用于推理的 AzureML 环境：无法将 pip 包添加到依赖项
AzureML Environment for Inference : can't add pip packages to dependencies
我找不到将依赖项添加到 ML 推理的 Azure 容器实例的正确方法。
我基本上是按照本教程开始的：Train and deploy an image classification model with an example Jupyter Notebook
它工作正常。
现在我想部署经过训练的 TensorFlow 模型进行推理。我尝试了很多方法，但我始终无法将 python 依赖项添加到环境中。
来自 TensorFlow 精选环境
使用AzureML-tensorflow-2.4-ubuntu18.04-py37-cpu-inference：
from azureml.core import Workspace
# connect to your workspace
ws = Workspace.from_config()
# names
experiment_name = "my-experiment"
model_name = "my-model"
env_version="1"
env_name="my-env-"+env_version
service_name = str.lower(model_name + "-service-" + env_version)
# create environment for the deploy
from azureml.core.environment import Environment, DEFAULT_CPU_IMAGE
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core.webservice import AciWebservice
# get a curated environment
env = Environment.get(
workspace=ws,
name="AzureML-tensorflow-2.4-ubuntu18.04-py37-cpu-inference",
# )
custom_env = env.clone(env_name)
custom_env.inferencing_stack_version='latest'
# add packages
conda_dep = CondaDependencies()
python_packages = ['joblib', 'numpy', 'os', 'json', 'tensorflow']
for package in python_packages:
conda_dep.add_pip_package(package)
conda_dep.add_conda_package(package)
# Adds dependencies to PythonSection of env
custom_env.python.user_managed_dependencies=True
custom_env.python.conda_dependencies=conda_dep
custom_env.register(workspace=ws)
# create deployment config i.e. compute resources
aciconfig = AciWebservice.deploy_configuration(
cpu_cores=1,
memory_gb=1,
tags={"experiment": experiment_name, "model": model_name},
)
from azureml.core.model import InferenceConfig
from azureml.core.model import Model
# get the registered model
model = Model(ws, model_name)
# create an inference config i.e. the scoring script and environment
inference_config = InferenceConfig(entry_script="score.py", environment=custom_env)
# deploy the service
service = Model.deploy(
workspace=ws,
name=service_name,
models=[model],
inference_config=inference_config,
deployment_config=aciconfig,
)
service.wait_for_deployment(show_output=True)
我得到以下日志：
AzureML image information: tensorflow-2.4-ubuntu18.04-py37-cpu-inference:20220110.v1
PATH environment variable: /opt/miniconda/envs/amlenv/bin:/opt/miniconda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
PYTHONPATH environment variable:
Pip Dependencies
---------------
EdgeHubConnectionString and IOTEDGE_IOTHUBHOSTNAME are not set. Exiting...
2022-01-24T10:21:09,855130300+00:00 - iot-server/finish 1 0
2022-01-24T10:21:09,856870100+00:00 - Exit code 1 is normal. Not restarting iot-server.
absl-py==0.15.0
applicationinsights==0.11.10
astunparse==1.6.3
azureml-inference-server-http==0.4.2
cachetools==4.2.4
certifi==2021.10.8
charset-normalizer==2.0.10
click==8.0.3
Flask==1.0.3
flatbuffers==1.12
gast==0.3.3
google-auth==2.3.3
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
grpcio==1.32.0
gunicorn==20.1.0
h5py==2.10.0
idna==3.3
importlib-metadata==4.10.0
inference-schema==1.3.0
itsdangerous==2.0.1
Jinja2==3.0.3
Keras-Preprocessing==1.1.2
Markdown==3.3.6
MarkupSafe==2.0.1
numpy==1.19.5
oauthlib==3.1.1
opt-einsum==3.3.0
pandas==1.1.5
protobuf==3.19.1
pyasn1==0.4.8
pyasn1-modules==0.2.8
python-dateutil==2.8.2
pytz==2021.3
requests==2.27.1
requests-oauthlib==1.3.0
rsa==4.8
six==1.15.0
tensorboard==2.7.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorflow==2.4.0
tensorflow-estimator==2.4.0
termcolor==1.1.0
typing-extensions==3.7.4.3
urllib3==1.26.8
Werkzeug==2.0.2
wrapt==1.12.1
zipp==3.7.0
Entry script directory: /var/azureml-app/.
Dynamic Python package installation is disabled.
Starting AzureML Inference Server HTTP.
Azure ML Inferencing HTTP server v0.4.2
Server Settings
---------------
Entry Script Name: score.py
Model Directory: /var/azureml-app/azureml-models/my-model/1
Worker Count: 1
Worker Timeout (seconds): 300
Server Port: 31311
Application Insights Enabled: false
Application Insights Key: None
Server Routes
---------------
Liveness Probe: GET 127.0.0.1:31311/
Score: POST 127.0.0.1:31311/score
Starting gunicorn 20.1.0
Listening at: http://0.0.0.0:31311 (69)
Using worker: sync
Booting worker with pid: 100
Exception in worker process
Traceback (most recent call last):
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/arbiter.py", line 589, in spawn_worker
worker.init_process()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/workers/base.py", line 134, in init_process
self.load_wsgi()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/workers/base.py", line 146, in load_wsgi
self.wsgi = self.app.wsgi()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/app/base.py", line 67, in wsgi
self.callable = self.load()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/app/wsgiapp.py", line 58, in load
return self.load_wsgiapp()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/app/wsgiapp.py", line 48, in load_wsgiapp
return util.import_app(self.app_uri)
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/util.py", line 359, in import_app
mod = importlib.import_module(module)
File "/opt/miniconda/envs/amlenv/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/azureml_inference_server_http/server/entry.py", line 1, in <module>
import create_app
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/azureml_inference_server_http/server/create_app.py", line 4, in <module>
from routes_common import main
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/azureml_inference_server_http/server/routes_common.py", line 32, in <module>
from aml_blueprint import AMLBlueprint
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/azureml_inference_server_http/server/aml_blueprint.py", line 28, in <module>
main_module_spec.loader.exec_module(main)
File "/var/azureml-app/score.py", line 4, in <module>
import joblib
ModuleNotFoundError: No module named 'joblib'
Worker exiting (pid: 100)
Shutting down: Master
Reason: Worker failed to boot.
2022-01-24T10:21:13,851467800+00:00 - gunicorn/finish 3 0
2022-01-24T10:21:13,853259700+00:00 - Exit code 3 is not normal. Killing image.
来自 Conda 规范
与以前相同，但使用来自 Conda 规范的全新环境并更改 env_version 数字：
# ...
env_version="2"
# ...
custom_env = Environment.from_conda_specification(name=env_name, file_path="my-env.yml")
custom_env.docker.base_image = DEFAULT_CPU_IMAGE
# ...
与 my-env.yml :
name: my-env
dependencies:
- python
- pip:
- azureml-defaults
- azureml-sdk
- sklearn
- numpy
- matplotlib
- joblib
- uuid
- requests
- tensorflow
我得到这个日志：
2022-01-24T11:06:54,887886931+00:00 - iot-server/run
2022-01-24T11:06:54,891839877+00:00 - rsyslog/run
2022-01-24T11:06:54,893640998+00:00 - gunicorn/run
2022-01-24T11:06:54,912032812+00:00 - nginx/run
EdgeHubConnectionString and IOTEDGE_IOTHUBHOSTNAME are not set. Exiting...
2022-01-24T11:06:55,398420960+00:00 - iot-server/finish 1 0
2022-01-24T11:06:55,414425146+00:00 - Exit code 1 is normal. Not restarting iot-server.
PATH environment variable: /opt/miniconda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
PYTHONPATH environment variable:
Pip Dependencies
---------------
brotlipy==0.7.0
certifi==2020.6.20
cffi @ file:///tmp/build/80754af9/cffi_1605538037615/work
chardet @ file:///tmp/build/80754af9/chardet_1605303159953/work
conda==4.9.2
conda-package-handling @ file:///tmp/build/80754af9/conda-package-handling_1603018138503/work
cryptography @ file:///tmp/build/80754af9/cryptography_1605544449973/work
idna @ file:///tmp/build/80754af9/idna_1593446292537/work
pycosat==0.6.3
pycparser @ file:///tmp/build/80754af9/pycparser_1594388511720/work
pyOpenSSL @ file:///tmp/build/80754af9/pyopenssl_1605545627475/work
PySocks @ file:///tmp/build/80754af9/pysocks_1594394576006/work
requests @ file:///tmp/build/80754af9/requests_1592841827918/work
ruamel-yaml==0.15.87
six @ file:///tmp/build/80754af9/six_1605205313296/work
tqdm @ file:///tmp/build/80754af9/tqdm_1605303662894/work
urllib3 @ file:///tmp/build/80754af9/urllib3_1603305693037/work
Starting HTTP server
2022-01-24T11:06:59,701365128+00:00 - gunicorn/finish 127 0
./run: line 127: exec: gunicorn: not found
2022-01-24T11:06:59,706177784+00:00 - Exit code 127 is not normal. Killing image.
我真的不知道我错过了什么，而且我已经搜索了太久（Azure 文档，SO，...）。
感谢您的帮助！
编辑：我尝试过的解决方案的非详尽列表：
- How to create AzureML environement and add required packages
- how to use existing conda environment as a AzureML environment
- ...
- https://docs.microsoft.com/en-us/azure/machine-learning/concept-environments#environment-building-caching-and-reuse
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#add-packages-to-an-environment
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where?tabs=python#define-a-deployment-configuration
- ...
如果你想创建一个自定义环境，你可以使用下面的代码来设置环境配置。
创造环境
myenv = Environment(name="Environment")
myenv.docker.enabled = True
myenv.python.conda_dependencies = CondaDependencies.create(conda_packages = ['numpy','scikit-learn','pip','pandas'], pip_packages = ['azureml-defaults~= 1.34.0','azureml','azureml-core~= 1.34.0',"azureml-sdk",'inference-schema','azureml-telemetry~= 1.34.0','azureml- train-automl~= 1.34.0','azure-ml-api-sdk','python-dotenv','azureml-contrib-server','azureml-inference-server-http'])
我认为在 Azure 服务器中实施 joblib 存在一个小的安全问题，请不要在您的代码中加载它，它会 运行。
好的，我成功了：我从头开始并且成功了。
我不知道我之前的所有尝试都出了什么问题，这太糟糕了。
多个问题以及我（认为我）如何解决它们：
joblib ：实际上我不需要它来加载我的 Keras 模型。但问题不在于这个特定的库，而是我无法将依赖项添加到推理环境。Environment ：最后，我只能使用自定义环境来实现：Environment.from_conda_specification(name=version, file_path="conda_dependencies.yml") 。我无法将我的库（或指定特定的包版本）添加到“精选环境”。我不知道为什么...TensorFlow ：我遇到的最后一个问题是我在 AzureML Notebook 的 azureml_py38_PT_TF 内核 (tensorflow==2.7.0) 中训练和注册了我的模型，并尝试在推理中加载它 Docker 图片 (tensorflow==2.4.0)。所以我必须指定我想在推理图像中使用的 TensorFlow 版本（这需要解决之前的问题）。
最终成功的方法：
- notebook.ipynb
import uuid
from azureml.core import Workspace, Environment, Model
from azureml.core.webservice import AciWebservice
from azureml.core.model import InferenceConfig
version = "test-"+str(uuid.uuid4())[:8]
env = Environment.from_conda_specification(name=version, file_path="conda_dependencies.yml")
inference_config = InferenceConfig(entry_script="score.py", environment=env)
ws = Workspace.from_config()
model = Model(ws, model_name)
aci_config = AciWebservice.deploy_configuration(
cpu_cores=1,
memory_gb=1,
)
service = Model.deploy(
workspace=ws,
name=version,
models=[model],
inference_config=inference_config,
deployment_config=aci_config,
overwrite=True,
)
service.wait_for_deployment(show_output=True)
- conda_dependencies.yml
channels:
- conda-forge
dependencies:
- python=3.8
- pip:
- azureml-defaults
- azureml-sdk
- numpy
- tensorflow==2.7.0
- score.py
import os
import json
import numpy as np
import tensorflow as tf
def init():
global model
model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR"), "model/data/model")
model = tf.keras.models.load_model(model_path)
def run(raw_data):
data = np.array(json.loads(raw_data)["data"])
y_hat = model.predict(data)
return y_hat.tolist()
我找不到将依赖项添加到 ML 推理的 Azure 容器实例的正确方法。
我基本上是按照本教程开始的：Train and deploy an image classification model with an example Jupyter Notebook
它工作正常。
现在我想部署经过训练的 TensorFlow 模型进行推理。我尝试了很多方法，但我始终无法将 python 依赖项添加到环境中。
来自 TensorFlow 精选环境
使用AzureML-tensorflow-2.4-ubuntu18.04-py37-cpu-inference：
from azureml.core import Workspace
# connect to your workspace
ws = Workspace.from_config()
# names
experiment_name = "my-experiment"
model_name = "my-model"
env_version="1"
env_name="my-env-"+env_version
service_name = str.lower(model_name + "-service-" + env_version)
# create environment for the deploy
from azureml.core.environment import Environment, DEFAULT_CPU_IMAGE
from azureml.core.conda_dependencies import CondaDependencies
from azureml.core.webservice import AciWebservice
# get a curated environment
env = Environment.get(
workspace=ws,
name="AzureML-tensorflow-2.4-ubuntu18.04-py37-cpu-inference",
# )
custom_env = env.clone(env_name)
custom_env.inferencing_stack_version='latest'
# add packages
conda_dep = CondaDependencies()
python_packages = ['joblib', 'numpy', 'os', 'json', 'tensorflow']
for package in python_packages:
conda_dep.add_pip_package(package)
conda_dep.add_conda_package(package)
# Adds dependencies to PythonSection of env
custom_env.python.user_managed_dependencies=True
custom_env.python.conda_dependencies=conda_dep
custom_env.register(workspace=ws)
# create deployment config i.e. compute resources
aciconfig = AciWebservice.deploy_configuration(
cpu_cores=1,
memory_gb=1,
tags={"experiment": experiment_name, "model": model_name},
)
from azureml.core.model import InferenceConfig
from azureml.core.model import Model
# get the registered model
model = Model(ws, model_name)
# create an inference config i.e. the scoring script and environment
inference_config = InferenceConfig(entry_script="score.py", environment=custom_env)
# deploy the service
service = Model.deploy(
workspace=ws,
name=service_name,
models=[model],
inference_config=inference_config,
deployment_config=aciconfig,
)
service.wait_for_deployment(show_output=True)
我得到以下日志：
AzureML image information: tensorflow-2.4-ubuntu18.04-py37-cpu-inference:20220110.v1
PATH environment variable: /opt/miniconda/envs/amlenv/bin:/opt/miniconda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
PYTHONPATH environment variable:
Pip Dependencies
---------------
EdgeHubConnectionString and IOTEDGE_IOTHUBHOSTNAME are not set. Exiting...
2022-01-24T10:21:09,855130300+00:00 - iot-server/finish 1 0
2022-01-24T10:21:09,856870100+00:00 - Exit code 1 is normal. Not restarting iot-server.
absl-py==0.15.0
applicationinsights==0.11.10
astunparse==1.6.3
azureml-inference-server-http==0.4.2
cachetools==4.2.4
certifi==2021.10.8
charset-normalizer==2.0.10
click==8.0.3
Flask==1.0.3
flatbuffers==1.12
gast==0.3.3
google-auth==2.3.3
google-auth-oauthlib==0.4.6
google-pasta==0.2.0
grpcio==1.32.0
gunicorn==20.1.0
h5py==2.10.0
idna==3.3
importlib-metadata==4.10.0
inference-schema==1.3.0
itsdangerous==2.0.1
Jinja2==3.0.3
Keras-Preprocessing==1.1.2
Markdown==3.3.6
MarkupSafe==2.0.1
numpy==1.19.5
oauthlib==3.1.1
opt-einsum==3.3.0
pandas==1.1.5
protobuf==3.19.1
pyasn1==0.4.8
pyasn1-modules==0.2.8
python-dateutil==2.8.2
pytz==2021.3
requests==2.27.1
requests-oauthlib==1.3.0
rsa==4.8
six==1.15.0
tensorboard==2.7.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
tensorflow==2.4.0
tensorflow-estimator==2.4.0
termcolor==1.1.0
typing-extensions==3.7.4.3
urllib3==1.26.8
Werkzeug==2.0.2
wrapt==1.12.1
zipp==3.7.0
Entry script directory: /var/azureml-app/.
Dynamic Python package installation is disabled.
Starting AzureML Inference Server HTTP.
Azure ML Inferencing HTTP server v0.4.2
Server Settings
---------------
Entry Script Name: score.py
Model Directory: /var/azureml-app/azureml-models/my-model/1
Worker Count: 1
Worker Timeout (seconds): 300
Server Port: 31311
Application Insights Enabled: false
Application Insights Key: None
Server Routes
---------------
Liveness Probe: GET 127.0.0.1:31311/
Score: POST 127.0.0.1:31311/score
Starting gunicorn 20.1.0
Listening at: http://0.0.0.0:31311 (69)
Using worker: sync
Booting worker with pid: 100
Exception in worker process
Traceback (most recent call last):
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/arbiter.py", line 589, in spawn_worker
worker.init_process()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/workers/base.py", line 134, in init_process
self.load_wsgi()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/workers/base.py", line 146, in load_wsgi
self.wsgi = self.app.wsgi()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/app/base.py", line 67, in wsgi
self.callable = self.load()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/app/wsgiapp.py", line 58, in load
return self.load_wsgiapp()
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/app/wsgiapp.py", line 48, in load_wsgiapp
return util.import_app(self.app_uri)
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/gunicorn/util.py", line 359, in import_app
mod = importlib.import_module(module)
File "/opt/miniconda/envs/amlenv/lib/python3.7/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "<frozen importlib._bootstrap>", line 1006, in _gcd_import
File "<frozen importlib._bootstrap>", line 983, in _find_and_load
File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 677, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 728, in exec_module
File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/azureml_inference_server_http/server/entry.py", line 1, in <module>
import create_app
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/azureml_inference_server_http/server/create_app.py", line 4, in <module>
from routes_common import main
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/azureml_inference_server_http/server/routes_common.py", line 32, in <module>
from aml_blueprint import AMLBlueprint
File "/opt/miniconda/envs/amlenv/lib/python3.7/site-packages/azureml_inference_server_http/server/aml_blueprint.py", line 28, in <module>
main_module_spec.loader.exec_module(main)
File "/var/azureml-app/score.py", line 4, in <module>
import joblib
ModuleNotFoundError: No module named 'joblib'
Worker exiting (pid: 100)
Shutting down: Master
Reason: Worker failed to boot.
2022-01-24T10:21:13,851467800+00:00 - gunicorn/finish 3 0
2022-01-24T10:21:13,853259700+00:00 - Exit code 3 is not normal. Killing image.
来自 Conda 规范
与以前相同，但使用来自 Conda 规范的全新环境并更改 env_version 数字：
# ...
env_version="2"
# ...
custom_env = Environment.from_conda_specification(name=env_name, file_path="my-env.yml")
custom_env.docker.base_image = DEFAULT_CPU_IMAGE
# ...
与 my-env.yml :
name: my-env
dependencies:
- python
- pip:
- azureml-defaults
- azureml-sdk
- sklearn
- numpy
- matplotlib
- joblib
- uuid
- requests
- tensorflow
我得到这个日志：
2022-01-24T11:06:54,887886931+00:00 - iot-server/run
2022-01-24T11:06:54,891839877+00:00 - rsyslog/run
2022-01-24T11:06:54,893640998+00:00 - gunicorn/run
2022-01-24T11:06:54,912032812+00:00 - nginx/run
EdgeHubConnectionString and IOTEDGE_IOTHUBHOSTNAME are not set. Exiting...
2022-01-24T11:06:55,398420960+00:00 - iot-server/finish 1 0
2022-01-24T11:06:55,414425146+00:00 - Exit code 1 is normal. Not restarting iot-server.
PATH environment variable: /opt/miniconda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
PYTHONPATH environment variable:
Pip Dependencies
---------------
brotlipy==0.7.0
certifi==2020.6.20
cffi @ file:///tmp/build/80754af9/cffi_1605538037615/work
chardet @ file:///tmp/build/80754af9/chardet_1605303159953/work
conda==4.9.2
conda-package-handling @ file:///tmp/build/80754af9/conda-package-handling_1603018138503/work
cryptography @ file:///tmp/build/80754af9/cryptography_1605544449973/work
idna @ file:///tmp/build/80754af9/idna_1593446292537/work
pycosat==0.6.3
pycparser @ file:///tmp/build/80754af9/pycparser_1594388511720/work
pyOpenSSL @ file:///tmp/build/80754af9/pyopenssl_1605545627475/work
PySocks @ file:///tmp/build/80754af9/pysocks_1594394576006/work
requests @ file:///tmp/build/80754af9/requests_1592841827918/work
ruamel-yaml==0.15.87
six @ file:///tmp/build/80754af9/six_1605205313296/work
tqdm @ file:///tmp/build/80754af9/tqdm_1605303662894/work
urllib3 @ file:///tmp/build/80754af9/urllib3_1603305693037/work
Starting HTTP server
2022-01-24T11:06:59,701365128+00:00 - gunicorn/finish 127 0
./run: line 127: exec: gunicorn: not found
2022-01-24T11:06:59,706177784+00:00 - Exit code 127 is not normal. Killing image.
我真的不知道我错过了什么，而且我已经搜索了太久（Azure 文档，SO，...）。
感谢您的帮助！
编辑：我尝试过的解决方案的非详尽列表：
- How to create AzureML environement and add required packages
- how to use existing conda environment as a AzureML environment
- ...
- https://docs.microsoft.com/en-us/azure/machine-learning/concept-environments#environment-building-caching-and-reuse
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments#add-packages-to-an-environment
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus
- https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where?tabs=python#define-a-deployment-configuration
- ...
如果你想创建一个自定义环境，你可以使用下面的代码来设置环境配置。
创造环境
myenv = Environment(name="Environment")
myenv.docker.enabled = True
myenv.python.conda_dependencies = CondaDependencies.create(conda_packages = ['numpy','scikit-learn','pip','pandas'], pip_packages = ['azureml-defaults~= 1.34.0','azureml','azureml-core~= 1.34.0',"azureml-sdk",'inference-schema','azureml-telemetry~= 1.34.0','azureml- train-automl~= 1.34.0','azure-ml-api-sdk','python-dotenv','azureml-contrib-server','azureml-inference-server-http'])
我认为在 Azure 服务器中实施 joblib 存在一个小的安全问题，请不要在您的代码中加载它，它会 运行。
好的，我成功了：我从头开始并且成功了。
我不知道我之前的所有尝试都出了什么问题，这太糟糕了。
多个问题以及我（认为我）如何解决它们：
joblib：实际上我不需要它来加载我的 Keras 模型。但问题不在于这个特定的库，而是我无法将依赖项添加到推理环境。Environment：最后，我只能使用自定义环境来实现：Environment.from_conda_specification(name=version, file_path="conda_dependencies.yml")。我无法将我的库（或指定特定的包版本）添加到“精选环境”。我不知道为什么...TensorFlow：我遇到的最后一个问题是我在 AzureML Notebook 的azureml_py38_PT_TF内核 (tensorflow==2.7.0) 中训练和注册了我的模型，并尝试在推理中加载它 Docker 图片 (tensorflow==2.4.0)。所以我必须指定我想在推理图像中使用的 TensorFlow 版本（这需要解决之前的问题）。
最终成功的方法：
- notebook.ipynb
import uuid
from azureml.core import Workspace, Environment, Model
from azureml.core.webservice import AciWebservice
from azureml.core.model import InferenceConfig
version = "test-"+str(uuid.uuid4())[:8]
env = Environment.from_conda_specification(name=version, file_path="conda_dependencies.yml")
inference_config = InferenceConfig(entry_script="score.py", environment=env)
ws = Workspace.from_config()
model = Model(ws, model_name)
aci_config = AciWebservice.deploy_configuration(
cpu_cores=1,
memory_gb=1,
)
service = Model.deploy(
workspace=ws,
name=version,
models=[model],
inference_config=inference_config,
deployment_config=aci_config,
overwrite=True,
)
service.wait_for_deployment(show_output=True)
- conda_dependencies.yml
channels:
- conda-forge
dependencies:
- python=3.8
- pip:
- azureml-defaults
- azureml-sdk
- numpy
- tensorflow==2.7.0
- score.py
import os
import json
import numpy as np
import tensorflow as tf
def init():
global model
model_path = os.path.join(os.getenv("AZUREML_MODEL_DIR"), "model/data/model")
model = tf.keras.models.load_model(model_path)
def run(raw_data):
data = np.array(json.loads(raw_data)["data"])
y_hat = model.predict(data)
return y_hat.tolist()