内衣网站建设详细方案,网络销售怎么做才能做好,电子商务网站建设评估工具,网页制作的基本步骤有哪些文章目录 简介使用model.tar.gz1.从huggingface上下载模型2.自定义代码3.打包为tar 文件4.上传model.tar.gz到S35.部署推理 使用hub1.在sagemaker上新建个jupyterlab2.上传官方示例ipynb文件3.指定HF_MODEL_ID和HF_TASK进行部署和推理 inference.py官方示例 简介
原始链接https://huggingface.co/docs/sagemaker/inference#deploy-with-modeldatahttps://docs.datarobot.com/en/docs/more-info/how-to/aws/sagemaker/sagemaker-deploy.html 这个可以是java环境或者python环境。
部署的都是从huggingface上的model或者根据huaggingface上的model进行fine-tune后的。
一般输入格式如下
text-classification request body{inputs: Camera - You are awarded a SiPix Digital Camera! call 09061221066 fromm landline. Delivery within 28 days.
}
question-answering request body{inputs: {question: What is used for inference?,context: My Name is Philipp and I live in Nuremberg. This model is used with sagemaker for inference.}
}
zero-shot classification request body{inputs: Hi, I recently bought a device from your company but it is not working as advertised and I would like to get reimbursed!,parameters: {candidate_labels: [refund,legal,faq]}
}所有官方示例 https://github.com/huggingface/notebooks/tree/main/sagemaker 推理工具 https://github.com/aws/sagemaker-huggingface-inference-toolkit 使用model.tar.gz
1.从huggingface上下载模型
由于模型文件比较大需要先安装git-lfs
CentOS7安装Git LFS的方法如下# 安装必要的软件包
sudo yum install curl-devel expat-devel gettext-devel openssl-devel zlib-devel
# 安装Git LFS
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.rpm.sh | sudo bash
# 安装
sudo yum install git-lfs
# 配置Git LFS
git lfs install
# 检测是否安装成功
git lfs version
如果出现版本信息说明安装成功。从huaggingface上clone你想使用的模型以https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2 为例子
git clone https://huggingface.co/sentence-transformers/all-MiniLM-L6-v22.自定义代码
允许用户覆盖 HuggingFaceHandlerService 的默认方法。您需要创建一个名为 code/ 的文件夹其中包含 inference.py 文件。
HuggingFaceHandlerService
目录结构如下
model.tar.gz/
|- pytorch_model.bin
|- ....
|- code/|- inference.py|- requirements.txt inference.py 文件包含自定义推理模块 requirements.txt 文件包含应添加的其他依赖项。自定义模块可以重写以下方法
model_fn(model_dir) 覆盖加载模型的默认方法。返回值 model 将在 predict 中用于预测。 predict 接收参数 model_dir 即解压后的 model.tar.gz 的路径。transform_fn(model, data, content_type, accept_type) 使用您的自定义实现覆盖默认转换函数。您需要在 transform_fn 中实现您自己的 preprocess 、 predict 和 postprocess 步骤。此方法不能与下面提到的 input_fn 、 predict_fn 或 output_fn 组合使用。input_fn(input_data, content_type) 覆盖默认的预处理方法。返回值 data 将在 predict 中用于预测。输入是 input_data 是您请求的原始正文。content_type 是请求标头中的内容类型。 predict_fn(processed_data, model) 覆盖默认的预测方法。返回值 predictions 将在 postprocess 中使用。输入是 processed_data 即 preprocess 的结果。output_fn(prediction, accept) 覆盖后处理的默认方法。返回值 result 将是您请求的响应例如 JSON 。输入是 predictions 是 predict 的结果。accept 是 HTTP 请求的返回接受类型例如 application/json 。
以下是包含 model_fn 、 input_fn 、 predict_fn 和 output_fn 的自定义推理模块的示例
from sagemaker_huggingface_inference_toolkit import decoder_encoderdef model_fn(model_dir):# implement custom code to load the modelloaded_model ...return loaded_model def input_fn(input_data, content_type):# decode the input data (e.g. JSON string - dict)data decoder_encoder.decode(input_data, content_type)return datadef predict_fn(data, model):# call your custom model with the dataoutputs model(data , ... )return predictionsdef output_fn(prediction, accept):# convert the model output to the desired output format (e.g. dict - JSON string)response decoder_encoder.encode(prediction, accept)return response仅使用 model_fn 和 transform_fn 自定义推理模块
from sagemaker_huggingface_inference_toolkit import decoder_encoderdef model_fn(model_dir):# implement custom code to load the modelloaded_model ...return loaded_model def transform_fn(model, input_data, content_type, accept):# decode the input data (e.g. JSON string - dict)data decoder_encoder.decode(input_data, content_type)# call your custom model with the dataoutputs model(data , ... ) # convert the model output to the desired output format (e.g. dict - JSON string)response decoder_encoder.encode(output, accept)return response重点这里的话我们 all-MiniLM-L6-v2的示例代码如下
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):token_embeddings model_output[0] #First element of model_output contains all token embeddingsinput_mask_expanded attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min1e-9)# Sentences we want sentence embeddings for
sentences [This is an example sentence, Each sentence is converted]# Load model from HuggingFace Hub
tokenizer AutoTokenizer.from_pretrained(sentence-transformers/all-MiniLM-L6-v2)
model AutoModel.from_pretrained(sentence-transformers/all-MiniLM-L6-v2)# Tokenize sentences
encoded_input tokenizer(sentences, paddingTrue, truncationTrue, return_tensorspt)# Compute token embeddings
with torch.no_grad():model_output model(**encoded_input)# Perform pooling
sentence_embeddings mean_pooling(model_output, encoded_input[attention_mask])# Normalize embeddings
sentence_embeddings F.normalize(sentence_embeddings, p2, dim1)print(Sentence embeddings:)
print(sentence_embeddings)我们需要改造下改为我们自己需要的自定义代码
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F# 这个方法直接同上
def mean_pooling(model_output, attention_mask):token_embeddings model_output[0] #First element of model_output contains all token embeddingsinput_mask_expanded attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min1e-9)# 覆盖 -- 模型加载 参考all-MiniLM-L6-v2给出的示例代码
def model_fn(model_dir):# Load model from HuggingFace Hubtokenizer AutoTokenizer.from_pretrained(model_dir)model AutoModel.from_pretrained(model_dir)return model, tokenizer
# 覆盖 -- 预测方法 参考all-MiniLM-L6-v2给出的示例代码
def predict_fn(data, model_and_tokenizer):# destruct model and tokenizermodel, tokenizer model_and_tokenizer# Tokenize sentencessentences data.pop(inputs, data)encoded_input tokenizer(sentences, paddingTrue, truncationTrue, return_tensorspt)# Compute token embeddingswith torch.no_grad():model_output model(**encoded_input)# Perform poolingsentence_embeddings mean_pooling(model_output, encoded_input[attention_mask])# Normalize embeddingssentence_embeddings F.normalize(sentence_embeddings, p2, dim1)# return dictonary, which will be json serializablereturn {vectors: sentence_embeddings[0].tolist()}3.打包为tar 文件
cd all-MiniLM-L6-v2
tar zcvf model.tar.gz *4.上传model.tar.gz到S3
5.部署推理
这里有好几种方式可选。
第一种在jupyterlab执行这个脚本替换model等参数即可。
https://github.com/huggingface/notebooks/blob/main/sagemaker/10_deploy_model_from_s3/deploy_transformer_model_from_s3.ipynb
第二种这个是吧上面所有步骤都包含了但是这种无法处理我们在私有环境fine-tune后的模型。
https://github.com/huggingface/notebooks/blob/main/sagemaker/17_custom_inference_script/sagemaker-notebook.ipynb
第三种可视化部署我重点介绍下这个吧
入口如下 注意下面的选项
容器框架根据实际情况选择这里我们就选择如图S3 URIIAM role: 可以去IAM创建角色 AmazonS3FullAccessAmazonSageMakerFullAccess 也可以去JumpStart中的model去复制过来。 使用hub 原文https://huggingface.co/docs/sagemaker/inference#deploy-a-model-from-the–hub 这种方式没有上面的方式灵活度高支持的model也没有上面的方式多。 1.在sagemaker上新建个jupyterlab
2.上传官方示例ipynb文件
https://github.com/huggingface/notebooks/blob/main/sagemaker/11_deploy_model_from_hf_hub/deploy_transformer_model_from_hf_hub.ipynb
3.指定HF_MODEL_ID和HF_TASK进行部署和推理
inference.py官方示例
https://sagemaker-examples.readthedocs.io/en/latest/sagemaker-script-mode/pytorch_bert/deploy_bert_outputs.html#Write-the-Inference-Script
import os
import json
from transformers import BertTokenizer, BertModeldef model_fn(model_dir):Load the model for inferencemodel_path os.path.join(model_dir, model/)# Load BERT tokenizer from disk.tokenizer BertTokenizer.from_pretrained(model_path)# Load BERT model from disk.model BertModel.from_pretrained(model_path)model_dict {model: model, tokenizer:tokenizer}return model_dictdef predict_fn(input_data, model):Apply model to the incoming requesttokenizer model[tokenizer]bert_model model[model]encoded_input tokenizer(input_data, return_tensorspt)return bert_model(**encoded_input)def input_fn(request_body, request_content_type):Deserialize and prepare the prediction inputif request_content_type application/json:request json.loads(request_body)else:request request_bodyreturn requestdef output_fn(prediction, response_content_type):Serialize and prepare the prediction outputif response_content_type application/json:response str(prediction)else:response str(prediction)return response
