无法使用自定义的预测例程将经过训练的模型部署到Google Cloud Ai平台：模型所需的内存超出了允许的范围

时间：2019-12-17 10:52:25

标签： google-cloud-platform pytorch google-cloud-ml gcp-ai-platform-training

我正在尝试使用自定义预测例程将预训练的pytorch model部署到ai平台。按照here中所述的说明进行操作后，部署失败并显示以下错误：

ERROR: (gcloud.beta.ai-platform.versions.create) Create Version failed. Bad model detected with error: Model requires more memory than allowed. Please try to decrease the model size and re-deploy. If you continue to have error, please contact Cloud ML.

模型文件夹的内容大83.89 MB，并且低于文档中描述的250 MB限制。文件夹中唯一的文件是模型的检查点文件（.pth）和自定义预测例程所需的tarball。

创建模型的命令：

gcloud beta ai-platform versions create pose_pytorch --model pose --runtime-version 1.15 --python-version 3.5 --origin gs://rcg-models/pytorch_pose_estimation --package-uris gs://rcg-models/pytorch_pose_estimation/my_custom_code-0.1.tar.gz --prediction-class predictor.MyPredictor

将运行时版本更改为1.14会导致相同的错误。 我已经尝试将Partition建议的--machine-type参数更改为mls1-c4-m2，但是仍然出现相同的错误。

生成my_custom_code-0.1.tar.gz的setup.py文件如下所示：

setup(
    name='my_custom_code',
    version='0.1',
    scripts=['predictor.py'],
    install_requires=["opencv-python", "torch"]
)

预测变量的相关代码段：

    def __init__(self, model):
        """Stores artifacts for prediction. Only initialized via `from_path`.
        """
        self._model = model
        self._client = storage.Client()

    @classmethod
    def from_path(cls, model_dir):
        """Creates an instance of MyPredictor using the given path.

        This loads artifacts that have been copied from your model directory in
        Cloud Storage. MyPredictor uses them during prediction.

        Args:
            model_dir: The local directory that contains the trained Keras
                model and the pickled preprocessor instance. These are copied
                from the Cloud Storage model directory you provide when you
                deploy a version resource.

        Returns:
            An instance of `MyPredictor`.
        """

        net = PoseEstimationWithMobileNet()
        checkpoint_path = os.path.join(model_dir, "checkpoint_iter_370000.pth")
        checkpoint = torch.load(checkpoint_path, map_location='cpu')
        load_state(net, checkpoint)

        return cls(net)

此外，我已经在AI平台中启用了该模型的日志记录，并且得到以下输出：

2019-12-17T09:28:06.208537Z OpenBLAS WARNING - could not determine the L2 cache size on this system, assuming 256k 
2019-12-17T09:28:13.474653Z WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/google/cloud/ml/prediction/frameworks/tf_prediction_lib.py:48: The name tf.saved_model.tag_constants.SERVING is deprecated. Please use tf.saved_model.SERVING instead. 
2019-12-17T09:28:13.474680Z {"textPayload":"","insertId":"5df89fad00073e383ced472a","resource":{"type":"cloudml_model_version","labels":{"project_id":"rcg-shopper","region":"","version_id":"lightweight_pose_pytorch","model_id":"pose"}},"timestamp":"2019-12-17T09:28:13.474680Z","logName":"projects/rcg-shopper/logs/ml.googleapis… 
2019-12-17T09:28:13.474807Z WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/google/cloud/ml/prediction/frameworks/tf_prediction_lib.py:50: The name tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY is deprecated. Please use tf.saved_model.DEFAULT_SERVING_SIGNATURE_DEF_KEY instead. 
2019-12-17T09:28:13.474829Z {"textPayload":"","insertId":"5df89fad00073ecd4836d6aa","resource":{"type":"cloudml_model_version","labels":{"project_id":"rcg-shopper","region":"","version_id":"lightweight_pose_pytorch","model_id":"pose"}},"timestamp":"2019-12-17T09:28:13.474829Z","logName":"projects/rcg-shopper/logs/ml.googleapis… 
2019-12-17T09:28:13.474918Z WARNING:tensorflow: 
2019-12-17T09:28:13.474927Z The TensorFlow contrib module will not be included in TensorFlow 2.0. 
2019-12-17T09:28:13.474934Z For more information, please see: 
2019-12-17T09:28:13.474941Z   * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md 
2019-12-17T09:28:13.474951Z   * https://github.com/tensorflow/addons 
2019-12-17T09:28:13.474958Z   * https://github.com/tensorflow/io (for I/O related ops) 
2019-12-17T09:28:13.474964Z If you depend on functionality not listed there, please file an issue. 
2019-12-17T09:28:13.474999Z {"textPayload":"","insertId":"5df89fad00073f778735d7c3","resource":{"type":"cloudml_model_version","labels":{"version_id":"lightweight_pose_pytorch","model_id":"pose","project_id":"rcg-shopper","region":""}},"timestamp":"2019-12-17T09:28:13.474999Z","logName":"projects/rcg-shopper/logs/ml.googleapis… 
2019-12-17T09:28:15.283483Z ERROR:root:Failed to import GA GRPC module. This is OK if the runtime version is 1.x 
2019-12-17T09:28:16.890923Z Copying gs://cml-489210249453-1560169483791188/models/pose/lightweight_pose_pytorch/15316451609316207868/user_code/my_custom_code-0.1.tar.gz... 
2019-12-17T09:28:16.891150Z / [0 files][    0.0 B/  8.4 KiB]                                                 
2019-12-17T09:28:17.007684Z / [1 files][  8.4 KiB/  8.4 KiB]                                                 
2019-12-17T09:28:17.009154Z Operation completed over 1 objects/8.4 KiB.                                       
2019-12-17T09:28:18.953923Z Processing /tmp/custom_code/my_custom_code-0.1.tar.gz 
2019-12-17T09:28:19.808897Z Collecting opencv-python 
2019-12-17T09:28:19.868579Z   Downloading https://files.pythonhosted.org/packages/d8/38/60de02a4c9013b14478a3f681a62e003c7489d207160a4d7df8705a682e7/opencv_python-4.1.2.30-cp37-cp37m-manylinux1_x86_64.whl (28.3MB) 
2019-12-17T09:28:21.537989Z Collecting torch 
2019-12-17T09:28:21.552871Z   Downloading https://files.pythonhosted.org/packages/f9/34/2107f342d4493b7107a600ee16005b2870b5a0a5a165bdf5c5e7168a16a6/torch-1.3.1-cp37-cp37m-manylinux1_x86_64.whl (734.6MB) 
2019-12-17T09:28:52.401619Z Collecting numpy>=1.14.5 
2019-12-17T09:28:52.412714Z   Downloading https://files.pythonhosted.org/packages/9b/af/4fc72f9d38e43b092e91e5b8cb9956d25b2e3ff8c75aed95df5569e4734e/numpy-1.17.4-cp37-cp37m-manylinux1_x86_64.whl (20.0MB) 
2019-12-17T09:28:53.550662Z Building wheels for collected packages: my-custom-code 
2019-12-17T09:28:53.550689Z   Building wheel for my-custom-code (setup.py): started 
2019-12-17T09:28:54.212558Z   Building wheel for my-custom-code (setup.py): finished with status 'done' 
2019-12-17T09:28:54.215365Z   Created wheel for my-custom-code: filename=my_custom_code-0.1-cp37-none-any.whl size=7791 sha256=fd9ecd472a6a24335fd24abe930a4e7d909e04bdc4cf770989143d92e7023f77 
2019-12-17T09:28:54.215482Z   Stored in directory: /tmp/pip-ephem-wheel-cache-i7sb0bmb/wheels/0d/6e/ba/bbee16521304fc5b017fa014665b9cae28da7943275a3e4b89 
2019-12-17T09:28:54.222017Z Successfully built my-custom-code 
2019-12-17T09:28:54.650218Z Installing collected packages: numpy, opencv-python, torch, my-custom-code

3 个答案:

答案 0 :(得分：2)

通过调整setup.py，我可以成功。基本上install_requires会尝试获取PyPI托管的torch软件包，这是一个巨大的GPU构建轮子，已经超出了部署配额。以下setup.py注入了从官方pytorch索引中获取CPU内置割炬的安装命令。

from setuptools import setup, find_packages
from setuptools.command.install import install as _install

INSTALL_REQUIRES = ['pillow']

CUSTOM_INSTALL_COMMANDS = [
    # Install torch here.
    [
        'python-default', '-m', 'pip', 'install', '--target=/tmp/custom_lib',
        '-b', '/tmp/pip_builds', 'torch==1.4.0+cpu', 'torchvision==0.5.0+cpu',
        '-f', 'https://download.pytorch.org/whl/torch_stable.html'
    ],
]

class Install(_install):
    def run(self):
        import sys
        if sys.platform == 'linux':
            import subprocess
            import logging
            for command in CUSTOM_INSTALL_COMMANDS:
                logging.info('Custom command: ' + ' '.join(command))
                result = subprocess.run(
                    command, check=True, stdout=subprocess.PIPE
                )
                logging.info(result.stdout.decode('utf-8', 'ignore'))
        _install.run(self)

setup(
    name='predictor',
    version='0.1',
    packages=find_packages(),
    install_requires=INSTALL_REQUIRES,
    cmdclass={'install': Install},
)

答案 1 :(得分：2)

在经历了数小时的老试验错误之后，我得出了与@kyamagu相同的结论，“ install_requires试图获取PyPI托管的割炬程序包，该程序包是GPU内置的巨大轮子，超出了部署配额。”

但是，他的解决方案对我不起作用。因此，经过多个小时的试用错误（由于缺少文档和错误的错误），我想出了以下解决方案：

我们需要获得大约100 MB的Pytorch的cpu内置轮子，而不是默认PyPI托管的700 MB GPU内置轮子。您可以在这里找到它们：https://download.pytorch.org/whl/cpu/torch_stable.html

下一步，我们需要将它们放置在gs存储中，然后将路径作为--package-uris的一部分给出，如下所示：

gcloud beta ai-platform versions create v17 \
    --model=newest \
    --origin=gs://bucket \
    --runtime-version=1.15 \
    --python-version=3.7 \
    --package-uris=gs://bucket/predictor-0.1.tar.gz,gs://bucket/torch-1.3.0+cpu-cp37-cp37m-linux_x86_64.whl \
    --prediction-class=predictor.MyPredictor \
    --machine-type=mls1-c4-m4

另外，请注意package-uris的顺序，predictor软件包应位于第一个位置，并且逗号后不应有空格。

希望这会有所帮助。欢呼！

答案 2 :(得分：2)

这是一个常见问题，我们知道这是一个痛点。请执行以下操作：

  1. torchvision具有torch作为依赖项，默认情况下，它从pypi中提取torch。

在部署模型时，即使您指向使用自定义ai平台torchvision包也可以做到，因为torchvision是由PyTorch团队构建的，因此配置为使用{{1 }}作为依赖项。来自pypi的torch依赖项提供了720mb的文件，因为它包含GPU单元

  1. 要解决＃1，您需要从源头build torch并告诉torchvision您想从哪里获得torchvision，您需要将其设置为torch网站，因为软件包较小。使用Python PEP-0440 direct references功能重建torch二进制文件。在torchvision setup.py中，我们有：
torchvision

更新pytorch_dep = 'torch' if os.getenv('PYTORCH_VERSION'): pytorch_dep += "==" + os.getenv('PYTORCH_VERSION') 中的setup.py以使用直接引用功能：

torchvision

*我已经为您完成了此操作* ，因此我构建了3个Wheel文件供您使用：

requirements = [
     #'numpy',
     #'six',
     #pytorch_dep,
     'torch @ https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl'
]

这些gs://dpe-sandbox/torchvision-0.4.0-cp37-cp37m-linux_x86_64.whl (torch 1.2.0, vision 0.4.0) gs://dpe-sandbox/torchvision-0.4.2-cp37-cp37m-linux_x86_64.whl (torch 1.2.0, vision 0.4.2) gs://dpe-sandbox/torchvision-0.5.0-cp37-cp37m-linux_x86_64.whl (torch 1.4.0 vision 0.5.0) 软件包将从火炬网站获取torchvision而不是pypi ：（例如：https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl）

  1. 在将模型部署到AI平台时更新模型torch，使其不包含setup.py或torch。

  2. 按如下所示重新部署模型：

torchvision

您可以将PYTORCH_VISION_PACKAGE=gs://dpe-sandbox/torchvision-0.5.0-cp37-cp37m-linux_x86_64.whl gcloud beta ai-platform versions create {MODEL_VERSION} --model={MODEL_NAME} \ --origin=gs://{BUCKET}/{GCS_MODEL_DIR} \ --python-version=3.7 \ --runtime-version={RUNTIME_VERSION} \ --machine-type=mls1-c4-m4 \ --package-uris=gs://{BUCKET}/{GCS_PACKAGE_URI},{PYTORCH_VISION_PACKAGE}\ --prediction-class={MODEL_CLASS} 更改为我在＃2中提到的任何选项

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