使用 Azure 机器学习微调小型语言模型 (SLM) Phi-3

news2024/9/22 21:37:41

目录

概述

动手实验室

概述

 

小型语言模型 (SLM) 的动机

  • 效率：SLM 的计算效率更高，需要的内存和存储空间更少，而且由于需要处理的参数更少，因此运行速度更快。
  • 成本：培训和部署 SLM 的成本较低，使其可供更广泛的企业使用，并适合边缘计算中的应用。
  • 可定制性：SLM 更适合专业应用，与大型模型相比，SLM 可以更轻松地针对特定任务进行微调· 未被充分开发的潜力：虽然大型模型已显示出明显的优势，但使用大型数据集训练的小型模型的潜力尚未得到充分开发。SLM 旨在展示小型模型在使用足够的数据进行训练时可以实现高性能。
  • 推理效率：较小的模型在推理过程中通常效率更高，这是在资源受限的实际应用中部署模型时的一个关键方面。这种效率包括更快的响应时间并降低计算和能源成本。
  • 研究可访问性：由于是开源的，而且体积较小，SLM 更容易被更多没有资源使用较大模型的研究人员使用。它为语言模型研究的实验和创新提供了一个平台，无需大量计算资源。
  • 架构和优化方面的进步：SLM 采用了各种架构和速度优化来提高计算效率。这些增强功能使 SLM 能够以更快的速度和更少的内存进行训练，从而可以在常见的 GPU 上进行训练。
  • 开源贡献：SLM 的作者已公开模型检查点和代码，为开源社区做出贡献并支持其他人的进一步进步和应用。
  • 终端用户应用程序：凭借其出色的性能和紧凑的尺寸，SLM 适用于终端用户应用程序，甚至可以在移动设备上使用，为广泛的应用程序提供轻量级平台。
  • 训练数据和流程：SLM 训练过程旨在有效且可重复，使用自然语言数据和代码数据的混合，旨在使预训练变得可访问且透明。

博客文章缩略图 1，标题为“使用 Azure 机器学习微调小型语言模型 (SLM) Phi-3”

Phi-3（微软研究院）

Phi-3 是微软研究院创建的 SLM，是 phi-2 的后继者。phi-3 在多个公开基准测试中表现良好（例如 phi-3 在 MMLU 中达到 69%），并支持长达 128k 的上下文。3.8B phi-3 mini 模型于 2024 年 4 月底首次发布，phi-3-small、phi-3-medium 和 phi-3-vision 于 5 月在 Microsoft Build 大会上亮相。

  • Phi-3-mini 是一个 3.8B 参数语言模型（128K 和 4K）。
  • Phi-3-small 是一个 7B 参数语言模型（128K 和 8K）。
  • Phi-3-medium 是一个14B 参数语言模型（128K 和 4K）。
  • Phi-3 vision 是一个具有语言和视觉功能的 4.2B 参数多模式模型。

在此示例中，我们将学习如何使用 QLoRA 对 phi-3-mini-4k-instruct 进行微调：使用 Flash Attention 对量化 LLM 进行高效微调。QLoRA 是一种高效的微调技术，可将预训练语言模型量化为 4 位，并附加经过微调的小型“低秩适配器”。这使得在单个 GPU 上微调多达 650 亿个参数的模型成为可能；尽管效率不高，但 QLoRA 的性能可与全精度微调相媲美，并在语言任务上取得了最先进的结果。

 

动手实验室

 

[第一步：准备]

让我们准备数据集。在本例中，我们将下载 ultrachat 数据集。

 

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">from</span> datasets <span style="color:#00e0e0">import</span> load_dataset
<span style="color:#00e0e0">from</span> random <span style="color:#00e0e0">import</span> randrange

<span style="color:#d4d0ab"># Load dataset from the hub</span>
dataset <span style="color:#00e0e0">=</span> load_dataset<span style="color:#fefefe">(</span><span style="color:#abe338">"HuggingFaceH4/ultrachat_200k"</span><span style="color:#fefefe">,</span> split<span style="color:#00e0e0">=</span><span style="color:#abe338">'train_sft[:2%]'</span><span style="color:#fefefe">)</span>

<span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span><span style="color:#abe338">f"dataset size: </span><span style="color:#fefefe">{</span><span style="color:#abe338">len</span><span style="color:#fefefe">(</span>dataset<span style="color:#fefefe">)</span><span style="color:#fefefe">}</span><span style="color:#abe338">"</span><span style="color:#fefefe">)</span>
<span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span>dataset<span style="color:#fefefe">[</span>randrange<span style="color:#fefefe">(</span><span style="color:#abe338">len</span><span style="color:#fefefe">(</span>dataset<span style="color:#fefefe">)</span><span style="color:#fefefe">)</span><span style="color:#fefefe">]</span><span style="color:#fefefe">)</span></code></span></span>

 

 

让我们使用较短版本的数据集来创建训练和测试示例。为了指导调整我们的模型，我们需要将结构化示例转换为通过指令描述的任务集合。我们定义一个，formatting_function它接受一个样本并返回一个带有格式指令的字符串。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python">dataset <span style="color:#00e0e0">=</span> dataset<span style="color:#fefefe">.</span>train_test_split<span style="color:#fefefe">(</span>test_size<span style="color:#00e0e0">=</span><span style="color:#00e0e0">0.2</span><span style="color:#fefefe">)</span>
train_dataset <span style="color:#00e0e0">=</span> dataset<span style="color:#fefefe">[</span><span style="color:#abe338">'train'</span><span style="color:#fefefe">]</span>
train_dataset<span style="color:#fefefe">.</span>to_json<span style="color:#fefefe">(</span><span style="color:#abe338">f"data/train.jsonl"</span><span style="color:#fefefe">)</span>
test_dataset <span style="color:#00e0e0">=</span> dataset<span style="color:#fefefe">[</span><span style="color:#abe338">'test'</span><span style="color:#fefefe">]</span>
test_dataset<span style="color:#fefefe">.</span>to_json<span style="color:#fefefe">(</span><span style="color:#abe338">f"data/eval.jsonl"</span><span style="color:#fefefe">)</span></code></span></span>

 

 

让我们以 json 格式保存这个训练和测试数据集。现在让我们加载 Azure ML SDK。这将帮助我们创建必要的组件。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#d4d0ab"># import required libraries</span>
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>identity <span style="color:#00e0e0">import</span> DefaultAzureCredential<span style="color:#fefefe">,</span> InteractiveBrowserCredential
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml <span style="color:#00e0e0">import</span> MLClient<span style="color:#fefefe">,</span> Input
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>dsl <span style="color:#00e0e0">import</span> pipeline
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml <span style="color:#00e0e0">import</span> load_component
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml <span style="color:#00e0e0">import</span> command
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>entities <span style="color:#00e0e0">import</span> Data
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml <span style="color:#00e0e0">import</span> Input
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml <span style="color:#00e0e0">import</span> Output
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>constants <span style="color:#00e0e0">import</span> AssetTypes</code></span></span>

 

 

现在让我们创建工作区客户端。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python">credential <span style="color:#00e0e0">=</span> DefaultAzureCredential<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
workspace_ml_client <span style="color:#00e0e0">=</span> <span style="color:#00e0e0">None</span>
<span style="color:#00e0e0">try</span><span style="color:#fefefe">:</span>
    workspace_ml_client <span style="color:#00e0e0">=</span> MLClient<span style="color:#fefefe">.</span>from_config<span style="color:#fefefe">(</span>credential<span style="color:#fefefe">)</span>
<span style="color:#00e0e0">except</span> Exception <span style="color:#00e0e0">as</span> ex<span style="color:#fefefe">:</span>
    <span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span>ex<span style="color:#fefefe">)</span>
    subscription_id<span style="color:#00e0e0">=</span> <span style="color:#abe338">"Enter your subscription_id"</span>
    resource_group <span style="color:#00e0e0">=</span> <span style="color:#abe338">"Enter your resource_group"</span>
    workspace<span style="color:#00e0e0">=</span> <span style="color:#abe338">"Enter your workspace name"</span>
    workspace_ml_client <span style="color:#00e0e0">=</span> MLClient<span style="color:#fefefe">(</span>credential<span style="color:#fefefe">,</span> subscription_id<span style="color:#fefefe">,</span> resource_group<span style="color:#fefefe">,</span> workspace<span style="color:#fefefe">)</span></code></span></span>

 

 

这里我们创建一个自定义的训练环境。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>entities <span style="color:#00e0e0">import</span> Environment<span style="color:#fefefe">,</span> BuildContext
env_docker_image <span style="color:#00e0e0">=</span> Environment<span style="color:#fefefe">(</span>
    image<span style="color:#00e0e0">=</span><span style="color:#abe338">"mcr.microsoft.com/azureml/curated/acft-hf-nlp-gpu:latest"</span><span style="color:#fefefe">,</span>
    conda_file<span style="color:#00e0e0">=</span><span style="color:#abe338">"environment/conda.yml"</span><span style="color:#fefefe">,</span>
    name<span style="color:#00e0e0">=</span><span style="color:#abe338">"llm-training"</span><span style="color:#fefefe">,</span>
    description<span style="color:#00e0e0">=</span><span style="color:#abe338">"Environment created for llm training."</span><span style="color:#fefefe">,</span>
<span style="color:#fefefe">)</span>
workspace_ml_client<span style="color:#fefefe">.</span>environments<span style="color:#fefefe">.</span>create_or_update<span style="color:#fefefe">(</span>env_docker_image<span style="color:#fefefe">)</span></code></span></span>

 

 

让我们看看conda.yaml

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-yaml"><span style="color:#ffd700">name</span><span style="color:#fefefe">:</span> model<span style="color:#fefefe">-</span>env
<span style="color:#ffd700">channels</span><span style="color:#fefefe">:</span>
  <span style="color:#fefefe">-</span> conda<span style="color:#fefefe">-</span>forge
<span style="color:#ffd700">dependencies</span><span style="color:#fefefe">:</span>
  <span style="color:#fefefe">-</span> python=3.8
  <span style="color:#fefefe">-</span> pip=24.0
  <span style="color:#fefefe">-</span> <span style="color:#ffd700">pip</span><span style="color:#fefefe">:</span>
    <span style="color:#fefefe">-</span> bitsandbytes==0.43.1
    <span style="color:#fefefe">-</span> transformers~=4.41
    <span style="color:#fefefe">-</span> peft~=0.11
    <span style="color:#fefefe">-</span> accelerate~=0.30
    <span style="color:#fefefe">-</span> trl==0.8.6
    <span style="color:#fefefe">-</span> einops==0.8.0
    <span style="color:#fefefe">-</span> datasets==2.19.1
    <span style="color:#fefefe">-</span> wandb==0.17.0
    <span style="color:#fefefe">-</span> mlflow==2.13.0
    <span style="color:#fefefe">-</span> azureml<span style="color:#fefefe">-</span>mlflow==1.56.0 
    <span style="color:#fefefe">-</span> torchvision==0.18.0    </code></span></span>

 

 

[第二步：训练]

让我们看一下训练脚本。我们将使用 Tim Dettmers 等人在论文“ QLoRA：用于语言生成的量化感知低秩适配器调整”中最近介绍的方法。QLoRA 是一种新技术，可以在不牺牲性能的情况下减少大型语言模型在微调过程中的内存占用。QLoRA 工作原理的 TL;DR; 如下：

  • 将预训练模型量化为 4 位并冻结。
  • 附加小型、可训练的适配器层。（LoRA）
  • 仅对适配器层进行微调，同时将冻结的量化模型用于上下文。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">%</span><span style="color:#00e0e0">%</span>writefile src<span style="color:#00e0e0">/</span>train<span style="color:#fefefe">.</span>py

<span style="color:#00e0e0">import</span> os
<span style="color:#d4d0ab">#import mlflow</span>
<span style="color:#00e0e0">import</span> argparse
<span style="color:#00e0e0">import</span> sys
<span style="color:#00e0e0">import</span> logging

<span style="color:#00e0e0">import</span> datasets
<span style="color:#00e0e0">from</span> datasets <span style="color:#00e0e0">import</span> load_dataset
<span style="color:#00e0e0">from</span> peft <span style="color:#00e0e0">import</span> LoraConfig
<span style="color:#00e0e0">import</span> torch
<span style="color:#00e0e0">import</span> transformers
<span style="color:#00e0e0">from</span> trl <span style="color:#00e0e0">import</span> SFTTrainer
<span style="color:#00e0e0">from</span> transformers <span style="color:#00e0e0">import</span> AutoModelForCausalLM<span style="color:#fefefe">,</span> AutoTokenizer<span style="color:#fefefe">,</span> TrainingArguments<span style="color:#fefefe">,</span> BitsAndBytesConfig
<span style="color:#00e0e0">from</span> datasets <span style="color:#00e0e0">import</span> load_dataset

logger <span style="color:#00e0e0">=</span> logging<span style="color:#fefefe">.</span>getLogger<span style="color:#fefefe">(</span>__name__<span style="color:#fefefe">)</span>


<span style="color:#d4d0ab">###################</span>
<span style="color:#d4d0ab"># Hyper-parameters</span>
<span style="color:#d4d0ab">###################</span>
training_config <span style="color:#00e0e0">=</span> <span style="color:#fefefe">{</span>
    <span style="color:#abe338">"bf16"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">True</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"do_eval"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">False</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"learning_rate"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">5.0e-06</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"log_level"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"info"</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"logging_steps"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">20</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"logging_strategy"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"steps"</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"lr_scheduler_type"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"cosine"</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"num_train_epochs"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"max_steps"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">-</span><span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"output_dir"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"./checkpoint_dir"</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"overwrite_output_dir"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">True</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"per_device_eval_batch_size"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">4</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"per_device_train_batch_size"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">4</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"remove_unused_columns"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">True</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"save_steps"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">100</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"save_total_limit"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"seed"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">0</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"gradient_checkpointing"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">True</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"gradient_checkpointing_kwargs"</span><span style="color:#fefefe">:</span><span style="color:#fefefe">{</span><span style="color:#abe338">"use_reentrant"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">False</span><span style="color:#fefefe">}</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"gradient_accumulation_steps"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"warmup_ratio"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">0.2</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">}</span>

peft_config <span style="color:#00e0e0">=</span> <span style="color:#fefefe">{</span>
    <span style="color:#abe338">"r"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">16</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"lora_alpha"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">32</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"lora_dropout"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">0.05</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"bias"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"none"</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"task_type"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"CAUSAL_LM"</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"target_modules"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"all-linear"</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">"modules_to_save"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">None</span><span style="color:#fefefe">,</span>
<span style="color:#fefefe">}</span>
train_conf <span style="color:#00e0e0">=</span> TrainingArguments<span style="color:#fefefe">(</span><span style="color:#00e0e0">**</span>training_config<span style="color:#fefefe">)</span>
peft_conf <span style="color:#00e0e0">=</span> LoraConfig<span style="color:#fefefe">(</span><span style="color:#00e0e0">**</span>peft_config<span style="color:#fefefe">)</span>

<span style="color:#d4d0ab">###############</span>
<span style="color:#d4d0ab"># Setup logging</span>
<span style="color:#d4d0ab">###############</span>
logging<span style="color:#fefefe">.</span>basicConfig<span style="color:#fefefe">(</span>
    <span style="color:#abe338">format</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"%(asctime)s - %(levelname)s - %(name)s - %(message)s"</span><span style="color:#fefefe">,</span>
    datefmt<span style="color:#00e0e0">=</span><span style="color:#abe338">"%Y-%m-%d %H:%M:%S"</span><span style="color:#fefefe">,</span>
    handlers<span style="color:#00e0e0">=</span><span style="color:#fefefe">[</span>logging<span style="color:#fefefe">.</span>StreamHandler<span style="color:#fefefe">(</span>sys<span style="color:#fefefe">.</span>stdout<span style="color:#fefefe">)</span><span style="color:#fefefe">]</span><span style="color:#fefefe">,</span>
<span style="color:#fefefe">)</span>
log_level <span style="color:#00e0e0">=</span> train_conf<span style="color:#fefefe">.</span>get_process_log_level<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
logger<span style="color:#fefefe">.</span>setLevel<span style="color:#fefefe">(</span>log_level<span style="color:#fefefe">)</span>
datasets<span style="color:#fefefe">.</span>utils<span style="color:#fefefe">.</span>logging<span style="color:#fefefe">.</span>set_verbosity<span style="color:#fefefe">(</span>log_level<span style="color:#fefefe">)</span>
transformers<span style="color:#fefefe">.</span>utils<span style="color:#fefefe">.</span>logging<span style="color:#fefefe">.</span>set_verbosity<span style="color:#fefefe">(</span>log_level<span style="color:#fefefe">)</span>
transformers<span style="color:#fefefe">.</span>utils<span style="color:#fefefe">.</span>logging<span style="color:#fefefe">.</span>enable_default_handler<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
transformers<span style="color:#fefefe">.</span>utils<span style="color:#fefefe">.</span>logging<span style="color:#fefefe">.</span>enable_explicit_format<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>

<span style="color:#d4d0ab"># Log on each process a small summary</span>
logger<span style="color:#fefefe">.</span>warning<span style="color:#fefefe">(</span>
    <span style="color:#abe338">f"Process rank: </span><span style="color:#fefefe">{</span>train_conf<span style="color:#fefefe">.</span>local_rank<span style="color:#fefefe">}</span><span style="color:#abe338">, device: </span><span style="color:#fefefe">{</span>train_conf<span style="color:#fefefe">.</span>device<span style="color:#fefefe">}</span><span style="color:#abe338">, n_gpu: </span><span style="color:#fefefe">{</span>train_conf<span style="color:#fefefe">.</span>n_gpu<span style="color:#fefefe">}</span><span style="color:#abe338">"</span>
    <span style="color:#00e0e0">+</span> <span style="color:#abe338">f" distributed training: </span><span style="color:#fefefe">{</span><span style="color:#abe338">bool</span><span style="color:#fefefe">(</span>train_conf<span style="color:#fefefe">.</span>local_rank <span style="color:#00e0e0">!=</span> <span style="color:#00e0e0">-</span><span style="color:#00e0e0">1</span><span style="color:#fefefe">)</span><span style="color:#fefefe">}</span><span style="color:#abe338">, 16-bits training: </span><span style="color:#fefefe">{</span>train_conf<span style="color:#fefefe">.</span>fp16<span style="color:#fefefe">}</span><span style="color:#abe338">"</span>
<span style="color:#fefefe">)</span>
logger<span style="color:#fefefe">.</span>info<span style="color:#fefefe">(</span><span style="color:#abe338">f"Training/evaluation parameters </span><span style="color:#fefefe">{</span>train_conf<span style="color:#fefefe">}</span><span style="color:#abe338">"</span><span style="color:#fefefe">)</span>
logger<span style="color:#fefefe">.</span>info<span style="color:#fefefe">(</span><span style="color:#abe338">f"PEFT parameters </span><span style="color:#fefefe">{</span>peft_conf<span style="color:#fefefe">}</span><span style="color:#abe338">"</span><span style="color:#fefefe">)</span>

<span style="color:#d4d0ab">################</span>
<span style="color:#d4d0ab"># Modle Loading</span>
<span style="color:#d4d0ab">################</span>
checkpoint_path <span style="color:#00e0e0">=</span> <span style="color:#abe338">"microsoft/Phi-3-mini-4k-instruct"</span>
<span style="color:#d4d0ab"># checkpoint_path = "microsoft/Phi-3-mini-128k-instruct"</span>
model_kwargs <span style="color:#00e0e0">=</span> <span style="color:#abe338">dict</span><span style="color:#fefefe">(</span>
    use_cache<span style="color:#00e0e0">=</span><span style="color:#00e0e0">False</span><span style="color:#fefefe">,</span>
    trust_remote_code<span style="color:#00e0e0">=</span><span style="color:#00e0e0">True</span><span style="color:#fefefe">,</span>
    attn_implementation<span style="color:#00e0e0">=</span><span style="color:#abe338">"flash_attention_2"</span><span style="color:#fefefe">,</span>  <span style="color:#d4d0ab"># loading the model with flash-attenstion support</span>
    torch_dtype<span style="color:#00e0e0">=</span>torch<span style="color:#fefefe">.</span>bfloat16<span style="color:#fefefe">,</span>
    device_map<span style="color:#00e0e0">=</span><span style="color:#00e0e0">None</span>
<span style="color:#fefefe">)</span>
model <span style="color:#00e0e0">=</span> AutoModelForCausalLM<span style="color:#fefefe">.</span>from_pretrained<span style="color:#fefefe">(</span>checkpoint_path<span style="color:#fefefe">,</span> <span style="color:#00e0e0">**</span>model_kwargs<span style="color:#fefefe">)</span>
tokenizer <span style="color:#00e0e0">=</span> AutoTokenizer<span style="color:#fefefe">.</span>from_pretrained<span style="color:#fefefe">(</span>checkpoint_path<span style="color:#fefefe">)</span>
tokenizer<span style="color:#fefefe">.</span>model_max_length <span style="color:#00e0e0">=</span> <span style="color:#00e0e0">2048</span>
tokenizer<span style="color:#fefefe">.</span>pad_token <span style="color:#00e0e0">=</span> tokenizer<span style="color:#fefefe">.</span>unk_token  <span style="color:#d4d0ab"># use unk rather than eos token to prevent endless generation</span>
tokenizer<span style="color:#fefefe">.</span>pad_token_id <span style="color:#00e0e0">=</span> tokenizer<span style="color:#fefefe">.</span>convert_tokens_to_ids<span style="color:#fefefe">(</span>tokenizer<span style="color:#fefefe">.</span>pad_token<span style="color:#fefefe">)</span>
tokenizer<span style="color:#fefefe">.</span>padding_side <span style="color:#00e0e0">=</span> <span style="color:#abe338">'right'</span>

<span style="color:#d4d0ab">##################</span>
<span style="color:#d4d0ab"># Data Processing</span>
<span style="color:#d4d0ab">##################</span>
<span style="color:#00e0e0">def</span> <span style="color:#ffd700">apply_chat_template</span><span style="color:#fefefe">(</span>
    example<span style="color:#fefefe">,</span>
    tokenizer<span style="color:#fefefe">,</span>
<span style="color:#fefefe">)</span><span style="color:#fefefe">:</span>
    messages <span style="color:#00e0e0">=</span> example<span style="color:#fefefe">[</span><span style="color:#abe338">"messages"</span><span style="color:#fefefe">]</span>
    <span style="color:#d4d0ab"># Add an empty system message if there is none</span>
    <span style="color:#00e0e0">if</span> messages<span style="color:#fefefe">[</span><span style="color:#00e0e0">0</span><span style="color:#fefefe">]</span><span style="color:#fefefe">[</span><span style="color:#abe338">"role"</span><span style="color:#fefefe">]</span> <span style="color:#00e0e0">!=</span> <span style="color:#abe338">"system"</span><span style="color:#fefefe">:</span>
        messages<span style="color:#fefefe">.</span>insert<span style="color:#fefefe">(</span><span style="color:#00e0e0">0</span><span style="color:#fefefe">,</span> <span style="color:#fefefe">{</span><span style="color:#abe338">"role"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"system"</span><span style="color:#fefefe">,</span> <span style="color:#abe338">"content"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">""</span><span style="color:#fefefe">}</span><span style="color:#fefefe">)</span>
    example<span style="color:#fefefe">[</span><span style="color:#abe338">"text"</span><span style="color:#fefefe">]</span> <span style="color:#00e0e0">=</span> tokenizer<span style="color:#fefefe">.</span>apply_chat_template<span style="color:#fefefe">(</span>
        messages<span style="color:#fefefe">,</span> tokenize<span style="color:#00e0e0">=</span><span style="color:#00e0e0">False</span><span style="color:#fefefe">,</span> add_generation_prompt<span style="color:#00e0e0">=</span><span style="color:#00e0e0">False</span><span style="color:#fefefe">)</span>
    <span style="color:#00e0e0">return</span> example



<span style="color:#00e0e0">def</span> <span style="color:#ffd700">main</span><span style="color:#fefefe">(</span>args<span style="color:#fefefe">)</span><span style="color:#fefefe">:</span>
    train_dataset <span style="color:#00e0e0">=</span> load_dataset<span style="color:#fefefe">(</span><span style="color:#abe338">'json'</span><span style="color:#fefefe">,</span> data_files<span style="color:#00e0e0">=</span>args<span style="color:#fefefe">.</span>train_file<span style="color:#fefefe">,</span> split<span style="color:#00e0e0">=</span><span style="color:#abe338">'train'</span><span style="color:#fefefe">)</span>
    test_dataset <span style="color:#00e0e0">=</span> load_dataset<span style="color:#fefefe">(</span><span style="color:#abe338">'json'</span><span style="color:#fefefe">,</span> data_files<span style="color:#00e0e0">=</span>args<span style="color:#fefefe">.</span>eval_file<span style="color:#fefefe">,</span> split<span style="color:#00e0e0">=</span><span style="color:#abe338">'train'</span><span style="color:#fefefe">)</span>
    column_names <span style="color:#00e0e0">=</span> <span style="color:#abe338">list</span><span style="color:#fefefe">(</span>train_dataset<span style="color:#fefefe">.</span>features<span style="color:#fefefe">)</span>

    processed_train_dataset <span style="color:#00e0e0">=</span> train_dataset<span style="color:#fefefe">.</span><span style="color:#abe338">map</span><span style="color:#fefefe">(</span>
        apply_chat_template<span style="color:#fefefe">,</span>
        fn_kwargs<span style="color:#00e0e0">=</span><span style="color:#fefefe">{</span><span style="color:#abe338">"tokenizer"</span><span style="color:#fefefe">:</span> tokenizer<span style="color:#fefefe">}</span><span style="color:#fefefe">,</span>
        num_proc<span style="color:#00e0e0">=</span><span style="color:#00e0e0">10</span><span style="color:#fefefe">,</span>
        remove_columns<span style="color:#00e0e0">=</span>column_names<span style="color:#fefefe">,</span>
        desc<span style="color:#00e0e0">=</span><span style="color:#abe338">"Applying chat template to train_sft"</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">)</span>

    processed_test_dataset <span style="color:#00e0e0">=</span> test_dataset<span style="color:#fefefe">.</span><span style="color:#abe338">map</span><span style="color:#fefefe">(</span>
        apply_chat_template<span style="color:#fefefe">,</span>
        fn_kwargs<span style="color:#00e0e0">=</span><span style="color:#fefefe">{</span><span style="color:#abe338">"tokenizer"</span><span style="color:#fefefe">:</span> tokenizer<span style="color:#fefefe">}</span><span style="color:#fefefe">,</span>
        num_proc<span style="color:#00e0e0">=</span><span style="color:#00e0e0">10</span><span style="color:#fefefe">,</span>
        remove_columns<span style="color:#00e0e0">=</span>column_names<span style="color:#fefefe">,</span>
        desc<span style="color:#00e0e0">=</span><span style="color:#abe338">"Applying chat template to test_sft"</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">)</span>

    <span style="color:#d4d0ab">###########</span>
    <span style="color:#d4d0ab"># Training</span>
    <span style="color:#d4d0ab">###########</span>
    trainer <span style="color:#00e0e0">=</span> SFTTrainer<span style="color:#fefefe">(</span>
        model<span style="color:#00e0e0">=</span>model<span style="color:#fefefe">,</span>
        args<span style="color:#00e0e0">=</span>train_conf<span style="color:#fefefe">,</span>
        peft_config<span style="color:#00e0e0">=</span>peft_conf<span style="color:#fefefe">,</span>
        train_dataset<span style="color:#00e0e0">=</span>processed_train_dataset<span style="color:#fefefe">,</span>
        eval_dataset<span style="color:#00e0e0">=</span>processed_test_dataset<span style="color:#fefefe">,</span>
        max_seq_length<span style="color:#00e0e0">=</span><span style="color:#00e0e0">2048</span><span style="color:#fefefe">,</span>
        dataset_text_field<span style="color:#00e0e0">=</span><span style="color:#abe338">"text"</span><span style="color:#fefefe">,</span>
        tokenizer<span style="color:#00e0e0">=</span>tokenizer<span style="color:#fefefe">,</span>
        packing<span style="color:#00e0e0">=</span><span style="color:#00e0e0">True</span>
    <span style="color:#fefefe">)</span>
    train_result <span style="color:#00e0e0">=</span> trainer<span style="color:#fefefe">.</span>train<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
    metrics <span style="color:#00e0e0">=</span> train_result<span style="color:#fefefe">.</span>metrics
    trainer<span style="color:#fefefe">.</span>log_metrics<span style="color:#fefefe">(</span><span style="color:#abe338">"train"</span><span style="color:#fefefe">,</span> metrics<span style="color:#fefefe">)</span>
    trainer<span style="color:#fefefe">.</span>save_metrics<span style="color:#fefefe">(</span><span style="color:#abe338">"train"</span><span style="color:#fefefe">,</span> metrics<span style="color:#fefefe">)</span>
    trainer<span style="color:#fefefe">.</span>save_state<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>


    <span style="color:#d4d0ab">#############</span>
    <span style="color:#d4d0ab"># Evaluation</span>
    <span style="color:#d4d0ab">#############</span>
    tokenizer<span style="color:#fefefe">.</span>padding_side <span style="color:#00e0e0">=</span> <span style="color:#abe338">'left'</span>
    metrics <span style="color:#00e0e0">=</span> trainer<span style="color:#fefefe">.</span>evaluate<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
    metrics<span style="color:#fefefe">[</span><span style="color:#abe338">"eval_samples"</span><span style="color:#fefefe">]</span> <span style="color:#00e0e0">=</span> <span style="color:#abe338">len</span><span style="color:#fefefe">(</span>processed_test_dataset<span style="color:#fefefe">)</span>
    trainer<span style="color:#fefefe">.</span>log_metrics<span style="color:#fefefe">(</span><span style="color:#abe338">"eval"</span><span style="color:#fefefe">,</span> metrics<span style="color:#fefefe">)</span>
    trainer<span style="color:#fefefe">.</span>save_metrics<span style="color:#fefefe">(</span><span style="color:#abe338">"eval"</span><span style="color:#fefefe">,</span> metrics<span style="color:#fefefe">)</span>


    <span style="color:#d4d0ab"># ############</span>
    <span style="color:#d4d0ab"># # Save model</span>
    <span style="color:#d4d0ab"># ############</span>
    os<span style="color:#fefefe">.</span>makedirs<span style="color:#fefefe">(</span>args<span style="color:#fefefe">.</span>model_dir<span style="color:#fefefe">,</span> exist_ok<span style="color:#00e0e0">=</span><span style="color:#00e0e0">True</span><span style="color:#fefefe">)</span>
    torch<span style="color:#fefefe">.</span>save<span style="color:#fefefe">(</span>model<span style="color:#fefefe">,</span> os<span style="color:#fefefe">.</span>path<span style="color:#fefefe">.</span>join<span style="color:#fefefe">(</span>args<span style="color:#fefefe">.</span>model_dir<span style="color:#fefefe">,</span> <span style="color:#abe338">"model.pt"</span><span style="color:#fefefe">)</span><span style="color:#fefefe">)</span>

<span style="color:#00e0e0">def</span> <span style="color:#ffd700">parse_args</span><span style="color:#fefefe">(</span><span style="color:#fefefe">)</span><span style="color:#fefefe">:</span>
    <span style="color:#d4d0ab"># setup argparse</span>
    parser <span style="color:#00e0e0">=</span> argparse<span style="color:#fefefe">.</span>ArgumentParser<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>

    <span style="color:#d4d0ab"># add arguments</span>
    parser<span style="color:#fefefe">.</span>add_argument<span style="color:#fefefe">(</span><span style="color:#abe338">"--train-file"</span><span style="color:#fefefe">,</span> <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">str</span><span style="color:#fefefe">,</span> <span style="color:#abe338">help</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"Input data for training"</span><span style="color:#fefefe">)</span>
    parser<span style="color:#fefefe">.</span>add_argument<span style="color:#fefefe">(</span><span style="color:#abe338">"--eval-file"</span><span style="color:#fefefe">,</span> <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">str</span><span style="color:#fefefe">,</span> <span style="color:#abe338">help</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"Input data for eval"</span><span style="color:#fefefe">)</span>
    parser<span style="color:#fefefe">.</span>add_argument<span style="color:#fefefe">(</span><span style="color:#abe338">"--model-dir"</span><span style="color:#fefefe">,</span> <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">str</span><span style="color:#fefefe">,</span> default<span style="color:#00e0e0">=</span><span style="color:#abe338">"./"</span><span style="color:#fefefe">,</span> <span style="color:#abe338">help</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"output directory for model"</span><span style="color:#fefefe">)</span>
    parser<span style="color:#fefefe">.</span>add_argument<span style="color:#fefefe">(</span><span style="color:#abe338">"--epochs"</span><span style="color:#fefefe">,</span> default<span style="color:#00e0e0">=</span><span style="color:#00e0e0">10</span><span style="color:#fefefe">,</span> <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">int</span><span style="color:#fefefe">,</span> <span style="color:#abe338">help</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"number of epochs"</span><span style="color:#fefefe">)</span>
    parser<span style="color:#fefefe">.</span>add_argument<span style="color:#fefefe">(</span>
        <span style="color:#abe338">"--batch-size"</span><span style="color:#fefefe">,</span>
        default<span style="color:#00e0e0">=</span><span style="color:#00e0e0">16</span><span style="color:#fefefe">,</span>
        <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">int</span><span style="color:#fefefe">,</span>
        <span style="color:#abe338">help</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"mini batch size for each gpu/process"</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">)</span>
    parser<span style="color:#fefefe">.</span>add_argument<span style="color:#fefefe">(</span><span style="color:#abe338">"--learning-rate"</span><span style="color:#fefefe">,</span> default<span style="color:#00e0e0">=</span><span style="color:#00e0e0">0.001</span><span style="color:#fefefe">,</span> <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">float</span><span style="color:#fefefe">,</span> <span style="color:#abe338">help</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"learning rate"</span><span style="color:#fefefe">)</span>
    parser<span style="color:#fefefe">.</span>add_argument<span style="color:#fefefe">(</span><span style="color:#abe338">"--momentum"</span><span style="color:#fefefe">,</span> default<span style="color:#00e0e0">=</span><span style="color:#00e0e0">0.9</span><span style="color:#fefefe">,</span> <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">float</span><span style="color:#fefefe">,</span> <span style="color:#abe338">help</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"momentum"</span><span style="color:#fefefe">)</span>
    parser<span style="color:#fefefe">.</span>add_argument<span style="color:#fefefe">(</span>
        <span style="color:#abe338">"--print-freq"</span><span style="color:#fefefe">,</span>
        default<span style="color:#00e0e0">=</span><span style="color:#00e0e0">200</span><span style="color:#fefefe">,</span>
        <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">int</span><span style="color:#fefefe">,</span>
        <span style="color:#abe338">help</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"frequency of printing training statistics"</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">)</span>

    <span style="color:#d4d0ab"># parse args</span>
    args <span style="color:#00e0e0">=</span> parser<span style="color:#fefefe">.</span>parse_args<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>

    <span style="color:#d4d0ab"># return args</span>
    <span style="color:#00e0e0">return</span> args


<span style="color:#d4d0ab"># run script</span>
<span style="color:#00e0e0">if</span> __name__ <span style="color:#00e0e0">==</span> <span style="color:#abe338">"__main__"</span><span style="color:#fefefe">:</span>
    <span style="color:#d4d0ab"># parse args</span>
    args <span style="color:#00e0e0">=</span> parse_args<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
    <span style="color:#d4d0ab"># call main function</span>
    main<span style="color:#fefefe">(</span>args<span style="color:#fefefe">)</span></code></span></span>

 

 

 

让我们创建一个训练计算。

 

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>entities <span style="color:#00e0e0">import</span> AmlCompute
<span style="color:#d4d0ab"># If you have a specific compute size to work with change it here. By default we use the 1 x A100 compute from the above list</span>

compute_cluster_size <span style="color:#00e0e0">=</span> <span style="color:#abe338">"Standard_NC24ads_A100_v4"</span>  <span style="color:#d4d0ab"># 1 x A100 (80GB)</span>
<span style="color:#d4d0ab"># If you already have a gpu cluster, mention it here. Else will create a new one with the name 'gpu-cluster-big'</span>
compute_cluster <span style="color:#00e0e0">=</span> <span style="color:#abe338">"gpu-a100"</span>
<span style="color:#00e0e0">try</span><span style="color:#fefefe">:</span>
    compute <span style="color:#00e0e0">=</span> ml_client<span style="color:#fefefe">.</span>compute<span style="color:#fefefe">.</span>get<span style="color:#fefefe">(</span>compute_cluster<span style="color:#fefefe">)</span>
    <span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span><span style="color:#abe338">"The compute cluster already exists! Reusing it for the current run"</span><span style="color:#fefefe">)</span>
<span style="color:#00e0e0">except</span> Exception <span style="color:#00e0e0">as</span> ex<span style="color:#fefefe">:</span>
    <span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span>
        <span style="color:#abe338">f"Looks like the compute cluster doesn't exist. Creating a new one with compute size </span><span style="color:#fefefe">{</span>compute_cluster_size<span style="color:#fefefe">}</span><span style="color:#abe338">!"</span>
    <span style="color:#fefefe">)</span>
    <span style="color:#00e0e0">try</span><span style="color:#fefefe">:</span>
        <span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span><span style="color:#abe338">"Attempt #1 - Trying to create a dedicated compute"</span><span style="color:#fefefe">)</span>
        compute <span style="color:#00e0e0">=</span> AmlCompute<span style="color:#fefefe">(</span>
            name<span style="color:#00e0e0">=</span>compute_cluster<span style="color:#fefefe">,</span>
            size<span style="color:#00e0e0">=</span>compute_cluster_size<span style="color:#fefefe">,</span>
            tier<span style="color:#00e0e0">=</span><span style="color:#abe338">"Dedicated"</span><span style="color:#fefefe">,</span>
            max_instances<span style="color:#00e0e0">=</span><span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>  <span style="color:#d4d0ab"># For multi node training set this to an integer value more than 1</span>
        <span style="color:#fefefe">)</span>
        ml_client<span style="color:#fefefe">.</span>compute<span style="color:#fefefe">.</span>begin_create_or_update<span style="color:#fefefe">(</span>compute<span style="color:#fefefe">)</span><span style="color:#fefefe">.</span>wait<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
    <span style="color:#00e0e0">except</span> Exception <span style="color:#00e0e0">as</span> e<span style="color:#fefefe">:</span>
        <span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span><span style="color:#abe338">"Error"</span><span style="color:#fefefe">)</span></code></span></span>

 

 

一些有用的提示：

  • LoRA 等级不需要很高。（例如，r=256）根据我们的经验，8 或 16 作为基准就足够了。
  • 如果训练数据集较小，最好设置rank=alpha。通常，2*rank或4*rank训练在小数据集上往往不稳定。
  • 使用lora时，请将学习率设置得较小。不建议使用1e-3或2e-4之类的学习率。我们从8e-4或5e-5开始。
  • 与其设置更大的批处理大小，不如检查我们是否有足够的 GPU 内存。这是因为如果上下文长度很长（如 8K），则可能会发生 OOM（内存不足）。使用梯度检查点和梯度累积可以增加批处理大小。
  • 如果你对批量大小和内存很敏感，绝对不要坚持使用 Adam，包括低位 Adam。Adam 需要额外的 GPU 内存来计算第一和第二动量。SGD（随机梯度下降）收敛速度较慢，但​​不占用额外的 GPU 内存。

现在让我们在刚刚创建的 AML 计算中使用上述训练脚本调用计算作业。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml <span style="color:#00e0e0">import</span> command
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml <span style="color:#00e0e0">import</span> Input
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>entities <span style="color:#00e0e0">import</span> ResourceConfiguration

job <span style="color:#00e0e0">=</span> command<span style="color:#fefefe">(</span>
    inputs<span style="color:#00e0e0">=</span><span style="color:#abe338">dict</span><span style="color:#fefefe">(</span>
        train_file<span style="color:#00e0e0">=</span>Input<span style="color:#fefefe">(</span>
            <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"uri_file"</span><span style="color:#fefefe">,</span>
            path<span style="color:#00e0e0">=</span><span style="color:#abe338">"data/train.jsonl"</span><span style="color:#fefefe">,</span>
        <span style="color:#fefefe">)</span><span style="color:#fefefe">,</span>
        eval_file<span style="color:#00e0e0">=</span>Input<span style="color:#fefefe">(</span>
            <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"uri_file"</span><span style="color:#fefefe">,</span>
            path<span style="color:#00e0e0">=</span><span style="color:#abe338">"data/eval.jsonl"</span><span style="color:#fefefe">,</span>
        <span style="color:#fefefe">)</span><span style="color:#fefefe">,</span>        
        epoch<span style="color:#00e0e0">=</span><span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
        batchsize<span style="color:#00e0e0">=</span><span style="color:#00e0e0">64</span><span style="color:#fefefe">,</span>
        lr <span style="color:#00e0e0">=</span> <span style="color:#00e0e0">0.01</span><span style="color:#fefefe">,</span>
        momentum <span style="color:#00e0e0">=</span> <span style="color:#00e0e0">0.9</span><span style="color:#fefefe">,</span>
        prtfreq <span style="color:#00e0e0">=</span> <span style="color:#00e0e0">200</span><span style="color:#fefefe">,</span>
        output <span style="color:#00e0e0">=</span> <span style="color:#abe338">"./outputs"</span>
    <span style="color:#fefefe">)</span><span style="color:#fefefe">,</span>
    code<span style="color:#00e0e0">=</span><span style="color:#abe338">"./src"</span><span style="color:#fefefe">,</span>  <span style="color:#d4d0ab"># local path where the code is stored</span>
    compute <span style="color:#00e0e0">=</span> <span style="color:#abe338">'gpu-a100'</span><span style="color:#fefefe">,</span>
    command<span style="color:#00e0e0">=</span><span style="color:#abe338">"accelerate launch train.py --train-file ${{inputs.train_file}} --eval-file ${{inputs.eval_file}} --epochs ${{inputs.epoch}} --batch-size ${{inputs.batchsize}} --learning-rate ${{inputs.lr}} --momentum ${{inputs.momentum}} --print-freq ${{inputs.prtfreq}} --model-dir ${{inputs.output}}"</span><span style="color:#fefefe">,</span>
    environment<span style="color:#00e0e0">=</span><span style="color:#abe338">"azureml://registries/azureml/environments/acft-hf-nlp-gpu/versions/52"</span><span style="color:#fefefe">,</span>
    distribution<span style="color:#00e0e0">=</span><span style="color:#fefefe">{</span>
        <span style="color:#abe338">"type"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"PyTorch"</span><span style="color:#fefefe">,</span>
        <span style="color:#abe338">"process_count_per_instance"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">}</span><span style="color:#fefefe">,</span>
<span style="color:#fefefe">)</span>
returned_job  <span style="color:#00e0e0">=</span> workspace_ml_client<span style="color:#fefefe">.</span>jobs<span style="color:#fefefe">.</span>create_or_update<span style="color:#fefefe">(</span>job<span style="color:#fefefe">)</span>
workspace_ml_client<span style="color:#fefefe">.</span>jobs<span style="color:#fefefe">.</span>stream<span style="color:#fefefe">(</span>returned_job<span style="color:#fefefe">.</span>name<span style="color:#fefefe">)</span></code></span></span>

 

 

 

让我们看一下管道输出。

 

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#d4d0ab"># check if the `trained_model` output is available</span>
job_name <span style="color:#00e0e0">=</span> returned_job<span style="color:#fefefe">.</span>name
<span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span><span style="color:#abe338">"pipeline job outputs: "</span><span style="color:#fefefe">,</span> workspace_ml_client<span style="color:#fefefe">.</span>jobs<span style="color:#fefefe">.</span>get<span style="color:#fefefe">(</span>job_name<span style="color:#fefefe">)</span><span style="color:#fefefe">.</span>outputs<span style="color:#fefefe">)</span></code></span></span>

 

 

[步骤 3：终点]

一旦模型微调完毕，就可以在工作区中注册该作业来创建端点。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>entities <span style="color:#00e0e0">import</span> Model
<span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>constants <span style="color:#00e0e0">import</span> AssetTypes

run_model <span style="color:#00e0e0">=</span> Model<span style="color:#fefefe">(</span>
    path<span style="color:#00e0e0">=</span><span style="color:#abe338">f"azureml://jobs/</span><span style="color:#fefefe">{</span>job_name<span style="color:#fefefe">}</span><span style="color:#abe338">/outputs/artifacts/paths/outputs/mlflow_model_folder"</span><span style="color:#fefefe">,</span>
    name<span style="color:#00e0e0">=</span><span style="color:#abe338">"phi-3-finetuned"</span><span style="color:#fefefe">,</span>
    description<span style="color:#00e0e0">=</span><span style="color:#abe338">"Model created from run."</span><span style="color:#fefefe">,</span>
    <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span>AssetTypes<span style="color:#fefefe">.</span>MLFLOW_MODEL<span style="color:#fefefe">,</span>
<span style="color:#fefefe">)</span>
model <span style="color:#00e0e0">=</span> workspace_ml_client<span style="color:#fefefe">.</span>models<span style="color:#fefefe">.</span>create_or_update<span style="color:#fefefe">(</span>run_model<span style="color:#fefefe">)</span></code></span></span>

 

 

 

让我们创建端点。

 

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>entities <span style="color:#00e0e0">import</span> <span style="color:#fefefe">(</span>
    ManagedOnlineEndpoint<span style="color:#fefefe">,</span>
    IdentityConfiguration<span style="color:#fefefe">,</span>
    ManagedIdentityConfiguration<span style="color:#fefefe">,</span>
<span style="color:#fefefe">)</span>

<span style="color:#d4d0ab"># Check if the endpoint already exists in the workspace</span>
<span style="color:#00e0e0">try</span><span style="color:#fefefe">:</span>
    endpoint <span style="color:#00e0e0">=</span> workspace_ml_client<span style="color:#fefefe">.</span>online_endpoints<span style="color:#fefefe">.</span>get<span style="color:#fefefe">(</span>endpoint_name<span style="color:#fefefe">)</span>
    <span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span><span style="color:#abe338">"---Endpoint already exists---"</span><span style="color:#fefefe">)</span>
<span style="color:#00e0e0">except</span><span style="color:#fefefe">:</span>
    <span style="color:#d4d0ab"># Create an online endpoint if it doesn't exist</span>

    <span style="color:#d4d0ab"># Define the endpoint</span>
    endpoint <span style="color:#00e0e0">=</span> ManagedOnlineEndpoint<span style="color:#fefefe">(</span>
        name<span style="color:#00e0e0">=</span>endpoint_name<span style="color:#fefefe">,</span>
        description<span style="color:#00e0e0">=</span><span style="color:#abe338">f"Test endpoint for </span><span style="color:#fefefe">{</span>model<span style="color:#fefefe">.</span>name<span style="color:#fefefe">}</span><span style="color:#abe338">"</span><span style="color:#fefefe">,</span>
        identity<span style="color:#00e0e0">=</span>IdentityConfiguration<span style="color:#fefefe">(</span>
            <span style="color:#abe338">type</span><span style="color:#00e0e0">=</span><span style="color:#abe338">"user_assigned"</span><span style="color:#fefefe">,</span>
            user_assigned_identities<span style="color:#00e0e0">=</span><span style="color:#fefefe">[</span>ManagedIdentityConfiguration<span style="color:#fefefe">(</span>resource_id<span style="color:#00e0e0">=</span>uai_id<span style="color:#fefefe">)</span><span style="color:#fefefe">]</span><span style="color:#fefefe">,</span>
        <span style="color:#fefefe">)</span>
        <span style="color:#00e0e0">if</span> uai_id <span style="color:#00e0e0">!=</span> <span style="color:#abe338">""</span>
        <span style="color:#00e0e0">else</span> <span style="color:#00e0e0">None</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">)</span>

<span style="color:#d4d0ab"># Trigger the endpoint creation</span>
<span style="color:#00e0e0">try</span><span style="color:#fefefe">:</span>
    workspace_ml_client<span style="color:#fefefe">.</span>begin_create_or_update<span style="color:#fefefe">(</span>endpoint<span style="color:#fefefe">)</span><span style="color:#fefefe">.</span>wait<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
    <span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span><span style="color:#abe338">"\n---Endpoint created successfully---\n"</span><span style="color:#fefefe">)</span>
<span style="color:#00e0e0">except</span> Exception <span style="color:#00e0e0">as</span> err<span style="color:#fefefe">:</span>
    <span style="color:#00e0e0">raise</span> RuntimeError<span style="color:#fefefe">(</span>
        <span style="color:#abe338">f"Endpoint creation failed. Detailed Response:\n</span><span style="color:#fefefe">{</span>err<span style="color:#fefefe">}</span><span style="color:#abe338">"</span>
    <span style="color:#fefefe">)</span> <span style="color:#00e0e0">from</span> err</code></span></span>

 

 

 

一旦创建端点，我们就可以继续创建部署。

 

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#d4d0ab"># Initialize deployment parameters</span>

deployment_name <span style="color:#00e0e0">=</span> <span style="color:#abe338">"phi3-deploy"</span>
sku_name <span style="color:#00e0e0">=</span> <span style="color:#abe338">"Standard_NCs_v3"</span>

REQUEST_TIMEOUT_MS <span style="color:#00e0e0">=</span> <span style="color:#00e0e0">90000</span>

deployment_env_vars <span style="color:#00e0e0">=</span> <span style="color:#fefefe">{</span>
    <span style="color:#abe338">"SUBSCRIPTION_ID"</span><span style="color:#fefefe">:</span> subscription_id<span style="color:#fefefe">,</span>
    <span style="color:#abe338">"RESOURCE_GROUP_NAME"</span><span style="color:#fefefe">:</span> resource_group<span style="color:#fefefe">,</span>
    <span style="color:#abe338">"UAI_CLIENT_ID"</span><span style="color:#fefefe">:</span> uai_client_id<span style="color:#fefefe">,</span>
<span style="color:#fefefe">}</span></code></span></span>

 

 

为了进行推理，我们将使用不同的基础图像。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>entities <span style="color:#00e0e0">import</span> Model<span style="color:#fefefe">,</span> Environment
env <span style="color:#00e0e0">=</span> Environment<span style="color:#fefefe">(</span>
    image<span style="color:#00e0e0">=</span><span style="color:#abe338">'mcr.microsoft.com/azureml/curated/foundation-model-inference:latest'</span><span style="color:#fefefe">,</span>
    inference_config<span style="color:#00e0e0">=</span><span style="color:#fefefe">{</span>
        <span style="color:#abe338">"liveness_route"</span><span style="color:#fefefe">:</span> <span style="color:#fefefe">{</span><span style="color:#abe338">"port"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">5001</span><span style="color:#fefefe">,</span> <span style="color:#abe338">"path"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"/"</span><span style="color:#fefefe">}</span><span style="color:#fefefe">,</span>
        <span style="color:#abe338">"readiness_route"</span><span style="color:#fefefe">:</span> <span style="color:#fefefe">{</span><span style="color:#abe338">"port"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">5001</span><span style="color:#fefefe">,</span> <span style="color:#abe338">"path"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"/"</span><span style="color:#fefefe">}</span><span style="color:#fefefe">,</span>
        <span style="color:#abe338">"scoring_route"</span><span style="color:#fefefe">:</span> <span style="color:#fefefe">{</span><span style="color:#abe338">"port"</span><span style="color:#fefefe">:</span> <span style="color:#00e0e0">5001</span><span style="color:#fefefe">,</span> <span style="color:#abe338">"path"</span><span style="color:#fefefe">:</span> <span style="color:#abe338">"/score"</span><span style="color:#fefefe">}</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">}</span><span style="color:#fefefe">,</span>
<span style="color:#fefefe">)</span></code></span></span>

 

 

 

让我们部署模型

 

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python"><span style="color:#00e0e0">from</span> azure<span style="color:#fefefe">.</span>ai<span style="color:#fefefe">.</span>ml<span style="color:#fefefe">.</span>entities <span style="color:#00e0e0">import</span> <span style="color:#fefefe">(</span>
    OnlineRequestSettings<span style="color:#fefefe">,</span>
    CodeConfiguration<span style="color:#fefefe">,</span>
    ManagedOnlineDeployment<span style="color:#fefefe">,</span>
    ProbeSettings<span style="color:#fefefe">,</span>
    Environment
<span style="color:#fefefe">)</span>

deployment <span style="color:#00e0e0">=</span> ManagedOnlineDeployment<span style="color:#fefefe">(</span>
    name<span style="color:#00e0e0">=</span>deployment_name<span style="color:#fefefe">,</span>
    endpoint_name<span style="color:#00e0e0">=</span>endpoint_name<span style="color:#fefefe">,</span>
    model<span style="color:#00e0e0">=</span>model<span style="color:#fefefe">.</span><span style="color:#abe338">id</span><span style="color:#fefefe">,</span>
    instance_type<span style="color:#00e0e0">=</span>sku_name<span style="color:#fefefe">,</span>
    instance_count<span style="color:#00e0e0">=</span><span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
    <span style="color:#d4d0ab">#code_configuration=code_configuration,</span>
    environment <span style="color:#00e0e0">=</span> env<span style="color:#fefefe">,</span>
    environment_variables<span style="color:#00e0e0">=</span>deployment_env_vars<span style="color:#fefefe">,</span>
    request_settings<span style="color:#00e0e0">=</span>OnlineRequestSettings<span style="color:#fefefe">(</span>request_timeout_ms<span style="color:#00e0e0">=</span>REQUEST_TIMEOUT_MS<span style="color:#fefefe">)</span><span style="color:#fefefe">,</span>
    liveness_probe<span style="color:#00e0e0">=</span>ProbeSettings<span style="color:#fefefe">(</span>
        failure_threshold<span style="color:#00e0e0">=</span><span style="color:#00e0e0">30</span><span style="color:#fefefe">,</span>
        success_threshold<span style="color:#00e0e0">=</span><span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
        period<span style="color:#00e0e0">=</span><span style="color:#00e0e0">100</span><span style="color:#fefefe">,</span>
        initial_delay<span style="color:#00e0e0">=</span><span style="color:#00e0e0">500</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">)</span><span style="color:#fefefe">,</span>
    readiness_probe<span style="color:#00e0e0">=</span>ProbeSettings<span style="color:#fefefe">(</span>
        failure_threshold<span style="color:#00e0e0">=</span><span style="color:#00e0e0">30</span><span style="color:#fefefe">,</span>
        success_threshold<span style="color:#00e0e0">=</span><span style="color:#00e0e0">1</span><span style="color:#fefefe">,</span>
        period<span style="color:#00e0e0">=</span><span style="color:#00e0e0">100</span><span style="color:#fefefe">,</span>
        initial_delay<span style="color:#00e0e0">=</span><span style="color:#00e0e0">500</span><span style="color:#fefefe">,</span>
    <span style="color:#fefefe">)</span><span style="color:#fefefe">,</span>
<span style="color:#fefefe">)</span>

<span style="color:#d4d0ab"># Trigger the deployment creation</span>
<span style="color:#00e0e0">try</span><span style="color:#fefefe">:</span>
    workspace_ml_client<span style="color:#fefefe">.</span>begin_create_or_update<span style="color:#fefefe">(</span>deployment<span style="color:#fefefe">)</span><span style="color:#fefefe">.</span>wait<span style="color:#fefefe">(</span><span style="color:#fefefe">)</span>
    <span style="color:#00e0e0">print</span><span style="color:#fefefe">(</span><span style="color:#abe338">"\n---Deployment created successfully---\n"</span><span style="color:#fefefe">)</span>
<span style="color:#00e0e0">except</span> Exception <span style="color:#00e0e0">as</span> err<span style="color:#fefefe">:</span>
    <span style="color:#00e0e0">raise</span> RuntimeError<span style="color:#fefefe">(</span>
        <span style="color:#abe338">f"Deployment creation failed. Detailed Response:\n</span><span style="color:#fefefe">{</span>err<span style="color:#fefefe">}</span><span style="color:#abe338">"</span>
    <span style="color:#fefefe">)</span> <span style="color:#00e0e0">from</span> err</code></span></span>

 

 

如果您想删除端点，请参阅下面的代码。

 

<span style="background-color:#2b2b2b"><span style="color:#f8f8f2"><code class="language-python">workspace_ml_client<span style="color:#fefefe">.</span>online_deployments<span style="color:#fefefe">.</span>begin_delete<span style="color:#fefefe">(</span>name <span style="color:#00e0e0">=</span> deployment_name<span style="color:#fefefe">,</span> 
                                                    endpoint_name <span style="color:#00e0e0">=</span> endpoint_name<span style="color:#fefefe">)</span>
workspace_ml_client<span style="color:#fefefe">.</span>_online_endpoints<span style="color:#fefefe">.</span>begin_delete<span style="color:#fefefe">(</span>name <span style="color:#00e0e0">=</span> endpoint_name<span style="color:#fefefe">)</span></code></span></span>

 

本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处：http://www.coloradmin.cn/o/2127116.html

如若内容造成侵权/违法违规/事实不符，请联系多彩编程网进行投诉反馈，一经查实，立即删除！

相关文章

【MySQL】从0开始在Centos 7环境安装MySQL

【MySQL】从0开始在Centos 7环境安装MySQL

&#x1f984;个人主页:修修修也 &#x1f38f;所属专栏:MySQL ⚙️操作环境:Xshell (操作系统:CentOS 7.9 64位) 目录 准备步骤 卸载原有环境 安装步骤 获取MySQL官方yum源 安装MySQL yum源 结语 准备步骤 卸载原有环境 第一步登录云服务器(注意安装yum需要在root身份下…
阅读更多...
ctf.show靶场ssrf攻略

ctf.show靶场ssrf攻略

前言 欢迎来到我的博客 个人主页:北岭敲键盘的荒漠猫-CSDN博客 web351 解析:post传入url参数他就会访问。 解法: hackbar传入url参数写入https://127.0.0.1/flag.php web352 解析:post传入url参数&#xff0c;不能是127.0.0.1和localhost 解法:缩写127.1传入 web353 解析…
阅读更多...
FTP、SFTP安装，整合Springboot教程

FTP、SFTP安装，整合Springboot教程

文章目录 前言一、FTP、SFTP是什么&#xff1f;1.FTP2.SFTP 二、安装FTP1.安装vsftp服务2.启动服务并设置开机自启动3.开放防火墙和SELinux4.创建用户和FTP目录4.修改vsftpd.conf文件5.启动FTP服务6.问题 二、安装SFTP总结 前言 在一般项目开发工程中&#xff0c;我们大多数会…
阅读更多...
什么是数据治理？在企业数字化转型过程中有什么用？

什么是数据治理？在企业数字化转型过程中有什么用？

建设背景 有效的数据治理不仅能够确保数据的安全和质量&#xff0c;还能为企业提供深入的业务洞察&#xff0c;推动决策制定和创新。数据治理是数字化转型的基础&#xff0c;是数据资源成为数据资产的基础&#xff0c;只有经过了数据治理&#xff0c;相应的数据资源才能产生价…
阅读更多...
Ubuntu 24.04中安装virtualenv

Ubuntu 24.04中安装virtualenv

在Ubuntu 24.04中安装virtualenv&#xff0c;可以按照以下步骤进行&#xff1a; 1. 确保Python已安装‌&#xff1a; 在终端中输入python --version或python3 --version来检查Python的安装情况。 python3 --version2. 安装pip‌&#xff08;如果尚未安装&#xff09;&#x…
阅读更多...
【论文笔记】AutoLFADS (Nature Methods, 2022)

【论文笔记】AutoLFADS (Nature Methods, 2022)

相关链接&#xff1a; Is This Tutorial For You? - AutoLFADS TutorialDANDI ArchiveNonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology Abstract 通过深度神经群体动力学模型实现最先进的性能需要对每个数据集进行广泛的超参数调整。 Au…
阅读更多...
机器学习：opencv--图像金字塔

机器学习：opencv--图像金字塔

目录 一、图像金字塔 1.图像金字塔是什么&#xff1f; 2.有哪些常见类型&#xff1f; 3.金字塔的构建过程 4.图像金字塔的作用 二、图像金字塔中的操作 1.向下采样 2.向上采样 3.注意--无法复原 三、代码实现 1.高斯金字塔向下采样 2.高斯金字塔向上采样 3.无法复…
阅读更多...
JavaEE:文件内容操作(一)

JavaEE:文件内容操作(一)

文章目录 文件内容的读写---数据流字节流和字符流打开和关闭文件文件资源泄漏try with resources 文件内容的读写—数据流 文件内容的操作,读文件和写文件,都是操作系统本身提供了API,在Java中也进行了封装. Java中封装了操作文件的这些类,我们给它们起了个名字,叫做"文…
阅读更多...
药品识别与分类系统源码分享

药品识别与分类系统源码分享

药品识别与分类检测系统源码分享 [一条龙教学YOLOV8标注好的数据集一键训练_70全套改进创新点发刊_Web前端展示] 1.研究背景与意义 项目参考AAAI Association for the Advancement of Artificial Intelligence 项目来源AACV Association for the Advancement of Computer V…
阅读更多...
Vue | Vue深入浅出——Vue中的render函数详解

Vue | Vue深入浅出——Vue中的render函数详解

1.render函数 在编写vue单文件的大多数情况下&#xff0c;我们都是使用template模板来创建HTML。然而在一些条件判断比较复杂的场景下&#xff0c;使用JavaScript去描绘HTML的生成逻辑会显得更加的简洁直观。 使用Vue官网的例子来简单说明&#xff1a; 如果自己在开发的时候…
阅读更多...
vscode配置django环境并创建django项目

vscode配置django环境并创建django项目

1、创建文件夹 创建文件夹 并在vscode打开 终端输入命令 “ python -m venv env ” 查看目录结构 2、创建项目 在终端输入 django-admin startproject 文件名(这里以myshop为例) 3、创建应用 在myshop打开终端 在终端输入 django-admin startapp 应用名 这里以app1为例…
阅读更多...
6个免费icon图标素材网站

6个免费icon图标素材网站

在这个数字化时代&#xff0c;优秀的图标设计对于提升用户体验至关重要。为了帮助设计师和开发者找到高质量的免费icon图标素材&#xff0c;我整理了以下6个实用的网站&#xff0c;让你轻松获取精美图标&#xff0c;助力你的设计工作。快来一起看看吧&#xff01; 1、菜鸟图库 …
阅读更多...
如何显示Dialog窗口

如何显示Dialog窗口

文章目录 1. 概念介绍2. 使用方法2.1 Overlay效果2.1 Dialog效果 3. 示例代码4. 内容总结 我们在上一章回中介绍了"使用get显示snackBar"相关的内容&#xff0c;本章回中将介绍使用get显示Dialog.闲话休提&#xff0c;让我们一起Talk Flutter吧。 1. 概念介绍 我们在…
阅读更多...
【卷起来】VUE3.0教程-07-异步请求处理（springboot后端）

【卷起来】VUE3.0教程-07-异步请求处理（springboot后端）

&#x1f332; 服务端接口准备 pom文件&#xff0c;引入mybatis/mybatis-plus相关依赖 <dependencies><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency>&…
阅读更多...
Split函数

Split函数

Split:可以将一个完整的字符串按照指定的分隔符划分为若干个子字符串。 String[] split(String regex) :将字符串全部拆分. String[] split(String regex, int limit) :将字符串以指定的格式&#xff0c;拆分为limit 组 代码实例&#xff1a; 切割小数点&#xff0c;或\ ,需…
阅读更多...
每日单词记背

每日单词记背

2024年9月12日 1.discriminate&#xff1a;歧视&#xff0c;区别&#xff0c;分辨 discriminate against 歧视&#xff1b;排斥 discriminate between 区别 辨别 dis(区别)crim(罪犯)inate ->区别为罪犯->歧视 it is illegal to discriminate against women in any way.…
阅读更多...
【Linux修行路】信号的产生

【Linux修行路】信号的产生

目录 ⛳️推荐 一、信号的产生 二、产生信号的系统调用 2.1 kill——给指定的进程发送指定的信号 2.2 模拟实现指令 kill 2.3 raise——给调用的进程发送指定的信号 2.4 abort——给调用者发送 6 号信号 三、验证哪些信号不可以被捕捉 四、为什么除0和解引用空指针会给…
阅读更多...
数据库（DB、DBMS、SQL）

数据库（DB、DBMS、SQL）

今天我来讲解一下数据库和可视化数据库管理系统的使用 数据库概述 数据库 存储数据的仓库&#xff0c;数据是有组织的存储 DataBase (DB) 数据库管理系统 操纵和管理数据库的大型软件 DataBaseMangement System (DBMS) SQL 操作关系型数据库的编程语言&#xff0c;定义…
阅读更多...
探索最佳 Shell 工具：全面测评 Bash、Zsh、Fish、Tcsh 和 Ksh

探索最佳 Shell 工具：全面测评 Bash、Zsh、Fish、Tcsh 和 Ksh

感谢浪浪云支持发布 浪浪云活动链接 &#xff1a;https://langlangy.cn/?i8afa52 文章目录 1. 简介2. 测评工具3. 测评标准4. Bash 测评4.1 易用性4.2 功能特性4.3 性能4.4 可定制性4.5 社区和支持 5. Zsh 测评5.1 易用性5.2 功能特性5.3 性能5.4 可定制性5.5 社区和支持 6. F…
阅读更多...
C++设计模式——Builder Pattern建造者模式

C++设计模式——Builder Pattern建造者模式

一&#xff0c;建造者模式的定义 建造者模式&#xff0c;又被称为生成器模式&#xff0c;是一种创建型设计模式&#xff0c;它将复杂产品的构建过程分解为一系列简单的步骤&#xff0c;每个步骤由独立的建造者对象负责。 建造者模式常用于创建复杂的对象&#xff0c;它避免了…
阅读更多...
推荐文章
最新文章

Copyright @ 2022~2023