From 3b0d092f465f702e1172b36d4f3842e8556708bb Mon Sep 17 00:00:00 2001 From: windsonsea Date: Wed, 3 Jul 2024 11:25:48 +0800 Subject: [PATCH] update images in 0702-k8s-for-genai.md --- docs/zh/docs/blogs/0702-k8s-for-genai.md | 8 ++++---- docs/zh/docs/blogs/images/genai01.jpeg | Bin 0 -> 82622 bytes docs/zh/docs/blogs/images/genai01.png | Bin 19667 -> 0 bytes docs/zh/docs/blogs/images/genai02.jpeg | Bin 0 -> 78758 bytes docs/zh/docs/blogs/images/genai02.png | Bin 20709 -> 0 bytes docs/zh/docs/blogs/images/genai03.jpeg | Bin 0 -> 80869 bytes docs/zh/docs/blogs/images/genai03.png | Bin 20032 -> 0 bytes docs/zh/docs/blogs/images/genai04.jpeg | Bin 0 -> 86957 bytes docs/zh/docs/blogs/images/genai04.png | Bin 20119 -> 0 bytes 9 files changed, 4 insertions(+), 4 deletions(-) create mode 100644 docs/zh/docs/blogs/images/genai01.jpeg delete mode 100644 docs/zh/docs/blogs/images/genai01.png create mode 100644 docs/zh/docs/blogs/images/genai02.jpeg delete mode 100644 docs/zh/docs/blogs/images/genai02.png create mode 100644 docs/zh/docs/blogs/images/genai03.jpeg delete mode 100644 docs/zh/docs/blogs/images/genai03.png create mode 100644 docs/zh/docs/blogs/images/genai04.jpeg delete mode 100644 docs/zh/docs/blogs/images/genai04.png diff --git a/docs/zh/docs/blogs/0702-k8s-for-genai.md b/docs/zh/docs/blogs/0702-k8s-for-genai.md index 5bbdc2b..bb3ae8f 100644 --- a/docs/zh/docs/blogs/0702-k8s-for-genai.md +++ b/docs/zh/docs/blogs/0702-k8s-for-genai.md @@ -31,7 +31,7 @@ Kubernetes(K8s)不再只是一个运行工作负载(如 Web 应用和微 ### 模型预训练 -![模型预训练](./images/genai01.png) +![模型预训练](./images/genai01.jpeg) 在模型预训练阶段,K8s 凭借其无与伦比的可扩展性和韧性,为模型训练提供了坚实的基础。 K8s 可以根据资源需求自动扩缩的能力是其最大的优势之一,这正是 AI/ML 工作负载在面对海量算力需求时所急需的特性。 @@ -47,13 +47,13 @@ Jupyter Notebooks 和 VS Code 等工具对于 LLM 实验和提示工程来说是 ### 模型微调 -![模型微调](./images/genai02.png) +![模型微调](./images/genai02.jpeg) 虽然 K8s 拥有开发 LLM 所需的所有工具,但如今许多企业并非从头开始构建大语言模型,而是选用现有的一些模型,根据企业特定环境对模型进行定制和微调。在这种场景下,当您想对现有模型进行微调时,K8s 也是理想的选择,因为它非常具有动态性。与 Slurm 不同,K8s 可以并行处理多个工作负载,这使得训练过程更加高效。另一个优势体现在 K8s 为模型训练构建了丰富的工具生态。比如 Kubeflow(专为 Pytorch、Tensorflow 和 MPI 设计的 Operator)、KubeRay Operator 和 MLflow。 ### 模型部署 -![模型部署](./images/genai03.png) +![模型部署](./images/genai03.jpeg) 谈到 LLM 模型部署本身或模型推理服务,K8s 提供了一个简化的流程:您只需向数据科学家呈现一个端点。 网络栈简化了向外界发布模型的流程,轻松将模型推向消费侧。K8s 为模型部署提供了全面的工具集和丰富的生态, @@ -65,7 +65,7 @@ Jupyter Notebooks 和 VS Code 等工具对于 LLM 实验和提示工程来说是 ### 提示工程 -![提示工程](./images/genai04.png) +![提示工程](./images/genai04.jpeg) 优势不止于此。部署 LLM 模型后,K8s 在开发应用或让用户参与模型实验时能够提供增强的用户体验。 例如,使用 K8s 在 Gradio 或 Streamlit 等平台上托管应用几乎毫不费力, diff --git a/docs/zh/docs/blogs/images/genai01.jpeg b/docs/zh/docs/blogs/images/genai01.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..81bf5c8e450195235cf8fbae6910fd9b243c7799 GIT binary patch literal 82622 zcmeEuWmH^Umu3}i!66X5a19QH1u5L!-Q8V+yB6;57Tg2DJ;B`_0t5+=1j6vn*ZubU z^>ojCJ+pdd&8)fgzexri_HN1ONyG0Dvza!1EeF?B(r$ z?fRpW|Fs@oT0i#zu#f;{aPMG&AOH*&5EcvgJP08BLrpl?m+t}pd?5i55Ru{FQD9(E 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