无法使用自定义预测例程将经过训练的模型部署到 Google Cloud Ai-Platform:模型需要的内存超出允许范围 [英] Cannot deploy trained model to Google Cloud Ai-Platform with custom prediction routine: Model requires more memory than allowed
问题描述
我正在尝试部署一个预训练的 pytorch 模型到具有自定义预测例程的 AI Platform.按照此处所述的说明进行操作后，部署失败并显示以下内容错误:
I am trying to deploy a pretrained pytorch model to AI Platform with a custom prediction routine. After following the instructions described here the deployment fails with the following error:
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.
模型文件夹的内容为 83.89 MB 大，低于文档中描述的 250 MB 限制.该文件夹中的唯一文件是模型的检查点文件 (.pth) 和自定义预测例程所需的 tarball.
The contents of the model folder are 83.89 MB large and are below the 250 MB limit that's described in the documentation. The only files in the folder are the checkpoint file (.pth) for the model and the tarball required for the custom prediction routine.
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 --machine-type 参数更改为 mls1-c4-m2 就像 Parth 建议的那样，但我仍然遇到相同的错误.
Changing the runtime version to 1.14 leads to the same error.
I have tried changing the --machine-type argument to mls1-c4-m2 like Parth suggested but I still get the same error.
生成 my_custom_code-0.1.tar.gz 的 setup.py 文件如下所示:
The setup.py file that generates my_custom_code-0.1.tar.gz looks like this:
setup(
name='my_custom_code',
version='0.1',
scripts=['predictor.py'],
install_requires=["opencv-python", "torch"]
)
来自预测器的相关代码片段:
Relevant code snippet from the predictor:
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，我在 AI Platform 中为模型启用了日志记录，并获得以下输出:
Additionally I have enabled logging for the model in AI Platform and I get the following outputs:
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
推荐答案
这是一个常见问题，我们理解这是一个痛点.请执行以下操作:
This is a common problem and we understand this is a pain point. Please do the following:
torchvision有torch作为依赖项，默认情况下，它从 pypi 中拉取torch.
torchvisionhastorchas dependency and by default, it pullstorchfrom pypi.
在部署模型时，即使你指向使用自定义的ai-platform torchvision 包它也会这样做，因为torchvision 是由PyTorch 团队构建的，它是配置为使用 torch 作为依赖项.这个来自 pypi 的 torch 依赖项提供了一个 720mb 的文件，因为它包含了 GPU 单元
When deploying the model, even if you point to use custom ai-platform torchvision packages it will do it, since torchvision when is built by PyTorch team, it is configured to use torch as dependency. This torch dependency from pypi, gives a 720mb file because it includes the GPU units
- 要解决 #1，您需要构建
torchvision并告诉torchvision你想从哪里得到torch，你需要设置它去torch网站，因为包较小.使用 Python 重建torchvision二进制文件 PEP-0440 直接引用 功能.在torchvisionsetup.py 我们有:
- To solve #1, you need to build
torchvisionfrom source and telltorchvisionwhere you want to gettorchfrom, you need to set it to go to thetorchwebsite as the package is smaller. Rebuild thetorchvisionbinary using Python PEP-0440 direct references feature. Intorchvisionsetup.py we have:
pytorch_dep = 'torch'
if os.getenv('PYTORCH_VERSION'):
pytorch_dep += "==" + os.getenv('PYTORCH_VERSION')
更新 torchvision 中的 setup.py 以使用直接引用功能:
Update setup.py in torchvision to use direct references feature:
requirements = [
#'numpy',
#'six',
#pytorch_dep,
'torch @ https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl'
]
* 我已经为你做了这个*，所以我建立了 3 个你可以使用的轮文件:
* I already did this for you*, so I build 3 wheel files you can use:
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)
这些 torchvision 包将从 Torch 站点而不是 pypi 获取 torch:(例如:https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl)
These torchvision packages will get torch from the torch site instead of pypi: (Example: https://download.pytorch.org/whl/cpu/torch-1.4.0%2Bcpu-cp37-cp37m-linux_x86_64.whl)
在将模型部署到 AI Platform 时更新您的模型
setup.py，使其不包含torch或torchvision.
Update your model
setup.pywhen deploying the model to AI Platform so it does not includetorchnortorchvision.
重新部署模型如下:
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}
您可以将 PYTORCH_VISION_PACKAGE 更改为我在 #2 中提到的任何选项
You can change the PYTORCH_VISION_PACKAGE to any of the options I mentioned in #2
这篇关于无法使用自定义预测例程将经过训练的模型部署到 Google Cloud Ai-Platform:模型需要的内存超出允许范围的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
