[{"data":1,"prerenderedAt":392},["ShallowReactive",2],{"content-query-m2aDQoFhnQ":3},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"title":8,"description":9,"date":10,"cover":11,"type":12,"category":13,"body":14,"_type":386,"_id":387,"_source":388,"_file":389,"_stem":390,"_extension":391},"/technology-blogs/zh/3388","zh",false,"","基于ms香橙派AIpro实现垃圾回收AI识别方案六：SOWT分析与总结","七、昇思MindSpore大模型平台SOWT分析与总结：","2024-08-04","https://obs-mindspore-file.obs.cn-north-4.myhuaweicloud.com/file/2024/11/28/960cae8736ab47dd8491f4f0c92fd89f.png","technology-blogs","调试调优",{"type":15,"children":16,"toc":370},"root",[17,25,38,48,58,68,78,87,91,97,102,105,111,116,124,129,135,142,152,158,166,172,180,186,194,201,206,212,217,227,232,237,242,250,255,260,265,273,278,283,291,296,301,308,313,316,322,327,334,339,342,348,353,358,365],{"type":18,"tag":19,"props":20,"children":22},"element","h1",{"id":21},"基于ms香橙派aipro实现垃圾回收ai识别方案六sowt分析与总结",[23],{"type":24,"value":8},"text",{"type":18,"tag":26,"props":27,"children":28},"p",{},[29],{"type":18,"tag":30,"props":31,"children":35},"a",{"href":32,"rel":33},"https://www.hiascend.com/developer/blog/details/0272157975874351541",[34],"nofollow",[36],{"type":24,"value":37},"基于ms香橙派AIpro实现垃圾回收AI识别方案一：昇思MindSpore介绍",{"type":18,"tag":26,"props":39,"children":40},{},[41],{"type":18,"tag":30,"props":42,"children":45},{"href":43,"rel":44},"https://www.hiascend.com/developer/blog/details/02111171533919729183",[34],[46],{"type":24,"value":47},"基于ms香橙派AIpro实现垃圾回收AI识别方案二：昇思MindSpore如何使用",{"type":18,"tag":26,"props":49,"children":50},{},[51],{"type":18,"tag":30,"props":52,"children":55},{"href":53,"rel":54},"https://www.hiascend.com/developer/blog/details/0272158138234394548",[34],[56],{"type":24,"value":57},"基于ms香橙派AIpro实现垃圾回收AI识别方案三：昇思大模型平台jupyter快速入门体验",{"type":18,"tag":26,"props":59,"children":60},{},[61],{"type":18,"tag":30,"props":62,"children":65},{"href":63,"rel":64},"https://www.hiascend.com/developer/blog/details/0272158143705280549",[34],[66],{"type":24,"value":67},"基于ms香橙派AIpro实现垃圾回收AI识别方案四：MindSpore应用实践 - 基于MobileNetv2的垃圾分类",{"type":18,"tag":26,"props":69,"children":70},{},[71],{"type":18,"tag":30,"props":72,"children":75},{"href":73,"rel":74},"https://www.hiascend.com/developer/blog/details/0272158155371368550",[34],[76],{"type":24,"value":77},"基于ms香橙派AIpro实现垃圾回收AI识别方案五：香橙派OrangePi AIpro实践(基于MobileNetv2的垃圾分类)",{"type":18,"tag":26,"props":79,"children":80},{},[81],{"type":18,"tag":30,"props":82,"children":85},{"href":83,"rel":84},"https://www.hiascend.com/developer/blog/details/0265158167508388542",[34],[86],{"type":24,"value":8},{"type":18,"tag":88,"props":89,"children":90},"hr",{},[],{"type":18,"tag":92,"props":93,"children":95},"h2",{"id":94},"七昇思mindspore大模型平台sowt分析与总结",[96],{"type":24,"value":9},{"type":18,"tag":26,"props":98,"children":99},{},[100],{"type":24,"value":101},"上面可以体验一下我们从0到1可以快速的搭建属于自己的AI系统，非常的方便与快捷，这里分享一下以前自己搭建一个能使用的AI应用有多么繁琐。",{"type":18,"tag":88,"props":103,"children":104},{},[],{"type":18,"tag":92,"props":106,"children":108},{"id":107},"_71-stable-diffusion介绍",[109],{"type":24,"value":110},"7.1 Stable Diffusion介绍：",{"type":18,"tag":26,"props":112,"children":113},{},[114],{"type":24,"value":115},"Stable Diffusion是一种基于扩散过程的图像生成模型，可以生成高质量、高分辨率的图像。它通过模拟扩散过程，将噪声图像逐渐转化为目标图像。这种模型具有较强的稳定性和可控性，可以生成具有多样化效果和良好视觉效果的图像。",{"type":18,"tag":26,"props":117,"children":118},{},[119],{"type":18,"tag":120,"props":121,"children":123},"img",{"alt":7,"src":122},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/6a4/0ed/bc6/0421bb776e6a40edbc68b6389cfec54d.20240805013819.80608866702820031690305971718825:20240913012259:2400:21C4409A7C6048C90F985088E6D054AC9E1502179FF76F5201AF7633ECA91C4B.jpg",[],{"type":18,"tag":26,"props":125,"children":126},{},[127],{"type":24,"value":128},"Stable Diffusion 可以通过生成多样化、高质量的图像、修复损坏的图像、提高图像的分辨率和应用特定风格到图像上等方式，辅助视觉创意的实现，它为视觉艺术家、设计师等提供更多的创作工具和素材，促进视觉艺术领域的创新和发展。",{"type":18,"tag":92,"props":130,"children":132},{"id":131},"_72-对比一下gpu服务器自行部署痛点",[133],{"type":24,"value":134},"7.2 对比一下GPU服务器自行部署痛点：",{"type":18,"tag":136,"props":137,"children":139},"h3",{"id":138},"_721-安装基本软件",[140],{"type":24,"value":141},"7.2.1 安装基本软件：",{"type":18,"tag":143,"props":144,"children":146},"pre",{"code":145},"sudo apt install wget git\n",[147],{"type":18,"tag":148,"props":149,"children":150},"code",{"__ignoreMap":7},[151],{"type":24,"value":145},{"type":18,"tag":136,"props":153,"children":155},{"id":154},"_722-安装python-3106",[156],{"type":24,"value":157},"7.2.2 安装python 3.10.6：",{"type":18,"tag":143,"props":159,"children":161},{"code":160},"# 安装依赖\nsudo apt install wget git python3 python3-venv\n# 删除默认的低版本\nwhich python3\nsudo rm /usr/bin/python\n# 配置软链接\nls -lh /usr/bin | grep python\nln -s /usr/bin/python3 /usr/bin/python\n# 若是GPU环境的用户需要安装与cuda版本对应的torch\npip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117\n# pip换源\npip config set global.index-url https://mirrors.ustc.edu.cn/pypi/web/simple\n# 安装对应依赖\n\npip install -r requirements_versions.txt\n# 建立虚拟环境\nsudo apt-get install python3.5-venv\npython3 -m venv_name\nsource venv_name/bin/activate\n",[162],{"type":18,"tag":148,"props":163,"children":164},{"__ignoreMap":7},[165],{"type":24,"value":160},{"type":18,"tag":136,"props":167,"children":169},{"id":168},"_723-安装cuda",[170],{"type":24,"value":171},"7.2.3 安装CUDA：",{"type":18,"tag":143,"props":173,"children":175},{"code":174},"# 下载Cuda\nwget https://developer.download.nvidia.com/compute/cuda/11.8.0/local_installers/cuda_11.8.0_520.61.05_linux.run\n# 安装cuda\nsudo sh cuda_11.8.0_520.61.05_linux.run\n# 配置环境变量\n# 增加下面两行内容，并保存\nvim ~/.bashrc\nexport PATH=/usr/local/cuda-11.8/bin:$PATH\nexport LD_LIBRARY_PATH=/usr/local/cuda-11.8/lib64:$LD_LIBRARY_PATH\n# 使配置文件生效\nsource ~/.bashrc\n",[176],{"type":18,"tag":148,"props":177,"children":178},{"__ignoreMap":7},[179],{"type":24,"value":174},{"type":18,"tag":136,"props":181,"children":183},{"id":182},"_724-安装stable-diffusion",[184],{"type":24,"value":185},"7.2.4 安装stable diffusion：",{"type":18,"tag":143,"props":187,"children":189},{"code":188},"# 拉取stable diffusion 代码：\ngit clone GitHub - AUTOMATIC1111/stable-diffusion-webui: Stable Diffusion web UI\n# 安装stable diffusion：\ncd stable-diffusion-webui/\n# 启动\n./webui.sh\n",[190],{"type":18,"tag":148,"props":191,"children":192},{"__ignoreMap":7},[193],{"type":24,"value":188},{"type":18,"tag":26,"props":195,"children":196},{},[197],{"type":18,"tag":120,"props":198,"children":200},{"alt":7,"src":199},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/6a4/0ed/bc6/0421bb776e6a40edbc68b6389cfec54d.20240805013839.50011077089910218558051936170483:20240913012259:2400:74DC33A98B3A1713C21FBD9DA3BA4B28D00F43B90C28E9A9218ED0A8EC643DF8.jpg",[],{"type":18,"tag":26,"props":202,"children":203},{},[204],{"type":24,"value":205},"以上是自行尝试购买某云GPU服务器，自己手动搭建环境，并运行stable diffusion。大概花费了差不多一个下午的时间，而且这个还是自己以前尝鲜有过经验的前提下。",{"type":18,"tag":92,"props":207,"children":209},{"id":208},"_72-搭建自己的ai应用的痛点",[210],{"type":24,"value":211},"7.2 搭建自己的AI应用的痛点：",{"type":18,"tag":26,"props":213,"children":214},{},[215],{"type":24,"value":216},"自己组建AI的痛点，无论是从概念设计到最终部署，都面临着一系列的痛点，主要包括以下点：",{"type":18,"tag":218,"props":219,"children":220},"ul",{},[221],{"type":18,"tag":222,"props":223,"children":224},"li",{},[225],{"type":24,"value":226},"①. 技术复杂性：",{"type":18,"tag":26,"props":228,"children":229},{},[230],{"type":24,"value":231},"(1). 算法选择与实现：选择合适的AI算法（如深度学习、机器学习、自然语言处理等），不同算法有不同的适用场景和性能表现，选择不当会导致最终应用的效果不佳。",{"type":18,"tag":26,"props":233,"children":234},{},[235],{"type":24,"value":236},"(2). 框架与库的选择：市面上存在多种AI框架和库（如TensorFlow、PyTorch等），每种框架都有其优缺点，选择合适的框架和库以支持应用需求是一个重要的决策。",{"type":18,"tag":26,"props":238,"children":239},{},[240],{"type":24,"value":241},"(3). 模型优化与部署：模型训练完成后，需要对其进行优化以提高性能和准确性，并考虑如何将其部署到生产环境中，其中包括选择合适的硬件平台、处理并发请求、优化响应时间等。",{"type":18,"tag":218,"props":243,"children":244},{},[245],{"type":18,"tag":222,"props":246,"children":247},{},[248],{"type":24,"value":249},"②. 资源限制：",{"type":18,"tag":26,"props":251,"children":252},{},[253],{"type":24,"value":254},"(1). 计算资源：AI应用的训练和推理过程通常需要大量的计算资源，包括CPU、GPU甚至TPU，这些资源成本高昂。",{"type":18,"tag":26,"props":256,"children":257},{},[258],{"type":24,"value":259},"(2). 存储资源：训练数据和模型本身也需要大量的存储空间，随着数据量的增加，存储成本也会不断上升。",{"type":18,"tag":26,"props":261,"children":262},{},[263],{"type":24,"value":264},"(3). 技术人才：AI应用的开发需要专业的技术人才，包括数据科学家、机器学习工程师等，往往这些人才在市场上供不应求，且成本较高。",{"type":18,"tag":218,"props":266,"children":267},{},[268],{"type":18,"tag":222,"props":269,"children":270},{},[271],{"type":24,"value":272},"③. 数据问题：",{"type":18,"tag":26,"props":274,"children":275},{},[276],{"type":24,"value":277},"(1). 数据获取：高质量的数据是训练AI模型的关键。然而，在实际应用中，获取足够数量的高质量数据往往非常困难。",{"type":18,"tag":26,"props":279,"children":280},{},[281],{"type":24,"value":282},"(2). 数据标注：对于监督学习来说，数据标注是必不可少的一步。然而，数据标注既耗时又耗力，且需要专业知识。",{"type":18,"tag":218,"props":284,"children":285},{},[286],{"type":18,"tag":222,"props":287,"children":288},{},[289],{"type":24,"value":290},"④. 维护与更新：",{"type":18,"tag":26,"props":292,"children":293},{},[294],{"type":24,"value":295},"(1). 模型更新：随着新数据的不断涌现和算法的不断进步，AI模型需要定期更新以保持其准确性和有效性，这要求开发者具备持续维护的能力。",{"type":18,"tag":26,"props":297,"children":298},{},[299],{"type":24,"value":300},"(2). 性能监控：在生产环境中，AI应用的性能可能受到多种因素的影响（如硬件故障、网络延迟等）。因此，需要对应用的性能进行实时监控和调整。",{"type":18,"tag":26,"props":302,"children":303},{},[304],{"type":18,"tag":120,"props":305,"children":307},{"alt":7,"src":306},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/6a4/0ed/bc6/0421bb776e6a40edbc68b6389cfec54d.20240805061714.02555076160890936682369800590601:20240913012259:2400:42381D654F218B2A7A59BCF32F7C635547C371B3CF601E72AB1223B810047D41.jpg",[],{"type":18,"tag":26,"props":309,"children":310},{},[311],{"type":24,"value":312},"可以看到云GPU服务器自行搭建存在很多问题，而昇思MindSpore大模型平台提供了一套全场景深度学习的AI框架，使能从算法研究到生产部署全流程的开源AI框架。",{"type":18,"tag":88,"props":314,"children":315},{},[],{"type":18,"tag":92,"props":317,"children":319},{"id":318},"_73-昇思mindspore大模型平台sowt分析",[320],{"type":24,"value":321},"7.3 昇思MindSpore大模型平台SOWT分析：",{"type":18,"tag":26,"props":323,"children":324},{},[325],{"type":24,"value":326},"经过一个多月的昇思MindSpore大模型平台的使用与实践，可以很好的解决模型复杂度高、参数量大，对算力有极大的需求，基于昇思MindSpore大模型平台的主流框架进行大模型的开发，为大模型创新开发提供了完备的硬核技术，以下是个人的一个SOWT分析：",{"type":18,"tag":26,"props":328,"children":329},{},[330],{"type":18,"tag":120,"props":331,"children":333},{"alt":7,"src":332},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/6a4/0ed/bc6/0421bb776e6a40edbc68b6389cfec54d.20240805061739.35607510037836951073952936136780:20240913012259:2400:62B164E4AEB4D65C4FBD29D613025E653D3829E228EF36284810C8FC227C2A4B.jpg",[],{"type":18,"tag":26,"props":335,"children":336},{},[337],{"type":24,"value":338},"昇思MindSpore充分发挥原生支持大模型训练的能力，降低大模型创新开发门槛，为了让AI大模型更好地普惠大众，昇思社区打造了首个基于国产AI算力和框架、服务全球开发者的一站式大模型平台，将大模型的能力开放给开发者。",{"type":18,"tag":88,"props":340,"children":341},{},[],{"type":18,"tag":92,"props":343,"children":345},{"id":344},"总结",[346],{"type":24,"value":347},"总结：",{"type":18,"tag":26,"props":349,"children":350},{},[351],{"type":24,"value":352},"在如今向人工智能发展的变迁时代，“人工智能+”意味着人工智能只有赋能千行百业才能真正落地转化为价值，这也是昇思大模型平台AI框架的价值的体现所在。",{"type":18,"tag":26,"props":354,"children":355},{},[356],{"type":24,"value":357},"同时，昇思技术体系在基于香橙派开发板，提供系统化的案例、教程和支持，降低了开发者上手和打造应用的门槛，也将加速原生大模型的推广速度。",{"type":18,"tag":26,"props":359,"children":360},{},[361],{"type":18,"tag":120,"props":362,"children":364},{"alt":7,"src":363},"https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/6a4/0ed/bc6/0421bb776e6a40edbc68b6389cfec54d.20240805074938.96306866443575610671380796751687:20240913012259:2400:45B2A487AF7E07D45C78D823805A570FB17D028AF7EB4B02A18C936B84F8FAEC.jpg",[],{"type":18,"tag":26,"props":366,"children":367},{},[368],{"type":24,"value":369},"昇思人工智能框架作为算法应用和硬件算力之间的桥梁，持续帮助用户解决部署挑战。相信在不久的将来，伴随昇思MindSpore开源社区的壮大，生态圈持续繁荣，千行百业智能化产品将不断的发展。",{"title":7,"searchDepth":371,"depth":371,"links":372},4,[373,375,376,383,384,385],{"id":94,"depth":374,"text":9},2,{"id":107,"depth":374,"text":110},{"id":131,"depth":374,"text":134,"children":377},[378,380,381,382],{"id":138,"depth":379,"text":141},3,{"id":154,"depth":379,"text":157},{"id":168,"depth":379,"text":171},{"id":182,"depth":379,"text":185},{"id":208,"depth":374,"text":211},{"id":318,"depth":374,"text":321},{"id":344,"depth":374,"text":347},"markdown","content:technology-blogs:zh:3388.md","content","technology-blogs/zh/3388.md","technology-blogs/zh/3388","md",1776506129206]