[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-samjabrahams--tensorflow-on-raspberry-pi":3,"tool-samjabrahams--tensorflow-on-raspberry-pi":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":80,"owner_email":81,"owner_twitter":81,"owner_website":82,"owner_url":83,"languages":84,"stars":89,"forks":90,"last_commit_at":91,"license":92,"difficulty_score":93,"env_os":94,"env_gpu":95,"env_ram":95,"env_deps":96,"category_tags":102,"github_topics":103,"view_count":23,"oss_zip_url":81,"oss_zip_packed_at":81,"status":16,"created_at":107,"updated_at":108,"faqs":109,"releases":139},2301,"samjabrahams\u002Ftensorflow-on-raspberry-pi","tensorflow-on-raspberry-pi","TensorFlow for Raspberry Pi","tensorflow-on-raspberry-pi 是一个曾致力于让谷歌深度学习框架 TensorFlow 在树莓派（Raspberry Pi）上运行的开源项目。它的核心目标是解决早期版本中 TensorFlow 缺乏对树莓派硬件官方支持的问题，帮助开发者在资源受限的边缘设备上部署和运行机器学习模型。\n\n随着 TensorFlow 1.9 版本的发布，官方已正式提供适用于树莓派的 Python 安装包，这意味着用户现在可以直接通过标准管道安装，无需再依赖此第三方仓库。因此，该项目目前已停止更新，转而推荐大家使用官方源。尽管如此，它在社区发展初期起到了重要的桥梁作用，激发了大量关于边缘计算和嵌入式 AI 的探索。\n\n适合使用该工具历史版本或关注其演进过程的主要是嵌入式系统开发者、物联网工程师以及希望在低成本硬件上尝试机器学习的研究人员。对于普通用户而言，若需在树莓派上运行 TensorFlow，建议直接采用官方支持的最新版本以获得更好的稳定性和性能。虽然项目已完成使命，但它见证了开源社区如何推动技术普及，是边缘智能发展历程中值得铭记的一笔。","# TensorFlow on Raspberry Pi\n\n## It's officially supported!\n\nAs of TensorFlow 1.9, Python wheels for TensorFlow are being [officially supported](https:\u002F\u002Fmedium.com\u002Ftensorflow\u002Ftensorflow-1-9-officially-supports-the-raspberry-pi-b91669b0aa0). As such, this repository is no longer recommended for your TensorFlow on RPi needs; use the official sources!\n\n## Pip installation\n\nYou can install the official wheel with the following commands, assuming you are using Raspbian 9:\n\n```bash\nsudo apt install libatlas-base-dev\npip3 install tensorflow\n```\n\n[Check out the official TensorFlow website for more information.](https:\u002F\u002Fwww.tensorflow.org\u002Finstall\u002Finstall_raspbian)\n\n---\n\nIt was a fun ride! With Raspberry Pi support now official, I will no longer be looking to update this repository. I'm sorry that I wasn't able to continue maintaining the repo as much as I wanted, but it was amazing watching the community continue to thrive.\n\n-Sam","# 树莓派上的 TensorFlow\n\n## 现已正式支持！\n\n自 TensorFlow 1.9 版本起，TensorFlow 的 Python wheel 文件已获得[官方支持](https:\u002F\u002Fmedium.com\u002Ftensorflow\u002Ftensorflow-1-9-officially-supports-the-raspberry-pi-b91669b0aa0)。因此，不再推荐使用本仓库来满足您在树莓派上运行 TensorFlow 的需求；请改用官方源！\n\n## 使用 pip 安装\n\n假设您正在使用 Raspbian 9 系统，可以通过以下命令安装官方提供的 wheel 包：\n\n```bash\nsudo apt install libatlas-base-dev\npip3 install tensorflow\n```\n\n[更多相关信息，请参阅 TensorFlow 官方网站。](https:\u002F\u002Fwww.tensorflow.org\u002Finstall\u002Finstall_raspbian)\n\n---\n\n这段旅程非常有趣！既然树莓派支持已成为官方功能，我将不再更新此仓库。很抱歉未能按照我的预期持续维护该仓库，但看到社区不断蓬勃发展，仍然令人倍感欣慰。\n\n—— Sam","# TensorFlow on Raspberry Pi 快速上手指南\n\n> **重要提示**：自 TensorFlow 1.9 版本起，树莓派已获得官方支持。本仓库不再维护，强烈建议直接使用官方提供的 Python wheel 包进行安装。\n\n## 环境准备\n\n- **操作系统**：Raspbian 9 (Stretch) 或更高版本\n- **硬件**：Raspberry Pi (推荐 Pi 3 B+ 或 Pi 4)\n- **Python 版本**：Python 3.x\n- **前置依赖**：需安装线性代数库以优化性能\n\n## 安装步骤\n\n请确保系统软件源已更新，然后执行以下命令安装必要依赖和 TensorFlow：\n\n```bash\nsudo apt update\nsudo apt install libatlas-base-dev\npip3 install tensorflow\n```\n\n> **国内加速建议**：如果下载速度较慢，建议使用国内镜像源（如清华源或阿里源）安装：\n> ```bash\n> pip3 install tensorflow -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n> ```\n\n## 基本使用\n\n安装完成后，即可在 Python 中导入并使用 TensorFlow。以下是一个最简单的验证示例：\n\n```python\nimport tensorflow as tf\n\n# 创建一个简单的常量操作\nhello = tf.constant('Hello, TensorFlow on Raspberry Pi!')\n\n# 启动会话并运行（适用于 TF 1.x）\nwith tf.Session() as sess:\n    print(sess.run(hello))\n```\n\n*注：如果您使用的是 TensorFlow 2.x 版本，代码将更加简洁，默认采用即时执行模式（Eager Execution），无需显式创建 Session。*","一位嵌入式开发者试图在树莓派上部署实时垃圾分类模型，以便在社区智能垃圾桶中实现自动识别功能。\n\n### 没有 tensorflow-on-raspberry-pi 时\n- **安装门槛极高**：开发者需要手动交叉编译 TensorFlow 源码，在树莓派有限的算力下，编译过程往往耗时数天且极易因内存不足而失败。\n- **依赖环境混乱**：缺乏预编译的 Python wheel 包，必须手动解决 BLAS 库（如 Atlas）等底层依赖的版本冲突问题，调试过程令人崩溃。\n- **社区支持断层**：由于缺乏官方背书，遇到报错时只能查阅过时的第三方教程，解决方案往往不兼容最新的 Raspbian 系统版本。\n- **原型验证受阻**：大量的时间被浪费在环境搭建而非算法优化上，导致项目迟迟无法进入实地测试阶段。\n\n### 使用 tensorflow-on-raspberry-pi 后\n- **一键快速部署**：借助官方支持的 Python wheel 包，仅需两条 pip 命令即可完成安装，将环境配置时间从数天缩短至几分钟。\n- **依赖自动管理**：安装包已预先处理好 libatlas-base-dev 等关键底层依赖，彻底消除了手动编译和链接库的繁琐步骤。\n- **生态稳定可靠**：基于 TensorFlow 1.9 及后续版本的官方支持，开发者可直接参考主流文档，确保代码在 Raspbian 9 等系统上稳定运行。\n- **聚焦核心业务**：节省下来的精力可全部投入到模型量化与推理加速上，迅速完成了从原型开发到户外真实场景的落地验证。\n\ntensorflow-on-raspberry-pi 通过提供官方预编译支持，彻底扫清了在边缘设备上部署深度学习模型的工程障碍，让开发者能专注于创新应用本身。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsamjabrahams_tensorflow-on-raspberry-pi_c9ff9658.png","samjabrahams","Sam Abrahams","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fsamjabrahams_52ca2d58.jpg","AI \u002F ML, robotics, autonomous vehicles. Longtime engineer at Cruise.","Cruise","San Francisco, CA",null,"http:\u002F\u002Fwww.memdump.io","https:\u002F\u002Fgithub.com\u002Fsamjabrahams",[85],{"name":86,"color":87,"percentage":88},"Python","#3572A5",100,2259,492,"2026-04-03T03:43:51","NOASSERTION",1,"Linux","未说明",{"notes":97,"python":98,"dependencies":99},"该项目已不再维护，因为自 TensorFlow 1.9 起官方已支持 Raspberry Pi。建议直接使用官方源进行安装。示例环境基于 Raspbian 9 系统。","3.x (通过 pip3 安装)",[100,101],"libatlas-base-dev","tensorflow>=1.9",[13],[104,105,106],"raspberry-pi","tensorflow","machine-learning","2026-03-27T02:49:30.150509","2026-04-06T07:13:10.112094",[110,115,120,125,130,135],{"id":111,"question_zh":112,"answer_zh":113,"source_url":114},10559,"从源码构建 TensorFlow 时出现沙盒执行警告（Sandboxed execution is not supported），这会影响构建吗？如何解决？","该警告通常不会阻止构建，但为了确保某些库能正确编译，建议执行以下操作：\n1. 删除 tensorflow\u002Fcore\u002Fplatform\u002Fplatform.h 文件中的第 45 行代码。\n2. 使用以下命令进行构建，其中 --copt=\"-mfpu=neon\" 标志对于树莓派等 ARM 设备至关重要：\nbazel build -c opt --copt=\"-mfpu=neon\" --local_resources 1024,1.0,1.0 --verbose_failures tensorflow\u002Ftools\u002Fpip_package:build_pip_package","https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi\u002Fissues\u002F23",{"id":116,"question_zh":117,"answer_zh":118,"source_url":119},10560,"在树莓派上编译 Bazel 时遇到 \"java.lang.OutOfMemoryError: Java heap space\" 错误怎么办？","这是由于 Java 堆内存不足导致的。解决方法是在运行 .\u002Fcompile.sh 之前，修改 bazel\u002Fscripts\u002Fbootstrap\u002Fcompile.sh 文件，增加 javac 的堆大小设置（例如调整 -J-Xmx 参数）。虽然这不是完美的解决方案，但在树莓派 2 等设备上通常有效。","https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi\u002Fissues\u002F5",{"id":121,"question_zh":122,"answer_zh":123,"source_url":124},10561,"使用 pip 安装 TensorFlow 时提示 \"No distributions at all found\" 或找不到下载源，如何解决？","这通常是因为系统自带的 pip 版本过旧。请尝试先升级 pip 到最新版本，命令如下：\nsudo pip install --upgrade pip\n升级完成后再次尝试安装 TensorFlow wheel 包。","https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi\u002Fissues\u002F10",{"id":126,"question_zh":127,"answer_zh":128,"source_url":129},10562,"pip 安装 .whl 文件时报错 \"BadZipfile: File is not a zip file\" 是什么原因？","这个错误通常意味着下载的 .whl 文件已损坏或不完整（例如下载过程中中断）。解决方案是重新下载该文件，确保下载过程完整无误，然后再尝试安装。","https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi\u002Fissues\u002F58",{"id":131,"question_zh":132,"answer_zh":133,"source_url":134},10563,"在树莓派上构建 Bazel 时进程卡住或在特定步骤（如 [1342\u002F1396]）失败，如何处理？","这通常是由于内存不足导致的。建议采取以下措施：\n1. 增加交换空间（Swap）的大小。\n2. 修改编译脚本中的内存限制，将 -J-Xmx500M 改为更大的值（如 -J-Xmx2048M）。\n3. 确保使用的是较新版本的 NOOBS 或 Raspbian 系统，旧版本可能存在兼容性问题。","https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi\u002Fissues\u002F39",{"id":136,"question_zh":137,"answer_zh":138,"source_url":114},10564,"从源码构建 TensorFlow 时遇到 gcc 编译错误（C++ compilation failed），特别是涉及内部库时怎么办？","这类编译错误通常与缺少必要的编译器标志有关。请确保在 bazel build 命令中添加了 --copt=\"-mfpu=neon\" 参数，这对于在树莓派（ARM 架构）上正确编译某些底层库是必须的。同时，检查是否已按照指南删除了 platform.h 中导致冲突的代码行。",[140,145,150,155,160,165,170,175,180,185],{"id":141,"version":142,"summary_zh":143,"released_at":144},71118,"v1.1.0","### Updates to this Project\r\n\r\n- No longer use OpenJDK (instead using the default Oracle JDK included with Raspbian)\r\n- Updated `.\u002Fconfigure` script to show all options used.\r\n- Added note that it's alright to skip the automatic `bazel clean` after running `.\u002Fconfigure`\r\n\r\n### TensorFlow Updates\r\n\r\nSee [the official release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv1.1.0) for details on latest supported features, updates, and changes to TensorFlow itself.\r\n\r\n### Version of Rasbpian used in the binaries.:\r\n\r\n```\r\nRaspbian 8.0 \"Jessie\"\r\nRelease: March 2, 2017\r\nInstalled via NOOBS 2.3\r\n```\r\n\r\n[Raspbian release notes](http:\u002F\u002Fdownloads.raspberrypi.org\u002Fraspbian\u002Frelease_notes.txt)\r\n","2017-04-30T19:03:29",{"id":146,"version":147,"summary_zh":148,"released_at":149},71119,"v1.0.1","### Updates to this Project\r\n- Users no longer have to build `protoc` from source in order to build Bazel\u002FTensorFlow\r\n- ~~Instructions using `sudo pip install...` have been changed to use `pip install --user...` instead (#72)~~\r\n    - This caused issues, so it has been reverted for now.\r\n- More information about the version of Raspbian used has been added to the README, as well as the release notes (#73).\r\n\r\n### TensorFlow Updates\r\n\r\nSee [the official release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv1.0.1) for details on latest supported features, updates, and changes to TensorFlow itself.\r\n\r\n### Version of Rasbpian used in the binaries.:\r\n\r\n```\r\nRaspbian 8.0 \"Jessie\"\r\nRelease: March 2, 2017\r\nInstalled via NOOBS 2.3\r\n```\r\n\r\n[Raspbian release notes](http:\u002F\u002Fdownloads.raspberrypi.org\u002Fraspbian\u002Frelease_notes.txt)\r\n","2017-04-06T02:01:48",{"id":151,"version":152,"summary_zh":153,"released_at":154},71120,"v1.0.0","### Updates to this Project\r\n- When building from scratch, users have to switch the Numeric JS library protocol from `https` to `http`\r\n- SHA-256 checksums for binaries will be included in this and future releases.\r\n\r\n### TensorFlow Updates\r\n\r\nSee [the official release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv1.0.0) for details on latest supported features, updates, and changes to TensorFlow itself.\r\n","2017-04-14T18:38:47",{"id":156,"version":157,"summary_zh":158,"released_at":159},71121,"v0.12.1","### Updates to this Project\n- No longer have to build protobuf Java plugin\n- `protoc` is installed to `\u002Fusr\u002Flocal\u002Fbin\u002F` instead of `\u002Fusr\u002Fbin\u002F`\n- No longer have to compile gRPC manually\n- Bazel source files are downloaded as a distribution zip file instead of cloning from GitHub\n- Most of the finagling to compile Bazel has been removed\n- Added a donation link to the README\n\n### TensorFlow Updates\n\nSee [the official release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002F0.12.0-rc0) for details on latest supported features, updates, and changes to TensorFlow itself.\n","2017-01-20T23:56:09",{"id":161,"version":162,"summary_zh":163,"released_at":164},71122,"v0.11.0","### Updates to this Project\n- Removed `archive` and `bin` directories from repository, as the large files were causing slow pushes\u002Fpulls\n  - Previous versions are still available from the [releases page](https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi\u002Freleases)\n- Build tested with fresh install using NOOBS 2.1\n\n### TensorFlow Updates\n\nSee [the official release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv0.11.0rc0) for details on latest supported features, updates, and changes to TensorFlow itself.\n","2016-12-05T05:11:19",{"id":166,"version":167,"summary_zh":168,"released_at":169},71123,"v0.10.0","### Updates to this Project\n- Updated [GUIDE.md](https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi\u002Fblob\u002Fmaster\u002FGUIDE.md) to compile gRPC from scratch, as well as update Bazel build instructions in order to work for newer versions of Bazel.\n- Final TensorFlow Bazel build now uses the flags `--copt=\"-mfpu=neon-vfpv4\"`, `--copt=\"-funsafe-math-optimizations\"`, and `--copt=\"-ftree-vectorize\"`\n\n### TensorFlow Updates\n\nSee [the official release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv0.10.0rc0) for details on latest supported features, updates, and changes to TensorFlow itself.\n","2016-11-10T19:18:19",{"id":171,"version":172,"summary_zh":173,"released_at":174},71124,"v0.9.0","### Updates to this Project\n- In order to build from source, [this line](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Fblob\u002Fr0.9\u002Ftensorflow\u002Fcore\u002Fplatform\u002Fplatform.h#L45) must be deleted from tensorflow\u002Fcore\u002Fplatform\u002Fplatform.h. Otherwise there will be errors on compilation\n- The Bazel build option `--copt=\"-mfpu=neon\"` is added back in, as new compatibility was introduced into Eigen. Without this option, the compiler will throw errors complaining about not knowing how to handle certain variables.\n\n### TensorFlow Updates\n\nSee [the official release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv0.9.0) for details on latest supported features, updates, and changes to TensorFlow itself.\n","2016-07-01T05:33:50",{"id":176,"version":177,"summary_zh":178,"released_at":179},71125,"v0.9.0rc0","### Updates to this Project\n- The NEON flag to GCC does not work due to changes related to use of the `Eigen::half` (16-bit floating point) type inside of TensorFlow. Because of this, the `--copt=\"-mfpu=neon\"` flag used when building TensorFlow has been removed from [GUIDE.md](https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi\u002Fblob\u002Fmaster\u002FGUIDE.md). Hopefully we'll find new compiler instructions that can help optimize TensorFlow.\n\n### TensorFlow Updates\n\nSee [the official pre-release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv0.9.0rc0) for details on latest supported features, updates, and changes to TensorFlow itself.\n","2016-06-16T23:16:41",{"id":181,"version":182,"summary_zh":183,"released_at":184},71126,"v0.8.0","### TensorFlow Updates\n\nSee [the official release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv0.8.0rc0) for details on latest supported features, updates, and changes to TensorFlow itself.\n\n---\n\n### Fixes on Top of RC0\n\n#### `tf.train.ClusterSpec` no longer throws an `AttributeError` when used as a parameter to `tf.train.Server`\n\nYou should be able to use the distributed capabilities as you would on any other system!\n","2016-05-04T20:04:03",{"id":186,"version":187,"summary_zh":188,"released_at":189},71127,"v0.8.0rc0","### Updates to this Project\n- Latest binaries are now available in the `bin` directory\n- Old binaries will now be kept in the `archive` directory\n- Figured out how to build the latest version of [Bazel](http:\u002F\u002Fbazel.io\u002F) natively on RPi3- the Bazel portion of GUIDE.md has been updated\n- We should be able to release new binaries to coincide with the official release now that we have Bazel version>0.2  running on RPi3. \n  - In the future, the goal will be to release within a week of the official release binaries.\n  - Future binaries will always be built at the exact commit of the official release. You probably didn't notice, but the 0.7.1 release was built at HEAD somewhere in the middle :)\n\n### TensorFlow Updates\n\nSee [the official pre-release notes](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Ftensorflow\u002Freleases\u002Ftag\u002Fv0.8.0rc0) for details on latest supported features, updates, and changes to TensorFlow itself.\n\n---\n\n### Notable quirks:\n\n##### `tf.train.ClusterSpec` throws AttributeError when used as a parameter to `tf.train.Server`\n\nFor whatever reason, it seems that the internal `_cluster_spec` parameter wants to hide itself when used as an input to the constructor of `tf.train.Server`. Hopefully we'll be able to figure out what the deal is, but in the meantime, it is possible to start distributed servers by just passing in the ClusterSpec dictionary directly. For example, instead of declaring a server like this:\n\n``` python\ncluster = tf.train.ClusterSpec({\"local\": [\"localhost:2222\", \"localhost:2223\"]})\nserver = tf.train.Server(cluster, job_name=\"local\", task_index=0)\n```\n\nYou can declare it directly like this:\n\n``` python\nserver = tf.train.Server({\"local\": [\"localhost:2222\", \"localhost:2223\"]}, job_name=\"local\", task_index=0)\n```\n","2016-04-23T08:09:28"]