如何在Azure ML中使用MLflow加载已记录/保存的模型？

huangapple
go评论93阅读模式
英文:

How to use MLlfow to load the logged/saved model in Azure ML?

问题

我想部署经过训练的 ML 模型通过 AZURE ML 在线端点。

我已经在工作空间上注册了我的模型。

现在当我尝试使用 cutome score.py 来加载模型时，我得到以下错误 -

错误信息显示在 /azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/azureml_inference_server_http/server/user_script.py 中，需要在其中更新 map_location=torch.device('cpu')。但是 mlflow.pyfunc.load_model() 没有参数可以访问 map_location，因此需要在代码中找到合适的位置进行更新。

英文:

I want to deploy the trained ML model via AZURE ML online endppoints.

I have already registered my model on the workspace.

Now I am getting following error when I am trying to load the model using cutome score.py for mlflow.pyfunc.load_model()

This is my code -

  1. model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR"), "use-case1-model")
  2. model = mlflow.pyfunc.load_model(model_path)

score.py

  1. import logging
  2. import os
  3. import json
  4. import mlflow
  5. from io import StringIO
  6. from mlflow.pyfunc.scoring_server import infer_and_parse_json_input, predictions_to_json
  7. import sys
  8. from time import strftime, localtime
  9. from collections import Counter
  10. from pytorch_transformers import BertTokenizer
  11. import random
  12. import numpy as np 
  13. import torch 
  14. from tqdm import tqdm
  16. def init():
  17.     global model
  18.     # "model" is the path of the mlflow artifacts when the model was registered. For automl
  19.     # models, this is generally "mlflow-model".
  20.     model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR"), "use-case1-model")
  21.     model = mlflow.pyfunc.load_model(model_path)
  22.     logging.info("Init complete")
  25. def run(raw_data):
  26.     data = json.loads(raw_data)
  27.     title = json.dumps(data["title"])
  28.     att = json.dumps(data["attributes"])
  30.     output = model.predict([tensor_t,tensor_a])
  32.     predict_list = output.tolist()[0]
  33.     
  34.     result = StringIO()
  35.     predictions_to_json(predict_list,result)
  36.     return result.getvalue()

Error that I am getting -

  1. File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/azureml_inference_server_http/server/user_script.py", line 117, in invoke_init
  2.     self._user_init()
  3.   File "/var/azureml-app/dependencies/score.py", line 21, in init
  4.     model = mlflow.pyfunc.load_model(model_path)
  5.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/mlflow/pyfunc/__init__.py", line 735, in load_model
  6.     model_impl = importlib.import_module(conf[MAIN])._load_pyfunc(data_path)
  7.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/mlflow/pytorch/__init__.py", line 735, in _load_pyfunc
  8.     return _PyTorchWrapper(_load_model(path, **kwargs))
  9.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/mlflow/pytorch/__init__.py", line 643, in _load_model
  10.     return torch.load(model_path, **kwargs)
  11.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/torch/serialization.py", line 809, in load
  12.     return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
  13.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/torch/serialization.py", line 1172, in _load
  14.     result = unpickler.load()
  15.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/torch/serialization.py", line 1142, in persistent_load
  16.     typed_storage = load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location))
  17.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/torch/serialization.py", line 1116, in load_tensor
  18.     wrap_storage=restore_location(storage, location),
  19.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/torch/serialization.py", line 217, in default_restore_location
  20.     result = fn(storage, location)
  21.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/torch/serialization.py", line 182, in _cuda_deserialize
  22.     device = validate_cuda_device(location)
  23.   File "/azureml-envs/azureml_9a3b1e0a66d72d612aebc12b4a285f72/lib/python3.9/site-packages/torch/serialization.py", line 166, in validate_cuda_device
  24.     raise RuntimeError('Attempting to deserialize object on a CUDA '
  25. RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.

How and where can I update map_location=torch.device('cpu') ? mlflow.pyfunc.load_model() doesnt have a parameter to access map_location and as the packages is installed in docker i cannot make changes to serilaization.py

答案1

得分: 0

根据错误日志，您正在尝试在CUDA设备上反序列化一个对象，但torch.cuda.is_available()返回False，这是因为您在仅CPU的机器上运行。要解决此问题，您需要更新torch.load函数以指定map_location=torch.device('cpu')来将存储映射到CPU。

由于mlflow.pyfunc.load_model()函数没有map_location参数，您可以使用一个**kwargs参数，该参数可以传递任何额外的关键字参数给torch.load()函数。

要解决这个问题，在您的score.py文件中添加*{'map_location': torch.device('cpu')}。

  1. model = mlflow.pyfunc.load_model(model_path, *{'map_location': torch.device('cpu')})

或者使用下面的代码（更新后的解决方案）：

  1. model = mlflow.pytorch.load_model(model_path, map_location=torch.device('cpu'))

示例：

  1. import mlflow
  2. import torch
  3. path = "./deploy/credit_defaults_model/"
  4. model = mlflow.pyfunc.load_model(path, *{'map_location': torch.device('cpu')})
英文:

As per the error logs, you are attempting to deserialize an object on a CUDA device, but torch.cuda.is_available() is returning False, which is due to running on a CPU only machine. To resolve this issue, you need to update the torch.load function to specify map_location=torch.device('cpu') to map the storages to the CPU.

Since the mlflow.pyfunc.load_model() function does not have a map_location argument, you can use a **kwargs argument that can pass any additional keyword arguments to the torch.load() function.

To solve the issue, add *{'map_location': torch.device('cpu')}in your score.py file.

  1. model = mlflow.pyfunc.load_model(model_path, *{'map_location': torch.device('cpu')})

or Use below code:(Updated solution)

model = mlflow.pytorch.load_model(model_path, map_location=torch.device('cpu'))

Example:

  1. import  mlflow
  2. import  torch
  3. path="./deploy/credit_defaults_model/"
  4. model = mlflow.pyfunc.load_model(path, *{'map_location': torch.device('cpu')})

huangapple
  • 本文由 发表于 2023年5月25日 21:37:33
  • 转载请务必保留本文链接：https://go.coder-hub.com/76332923.html
  • azure
  • azure-machine-learning-service
  • mlflow
匿名

发表评论

匿名网友

:?: :razz: :sad: :evil: :!: :smile: :oops: :grin: :eek: :shock: :???: :cool: :lol: :mad: :twisted: :roll: :wink: :idea: :arrow: :neutral: :cry: :mrgreen:

确定