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集装箱火炬工人下载启动时下载新的序列化文件

发布于 2025-02-10 20:26:18 字数 8065 浏览 1 评论 0 原文

我试图通过此示例，使用验证的快速RCNN模型构建一个运行火炬的容器，以在多合一的Dockerfile中进行对象检测： https://github.com/github.com/pytorch/pytorch/pytorch/serve/serve/serve/serve/tree/主/示例/object_detector/fast-rcnn

dockerfile ：

FROM pytorch/torchserve:latest

COPY ["config.properties", "model.py", "fasterrcnn_resnet50_fpn_coco-258fb6c6.pth", "index_to_name.json", "/home/model-server/"]

RUN torch-model-archiver \
  --model-name=fastrcnn \
  --version=1.0 \
  --model-file=/home/model-server/model.py \
  --serialized-file=/home/model-server/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth \
  --handler=object_detector \
  --extra-files=/home/model-server/index_to_name.json \
  --export-path=/home/model-server/model-store

RUN rm model.py fasterrcnn_resnet50_fpn_coco-258fb6c6.pth index_to_name.json

CMD ["torchserve", \
     "--start", \
     "--model-store", "model-store", \
     "--ts-config", "config.properties", \
     "--models", "fastrcnn=fastrcnn.mar"]

model.py.py 和 index_to_name.json 是从示例（< a href =“ https://github.com/pytorch/serve/serve/tree/master/master/examples/object_detector/fast-rcnn” rel =“ nofollow noreferrer”> https://github.com/github.com/pytorc.com/pytorch/pytorch/pytorch/serve/serve/tree/tree/master/master/master/master/master/master /示例/object_detector/fast-rcnn ）并放置在根目录中。

fasterrcnn_resnet50_fpn_coco-258fb6c6.pth can be downloaded from https:// downlation.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth

config.properties ：

inference_address=http://0.0.0.0:8080
management_address=http://0.0.0.0:8081
metrics_address=http://0.0.0.0:8082
number_of_netty_threads=32
job_queue_size=1000
model_store=/home/model-server/model-store
workflow_store=/home/model-server/wf-store
default_workers_per_model=1

使用： docker build-tag aio-fastrcnn。 运行良好（多合一的AIO）。

运行容器： docker run -rm -It -P 8080：8080 -P 8081：8081 -name fastrcnn aio -fastrcnn：最新 也可以运行良好，但是在启动期间，工人下载了另一个序列化模型。

model_log.log ：

model-server@15838dd41e69:~$ cat logs/model_log.log
2022-06-26T13:45:07,171 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9000
2022-06-26T13:45:07,172 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - [PID]35
2022-06-26T13:45:07,173 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - Torch worker started.
2022-06-26T13:45:07,173 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - Python runtime: 3.8.0
2022-06-26T13:45:07,191 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9000.
2022-06-26T13:45:07,242 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - model_name: fastrcnn, batchSize: 1
2022-06-26T13:45:07,809 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - generated new fontManager
2022-06-26T13:45:08,757 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG - Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /home/model-server/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
2022-06-26T13:45:08,919 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -
2022-06-26T13:45:09,021 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -   0%|          | 0.00/97.8M [00:00<?, ?B/s]
2022-06-26T13:45:09,125 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -   0%|          | 280k/97.8M [00:00<00:36, 2.81MB/s]
2022-06-26T13:45:09,230 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -   1%|          | 592k/97.8M [00:00<00:34, 2.97MB/s]
2022-06-26T13:45:09,341 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -   1%|          | 960k/97.8M [00:00<00:31, 3.26MB/s]
...
2022-06-26T13:45:45,230 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -  99%|█████████▉| 96.7M/97.8M [00:36<00:00, 2.68MB/s]
2022-06-26T13:45:45,344 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -  99%|█████████▉| 97.0M/97.8M [00:36<00:00, 2.67MB/s]
2022-06-26T13:45:45,449 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -  99%|█████████▉| 97.2M/97.8M [00:36<00:00, 2.63MB/s]
2022-06-26T13:45:45,547 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG - 100%|█████████▉| 97.5M/97.8M [00:36<00:00, 2.68MB/s]
2022-06-26T13:45:45,548 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG - 100%|██████████| 97.8M/97.8M [00:36<00:00, 2.80MB/s]

下载文件后，服​​务器运行良好，可以进行调整并运行正确的模型。

ping：

$ curl http://localhost:8080/ping -UseBasicParsing


StatusCode        : 200
StatusDescription : OK
Content           : {
                      "status": "Healthy"
                    }
...

模型：

$ curl http://localhost:8081/models -UseBasicParsing


StatusCode        : 200
StatusDescription : OK
Content           : {
                      "models": [
                        {
                          "modelName": "fastrcnn",
                          "modelUrl": "fastrcnn.mar"
                        }
                      ]
                    }
...

我不明白为什么下载了这个新的序列化文件。我以为火炬模型-Archiver 背后的想法是将所有必要的文件组合到一个文件中。我是否从根本上误解了Torchserve或Docker的工作方式？

startup_log.log ：

model-server@15838dd41e69:~$ cat logs/config/20220626134506612-startup.cfg
#Saving snapshot
#Sun Jun 26 13:45:06 GMT 2022
inference_address=http\://0.0.0.0\:8080
default_workers_per_model=1
load_models=fastrcnn\=fastrcnn.mar
model_store=model-store
number_of_gpu=0
job_queue_size=1000
python=/home/venv/bin/python
model_snapshot={\n  "name"\: "20220626134506612-startup.cfg",\n  "modelCount"\: 1,\n  "created"\: 1656251106614,\n  "models"\: {\n    "fastrcnn"\: {\n      "1.0"\: {\n        "defaultVersion"\: true,\n        "marName"\: "fastrcnn.mar",\n        "minWorkers"\: 1,\n        "maxWorkers"\: 1,\n        "batchSize"\: 1,\n        "maxBatchDelay"\: 100,\n        "responseTimeout"\: 120\n      }\n    }\n  }\n}
tsConfigFile=config.properties
version=0.6.0
workflow_store=model-store
number_of_netty_threads=32
management_address=http\://0.0.0.0\:8081
metrics_address=http\://0.0.0.0\:8082

我也尝试了这些步骤更换 fasterrcnn_resnet50_fpn_coco-258fb6c6.pth 和 resnet50-0676ba61.pth 在所有必要的地方。但是工人仍然下载 resnet50-0676ba61.pth 在启动期间。

resnet50-0676ba61.pth 在模型构建过程中需要，并且pytorch检查文件是否在 model_dir （如果不是下载）中。我将Dockerfile更新以复制 resnet50-0676ba61.pth 中的火炬容器，并将其作为超额文件包含在火炬模型Arac​​hiver命令中。 dockerfile ：

FROM pytorch/torchserve:latest

COPY ["config.properties", \
      "model.py", \
      "fasterrcnn_resnet50_fpn_coco-258fb6c6.pth", \
      "resnet50-0676ba61.pth", \
      "index_to_name.json", \
      "/home/model-server/"]

RUN torch-model-archiver \
  --model-name=fastrcnn \
  --version=1.0 \
  --model-file=/home/model-server/model.py \
  --serialized-file=/home/model-server/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth \
  --handler=object_detector \
  --extra-files=/home/model-server/resnet50-0676ba61.pth,/home/model-server/index_to_name.json \
  --export-path=/home/model-server/model-store

CMD ["torchserve", \
     "--start", \
     "--model-store", "model-store", \
     "--ts-config", "config.properties", \
     "--models", "fastrcnn=fastrcnn.mar"]

根据 resnet50-0676ba61.pth 应该可以访问，但仍会在每家初创公司下载它。

原文

I am trying to build a container running torchserve with the pretrained fast-rcnn model for object detection in a all-in-one Dockerfile, based on this example:
https://github.com/pytorch/serve/tree/master/examples/object_detector/fast-rcnn

Dockerfile:

FROM pytorch/torchserve:latest

COPY ["config.properties", "model.py", "fasterrcnn_resnet50_fpn_coco-258fb6c6.pth", "index_to_name.json", "/home/model-server/"]

RUN torch-model-archiver \
  --model-name=fastrcnn \
  --version=1.0 \
  --model-file=/home/model-server/model.py \
  --serialized-file=/home/model-server/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth \
  --handler=object_detector \
  --extra-files=/home/model-server/index_to_name.json \
  --export-path=/home/model-server/model-store

RUN rm model.py fasterrcnn_resnet50_fpn_coco-258fb6c6.pth index_to_name.json

CMD ["torchserve", \
     "--start", \
     "--model-store", "model-store", \
     "--ts-config", "config.properties", \
     "--models", "fastrcnn=fastrcnn.mar"]

model.py and index_to_name.json are taken from the example (https://github.com/pytorch/serve/tree/master/examples/object_detector/fast-rcnn) and placed in the root directory.

fasterrcnn_resnet50_fpn_coco-258fb6c6.pth can be downloaded from https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth

config.properties:

inference_address=http://0.0.0.0:8080
management_address=http://0.0.0.0:8081
metrics_address=http://0.0.0.0:8082
number_of_netty_threads=32
job_queue_size=1000
model_store=/home/model-server/model-store
workflow_store=/home/model-server/wf-store
default_workers_per_model=1

Building the container with:
docker build --tag aio-fastrcnn .
runs fine (aio for all-in-one).

Running the container with:
docker run --rm -it -p 8080:8080 -p 8081:8081 --name fastrcnn aio-fastrcnn:latest
also runs fine, but during start-up the worker downloads a different serialized model.

model_log.log:

model-server@15838dd41e69:~$ cat logs/model_log.log
2022-06-26T13:45:07,171 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9000
2022-06-26T13:45:07,172 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - [PID]35
2022-06-26T13:45:07,173 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - Torch worker started.
2022-06-26T13:45:07,173 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - Python runtime: 3.8.0
2022-06-26T13:45:07,191 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9000.
2022-06-26T13:45:07,242 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - model_name: fastrcnn, batchSize: 1
2022-06-26T13:45:07,809 [INFO ] W-9000-fastrcnn_1.0-stdout MODEL_LOG - generated new fontManager
2022-06-26T13:45:08,757 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG - Downloading: "https://download.pytorch.org/models/resnet50-0676ba61.pth" to /home/model-server/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth
2022-06-26T13:45:08,919 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -
2022-06-26T13:45:09,021 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -   0%|          | 0.00/97.8M [00:00<?, ?B/s]
2022-06-26T13:45:09,125 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -   0%|          | 280k/97.8M [00:00<00:36, 2.81MB/s]
2022-06-26T13:45:09,230 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -   1%|          | 592k/97.8M [00:00<00:34, 2.97MB/s]
2022-06-26T13:45:09,341 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -   1%|          | 960k/97.8M [00:00<00:31, 3.26MB/s]
...
2022-06-26T13:45:45,230 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -  99%|█████████▉| 96.7M/97.8M [00:36<00:00, 2.68MB/s]
2022-06-26T13:45:45,344 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -  99%|█████████▉| 97.0M/97.8M [00:36<00:00, 2.67MB/s]
2022-06-26T13:45:45,449 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG -  99%|█████████▉| 97.2M/97.8M [00:36<00:00, 2.63MB/s]
2022-06-26T13:45:45,547 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG - 100%|█████████▉| 97.5M/97.8M [00:36<00:00, 2.68MB/s]
2022-06-26T13:45:45,548 [WARN ] W-9000-fastrcnn_1.0-stderr MODEL_LOG - 100%|██████████| 97.8M/97.8M [00:36<00:00, 2.80MB/s]

https://download.pytorch.org/models/resnet50-0676ba61.pth

Once the file is downloaded the server runs fine, can be pinged and runs the correct model.

Ping:

$ curl http://localhost:8080/ping -UseBasicParsing


StatusCode        : 200
StatusDescription : OK
Content           : {
                      "status": "Healthy"
                    }
...

Model:

$ curl http://localhost:8081/models -UseBasicParsing


StatusCode        : 200
StatusDescription : OK
Content           : {
                      "models": [
                        {
                          "modelName": "fastrcnn",
                          "modelUrl": "fastrcnn.mar"
                        }
                      ]
                    }
...

I don't understand why this new serialized file is downloaded. I thought the idea behind the torch-model-archiver was to combine all necessary files into a single one. Have I fundamentally misunderstood something about how torchserve or docker works?

startup_log.log:

model-server@15838dd41e69:~$ cat logs/config/20220626134506612-startup.cfg
#Saving snapshot
#Sun Jun 26 13:45:06 GMT 2022
inference_address=http\://0.0.0.0\:8080
default_workers_per_model=1
load_models=fastrcnn\=fastrcnn.mar
model_store=model-store
number_of_gpu=0
job_queue_size=1000
python=/home/venv/bin/python
model_snapshot={\n  "name"\: "20220626134506612-startup.cfg",\n  "modelCount"\: 1,\n  "created"\: 1656251106614,\n  "models"\: {\n    "fastrcnn"\: {\n      "1.0"\: {\n        "defaultVersion"\: true,\n        "marName"\: "fastrcnn.mar",\n        "minWorkers"\: 1,\n        "maxWorkers"\: 1,\n        "batchSize"\: 1,\n        "maxBatchDelay"\: 100,\n        "responseTimeout"\: 120\n      }\n    }\n  }\n}
tsConfigFile=config.properties
version=0.6.0
workflow_store=model-store
number_of_netty_threads=32
management_address=http\://0.0.0.0\:8081
metrics_address=http\://0.0.0.0\:8082

I have also tried these steps replacing
fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
with
resnet50-0676ba61.pth
in all necessary places. But the worker still downloads resnet50-0676ba61.pth during startup.

resnet50-0676ba61.pth is needed during model building and pytorch checks if the file is in the model_dir, if not it is downloaded. I updated the Dockerfile to copy resnet50-0676ba61.pth into the torchserve container and included it as an extra-file in the torch-model-archiver command.
Dockerfile:

FROM pytorch/torchserve:latest

COPY ["config.properties", \
      "model.py", \
      "fasterrcnn_resnet50_fpn_coco-258fb6c6.pth", \
      "resnet50-0676ba61.pth", \
      "index_to_name.json", \
      "/home/model-server/"]

RUN torch-model-archiver \
  --model-name=fastrcnn \
  --version=1.0 \
  --model-file=/home/model-server/model.py \
  --serialized-file=/home/model-server/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth \
  --handler=object_detector \
  --extra-files=/home/model-server/resnet50-0676ba61.pth,/home/model-server/index_to_name.json \
  --export-path=/home/model-server/model-store

CMD ["torchserve", \
     "--start", \
     "--model-store", "model-store", \
     "--ts-config", "config.properties", \
     "--models", "fastrcnn=fastrcnn.mar"]

According to https://github.com/pytorch/serve/issues/633#issuecomment-677759331 resnet50-0676ba61.pth should be accessible but it still gets downloaded on every startup.

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