Head First 软件开发(Software Development) 4-6.5 US, Design, Version, AutomaticallyBuild

最新推荐文章于 2020-11-25 20:39:50 发布
原创 最新推荐文章于 2020-11-25 20:39:50 发布 · 1.1k 阅读 · 0 ·
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文章标签：

#任务 #版本控制系统 #脚本 #serialization #refactoring

本文介绍了软件开发中的用户故事与任务管理，强调了任务细化和每日同步的重要性。讨论了设计原则，如单一责任原则和DRY原则，以及重构在软件设计中的作用。此外，还详述了版本控制的重要性和使用，包括分支、标记和合并。最后，探讨了自动化构建工具在确保软件质量和团队协作中的角色。

Section 4 User Story & Task

Task Estimates 实际工作比US更细致

US是给客户看的, 描述软件需求. 每个US收集了一些特定任务, 每个任务是能组合成US的小功能代码

>Task Title +Rough Description+ Estimation(Planning Poker)

在估计过程开始时, 把US分解为Task有助于计划和估计增加信心. 可信的任务估计是可靠的 (hours)

>发现比较大的任务遗漏, 调整开发循环, 推迟和调整其他的开发循环(避免这种情况, 在开始计划时就把US划分成Task来做准确Estimation)

>Burndown rate 使用Task Stick Notes便利签, 栏目 -US/Tasks -正在进行中 -已完成 -趋势图 -递延的US/Task  Daily Sync up

开始任务 在Task Notes上写上SWD/TD的名字 从dependency最高的US开始  

>完成一个US比两个US完成一半要好, 所以分配任务最好是按一个US接一个US >确保白板的准确性

如果有几项任务是相关或者有依赖的, 最好是按优先级或者同时进行, 相关任务可以分配给同一个SWD, 增加效率. -某项任务的完成可能会为另外一项任务的开展提供决策依据, 相互影响

Daily Sync up >跟踪任务 每个人简报 >更新工作量完成情况 更新趋势图 >更新任务 把状态改变的任务移动到相应区域 >昨天的情况和今天的任务 >提出Issue 准备解决方案 >5-15 minutes

1 哪些完成了 2 有Issue么 3 今天的计划, 对于具体Issue的讨论可以在Daily Sync之后

要点 -每天Daily Sync -会议15mins内, 了解进展, 更新白板 -提出Issue -早会

计划外任务  (客户需求) 使用红色卡片, 放入In Progress区, 

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项目管理——Head First 软件开发
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Doc 下载： https://download.csdn.net/download/qccz123456/10567658   时间：2018年3月22日星期四 作者：清村常争   一、伟大的软件开发——让客户满意          除开宏大的想法外，大多数项目都有两个焦点：要花多少钱？要花多长时间？          大霹雳式（Big Bang）开发法：集中时间一口气开发完成。通...
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​​​deepseekv3
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原汁原味的英文版阅读 介绍软件开发的生命周期和重要步骤，headfirst系列的优秀作品。
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《Head First软件开发(中文版)》内容包括：伟大的软件开发：让客户满意、收集需求：知道客户需要什么、项目规划：为成功而筹划、使用情节和任务：开始你实际的工作、足够好的设计：以良好的设计完成工作、构建你的软件代码：测试和连续集成：智者千虑必有一失、测试驱动开发：让代码负起责来、结束开发循环：涓涓细流归大海
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忙里偷闲系列，在项目推进的过程中看到了这本书。本来只是翻翻看，但第一章就吸引到我的注意力，这本书里讲的内容完全就是目前的实际场景。。。原来，真实世界就是如此。书的出版时间在2010年，快10年前的故事，今天依旧在重复发生着。。。 1 伟大的软件开发——让客户满意 从bigbang 到 Iteration 的进阶。 几个原则： 客户的需求总是在不停变：唯一不变的内容就是变化； 需求不可能一次整...
Table of Contents Introduction Model Summary Model Downloads Evaluation Results Chat Website & API Platform How to Run Locally License Citation Contact 1. Introduction We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. 2. Model Summary Architecture: Innovative Load Balancing Strategy and Training Objective On top of the efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which minimizes the performance degradation that arises from encouraging load balancing. We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance. It can also be used for speculative decoding for inference acceleration. Pre-Training: Towards Ultimate Training Efficiency We design an FP8 mixed precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model. Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap. This significantly enhances our training efficiency and reduces the training costs, enabling us to further scale up the model size without additional overhead. At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours. Post-Training: Knowledge Distillation from DeepSeek-R1 We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3. 3. Model Downloads Model #Total Params #Activated Params Context Length Download DeepSeek-V3-Base 671B 37B 128K 🤗 Hugging Face DeepSeek-V3 671B 37B 128K 🤗 Hugging Face Note The total size of DeepSeek-V3 models on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: How_to Run_Locally. For developers looking to dive deeper, we recommend exploring README_WEIGHTS.md for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active development within the community, and we welcome your contributions and feedback. 4. Evaluation Results Base Model Standard Benchmarks Benchmark (Metric) # Shots DeepSeek-V2 Qwen2.5 72B LLaMA3.1 405B DeepSeek-V3 Architecture - MoE Dense Dense MoE # Activated Params - 21B 72B 405B 37B # Total Params - 236B 72B 405B 671B English Pile-test (BPB) - 0.606 0.638 0.542 0.548 BBH (EM) 3-shot 78.8 79.8 82.9 87.5 MMLU (Acc.) 5-shot 78.4 85.0 84.4 87.1 MMLU-Redux (Acc.) 5-shot 75.6 83.2 81.3 86.2 MMLU-Pro (Acc.) 5-shot 51.4 58.3 52.8 64.4 DROP (F1) 3-shot 80.4 80.6 86.0 89.0 ARC-Easy (Acc.) 25-shot 97.6 98.4 98.4 98.9 ARC-Challenge (Acc.) 25-shot 92.2 94.5 95.3 95.3 HellaSwag (Acc.) 10-shot 87.1 84.8 89.2 88.9 PIQA (Acc.) 0-shot 83.9 82.6 85.9 84.7 WinoGrande (Acc.) 5-shot 86.3 82.3 85.2 84.9 RACE-Middle (Acc.) 5-shot 73.1 68.1 74.2 67.1 RACE-High (Acc.) 5-shot 52.6 50.3 56.8 51.3 TriviaQA (EM) 5-shot 80.0 71.9 82.7 82.9 NaturalQuestions (EM) 5-shot 38.6 33.2 41.5 40.0 AGIEval (Acc.) 0-shot 57.5 75.8 60.6 79.6 Code HumanEval (Pass@1) 0-shot 43.3 53.0 54.9 65.2 MBPP (Pass@1) 3-shot 65.0 72.6 68.4 75.4 LiveCodeBench-Base (Pass@1) 3-shot 11.6 12.9 15.5 19.4 CRUXEval-I (Acc.) 2-shot 52.5 59.1 58.5 67.3 CRUXEval-O (Acc.) 2-shot 49.8 59.9 59.9 69.8 Math GSM8K (EM) 8-shot 81.6 88.3 83.5 89.3 MATH (EM) 4-shot 43.4 54.4 49.0 61.6 MGSM (EM) 8-shot 63.6 76.2 69.9 79.8 CMath (EM) 3-shot 78.7 84.5 77.3 90.7 Chinese CLUEWSC (EM) 5-shot 82.0 82.5 83.0 82.7 C-Eval (Acc.) 5-shot 81.4 89.2 72.5 90.1 CMMLU (Acc.) 5-shot 84.0 89.5 73.7 88.8 CMRC (EM) 1-shot 77.4 75.8 76.0 76.3 C3 (Acc.) 0-shot 77.4 76.7 79.7 78.6 CCPM (Acc.) 0-shot 93.0 88.5 78.6 92.0 Multilingual MMMLU-non-English (Acc.) 5-shot 64.0 74.8 73.8 79.4 Note Best results are shown in bold. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 achieves the best performance on most benchmarks, especially on math and code tasks. For more evaluation details, please check our paper. Context Window Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 performs well across all context window lengths up to 128K. Chat Model Standard Benchmarks (Models larger than 67B) Benchmark (Metric) DeepSeek V2-0506 DeepSeek V2.5-0905 Qwen2.5 72B-Inst. Llama3.1 405B-Inst. Claude-3.5-Sonnet-1022 GPT-4o 0513 DeepSeek V3 Architecture MoE MoE Dense Dense - - MoE # Activated Params 21B 21B 72B 405B - - 37B # Total Params 236B 236B 72B 405B - - 671B English MMLU (EM) 78.2 80.6 85.3 88.6 88.3 87.2 88.5 MMLU-Redux (EM) 77.9 80.3 85.6 86.2 88.9 88.0 89.1 MMLU-Pro (EM) 58.5 66.2 71.6 73.3 78.0 72.6 75.9 DROP (3-shot F1) 83.0 87.8 76.7 88.7 88.3 83.7 91.6 IF-Eval (Prompt Strict) 57.7 80.6 84.1 86.0 86.5 84.3 86.1 GPQA-Diamond (Pass@1) 35.3 41.3 49.0 51.1 65.0 49.9 59.1 SimpleQA (Correct) 9.0 10.2 9.1 17.1 28.4 38.2 24.9 FRAMES (Acc.) 66.9 65.4 69.8 70.0 72.5 80.5 73.3 LongBench v2 (Acc.) 31.6 35.4 39.4 36.1 41.0 48.1 48.7 Code HumanEval-Mul (Pass@1) 69.3 77.4 77.3 77.2 81.7 80.5 82.6 LiveCodeBench (Pass@1-COT) 18.8 29.2 31.1 28.4 36.3 33.4 40.5 LiveCodeBench (Pass@1) 20.3 28.4 28.7 30.1 32.8 34.2 37.6 Codeforces (Percentile) 17.5 35.6 24.8 25.3 20.3 23.6 51.6 SWE Verified (Resolved) - 22.6 23.8 24.5 50.8 38.8 42.0 Aider-Edit (Acc.) 60.3 71.6 65.4 63.9 84.2 72.9 79.7 Aider-Polyglot (Acc.) - 18.2 7.6 5.8 45.3 16.0 49.6 Math AIME 2024 (Pass@1) 4.6 16.7 23.3 23.3 16.0 9.3 39.2 MATH-500 (EM) 56.3 74.7 80.0 73.8 78.3 74.6 90.2 CNMO 2024 (Pass@1) 2.8 10.8 15.9 6.8 13.1 10.8 43.2 Chinese CLUEWSC (EM) 89.9 90.4 91.4 84.7 85.4 87.9 90.9 C-Eval (EM) 78.6 79.5 86.1 61.5 76.7 76.0 86.5 C-SimpleQA (Correct) 48.5 54.1 48.4 50.4 51.3 59.3 64.8 Note All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times using varying temperature settings to derive robust final results. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance against frontier closed-source models. Open Ended Generation Evaluation Model Arena-Hard AlpacaEval 2.0 DeepSeek-V2.5-0905 76.2 50.5 Qwen2.5-72B-Instruct 81.2 49.1 LLaMA-3.1 405B 69.3 40.5 GPT-4o-0513 80.4 51.1 Claude-Sonnet-3.5-1022 85.2 52.0 DeepSeek-V3 85.5 70.0 Note English open-ended conversation evaluations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric. 5. Chat Website & API Platform You can chat with DeepSeek-V3 on DeepSeek's official website: chat.deepseek.com We also provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com 6. How to Run Locally DeepSeek-V3 can be deployed locally using the following hardware and open-source community software: DeepSeek-Infer Demo: We provide a simple and lightweight demo for FP8 and BF16 inference. SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon. LMDeploy: Enables efficient FP8 and BF16 inference for local and cloud deployment. TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon. vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism. LightLLM: Supports efficient single-node or multi-node deployment for FP8 and BF16. AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes. Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend devices in both INT8 and BF16. Since FP8 training is natively adopted in our framework, we only provide FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to perform the transformation. Here is an example of converting FP8 weights to BF16: cd inference python fp8_cast_bf16.py --input-fp8-hf-path /path/to/fp8_weights --output-bf16-hf-path /path/to/bf16_weights Note Hugging Face's Transformers has not been directly supported yet. 6.1 Inference with DeepSeek-Infer Demo (example only) System Requirements Note Linux with Python 3.10 only. Mac and Windows are not supported. Dependencies: torch==2.4.1 triton==3.0.0 transformers==4.46.3 safetensors==0.4.5 Model Weights & Demo Code Preparation First, clone our DeepSeek-V3 GitHub repository: git clone https://github.com/deepseek-ai/DeepSeek-V3.git Navigate to the inference folder and install dependencies listed in requirements.txt. Easiest way is to use a package manager like conda or uv to create a new virtual environment and install the dependencies. cd DeepSeek-V3/inference pip install -r requirements.txt Download the model weights from Hugging Face, and put them into /path/to/DeepSeek-V3 folder. Model Weights Conversion Convert Hugging Face model weights to a specific format: python convert.py --hf-ckpt-path /path/to/DeepSeek-V3 --save-path /path/to/DeepSeek-V3-Demo --n-experts 256 --model-parallel 16 Run Then you can chat with DeepSeek-V3: torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --interactive --temperature 0.7 --max-new-tokens 200 Or batch inference on a given file: torchrun --nnodes 2 --nproc-per-node 8 --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path /path/to/DeepSeek-V3-Demo --config configs/config_671B.json --input-file $FILE 6.2 Inference with SGLang (recommended) SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering state-of-the-art latency and throughput performance among open-source frameworks. Notably, SGLang v0.4.1 fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly versatile and robust solution. SGLang also supports multi-node tensor parallelism, enabling you to run this model on multiple network-connected machines. Multi-Token Prediction (MTP) is in development, and progress can be tracked in the optimization plan. Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3 6.3 Inference with LMDeploy (recommended) LMDeploy, a flexible and high-performance inference and serving framework tailored for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows. For comprehensive step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy#2960 6.4 Inference with TRT-LLM (recommended) TensorRT-LLM now supports the DeepSeek-V3 model, offering precision options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released soon. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/deepseek_v3. 6.5 Inference with vLLM (recommended) vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard techniques, vLLM offers pipeline parallelism allowing you to run this model on multiple machines connected by networks. For detailed guidance, please refer to the vLLM instructions. Please feel free to follow the enhancement plan as well. 6.6 Inference with LightLLM (recommended) LightLLM v1.0.1 supports single-machine and multi-machine tensor parallel deployment for DeepSeek-R1 (FP8/BF16) and provides mixed-precision deployment, with more quantization modes continuously integrated. For more details, please refer to LightLLM instructions. Additionally, LightLLM offers PD-disaggregation deployment for DeepSeek-V2, and the implementation of PD-disaggregation for DeepSeek-V3 is in development. 6.7 Recommended Inference Functionality with AMD GPUs In collaboration with the AMD team, we have achieved Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please refer to the SGLang instructions. 6.8 Recommended Inference Functionality with Huawei Ascend NPUs The MindIE framework from the Huawei Ascend community has successfully adapted the BF16 version of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the instructions here. 7. License This code repository is licensed under the MIT License. The use of DeepSeek-V3 Base/Chat models is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use. 8. Citation @misc{deepseekai2024deepseekv3technicalreport, title={DeepSeek-V3 Technical Report}, author={DeepSeek-AI}, year={2024}, eprint={2412.19437}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2412.19437}, } 9. Contact If you have any questions, please raise an issue or contact us at service@deepseek.com.
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大脑学习规则  大脑总是渴求新奇的事物, 搜索和等待不平常的事情出现. 1)理解越多, 需要记忆的越少 2)多做练习和笔记 3)阅读问题,解答问题 4)睡前阅读,消化处理 5)多喝水补充水分 6)讨论和谈论,解释给他人 7)注意大脑运转效率 8)感受事物,融入情景 9)Coding exercise  Codes: http://www.headfirstlabs.com/boo
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Section 10 Next Iteration 拥有可构建的软件片段, 完成Iteration, 通过所有测试(单元 系统 Black White) 按客户需求完成设计的代码 下一轮的计划 >关键 新加的US, 客户会增加US, 改变优先级 >时间效率值的更新 >工作量完成状况, 准备新白板 >下一个US >Defect >把上衣个白板上的内容归档 拍照orDoc 了解
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