nval trainDF = spark.read.parquet(\u0026quot;train_data\u0026quot;)\nval validationDF = spark.read.parquet(\u0026quot;val_data\u0026quot;)\nval scaler = new MinMaxScaler().setInputCol(\u0026quot;in\u0026quot;).setOutputCol(\u0026quot;value\u0026quot;)\nval estimator = NNEstimator(model, CrossEntropyCriterion()) \n .setBatchSize(128).setOptimMethod(new Adam()).setMaxEpoch(5)\nval pipeline = new Pipeline().setStages(Array(scaler, estimator))\n\nval pipelineModel = pipeline.fit(trainDF) \nval predictions = pipelineModel.transform(validationDF)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e 1. Call `initNNContext` at the beginning of the code: \u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecom\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eintel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eanalytics\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eNNContext\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003esc\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eNNContext\u003c/span\u003e.initNNContext()\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e 2. Define the deep learning model using Keras-style API in DLlib:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecom\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eintel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eanalytics\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003elayers\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003e_\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecom\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eintel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eanalytics\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eModel\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003einput\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eInput\u003c/span\u003e[\u003cspan class=\"pl-en\"\u003eFloat\u003c/span\u003e](inputShape \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eShape\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e)) \n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003edense\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eDense\u003c/span\u003e[\u003cspan class=\"pl-en\"\u003eFloat\u003c/span\u003e](\u003cspan class=\"pl-c1\"\u003e12\u003c/span\u003e).inputs(input) \n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eActivation\u003c/span\u003e[\u003cspan class=\"pl-en\"\u003eFloat\u003c/span\u003e](\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003esoftmax\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e).inputs(dense) \n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eModel\u003c/span\u003e(input, output)\n\n\u003cspan class=\"pl-c\"\u003e\u003cspan class=\"pl-c\"\u003e//\u003c/span\u003e 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eorg\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eapache\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003espark\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003esql\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eSparkSession\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eorg\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eapache\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003espark\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eml\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003efeature\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eMinMaxScaler\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eorg\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eapache\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003espark\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eml\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ePipeline\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecom\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eintel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eanalytics\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ennframes\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eNNEstimator\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecom\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eintel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eanalytics\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003enn\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eCrossEntropyCriterion\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecom\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eintel\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eanalytics\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eoptim\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eAdam\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003espark\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eSparkSession\u003c/span\u003e.builder().getOrCreate()\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003etrainDF\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e spark.read.parquet(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003etrain_data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003evalidationDF\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e spark.read.parquet(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003eval_data\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003escaler\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003enew\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMinMaxScaler\u003c/span\u003e().setInputCol(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003ein\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e).setOutputCol(\u003cspan class=\"pl-s\"\u003e\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003evalue\u003cspan class=\"pl-pds\"\u003e\"\u003c/span\u003e\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eestimator\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eNNEstimator\u003c/span\u003e(model, \u003cspan class=\"pl-en\"\u003eCrossEntropyCriterion\u003c/span\u003e()) \n .setBatchSize(\u003cspan class=\"pl-c1\"\u003e128\u003c/span\u003e).setOptimMethod(\u003cspan class=\"pl-k\"\u003enew\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eAdam\u003c/span\u003e()).setMaxEpoch(\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003epipeline\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-k\"\u003enew\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ePipeline\u003c/span\u003e().setStages(\u003cspan class=\"pl-en\"\u003eArray\u003c/span\u003e(scaler, estimator))\n\n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003epipelineModel\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e pipeline.fit(trainDF) \n\u003cspan class=\"pl-k\"\u003eval\u003c/span\u003e \u003cspan class=\"pl-v\"\u003epredictions\u003c/span\u003e \u003cspan class=\"pl-k\"\u003e=\u003c/span\u003e pipelineModel.transform(validationDF)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cdetails\u003e\u003csummary\u003eShow DLlib Python example\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003eYou can build distributed deep learning applications for Spark using \u003cem\u003eDLlib\u003c/em\u003e Python APIs in 3 simple steps:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# 1. Call `init_nncontext` at the beginning of the code:\nfrom bigdl.dllib.nncontext import init_nncontext\nsc = init_nncontext()\n\n# 2. Define the deep learning model using Keras-style API in DLlib:\nfrom bigdl.dllib.keras.layers import Input, Dense, Activation\nfrom bigdl.dllib.keras.models import Model\ninput = Input(shape=(10,))\ndense = Dense(12)(input)\noutput = Activation(\u0026quot;softmax\u0026quot;)(dense)\nmodel = Model(input, output)\n\n# 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs\nfrom pyspark.sql import SparkSession\nfrom pyspark.ml.feature import MinMaxScaler\nfrom pyspark.ml import Pipeline\nfrom bigdl.dllib.nnframes import NNEstimator\nfrom bigdl.dllib.nn.criterion import CrossEntropyCriterion\nfrom bigdl.dllib.optim.optimizer import Adam\nspark = SparkSession.builder.getOrCreate()\ntrain_df = spark.read.parquet(\u0026quot;train_data\u0026quot;)\nvalidation_df = spark.read.parquet(\u0026quot;val_data\u0026quot;)\nscaler = MinMaxScaler().setInputCol(\u0026quot;in\u0026quot;).setOutputCol(\u0026quot;value\u0026quot;)\nestimator = NNEstimator(model, CrossEntropyCriterion())\\\n .setBatchSize(128)\\\n .setOptimMethod(Adam())\\\n .setMaxEpoch(5)\npipeline = Pipeline(stages=[scaler, estimator])\n\npipelineModel = pipeline.fit(train_df)\npredictions = pipelineModel.transform(validation_df)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# 1. Call `init_nncontext` at the beginning of the code:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enncontext\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003einit_nncontext\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003esc\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003einit_nncontext\u003c/span\u003e()\n\n\u003cspan class=\"pl-c\"\u003e# 2. Define the deep learning model using Keras-style API in DLlib:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003elayers\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eInput\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eDense\u003c/span\u003e, \u003cspan class=\"pl-v\"\u003eActivation\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ekeras\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003emodels\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eModel\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003einput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eInput\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eshape\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e,))\n\u003cspan class=\"pl-s1\"\u003edense\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eDense\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e12\u003c/span\u003e)(\u003cspan class=\"pl-s1\"\u003einput\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eActivation\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"softmax\"\u003c/span\u003e)(\u003cspan class=\"pl-s1\"\u003edense\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eModel\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003einput\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eoutput\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epyspark\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003esql\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSparkSession\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epyspark\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eml\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003efeature\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eMinMaxScaler\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epyspark\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eml\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003ePipeline\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ennframes\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eNNEstimator\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003enn\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003ecriterion\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eCrossEntropyCriterion\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edllib\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eoptim\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eoptimizer\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAdam\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003espark\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eSparkSession\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003ebuilder\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003egetOrCreate\u003c/span\u003e()\n\u003cspan class=\"pl-s1\"\u003etrain_df\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espark\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eparquet\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"train_data\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003evalidation_df\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003espark\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eread\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eparquet\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"val_data\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003escaler\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eMinMaxScaler\u003c/span\u003e().\u003cspan class=\"pl-c1\"\u003esetInputCol\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"in\"\u003c/span\u003e).\u003cspan class=\"pl-c1\"\u003esetOutputCol\u003c/span\u003e(\u003cspan class=\"pl-s\"\u003e\"value\"\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003eestimator\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eNNEstimator\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e, \u003cspan class=\"pl-en\"\u003eCrossEntropyCriterion\u003c/span\u003e())\\\n .\u003cspan class=\"pl-c1\"\u003esetBatchSize\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e128\u003c/span\u003e)\\\n .\u003cspan class=\"pl-c1\"\u003esetOptimMethod\u003c/span\u003e(\u003cspan class=\"pl-en\"\u003eAdam\u003c/span\u003e())\\\n .\u003cspan class=\"pl-c1\"\u003esetMaxEpoch\u003c/span\u003e(\u003cspan class=\"pl-c1\"\u003e5\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003epipeline\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ePipeline\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003estages\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e[\u003cspan class=\"pl-s1\"\u003escaler\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eestimator\u003c/span\u003e])\n\n\u003cspan class=\"pl-s1\"\u003epipelineModel\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipeline\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_df\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003epredictions\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003epipelineModel\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etransform\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003evalidation_df\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eSee DLlib \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/DLlib/Overview/nnframes.html\" rel=\"nofollow\"\u003eNNFrames\u003c/a\u003e and \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/DLlib/Overview/keras-api.html\" rel=\"nofollow\"\u003eKeras API\u003c/a\u003e user guides for more details.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eChronos\u003c/h3\u003e\u003ca id=\"user-content-chronos\" class=\"anchor\" aria-label=\"Permalink: Chronos\" href=\"#chronos\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003cem\u003eChronos\u003c/em\u003e library makes it easy to build fast, accurate and scalable \u003cstrong\u003etime series analysis\u003c/strong\u003e applications (with AutoML).\u003c/p\u003e\n\u003cdetails\u003e\u003csummary\u003eShow Chronos example\u003c/summary\u003e\n\u003cbr\u003e\n\u003cp dir=\"auto\"\u003eYou can train a time series forecaster using \u003cem\u003eChronos\u003c/em\u003e in 3 simple steps:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"from bigdl.chronos.forecaster import TCNForecaster \nfrom bigdl.chronos.data.repo_dataset import get_public_dataset\n\n# 1. Process time series data using `TSDataset`\ntsdata_train, tsdata_val, tsdata_test = get_public_dataset(name='nyc_taxi')\nfor tsdata in [tsdata_train, tsdata_val, tsdata_test]:\n data.roll(lookback=100, horizon=1)\n\n# 2. Create a `TCNForecaster` (automatically configured based on train_data)\nforecaster = TCNForecaster.from_tsdataset(train_data)\n\n# 3. Train the forecaster for prediction\nforecaster.fit(train_data)\n\npred = forecaster.predict(test_data)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003echronos\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eforecaster\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTCNForecaster\u003c/span\u003e \n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003echronos\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003erepo_dataset\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eget_public_dataset\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# 1. Process time series data using `TSDataset`\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003etsdata_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etsdata_val\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etsdata_test\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eget_public_dataset\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003ename\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'nyc_taxi'\u003c/span\u003e)\n\u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etsdata\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e [\u003cspan class=\"pl-s1\"\u003etsdata_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etsdata_val\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003etsdata_test\u003c/span\u003e]:\n \u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eroll\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003elookback\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e100\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003ehorizon\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# 2. Create a `TCNForecaster` (automatically configured based on train_data)\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eforecaster\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eTCNForecaster\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efrom_tsdataset\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e)\n\n\u003cspan class=\"pl-c\"\u003e# 3. Train the forecaster for prediction\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eforecaster\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etrain_data\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003epred\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eforecaster\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etest_data\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo apply AutoML, use \u003ccode\u003eAutoTSEstimator\u003c/code\u003e instead of normal forecasters.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"# Create and fit an `AutoTSEstimator`\nfrom bigdl.chronos.autots import AutoTSEstimator\nautotsest = AutoTSEstimator(model=\u0026quot;tcn\u0026quot;, future_seq_len=10)\n\ntsppl = autotsest.fit(data=tsdata_train, validation_data=tsdata_val)\npred = tsppl.predict(tsdata_test)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e# Create and fit an `AutoTSEstimator`\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003efrom\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ebigdl\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003echronos\u003c/span\u003e.\u003cspan class=\"pl-s1\"\u003eautots\u003c/span\u003e \u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-v\"\u003eAutoTSEstimator\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eautotsest\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eAutoTSEstimator\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003emodel\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"tcn\"\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003efuture_seq_len\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e10\u003c/span\u003e)\n\n\u003cspan class=\"pl-s1\"\u003etsppl\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eautotsest\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003efit\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003edata\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etsdata_train\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003evalidation_data\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s1\"\u003etsdata_val\u003c/span\u003e)\n\u003cspan class=\"pl-s1\"\u003epred\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etsppl\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003epredict\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003etsdata_test\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003c/details\u003e \n\u003cp dir=\"auto\"\u003e\u003cem\u003eSee Chronos \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/Chronos/index.html\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e and \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-autotsest-quickstart.html\" rel=\"nofollow\"\u003equick start\u003c/a\u003e for more details.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFriesian\u003c/h3\u003e\u003ca id=\"user-content-friesian\" class=\"anchor\" aria-label=\"Permalink: Friesian\" href=\"#friesian\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe \u003cem\u003eFriesian\u003c/em\u003e library makes it easy to build end-to-end, large-scale \u003cstrong\u003erecommedation system\u003c/strong\u003e (including \u003cem\u003eoffline\u003c/em\u003e feature transformation and traning, \u003cem\u003enear-line\u003c/em\u003e feature and model update, and \u003cem\u003eonline\u003c/em\u003e serving pipeline).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eSee Freisian \u003ca href=\"https://github.com/intel-analytics/BigDL-2.x/blob/main/python/friesian/README.md\"\u003ereadme\u003c/a\u003e for more details.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePPML\u003c/h3\u003e\u003ca id=\"user-content-ppml\" class=\"anchor\" aria-label=\"Permalink: PPML\" href=\"#ppml\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eBigDL PPML\u003c/em\u003e provides a \u003cstrong\u003ehardware (Intel SGX) protected\u003c/strong\u003e \u003cem\u003eTrusted Cluster Environment\u003c/em\u003e for running distributed Big Data \u0026amp; AI applications (in a secure fashion on private or public cloud).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003cem\u003eSee PPML \u003ca href=\"https://bigdl.readthedocs.io/en/latest/doc/PPML/Overview/ppml.html\" rel=\"nofollow\"\u003euser guide\u003c/a\u003e and \u003ca href=\"https://github.com/intel-analytics/BigDL-2.x/blob/main/ppml/README.md\"\u003etutorial\u003c/a\u003e for more details.\u003c/em\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting Support\u003c/h2\u003e\u003ca id=\"user-content-getting-support\" class=\"anchor\" aria-label=\"Permalink: Getting Support\" href=\"#getting-support\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"mailto:bigdl-user-group+subscribe@googlegroups.com\"\u003eMail List\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://groups.google.com/forum/#!forum/bigdl-user-group\" rel=\"nofollow\"\u003eUser Group\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/intel-analytics/BigDL-2.x/issues\"\u003eGithub Issues\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003chr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCitation\u003c/h2\u003e\u003ca id=\"user-content-citation\" class=\"anchor\" aria-label=\"Permalink: Citation\" href=\"#citation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you've found BigDL useful for your project, you may cite our papers as follows:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cem\u003e\u003ca href=\"https://arxiv.org/abs/2204.01715\" rel=\"nofollow\"\u003eBigDL 2.0\u003c/a\u003e: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster\u003c/em\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@INPROCEEDINGS{9880257,\n title={BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster}, \n author={Dai, Jason Jinquan and Ding, Ding and Shi, Dongjie and Huang, Shengsheng and Wang, Jiao and Qiu, Xin and Huang, Kai and Song, Guoqiong and Wang, Yang and Gong, Qiyuan and Song, Jiaming and Yu, Shan and Zheng, Le and Chen, Yina and Deng, Junwei and Song, Ge},\n booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, \n year={2022},\n pages={21407-21414},\n doi={10.1109/CVPR52688.2022.02076}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@INPROCEEDINGS{9880257,\n title={BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster}, \n author={Dai, Jason Jinquan and Ding, Ding and Shi, Dongjie and Huang, Shengsheng and Wang, Jiao and Qiu, Xin and Huang, Kai and Song, Guoqiong and Wang, Yang and Gong, Qiyuan and Song, Jiaming and Yu, Shan and Zheng, Le and Chen, Yina and Deng, Junwei and Song, Ge},\n booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, \n year={2022},\n pages={21407-21414},\n doi={10.1109/CVPR52688.2022.02076}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003cem\u003e\u003ca href=\"https://arxiv.org/abs/1804.05839\" rel=\"nofollow\"\u003eBigDL\u003c/a\u003e: A Distributed Deep Learning Framework for Big Data\u003c/em\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"@INPROCEEDINGS{10.1145/3357223.3362707,\n title = {BigDL: A Distributed Deep Learning Framework for Big Data},\n author = {Dai, Jason Jinquan and Wang, Yiheng and Qiu, Xin and Ding, Ding and Zhang, Yao and Wang, Yanzhang and Jia, Xianyan and Zhang, Cherry Li and Wan, Yan and Li, Zhichao and Wang, Jiao and Huang, Shengsheng and Wu, Zhongyuan and Wang, Yang and Yang, Yuhao and She, Bowen and Shi, Dongjie and Lu, Qi and Huang, Kai and Song, Guoqiong},\n booktitle = {Proceedings of the ACM Symposium on Cloud Computing (SoCC)},\n year = {2019},\n pages = {50–60},\n doi = {10.1145/3357223.3362707}\n}\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e@INPROCEEDINGS{10.1145/3357223.3362707,\n title = {BigDL: A Distributed Deep Learning Framework for Big Data},\n author = {Dai, Jason Jinquan and Wang, Yiheng and Qiu, Xin and Ding, Ding and Zhang, Yao and Wang, Yanzhang and Jia, Xianyan and Zhang, Cherry Li and Wan, Yan and Li, Zhichao and Wang, Jiao and Huang, Shengsheng and Wu, Zhongyuan and Wang, Yang and Yang, Yuhao and She, Bowen and Shi, Dongjie and Lu, Qi and Huang, Kai and Song, Guoqiong},\n booktitle = {Proceedings of the ACM Symposium on Cloud Computing (SoCC)},\n year = {2019},\n pages = {50–60},\n doi = {10.1145/3357223.3362707}\n}\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":2,"text":"Overview","anchor":"overview","htmlText":"Overview"},{"level":2,"text":"Choosing the right BigDL library","anchor":"choosing-the-right-bigdl-library","htmlText":"Choosing the right BigDL library"},{"level":2,"text":"Installing","anchor":"installing","htmlText":"Installing"},{"level":2,"text":"Getting Started","anchor":"getting-started","htmlText":"Getting Started"},{"level":3,"text":"Orca","anchor":"orca","htmlText":"Orca"},{"level":3,"text":"Nano","anchor":"nano","htmlText":"Nano"},{"level":3,"text":"DLlib","anchor":"dllib","htmlText":"DLlib"},{"level":3,"text":"Chronos","anchor":"chronos","htmlText":"Chronos"},{"level":3,"text":"Friesian","anchor":"friesian","htmlText":"Friesian"},{"level":3,"text":"PPML","anchor":"ppml","htmlText":"PPML"},{"level":2,"text":"Getting Support","anchor":"getting-support","htmlText":"Getting Support"},{"level":2,"text":"Citation","anchor":"citation","htmlText":"Citation"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fintel%2FBigDL"}},{"displayName":"LICENSE","repoName":"BigDL","refName":"main","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fintel%2FBigDL"}},{"displayName":"SECURITY.md","repoName":"BigDL","refName":"main","path":"SECURITY.md","preferredFileType":"security","tabName":"Security","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fintel%2FBigDL"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-7d7eb7c71814.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-708ec8ade250.js","githubDevUrl":null,"enabled_features":{"copilot_workspace":null,"code_nav_ui_events":false,"overview_shared_code_dropdown_button":true,"react_blob_overlay":false,"accessible_code_button":true,"github_models_repo_integration":false}}}}</script> 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0-1.5h2.5V3.5h-.984a.245.245 0 0 0-.124.033l-1.289.737c-.265.15-.564.23-.869.23h-.162l2.112 4.692a.75.75 0 0 1-.154.838l-.53-.53.529.531-.001.002-.002.002-.006.006-.016.015-.045.04c-.21.176-.441.327-.686.45C4.556 10.78 3.88 11 3 11a4.498 4.498 0 0 1-2.023-.454 3.544 3.544 0 0 1-.686-.45l-.045-.04-.016-.015-.006-.006-.004-.004v-.001a.75.75 0 0 1-.154-.838L2.178 4.5H1.75a.75.75 0 0 1 0-1.5h2.234a.249.249 0 0 0 .125-.033l1.288-.737c.265-.15.564-.23.869-.23h.984V.75a.75.75 0 0 1 1.5 0Zm2.945 8.477c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L13 6.327Zm-10 0c.285.135.718.273 1.305.273s1.02-.138 1.305-.273L3 6.327Z"></path></svg></span><span data-component="text" data-content="Security">Security</span></a></li></ul></nav><button style="--button-color:fg.subtle" type="button" aria-label="Outline" aria-haspopup="true" aria-expanded="false" tabindex="0" class="Box-sc-g0xbh4-0 cwoBXV prc-Button-ButtonBase-c50BI" data-loading="false" data-size="medium" data-variant="invisible" aria-describedby=":Rr9ab:-loading-announcement" id=":Rr9ab:"><svg aria-hidden="true" focusable="false" class="octicon octicon-list-unordered" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M5.75 2.5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5Zm0 5h8.5a.75.75 0 0 1 0 1.5h-8.5a.75.75 0 0 1 0-1.5ZM2 14a1 1 0 1 1 0-2 1 1 0 0 1 0 2Zm1-6a1 1 0 1 1-2 0 1 1 0 0 1 2 0ZM2 4a1 1 0 1 1 0-2 1 1 0 0 1 0 2Z"></path></svg></button></div><div class="Box-sc-g0xbh4-0 QkQOb js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-alert markdown-alert-important" dir="auto"><p class="markdown-alert-title" dir="auto"><svg class="octicon octicon-report mr-2" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="M0 1.75C0 .784.784 0 1.75 0h12.5C15.216 0 16 .784 16 1.75v9.5A1.75 1.75 0 0 1 14.25 13H8.06l-2.573 2.573A1.458 1.458 0 0 1 3 14.543V13H1.75A1.75 1.75 0 0 1 0 11.25Zm1.75-.25a.25.25 0 0 0-.25.25v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25Zm7 2.25v2.5a.75.75 0 0 1-1.5 0v-2.5a.75.75 0 0 1 1.5 0ZM9 9a1 1 0 1 1-2 0 1 1 0 0 1 2 0Z"></path></svg>Important</p><p dir="auto"><em><strong><code>bigdl-llm</code> has now become <code>ipex-llm</code>, and our future development will move to the <a href="https://github.com/intel-analytics/ipex-llm">IPEX-LLM</a> project.</strong></em></p> </div> <hr> <div align="center" dir="auto"> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer nofollow" href="https://camo.githubusercontent.com/df7fa1f3085ff259f05c26ad3f54eecb7743a032b3cb3a62922588ff09b6d67e/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f626967646c5f6c6f676f2e6a7067"><img src="https://camo.githubusercontent.com/df7fa1f3085ff259f05c26ad3f54eecb7743a032b3cb3a62922588ff09b6d67e/68747470733a2f2f6c6c6d2d6173736574732e72656164746865646f63732e696f2f656e2f6c61746573742f5f696d616765732f626967646c5f6c6f676f2e6a7067" height="140px" data-canonical-src="https://llm-assets.readthedocs.io/en/latest/_images/bigdl_logo.jpg" style="max-width: 100%;"></a><br></p> </div> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Overview</h2><a id="user-content-overview" class="anchor" aria-label="Permalink: Overview" href="#overview"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">BigDL seamlessly scales your data analytics & AI applications from laptop to cloud, with the following libraries:</p> <ul dir="auto"> <li> <p dir="auto"><code>LLM</code> <em><strong>(deprecated - please use <a href="https://github.com/intel-analytics/ipex-llm">IPEX-LLM</a> instead)</strong></em>: Optimizaed large language model library for Intel CPU and GPU</p> </li> <li> <p dir="auto"><a href="#orca">Orca</a>: Distributed Big Data & AI (TF & PyTorch) Pipeline on Spark and Ray</p> </li> <li> <p dir="auto"><a href="#nano">Nano</a>: Transparent Acceleration of Tensorflow & PyTorch Programs on Intel CPU/GPU</p> </li> <li> <p dir="auto"><a href="#dllib">DLlib</a>: “Equivalent of Spark MLlib” for Deep Learning</p> </li> <li> <p dir="auto"><a href="#chronos">Chronos</a>: Scalable Time Series Analysis using AutoML</p> </li> <li> <p dir="auto"><a href="#friesian">Friesian</a>: End-to-End Recommendation Systems</p> </li> <li> <p dir="auto"><a href="#ppml">PPML</a>: Secure Big Data and AI (with SGX/TDX Hardware Security)</p> </li> </ul> <p dir="auto">For more information, you may <a href="https://bigdl.readthedocs.io/" rel="nofollow">read the docs</a>.</p> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Choosing the right BigDL library</h2><a id="user-content-choosing-the-right-bigdl-library" class="anchor" aria-label="Permalink: Choosing the right BigDL library" href="#choosing-the-right-bigdl-library"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <section class="js-render-needs-enrichment render-needs-enrichment position-relative" data-identity="f2abcb8f-af5a-426f-9f7a-1d61302a831c" data-host="https://viewscreen.githubusercontent.com" data-src="https://viewscreen.githubusercontent.com/markdown/mermaid?docs_host=https%3A%2F%2Fdocs.github.com" data-type="mermaid" aria-label="mermaid rendered output container"> <div class="js-render-enrichment-target" data-json="{"data":"flowchart TD;\n Feature1{{HW Secured Big Data &amp; AI?}};\n Feature1-- No --&gt;Feature2{{Python vs. Scala/Java?}};\n Feature1-- \"Yes\" --&gt;ReferPPML([&lt;em&gt;&lt;strong&gt;PPML&lt;/strong&gt;&lt;/em&gt;]);\n Feature2-- Python --&gt;Feature3{{What type of application?}};\n Feature2-- Scala/Java --&gt;ReferDLlib([&lt;em&gt;&lt;strong&gt;DLlib&lt;/strong&gt;&lt;/em&gt;]);\n Feature3-- \"Large Language Model\" --&gt;ReferLLM([&lt;em&gt;&lt;strong&gt;LLM&lt;/strong&gt;&lt;/em&gt;]);\n Feature3-- \"Big Data + AI (TF/PyTorch)\" --&gt;ReferOrca([&lt;em&gt;&lt;strong&gt;Orca&lt;/strong&gt;&lt;/em&gt;]);\n Feature3-- Accelerate TensorFlow / PyTorch --&gt;ReferNano([&lt;em&gt;&lt;strong&gt;Nano&lt;/strong&gt;&lt;/em&gt;]);\n Feature3-- DL for Spark MLlib --&gt;ReferDLlib2([&lt;em&gt;&lt;strong&gt;DLlib&lt;/strong&gt;&lt;/em&gt;]);\n Feature3-- High Level App Framework --&gt;Feature4{{Domain?}};\n Feature4-- Time Series --&gt;ReferChronos([&lt;em&gt;&lt;strong&gt;Chronos&lt;/strong&gt;&lt;/em&gt;]);\n Feature4-- Recommender System --&gt;ReferFriesian([&lt;em&gt;&lt;strong&gt;Friesian&lt;/strong&gt;&lt;/em&gt;]);\n \n click ReferLLM \"https://github.com/intel-analytics/ipex-llm\"\n click ReferNano \"https://github.com/intel-analytics/BigDL-2.x#nano\"\n click ReferOrca \"https://github.com/intel-analytics/BigDL-2.x#orca\"\n click ReferDLlib \"https://github.com/intel-analytics/BigDL-2.x#dllib\"\n click ReferDLlib2 \"https://github.com/intel-analytics/BigDL-2.x#dllib\"\n click ReferChronos \"https://github.com/intel-analytics/BigDL-2.x#chronos\"\n click ReferFriesian \"https://github.com/intel-analytics/BigDL-2.x#friesian\"\n click ReferPPML \"https://github.com/intel-analytics/BigDL-2.x#ppml\"\n \n classDef ReferStyle1 fill:#5099ce,stroke:#5099ce;\n classDef Feature fill:#FFF,stroke:#08409c,stroke-width:1px;\n class ReferLLM,ReferNano,ReferOrca,ReferDLlib,ReferDLlib2,ReferChronos,ReferFriesian,ReferPPML ReferStyle1;\n class Feature1,Feature2,Feature3,Feature4,Feature5,Feature6,Feature7 Feature;\n \n"}" data-plain="flowchart TD; Feature1{{HW Secured Big Data & AI?}}; Feature1-- No -->Feature2{{Python vs. Scala/Java?}}; Feature1-- "Yes" -->ReferPPML([<em><strong>PPML</strong></em>]); Feature2-- Python -->Feature3{{What type of application?}}; Feature2-- Scala/Java -->ReferDLlib([<em><strong>DLlib</strong></em>]); Feature3-- "Large Language Model" -->ReferLLM([<em><strong>LLM</strong></em>]); Feature3-- "Big Data + AI (TF/PyTorch)" -->ReferOrca([<em><strong>Orca</strong></em>]); Feature3-- Accelerate TensorFlow / PyTorch -->ReferNano([<em><strong>Nano</strong></em>]); Feature3-- DL for Spark MLlib -->ReferDLlib2([<em><strong>DLlib</strong></em>]); Feature3-- High Level App Framework -->Feature4{{Domain?}}; Feature4-- Time Series -->ReferChronos([<em><strong>Chronos</strong></em>]); Feature4-- Recommender System -->ReferFriesian([<em><strong>Friesian</strong></em>]); click ReferLLM "https://github.com/intel-analytics/ipex-llm" click ReferNano "https://github.com/intel-analytics/BigDL-2.x#nano" click ReferOrca "https://github.com/intel-analytics/BigDL-2.x#orca" click ReferDLlib "https://github.com/intel-analytics/BigDL-2.x#dllib" click ReferDLlib2 "https://github.com/intel-analytics/BigDL-2.x#dllib" click ReferChronos "https://github.com/intel-analytics/BigDL-2.x#chronos" click ReferFriesian "https://github.com/intel-analytics/BigDL-2.x#friesian" click ReferPPML "https://github.com/intel-analytics/BigDL-2.x#ppml" classDef ReferStyle1 fill:#5099ce,stroke:#5099ce; classDef Feature fill:#FFF,stroke:#08409c,stroke-width:1px; class ReferLLM,ReferNano,ReferOrca,ReferDLlib,ReferDLlib2,ReferChronos,ReferFriesian,ReferPPML ReferStyle1; class Feature1,Feature2,Feature3,Feature4,Feature5,Feature6,Feature7 Feature; " dir="auto"> <div class="render-plaintext-hidden" dir="auto"> <pre lang="mermaid" aria-label="Raw mermaid code">flowchart TD; Feature1{{HW Secured Big Data & AI?}}; Feature1-- No -->Feature2{{Python vs. Scala/Java?}}; Feature1-- "Yes" -->ReferPPML([<em><strong>PPML</strong></em>]); Feature2-- Python -->Feature3{{What type of application?}}; Feature2-- Scala/Java -->ReferDLlib([<em><strong>DLlib</strong></em>]); Feature3-- "Large Language Model" -->ReferLLM([<em><strong>LLM</strong></em>]); Feature3-- "Big Data + AI (TF/PyTorch)" -->ReferOrca([<em><strong>Orca</strong></em>]); Feature3-- Accelerate TensorFlow / PyTorch -->ReferNano([<em><strong>Nano</strong></em>]); Feature3-- DL for Spark MLlib -->ReferDLlib2([<em><strong>DLlib</strong></em>]); Feature3-- High Level App Framework -->Feature4{{Domain?}}; Feature4-- Time Series -->ReferChronos([<em><strong>Chronos</strong></em>]); Feature4-- Recommender System -->ReferFriesian([<em><strong>Friesian</strong></em>]); click ReferLLM "https://github.com/intel-analytics/ipex-llm" click ReferNano "https://github.com/intel-analytics/BigDL-2.x#nano" click ReferOrca "https://github.com/intel-analytics/BigDL-2.x#orca" click ReferDLlib "https://github.com/intel-analytics/BigDL-2.x#dllib" click ReferDLlib2 "https://github.com/intel-analytics/BigDL-2.x#dllib" click ReferChronos "https://github.com/intel-analytics/BigDL-2.x#chronos" click ReferFriesian "https://github.com/intel-analytics/BigDL-2.x#friesian" click ReferPPML "https://github.com/intel-analytics/BigDL-2.x#ppml" classDef ReferStyle1 fill:#5099ce,stroke:#5099ce; classDef Feature fill:#FFF,stroke:#08409c,stroke-width:1px; class ReferLLM,ReferNano,ReferOrca,ReferDLlib,ReferDLlib2,ReferChronos,ReferFriesian,ReferPPML ReferStyle1; class Feature1,Feature2,Feature3,Feature4,Feature5,Feature6,Feature7 Feature; </pre> </div> </div> <span class="js-render-enrichment-loader d-flex flex-justify-center flex-items-center width-full" style="min-height:100px" role="presentation"> <span data-view-component="true"> <svg style="box-sizing: content-box; color: var(--color-icon-primary);" width="16" height="16" viewBox="0 0 16 16" fill="none" aria-hidden="true" data-view-component="true" class="octospinner mx-auto anim-rotate"> <circle cx="8" cy="8" r="7" stroke="currentColor" stroke-opacity="0.25" stroke-width="2" vector-effect="non-scaling-stroke" fill="none"></circle> <path d="M15 8a7.002 7.002 0 00-7-7" stroke="currentColor" stroke-width="2" stroke-linecap="round" vector-effect="non-scaling-stroke"></path> </svg> <span class="sr-only">Loading</span> </span> </span> </section> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Installing</h2><a id="user-content-installing" class="anchor" aria-label="Permalink: Installing" href="#installing"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto">To install BigDL, we recommend using <a href="https://docs.conda.io/projects/conda/en/latest/user-guide/install/" rel="nofollow">conda</a> environment:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="conda create -n my_env conda activate my_env pip install bigdl"><pre>conda create -n my_env conda activate my_env pip install bigdl</pre></div> <p dir="auto">To install latest nightly build, use <code>pip install --pre --upgrade bigdl</code>; see <a href="https://bigdl.readthedocs.io/en/latest/doc/UserGuide/python.html" rel="nofollow">Python</a> and <a href="https://bigdl.readthedocs.io/en/latest/doc/UserGuide/scala.html" rel="nofollow">Scala</a> user guide for more details.</p> </li> <li> <p dir="auto">To install each individual library, such as Chronos, use <code>pip install bigdl-chronos</code>; see the <a href="https://bigdl.readthedocs.io/" rel="nofollow">document website</a> for more details.</p> </li> </ul> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting Started</h2><a id="user-content-getting-started" class="anchor" aria-label="Permalink: Getting Started" href="#getting-started"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Orca</h3><a id="user-content-orca" class="anchor" aria-label="Permalink: Orca" href="#orca"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li> <p dir="auto">The <em>Orca</em> library seamlessly scales out your single node <strong>TensorFlow</strong>, <strong>PyTorch</strong> or <strong>OpenVINO</strong> programs across large clusters (so as to process distributed Big Data).</p> <details><summary>Show Orca example</summary> <br> <p dir="auto">You can build end-to-end, distributed data processing & AI programs using <em>Orca</em> in 4 simple steps:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop) from bigdl.orca import init_orca_context, OrcaContext sc = init_orca_context(cluster_mode="k8s", cores=4, memory="10g", num_nodes=2) # 2. Perform distribtued data processing (supporting Spark DataFrames, # TensorFlow Dataset, PyTorch DataLoader, Ray Dataset, Pandas, Pillow, etc.) spark = OrcaContext.get_spark_session() df = spark.read.parquet(file_path) df = df.withColumn('label', df.label-1) ... # 3. Build deep learning models using standard framework APIs # (supporting TensorFlow, PyTorch, Keras, OpenVino, etc.) from tensorflow import keras ... model = keras.models.Model(inputs=[user, item], outputs=predictions) model.compile(...) # 4. Use Orca Estimator for distributed training/inference from bigdl.orca.learn.tf.estimator import Estimator est = Estimator.from_keras(keras_model=model) est.fit(data=df, feature_cols=['user', 'item'], label_cols=['label'], ...)"><pre><span class="pl-c"># 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop)</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">orca</span> <span class="pl-k">import</span> <span class="pl-s1">init_orca_context</span>, <span class="pl-v">OrcaContext</span> <span class="pl-s1">sc</span> <span class="pl-c1">=</span> <span class="pl-en">init_orca_context</span>(<span class="pl-s1">cluster_mode</span><span class="pl-c1">=</span><span class="pl-s">"k8s"</span>, <span class="pl-s1">cores</span><span class="pl-c1">=</span><span class="pl-c1">4</span>, <span class="pl-s1">memory</span><span class="pl-c1">=</span><span class="pl-s">"10g"</span>, <span class="pl-s1">num_nodes</span><span class="pl-c1">=</span><span class="pl-c1">2</span>) <span class="pl-c"># 2. Perform distribtued data processing (supporting Spark DataFrames,</span> <span class="pl-c"># TensorFlow Dataset, PyTorch DataLoader, Ray Dataset, Pandas, Pillow, etc.)</span> <span class="pl-s1">spark</span> <span class="pl-c1">=</span> <span class="pl-v">OrcaContext</span>.<span class="pl-c1">get_spark_session</span>() <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">spark</span>.<span class="pl-c1">read</span>.<span class="pl-c1">parquet</span>(<span class="pl-s1">file_path</span>) <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">df</span>.<span class="pl-c1">withColumn</span>(<span class="pl-s">'label'</span>, <span class="pl-s1">df</span>.<span class="pl-c1">label</span><span class="pl-c1">-</span><span class="pl-c1">1</span>) ... <span class="pl-c"># 3. Build deep learning models using standard framework APIs</span> <span class="pl-c"># (supporting TensorFlow, PyTorch, Keras, OpenVino, etc.)</span> <span class="pl-k">from</span> <span class="pl-s1">tensorflow</span> <span class="pl-k">import</span> <span class="pl-s1">keras</span> ... <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-s1">keras</span>.<span class="pl-c1">models</span>.<span class="pl-c1">Model</span>(<span class="pl-s1">inputs</span><span class="pl-c1">=</span>[<span class="pl-s1">user</span>, <span class="pl-s1">item</span>], <span class="pl-s1">outputs</span><span class="pl-c1">=</span><span class="pl-s1">predictions</span>) <span class="pl-s1">model</span>.<span class="pl-c1">compile</span>(...) <span class="pl-c"># 4. Use Orca Estimator for distributed training/inference</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">orca</span>.<span class="pl-s1">learn</span>.<span class="pl-s1">tf</span>.<span class="pl-s1">estimator</span> <span class="pl-k">import</span> <span class="pl-v">Estimator</span> <span class="pl-s1">est</span> <span class="pl-c1">=</span> <span class="pl-v">Estimator</span>.<span class="pl-c1">from_keras</span>(<span class="pl-s1">keras_model</span><span class="pl-c1">=</span><span class="pl-s1">model</span>) <span class="pl-s1">est</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">df</span>, <span class="pl-s1">feature_cols</span><span class="pl-c1">=</span>[<span class="pl-s">'user'</span>, <span class="pl-s">'item'</span>], <span class="pl-s1">label_cols</span><span class="pl-c1">=</span>[<span class="pl-s">'label'</span>], ...)</pre></div> </details> <p dir="auto"><em>See Orca <a href="https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/orca.html" rel="nofollow">user guide</a>, as well as <a href="https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/tf2keras-quickstart.html" rel="nofollow">TensorFlow</a> and <a href="https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/pytorch-quickstart.html" rel="nofollow">PyTorch</a> quickstarts, for more details.</em></p> </li> <li> <p dir="auto">In addition, you can also run standard <strong>Ray</strong> programs on Spark cluster using <em><strong>RayOnSpark</strong></em> in Orca.</p> <details><summary>Show RayOnSpark example</summary> <br> <p dir="auto">You can not only run Ray program on Spark cluster, but also write Ray code inline with Spark code (so as to process the in-memory Spark RDDs or DataFrames) using <em>RayOnSpark</em> in Orca.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop) from bigdl.orca import init_orca_context, OrcaContext sc = init_orca_context(cluster_mode="yarn", cores=4, memory="10g", num_nodes=2, init_ray_on_spark=True) # 2. Distribtued data processing using Spark spark = OrcaContext.get_spark_session() df = spark.read.parquet(file_path).withColumn(...) # 3. Convert Spark DataFrame to Ray Dataset from bigdl.orca.data import spark_df_to_ray_dataset dataset = spark_df_to_ray_dataset(df) # 4. Use Ray to operate on Ray Datasets import ray @ray.remote def consume(data) -> int: num_batches = 0 for batch in data.iter_batches(batch_size=10): num_batches += 1 return num_batches print(ray.get(consume.remote(dataset)))"><pre><span class="pl-c"># 1. Initilize Orca Context (to run your program on K8s, YARN or local laptop)</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">orca</span> <span class="pl-k">import</span> <span class="pl-s1">init_orca_context</span>, <span class="pl-v">OrcaContext</span> <span class="pl-s1">sc</span> <span class="pl-c1">=</span> <span class="pl-en">init_orca_context</span>(<span class="pl-s1">cluster_mode</span><span class="pl-c1">=</span><span class="pl-s">"yarn"</span>, <span class="pl-s1">cores</span><span class="pl-c1">=</span><span class="pl-c1">4</span>, <span class="pl-s1">memory</span><span class="pl-c1">=</span><span class="pl-s">"10g"</span>, <span class="pl-s1">num_nodes</span><span class="pl-c1">=</span><span class="pl-c1">2</span>, <span class="pl-s1">init_ray_on_spark</span><span class="pl-c1">=</span><span class="pl-c1">True</span>) <span class="pl-c"># 2. Distribtued data processing using Spark</span> <span class="pl-s1">spark</span> <span class="pl-c1">=</span> <span class="pl-v">OrcaContext</span>.<span class="pl-c1">get_spark_session</span>() <span class="pl-s1">df</span> <span class="pl-c1">=</span> <span class="pl-s1">spark</span>.<span class="pl-c1">read</span>.<span class="pl-c1">parquet</span>(<span class="pl-s1">file_path</span>).<span class="pl-c1">withColumn</span>(...) <span class="pl-c"># 3. Convert Spark DataFrame to Ray Dataset</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">orca</span>.<span class="pl-s1">data</span> <span class="pl-k">import</span> <span class="pl-s1">spark_df_to_ray_dataset</span> <span class="pl-s1">dataset</span> <span class="pl-c1">=</span> <span class="pl-en">spark_df_to_ray_dataset</span>(<span class="pl-s1">df</span>) <span class="pl-c"># 4. Use Ray to operate on Ray Datasets</span> <span class="pl-k">import</span> <span class="pl-s1">ray</span> <span class="pl-en">@<span class="pl-s1">ray</span>.<span class="pl-c1">remote</span></span> <span class="pl-k">def</span> <span class="pl-en">consume</span>(<span class="pl-s1">data</span>) <span class="pl-c1">-></span> <span class="pl-smi">int</span>: <span class="pl-s1">num_batches</span> <span class="pl-c1">=</span> <span class="pl-c1">0</span> <span class="pl-k">for</span> <span class="pl-s1">batch</span> <span class="pl-c1">in</span> <span class="pl-s1">data</span>.<span class="pl-c1">iter_batches</span>(<span class="pl-s1">batch_size</span><span class="pl-c1">=</span><span class="pl-c1">10</span>): <span class="pl-s1">num_batches</span> <span class="pl-c1">+=</span> <span class="pl-c1">1</span> <span class="pl-k">return</span> <span class="pl-s1">num_batches</span> <span class="pl-en">print</span>(<span class="pl-s1">ray</span>.<span class="pl-c1">get</span>(<span class="pl-s1">consume</span>.<span class="pl-c1">remote</span>(<span class="pl-s1">dataset</span>)))</pre></div> </details> <p dir="auto"><em>See RayOnSpark <a href="https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/ray.html" rel="nofollow">user guide</a> and <a href="https://bigdl.readthedocs.io/en/latest/doc/Orca/Howto/ray-quickstart.html" rel="nofollow">quickstart</a> for more details.</em></p> </li> </ul> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Nano</h3><a id="user-content-nano" class="anchor" aria-label="Permalink: Nano" href="#nano"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can transparently accelerate your TensorFlow or PyTorch programs on your laptop or server using <em>Nano</em>. With minimum code changes, <em>Nano</em> automatically applies modern CPU optimizations (e.g., SIMD, multiprocessing, low precision, etc.) to standard TensorFlow and PyTorch code, with up-to 10x speedup.</p> <details><summary>Show Nano inference example</summary> <br> <p dir="auto">You can automatically optimize a trained PyTorch model for inference or deployment using <em>Nano</em>:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="model = ResNet18().load_state_dict(...) train_dataloader = ... val_dataloader = ... def accuracy (pred, target): ... from bigdl.nano.pytorch import InferenceOptimizer optimizer = InferenceOptimizer() optimizer.optimize(model, training_data=train_dataloader, validation_data=val_dataloader, metric=accuracy) new_model, config = optimizer.get_best_model() optimizer.summary()"><pre><span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-en">ResNet18</span>().<span class="pl-c1">load_state_dict</span>(...) <span class="pl-s1">train_dataloader</span> <span class="pl-c1">=</span> ... <span class="pl-s1">val_dataloader</span> <span class="pl-c1">=</span> ... <span class="pl-k">def</span> <span class="pl-en">accuracy</span> (<span class="pl-s1">pred</span>, <span class="pl-s1">target</span>): ... <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">nano</span>.<span class="pl-s1">pytorch</span> <span class="pl-k">import</span> <span class="pl-v">InferenceOptimizer</span> <span class="pl-s1">optimizer</span> <span class="pl-c1">=</span> <span class="pl-en">InferenceOptimizer</span>() <span class="pl-s1">optimizer</span>.<span class="pl-c1">optimize</span>(<span class="pl-s1">model</span>, <span class="pl-s1">training_data</span><span class="pl-c1">=</span><span class="pl-s1">train_dataloader</span>, <span class="pl-s1">validation_data</span><span class="pl-c1">=</span><span class="pl-s1">val_dataloader</span>, <span class="pl-s1">metric</span><span class="pl-c1">=</span><span class="pl-s1">accuracy</span>) <span class="pl-s1">new_model</span>, <span class="pl-s1">config</span> <span class="pl-c1">=</span> <span class="pl-s1">optimizer</span>.<span class="pl-c1">get_best_model</span>() <span class="pl-s1">optimizer</span>.<span class="pl-c1">summary</span>()</pre></div> <p dir="auto">The output of <code>optimizer.summary()</code> will be something like:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content=" -------------------------------- ---------------------- -------------- ---------------------- | method | status | latency(ms) | metric value | -------------------------------- ---------------------- -------------- ---------------------- | original | successful | 45.145 | 0.975 | | bf16 | successful | 27.549 | 0.975 | | static_int8 | successful | 11.339 | 0.975 | | jit_fp32_ipex | successful | 40.618 | 0.975* | | jit_fp32_ipex_channels_last | successful | 19.247 | 0.975* | | jit_bf16_ipex | successful | 10.149 | 0.975 | | jit_bf16_ipex_channels_last | successful | 9.782 | 0.975 | | openvino_fp32 | successful | 22.721 | 0.975* | | openvino_int8 | successful | 5.846 | 0.962 | | onnxruntime_fp32 | successful | 20.838 | 0.975* | | onnxruntime_int8_qlinear | successful | 7.123 | 0.981 | -------------------------------- ---------------------- -------------- ---------------------- * means we assume the metric value of the traced model does not change, so we don't recompute metric value to save time. Optimization cost 60.8s in total."><pre class="notranslate"><code> -------------------------------- ---------------------- -------------- ---------------------- | method | status | latency(ms) | metric value | -------------------------------- ---------------------- -------------- ---------------------- | original | successful | 45.145 | 0.975 | | bf16 | successful | 27.549 | 0.975 | | static_int8 | successful | 11.339 | 0.975 | | jit_fp32_ipex | successful | 40.618 | 0.975* | | jit_fp32_ipex_channels_last | successful | 19.247 | 0.975* | | jit_bf16_ipex | successful | 10.149 | 0.975 | | jit_bf16_ipex_channels_last | successful | 9.782 | 0.975 | | openvino_fp32 | successful | 22.721 | 0.975* | | openvino_int8 | successful | 5.846 | 0.962 | | onnxruntime_fp32 | successful | 20.838 | 0.975* | | onnxruntime_int8_qlinear | successful | 7.123 | 0.981 | -------------------------------- ---------------------- -------------- ---------------------- * means we assume the metric value of the traced model does not change, so we don't recompute metric value to save time. Optimization cost 60.8s in total. </code></pre></div> </details> <details><summary>Show Nano Training example</summary> <br> You may easily accelerate PyTorch training (e.g., IPEX, BF16, Multi-Instance Training, etc.) using Nano: <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="model = ResNet18() optimizer = torch.optim.SGD(...) train_loader = ... val_loader = ... from bigdl.nano.pytorch import TorchNano # Define your training loop inside `TorchNano.train` class Trainer(TorchNano): def train(self): # call `setup` to prepare for model, optimizer(s) and dataloader(s) for accelerated training model, optimizer, (train_loader, val_loader) = self.setup(model, optimizer, train_loader, val_loader) for epoch in range(num_epochs): model.train() for data, target in train_loader: optimizer.zero_grad() output = model(data) # replace the loss.backward() with self.backward(loss) loss = loss_fuc(output, target) self.backward(loss) optimizer.step() # Accelerated training (IPEX, BF16 and Multi-Instance Training) Trainer(use_ipex=True, precision='bf16', num_processes=2).train()"><pre><span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-en">ResNet18</span>() <span class="pl-s1">optimizer</span> <span class="pl-c1">=</span> <span class="pl-s1">torch</span>.<span class="pl-c1">optim</span>.<span class="pl-c1">SGD</span>(...) <span class="pl-s1">train_loader</span> <span class="pl-c1">=</span> ... <span class="pl-s1">val_loader</span> <span class="pl-c1">=</span> ... <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">nano</span>.<span class="pl-s1">pytorch</span> <span class="pl-k">import</span> <span class="pl-v">TorchNano</span> <span class="pl-c"># Define your training loop inside `TorchNano.train`</span> <span class="pl-k">class</span> <span class="pl-v">Trainer</span>(<span class="pl-v">TorchNano</span>): <span class="pl-k">def</span> <span class="pl-en">train</span>(<span class="pl-s1">self</span>): <span class="pl-c"># call `setup` to prepare for model, optimizer(s) and dataloader(s) for accelerated training</span> <span class="pl-s1">model</span>, <span class="pl-s1">optimizer</span>, (<span class="pl-s1">train_loader</span>, <span class="pl-s1">val_loader</span>) <span class="pl-c1">=</span> <span class="pl-s1">self</span>.<span class="pl-c1">setup</span>(<span class="pl-s1">model</span>, <span class="pl-s1">optimizer</span>, <span class="pl-s1">train_loader</span>, <span class="pl-s1">val_loader</span>) <span class="pl-k">for</span> <span class="pl-s1">epoch</span> <span class="pl-c1">in</span> <span class="pl-en">range</span>(<span class="pl-s1">num_epochs</span>): <span class="pl-s1">model</span>.<span class="pl-c1">train</span>() <span class="pl-k">for</span> <span class="pl-s1">data</span>, <span class="pl-s1">target</span> <span class="pl-c1">in</span> <span class="pl-s1">train_loader</span>: <span class="pl-s1">optimizer</span>.<span class="pl-c1">zero_grad</span>() <span class="pl-s1">output</span> <span class="pl-c1">=</span> <span class="pl-en">model</span>(<span class="pl-s1">data</span>) <span class="pl-c"># replace the loss.backward() with self.backward(loss) </span> <span class="pl-s1">loss</span> <span class="pl-c1">=</span> <span class="pl-en">loss_fuc</span>(<span class="pl-s1">output</span>, <span class="pl-s1">target</span>) <span class="pl-s1">self</span>.<span class="pl-c1">backward</span>(<span class="pl-s1">loss</span>) <span class="pl-s1">optimizer</span>.<span class="pl-c1">step</span>() <span class="pl-c"># Accelerated training (IPEX, BF16 and Multi-Instance Training)</span> <span class="pl-en">Trainer</span>(<span class="pl-s1">use_ipex</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">precision</span><span class="pl-c1">=</span><span class="pl-s">'bf16'</span>, <span class="pl-s1">num_processes</span><span class="pl-c1">=</span><span class="pl-c1">2</span>).<span class="pl-c1">train</span>()</pre></div> </details> <p dir="auto"><em>See Nano <a href="https://bigdl.readthedocs.io/en/latest/doc/Nano/Overview/nano.html" rel="nofollow">user guide</a> and <a href="https://github.com/intel-analytics/BigDL-2.x/tree/main/python/nano/tutorial">tutotial</a> for more details.</em></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">DLlib</h3><a id="user-content-dllib" class="anchor" aria-label="Permalink: DLlib" href="#dllib"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">With <em>DLlib</em>, you can write distributed deep learning applications as standard (<strong>Scala</strong> or <strong>Python</strong>) Spark programs, using the same <strong>Spark DataFrames</strong> and <strong>ML Pipeline</strong> APIs.</p> <details><summary>Show DLlib Scala example</summary> <br> <p dir="auto">You can build distributed deep learning applications for Spark using <em>DLlib</em> Scala APIs in 3 simple steps:</p> <div class="highlight highlight-source-scala notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="// 1. Call `initNNContext` at the beginning of the code: import com.intel.analytics.bigdl.dllib.NNContext val sc = NNContext.initNNContext() // 2. Define the deep learning model using Keras-style API in DLlib: import com.intel.analytics.bigdl.dllib.keras.layers._ import com.intel.analytics.bigdl.dllib.keras.Model val input = Input[Float](inputShape = Shape(10)) val dense = Dense[Float](12).inputs(input) val output = Activation[Float]("softmax").inputs(dense) val model = Model(input, output) // 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs import org.apache.spark.sql.SparkSession import org.apache.spark.ml.feature.MinMaxScaler import org.apache.spark.ml.Pipeline import com.intel.analytics.bigdl.dllib.nnframes.NNEstimator import com.intel.analytics.bigdl.dllib.nn.CrossEntropyCriterion import com.intel.analytics.bigdl.dllib.optim.Adam val spark = SparkSession.builder().getOrCreate() val trainDF = spark.read.parquet("train_data") val validationDF = spark.read.parquet("val_data") val scaler = new MinMaxScaler().setInputCol("in").setOutputCol("value") val estimator = NNEstimator(model, CrossEntropyCriterion()) .setBatchSize(128).setOptimMethod(new Adam()).setMaxEpoch(5) val pipeline = new Pipeline().setStages(Array(scaler, estimator)) val pipelineModel = pipeline.fit(trainDF) val predictions = pipelineModel.transform(validationDF)"><pre><span class="pl-c"><span class="pl-c">//</span> 1. Call `initNNContext` at the beginning of the code: </span> <span class="pl-k">import</span> <span class="pl-en">com</span>.<span class="pl-en">intel</span>.<span class="pl-en">analytics</span>.<span class="pl-en">bigdl</span>.<span class="pl-en">dllib</span>.<span class="pl-en">NNContext</span> <span class="pl-k">val</span> <span class="pl-v">sc</span> <span class="pl-k">=</span> <span class="pl-en">NNContext</span>.initNNContext() <span class="pl-c"><span class="pl-c">//</span> 2. Define the deep learning model using Keras-style API in DLlib:</span> <span class="pl-k">import</span> <span class="pl-en">com</span>.<span class="pl-en">intel</span>.<span class="pl-en">analytics</span>.<span class="pl-en">bigdl</span>.<span class="pl-en">dllib</span>.<span class="pl-en">keras</span>.<span class="pl-en">layers</span>.<span class="pl-en">_</span> <span class="pl-k">import</span> <span class="pl-en">com</span>.<span class="pl-en">intel</span>.<span class="pl-en">analytics</span>.<span class="pl-en">bigdl</span>.<span class="pl-en">dllib</span>.<span class="pl-en">keras</span>.<span class="pl-en">Model</span> <span class="pl-k">val</span> <span class="pl-v">input</span> <span class="pl-k">=</span> <span class="pl-en">Input</span>[<span class="pl-en">Float</span>](inputShape <span class="pl-k">=</span> <span class="pl-en">Shape</span>(<span class="pl-c1">10</span>)) <span class="pl-k">val</span> <span class="pl-v">dense</span> <span class="pl-k">=</span> <span class="pl-en">Dense</span>[<span class="pl-en">Float</span>](<span class="pl-c1">12</span>).inputs(input) <span class="pl-k">val</span> <span class="pl-v">output</span> <span class="pl-k">=</span> <span class="pl-en">Activation</span>[<span class="pl-en">Float</span>](<span class="pl-s"><span class="pl-pds">"</span>softmax<span class="pl-pds">"</span></span>).inputs(dense) <span class="pl-k">val</span> <span class="pl-v">model</span> <span class="pl-k">=</span> <span class="pl-en">Model</span>(input, output) <span class="pl-c"><span class="pl-c">//</span> 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs</span> <span class="pl-k">import</span> <span class="pl-en">org</span>.<span class="pl-en">apache</span>.<span class="pl-en">spark</span>.<span class="pl-en">sql</span>.<span class="pl-en">SparkSession</span> <span class="pl-k">import</span> <span class="pl-en">org</span>.<span class="pl-en">apache</span>.<span class="pl-en">spark</span>.<span class="pl-en">ml</span>.<span class="pl-en">feature</span>.<span class="pl-en">MinMaxScaler</span> <span class="pl-k">import</span> <span class="pl-en">org</span>.<span class="pl-en">apache</span>.<span class="pl-en">spark</span>.<span class="pl-en">ml</span>.<span class="pl-en">Pipeline</span> <span class="pl-k">import</span> <span class="pl-en">com</span>.<span class="pl-en">intel</span>.<span class="pl-en">analytics</span>.<span class="pl-en">bigdl</span>.<span class="pl-en">dllib</span>.<span class="pl-en">nnframes</span>.<span class="pl-en">NNEstimator</span> <span class="pl-k">import</span> <span class="pl-en">com</span>.<span class="pl-en">intel</span>.<span class="pl-en">analytics</span>.<span class="pl-en">bigdl</span>.<span class="pl-en">dllib</span>.<span class="pl-en">nn</span>.<span class="pl-en">CrossEntropyCriterion</span> <span class="pl-k">import</span> <span class="pl-en">com</span>.<span class="pl-en">intel</span>.<span class="pl-en">analytics</span>.<span class="pl-en">bigdl</span>.<span class="pl-en">dllib</span>.<span class="pl-en">optim</span>.<span class="pl-en">Adam</span> <span class="pl-k">val</span> <span class="pl-v">spark</span> <span class="pl-k">=</span> <span class="pl-en">SparkSession</span>.builder().getOrCreate() <span class="pl-k">val</span> <span class="pl-v">trainDF</span> <span class="pl-k">=</span> spark.read.parquet(<span class="pl-s"><span class="pl-pds">"</span>train_data<span class="pl-pds">"</span></span>) <span class="pl-k">val</span> <span class="pl-v">validationDF</span> <span class="pl-k">=</span> spark.read.parquet(<span class="pl-s"><span class="pl-pds">"</span>val_data<span class="pl-pds">"</span></span>) <span class="pl-k">val</span> <span class="pl-v">scaler</span> <span class="pl-k">=</span> <span class="pl-k">new</span> <span class="pl-en">MinMaxScaler</span>().setInputCol(<span class="pl-s"><span class="pl-pds">"</span>in<span class="pl-pds">"</span></span>).setOutputCol(<span class="pl-s"><span class="pl-pds">"</span>value<span class="pl-pds">"</span></span>) <span class="pl-k">val</span> <span class="pl-v">estimator</span> <span class="pl-k">=</span> <span class="pl-en">NNEstimator</span>(model, <span class="pl-en">CrossEntropyCriterion</span>()) .setBatchSize(<span class="pl-c1">128</span>).setOptimMethod(<span class="pl-k">new</span> <span class="pl-en">Adam</span>()).setMaxEpoch(<span class="pl-c1">5</span>) <span class="pl-k">val</span> <span class="pl-v">pipeline</span> <span class="pl-k">=</span> <span class="pl-k">new</span> <span class="pl-en">Pipeline</span>().setStages(<span class="pl-en">Array</span>(scaler, estimator)) <span class="pl-k">val</span> <span class="pl-v">pipelineModel</span> <span class="pl-k">=</span> pipeline.fit(trainDF) <span class="pl-k">val</span> <span class="pl-v">predictions</span> <span class="pl-k">=</span> pipelineModel.transform(validationDF)</pre></div> </details> <details><summary>Show DLlib Python example</summary> <br> <p dir="auto">You can build distributed deep learning applications for Spark using <em>DLlib</em> Python APIs in 3 simple steps:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# 1. Call `init_nncontext` at the beginning of the code: from bigdl.dllib.nncontext import init_nncontext sc = init_nncontext() # 2. Define the deep learning model using Keras-style API in DLlib: from bigdl.dllib.keras.layers import Input, Dense, Activation from bigdl.dllib.keras.models import Model input = Input(shape=(10,)) dense = Dense(12)(input) output = Activation("softmax")(dense) model = Model(input, output) # 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs from pyspark.sql import SparkSession from pyspark.ml.feature import MinMaxScaler from pyspark.ml import Pipeline from bigdl.dllib.nnframes import NNEstimator from bigdl.dllib.nn.criterion import CrossEntropyCriterion from bigdl.dllib.optim.optimizer import Adam spark = SparkSession.builder.getOrCreate() train_df = spark.read.parquet("train_data") validation_df = spark.read.parquet("val_data") scaler = MinMaxScaler().setInputCol("in").setOutputCol("value") estimator = NNEstimator(model, CrossEntropyCriterion())\ .setBatchSize(128)\ .setOptimMethod(Adam())\ .setMaxEpoch(5) pipeline = Pipeline(stages=[scaler, estimator]) pipelineModel = pipeline.fit(train_df) predictions = pipelineModel.transform(validation_df)"><pre><span class="pl-c"># 1. Call `init_nncontext` at the beginning of the code:</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">dllib</span>.<span class="pl-s1">nncontext</span> <span class="pl-k">import</span> <span class="pl-s1">init_nncontext</span> <span class="pl-s1">sc</span> <span class="pl-c1">=</span> <span class="pl-en">init_nncontext</span>() <span class="pl-c"># 2. Define the deep learning model using Keras-style API in DLlib:</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">dllib</span>.<span class="pl-s1">keras</span>.<span class="pl-s1">layers</span> <span class="pl-k">import</span> <span class="pl-v">Input</span>, <span class="pl-v">Dense</span>, <span class="pl-v">Activation</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">dllib</span>.<span class="pl-s1">keras</span>.<span class="pl-s1">models</span> <span class="pl-k">import</span> <span class="pl-v">Model</span> <span class="pl-s1">input</span> <span class="pl-c1">=</span> <span class="pl-en">Input</span>(<span class="pl-s1">shape</span><span class="pl-c1">=</span>(<span class="pl-c1">10</span>,)) <span class="pl-s1">dense</span> <span class="pl-c1">=</span> <span class="pl-en">Dense</span>(<span class="pl-c1">12</span>)(<span class="pl-s1">input</span>) <span class="pl-s1">output</span> <span class="pl-c1">=</span> <span class="pl-en">Activation</span>(<span class="pl-s">"softmax"</span>)(<span class="pl-s1">dense</span>) <span class="pl-s1">model</span> <span class="pl-c1">=</span> <span class="pl-en">Model</span>(<span class="pl-s1">input</span>, <span class="pl-s1">output</span>) <span class="pl-c"># 3. Use `NNEstimator` to train/predict/evaluate the model using Spark DataFrame and ML pipeline APIs</span> <span class="pl-k">from</span> <span class="pl-s1">pyspark</span>.<span class="pl-s1">sql</span> <span class="pl-k">import</span> <span class="pl-v">SparkSession</span> <span class="pl-k">from</span> <span class="pl-s1">pyspark</span>.<span class="pl-s1">ml</span>.<span class="pl-s1">feature</span> <span class="pl-k">import</span> <span class="pl-v">MinMaxScaler</span> <span class="pl-k">from</span> <span class="pl-s1">pyspark</span>.<span class="pl-s1">ml</span> <span class="pl-k">import</span> <span class="pl-v">Pipeline</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">dllib</span>.<span class="pl-s1">nnframes</span> <span class="pl-k">import</span> <span class="pl-v">NNEstimator</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">dllib</span>.<span class="pl-s1">nn</span>.<span class="pl-s1">criterion</span> <span class="pl-k">import</span> <span class="pl-v">CrossEntropyCriterion</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">dllib</span>.<span class="pl-s1">optim</span>.<span class="pl-s1">optimizer</span> <span class="pl-k">import</span> <span class="pl-v">Adam</span> <span class="pl-s1">spark</span> <span class="pl-c1">=</span> <span class="pl-v">SparkSession</span>.<span class="pl-c1">builder</span>.<span class="pl-c1">getOrCreate</span>() <span class="pl-s1">train_df</span> <span class="pl-c1">=</span> <span class="pl-s1">spark</span>.<span class="pl-c1">read</span>.<span class="pl-c1">parquet</span>(<span class="pl-s">"train_data"</span>) <span class="pl-s1">validation_df</span> <span class="pl-c1">=</span> <span class="pl-s1">spark</span>.<span class="pl-c1">read</span>.<span class="pl-c1">parquet</span>(<span class="pl-s">"val_data"</span>) <span class="pl-s1">scaler</span> <span class="pl-c1">=</span> <span class="pl-en">MinMaxScaler</span>().<span class="pl-c1">setInputCol</span>(<span class="pl-s">"in"</span>).<span class="pl-c1">setOutputCol</span>(<span class="pl-s">"value"</span>) <span class="pl-s1">estimator</span> <span class="pl-c1">=</span> <span class="pl-en">NNEstimator</span>(<span class="pl-s1">model</span>, <span class="pl-en">CrossEntropyCriterion</span>())\ .<span class="pl-c1">setBatchSize</span>(<span class="pl-c1">128</span>)\ .<span class="pl-c1">setOptimMethod</span>(<span class="pl-en">Adam</span>())\ .<span class="pl-c1">setMaxEpoch</span>(<span class="pl-c1">5</span>) <span class="pl-s1">pipeline</span> <span class="pl-c1">=</span> <span class="pl-en">Pipeline</span>(<span class="pl-s1">stages</span><span class="pl-c1">=</span>[<span class="pl-s1">scaler</span>, <span class="pl-s1">estimator</span>]) <span class="pl-s1">pipelineModel</span> <span class="pl-c1">=</span> <span class="pl-s1">pipeline</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">train_df</span>) <span class="pl-s1">predictions</span> <span class="pl-c1">=</span> <span class="pl-s1">pipelineModel</span>.<span class="pl-c1">transform</span>(<span class="pl-s1">validation_df</span>)</pre></div> </details> <p dir="auto"><em>See DLlib <a href="https://bigdl.readthedocs.io/en/latest/doc/DLlib/Overview/nnframes.html" rel="nofollow">NNFrames</a> and <a href="https://bigdl.readthedocs.io/en/latest/doc/DLlib/Overview/keras-api.html" rel="nofollow">Keras API</a> user guides for more details.</em></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Chronos</h3><a id="user-content-chronos" class="anchor" aria-label="Permalink: Chronos" href="#chronos"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The <em>Chronos</em> library makes it easy to build fast, accurate and scalable <strong>time series analysis</strong> applications (with AutoML).</p> <details><summary>Show Chronos example</summary> <br> <p dir="auto">You can train a time series forecaster using <em>Chronos</em> in 3 simple steps:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="from bigdl.chronos.forecaster import TCNForecaster from bigdl.chronos.data.repo_dataset import get_public_dataset # 1. Process time series data using `TSDataset` tsdata_train, tsdata_val, tsdata_test = get_public_dataset(name='nyc_taxi') for tsdata in [tsdata_train, tsdata_val, tsdata_test]: data.roll(lookback=100, horizon=1) # 2. Create a `TCNForecaster` (automatically configured based on train_data) forecaster = TCNForecaster.from_tsdataset(train_data) # 3. Train the forecaster for prediction forecaster.fit(train_data) pred = forecaster.predict(test_data)"><pre><span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">chronos</span>.<span class="pl-s1">forecaster</span> <span class="pl-k">import</span> <span class="pl-v">TCNForecaster</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">chronos</span>.<span class="pl-s1">data</span>.<span class="pl-s1">repo_dataset</span> <span class="pl-k">import</span> <span class="pl-s1">get_public_dataset</span> <span class="pl-c"># 1. Process time series data using `TSDataset`</span> <span class="pl-s1">tsdata_train</span>, <span class="pl-s1">tsdata_val</span>, <span class="pl-s1">tsdata_test</span> <span class="pl-c1">=</span> <span class="pl-en">get_public_dataset</span>(<span class="pl-s1">name</span><span class="pl-c1">=</span><span class="pl-s">'nyc_taxi'</span>) <span class="pl-k">for</span> <span class="pl-s1">tsdata</span> <span class="pl-c1">in</span> [<span class="pl-s1">tsdata_train</span>, <span class="pl-s1">tsdata_val</span>, <span class="pl-s1">tsdata_test</span>]: <span class="pl-s1">data</span>.<span class="pl-c1">roll</span>(<span class="pl-s1">lookback</span><span class="pl-c1">=</span><span class="pl-c1">100</span>, <span class="pl-s1">horizon</span><span class="pl-c1">=</span><span class="pl-c1">1</span>) <span class="pl-c"># 2. Create a `TCNForecaster` (automatically configured based on train_data)</span> <span class="pl-s1">forecaster</span> <span class="pl-c1">=</span> <span class="pl-v">TCNForecaster</span>.<span class="pl-c1">from_tsdataset</span>(<span class="pl-s1">train_data</span>) <span class="pl-c"># 3. Train the forecaster for prediction</span> <span class="pl-s1">forecaster</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">train_data</span>) <span class="pl-s1">pred</span> <span class="pl-c1">=</span> <span class="pl-s1">forecaster</span>.<span class="pl-c1">predict</span>(<span class="pl-s1">test_data</span>)</pre></div> <p dir="auto">To apply AutoML, use <code>AutoTSEstimator</code> instead of normal forecasters.</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="# Create and fit an `AutoTSEstimator` from bigdl.chronos.autots import AutoTSEstimator autotsest = AutoTSEstimator(model="tcn", future_seq_len=10) tsppl = autotsest.fit(data=tsdata_train, validation_data=tsdata_val) pred = tsppl.predict(tsdata_test)"><pre><span class="pl-c"># Create and fit an `AutoTSEstimator`</span> <span class="pl-k">from</span> <span class="pl-s1">bigdl</span>.<span class="pl-s1">chronos</span>.<span class="pl-s1">autots</span> <span class="pl-k">import</span> <span class="pl-v">AutoTSEstimator</span> <span class="pl-s1">autotsest</span> <span class="pl-c1">=</span> <span class="pl-en">AutoTSEstimator</span>(<span class="pl-s1">model</span><span class="pl-c1">=</span><span class="pl-s">"tcn"</span>, <span class="pl-s1">future_seq_len</span><span class="pl-c1">=</span><span class="pl-c1">10</span>) <span class="pl-s1">tsppl</span> <span class="pl-c1">=</span> <span class="pl-s1">autotsest</span>.<span class="pl-c1">fit</span>(<span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">tsdata_train</span>, <span class="pl-s1">validation_data</span><span class="pl-c1">=</span><span class="pl-s1">tsdata_val</span>) <span class="pl-s1">pred</span> <span class="pl-c1">=</span> <span class="pl-s1">tsppl</span>.<span class="pl-c1">predict</span>(<span class="pl-s1">tsdata_test</span>)</pre></div> </details> <p dir="auto"><em>See Chronos <a href="https://bigdl.readthedocs.io/en/latest/doc/Chronos/index.html" rel="nofollow">user guide</a> and <a href="https://bigdl.readthedocs.io/en/latest/doc/Chronos/QuickStart/chronos-autotsest-quickstart.html" rel="nofollow">quick start</a> for more details.</em></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Friesian</h3><a id="user-content-friesian" class="anchor" aria-label="Permalink: Friesian" href="#friesian"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The <em>Friesian</em> library makes it easy to build end-to-end, large-scale <strong>recommedation system</strong> (including <em>offline</em> feature transformation and traning, <em>near-line</em> feature and model update, and <em>online</em> serving pipeline).</p> <p dir="auto"><em>See Freisian <a href="https://github.com/intel-analytics/BigDL-2.x/blob/main/python/friesian/README.md">readme</a> for more details.</em></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">PPML</h3><a id="user-content-ppml" class="anchor" aria-label="Permalink: PPML" href="#ppml"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><em>BigDL PPML</em> provides a <strong>hardware (Intel SGX) protected</strong> <em>Trusted Cluster Environment</em> for running distributed Big Data & AI applications (in a secure fashion on private or public cloud).</p> <p dir="auto"><em>See PPML <a href="https://bigdl.readthedocs.io/en/latest/doc/PPML/Overview/ppml.html" rel="nofollow">user guide</a> and <a href="https://github.com/intel-analytics/BigDL-2.x/blob/main/ppml/README.md">tutorial</a> for more details.</em></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting Support</h2><a id="user-content-getting-support" class="anchor" aria-label="Permalink: Getting Support" href="#getting-support"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="mailto:bigdl-user-group+subscribe@googlegroups.com">Mail List</a></li> <li><a href="https://groups.google.com/forum/#!forum/bigdl-user-group" rel="nofollow">User Group</a></li> <li><a href="https://github.com/intel-analytics/BigDL-2.x/issues">Github Issues</a></li> </ul> <hr> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Citation</h2><a id="user-content-citation" class="anchor" aria-label="Permalink: Citation" href="#citation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you've found BigDL useful for your project, you may cite our papers as follows:</p> <ul dir="auto"> <li><em><a href="https://arxiv.org/abs/2204.01715" rel="nofollow">BigDL 2.0</a>: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster</em> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@INPROCEEDINGS{9880257, title={BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster}, author={Dai, Jason Jinquan and Ding, Ding and Shi, Dongjie and Huang, Shengsheng and Wang, Jiao and Qiu, Xin and Huang, Kai and Song, Guoqiong and Wang, Yang and Gong, Qiyuan and Song, Jiaming and Yu, Shan and Zheng, Le and Chen, Yina and Deng, Junwei and Song, Ge}, booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2022}, pages={21407-21414}, doi={10.1109/CVPR52688.2022.02076} }"><pre class="notranslate"><code>@INPROCEEDINGS{9880257, title={BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster}, author={Dai, Jason Jinquan and Ding, Ding and Shi, Dongjie and Huang, Shengsheng and Wang, Jiao and Qiu, Xin and Huang, Kai and Song, Guoqiong and Wang, Yang and Gong, Qiyuan and Song, Jiaming and Yu, Shan and Zheng, Le and Chen, Yina and Deng, Junwei and Song, Ge}, booktitle={2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2022}, pages={21407-21414}, doi={10.1109/CVPR52688.2022.02076} } </code></pre></div> </li> </ul> <ul dir="auto"> <li><em><a href="https://arxiv.org/abs/1804.05839" rel="nofollow">BigDL</a>: A Distributed Deep Learning Framework for Big Data</em> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="@INPROCEEDINGS{10.1145/3357223.3362707, title = {BigDL: A Distributed Deep Learning Framework for Big Data}, author = {Dai, Jason Jinquan and Wang, Yiheng and Qiu, Xin and Ding, Ding and Zhang, Yao and Wang, Yanzhang and Jia, Xianyan and Zhang, Cherry Li and Wan, Yan and Li, Zhichao and Wang, Jiao and Huang, Shengsheng and Wu, Zhongyuan and Wang, Yang and Yang, Yuhao and She, Bowen and Shi, Dongjie and Lu, Qi and Huang, Kai and Song, Guoqiong}, booktitle = {Proceedings of the ACM Symposium on Cloud Computing (SoCC)}, year = {2019}, pages = {50–60}, doi = {10.1145/3357223.3362707} }"><pre class="notranslate"><code>@INPROCEEDINGS{10.1145/3357223.3362707, title = {BigDL: A Distributed Deep Learning Framework for Big Data}, author = {Dai, Jason Jinquan and Wang, Yiheng and Qiu, Xin and Ding, Ding and Zhang, Yao and Wang, Yanzhang and Jia, Xianyan and Zhang, Cherry Li and Wan, Yan and Li, Zhichao and Wang, Jiao and Huang, Shengsheng and Wu, Zhongyuan and Wang, Yang and Yang, Yuhao and She, Bowen and Shi, Dongjie and Lu, Qi and Huang, Kai and Song, Guoqiong}, booktitle = {Proceedings of the ACM Symposium on Cloud Computing (SoCC)}, year = {2019}, pages = {50–60}, doi = {10.1145/3357223.3362707} } </code></pre></div> </li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="zxY/02NnRMnWF51fKMHEkU0H4OznEZbE7b0HT9I2MyzJ+jH0WxcSbYLHKCHTW1g1GcHBIpkU9is5jeJgA6YvNw==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> BigDL: Distributed TensorFlow, Keras and PyTorch on Apache Spark/Flink & Ray </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-link flex-shrink-0 mr-2"> <path d="m7.775 3.275 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