[DOCKER] I tried hosting a Pytorch sample model using TorchServe

Introduction

Torchserve is PyTorch's open source model service library developed in collaboration with AWS and Facebook. Just give the model class and weight file written in Pytorch to enable the host and provide the API endpoint. You can infer with the Inference API and manage your model with the Management API. See the TorchServe Documentation (https://pytorch.org/serve/index.html) for more information.

"Deploy a PyTorch model for large-scale inference using TorchServe" on the Amazon Web Services blog [https://aws.amazon.com/jp/blogs/news/deploying-pytorch-models-for- I read inference-at-scale-using-torchserve /) and tried running Torchserve on AWS EC2. In the following, I will introduce the procedure and its surroundings, and execution with docker.

reference

-[Deploy PyTorch models for large-scale inference using TorchServe](https://aws.amazon.com/jp/blogs/news/deploying-pytorch-models-for-inference-at-scale- using-torchserve /)

procedure

EC2 instance creation

Enter "Deep Learning AMI" in the AMI search bar to search for the AMI you want to use. This time, I used "Deep Learning AMI (Ubuntu 18.04) Version 30.0 --ami-0b1b56cbf0f8fcea3". I used "p2.xlarge" as the instance type. The security group is set up so that ssh and http can be connected from the development environment, and all other settings are left as default.

Environment

Log in to EC2 and build the environment.

~$ ls
LICENSE                README     examples  tools
Nvidia_Cloud_EULA.pdf  anaconda3  src       tutorials

torchserve requires Java 8 or later. Install Java 11 according to the tutorial. Switch the Java to be used after installation to Java 11.

~$ sudo apt-get install openjdk-11-jdk
~$ update-java-alternatives -l
java-1.11.0-openjdk-amd64      1111       /usr/lib/jvm/java-1.11.0-openjdk-amd64
java-1.8.0-openjdk-amd64       1081       /usr/lib/jvm/java-1.8.0-openjdk-amd64
~$ sudo update-alternatives --config java
There are 2 choices for the alternative java (providing /usr/bin/java).

  Selection    Path                                            Priority   Status
------------------------------------------------------------
  0            /usr/lib/jvm/java-11-openjdk-amd64/bin/java      1111      auto mode
* 1            /usr/lib/jvm/java-11-openjdk-amd64/bin/java      1111      manual mode
  2            /usr/lib/jvm/java-8-openjdk-amd64/jre/bin/java   1081      manual mode

Press <enter> to keep the current choice[*], or type selection number: 1     

Build a Python environment. Create a virtual environment. Install torchserve related libraries in the virtual environment.

~$ mkdir torchserve-examples
~$ cd torchserve-examples
~/torchserve-examples$ python -m venv venv
~/torchserve-examples$ source venv/bin/activate
(venv):~/torchserve-examples$ pip install torch torchtext torchvision sentencepiece psutil future
(venv):~/torchserve-examples$ pip install torchserve torch-model-archiver

Prepare the model to host. This time we will use the model published in the official repository.

(venv):~/torchserve-examples$ git clone https://github.com/pytorch/serve.git
(venv):~/torchserve-examples$ wget https://download.pytorch.org/models/densenet161-8d451a50.pth
(venv):~/torchserve-examples$ ls
densenet161-8d451a50.pth  serve  venv

The model used this time is stored in serve / examples / image_classifier / densenet_161 / model.py.

model.py

from torchvision.models.densenet import DenseNet


class ImageClassifier(DenseNet):
    def __init__(self):
        super(ImageClassifier, self).__init__(48, (6, 12, 36, 24), 96)

    def load_state_dict(self, state_dict, strict=True):
        # '.'s are no longer allowed in module names, but previous _DenseLayer
        # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
        # They are also in the checkpoints in model_urls. This pattern is used
        # to find such keys.
        # Credit - https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py#def _load_state_dict()
        import re
        pattern = re.compile(r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')

        for key in list(state_dict.keys()):
            res = pattern.match(key)
            if res:
                new_key = res.group(1) + res.group(2)
                state_dict[new_key] = state_dict[key]
                del state_dict[key]

        return super(ImageClassifier, self).load_state_dict(state_dict, strict)

In the previous _DenseLayer, the layer names were'norm.1','relu.1','conv.1','norm.2','relu.2', and'conv.2' with dots. However, dots are not available in the current _DenseLayer. It just inherits and renames DenseNet to use the old weights file in the current model. You can see the inheritance source DenseNet at here.

class DenseNet(nn.Module):
    r"""Densenet-BC model class, based on
    `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_

    Args:
        growth_rate (int) - how many filters to add each layer (`k` in paper)
        block_config (list of 4 ints) - how many layers in each pooling block
        num_init_features (int) - the number of filters to learn in the first convolution layer
        bn_size (int) - multiplicative factor for number of bottle neck layers
          (i.e. bn_size * k features in the bottleneck layer)
        drop_rate (float) - dropout rate after each dense layer
        num_classes (int) - number of classification classes
        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,
          but slower. Default: *False*. See `"paper" <https://arxiv.org/pdf/1707.06990.pdf>`_
    """

    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
                 num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, memory_efficient=False):

        super(DenseNet, self).__init__()

        # First convolution
        self.features = nn.Sequential(OrderedDict([
            ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2,
                                padding=3, bias=False)),
            ('norm0', nn.BatchNorm2d(num_init_features)),
            ('relu0', nn.ReLU(inplace=True)),
            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
        ]))

        # Each denseblock
        num_features = num_init_features
        for i, num_layers in enumerate(block_config):
            block = _DenseBlock(
                num_layers=num_layers,
                num_input_features=num_features,
                bn_size=bn_size,
                growth_rate=growth_rate,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient
            )
            self.features.add_module('denseblock%d' % (i + 1), block)
            num_features = num_features + num_layers * growth_rate
            if i != len(block_config) - 1:
                trans = _Transition(num_input_features=num_features,
                                    num_output_features=num_features // 2)
                self.features.add_module('transition%d' % (i + 1), trans)
                num_features = num_features // 2

        # Final batch norm
        self.features.add_module('norm5', nn.BatchNorm2d(num_features))

        # Linear layer
        self.classifier = nn.Linear(num_features, num_classes)

        # Official init from torch repo.
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                nn.init.constant_(m.bias, 0)

    def forward(self, x):
        features = self.features(x)
        out = F.relu(features, inplace=True)
        out = F.adaptive_avg_pool2d(out, (1, 1))
        out = torch.flatten(out, 1)
        out = self.classifier(out)
        return out


class _DenseBlock(nn.ModuleDict):
    _version = 2

    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, memory_efficient=False):
        super(_DenseBlock, self).__init__()
        for i in range(num_layers):
            layer = _DenseLayer(
                num_input_features + i * growth_rate,
                growth_rate=growth_rate,
                bn_size=bn_size,
                drop_rate=drop_rate,
                memory_efficient=memory_efficient,
            )
            self.add_module('denselayer%d' % (i + 1), layer)

    def forward(self, init_features):
        features = [init_features]
        for name, layer in self.items():
            new_features = layer(features)
            features.append(new_features)
        return torch.cat(features, 1)


class _DenseLayer(nn.Module):
    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False):
        super(_DenseLayer, self).__init__()
        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
        self.add_module('relu1', nn.ReLU(inplace=True)),
        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
                                           growth_rate, kernel_size=1, stride=1,
                                           bias=False)),
        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
        self.add_module('relu2', nn.ReLU(inplace=True)),
        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
                                           kernel_size=3, stride=1, padding=1,
                                           bias=False)),
        self.drop_rate = float(drop_rate)
        self.memory_efficient = memory_efficient

    def bn_function(self, inputs):
        # type: (List[Tensor]) -> Tensor
        concated_features = torch.cat(inputs, 1)
        bottleneck_output = self.conv1(self.relu1(self.norm1(concated_features)))  # noqa: T484
        return bottleneck_output

    # todo: rewrite when torchscript supports any
    def any_requires_grad(self, input):
        # type: (List[Tensor]) -> bool
        for tensor in input:
            if tensor.requires_grad:
                return True
        return False

    @torch.jit.unused  # noqa: T484
    def call_checkpoint_bottleneck(self, input):
        # type: (List[Tensor]) -> Tensor
        def closure(*inputs):
            return self.bn_function(inputs)

        return cp.checkpoint(closure, *input)

    @torch.jit._overload_method  # noqa: F811
    def forward(self, input):
        # type: (List[Tensor]) -> (Tensor)
        pass

    @torch.jit._overload_method  # noqa: F811
    def forward(self, input):
        # type: (Tensor) -> (Tensor)
        pass

    # torchscript does not yet support *args, so we overload method
    # allowing it to take either a List[Tensor] or single Tensor
    def forward(self, input):  # noqa: F811
        if isinstance(input, Tensor):
            prev_features = [input]
        else:
            prev_features = input

        if self.memory_efficient and self.any_requires_grad(prev_features):
            if torch.jit.is_scripting():
                raise Exception("Memory Efficient not supported in JIT")

            bottleneck_output = self.call_checkpoint_bottleneck(prev_features)
        else:
            bottleneck_output = self.bn_function(prev_features)

        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))
        if self.drop_rate > 0:
            new_features = F.dropout(new_features, p=self.drop_rate,
                                     training=self.training)
        return new_features

(Omitted below)

For the model to serve in this way, prepare a model class that inherits nn.Module. The above example was a bit confusing, so it's easy to see if you look at serve / examples / image_classifier / mnist / mnist.py.

mnist.py

import torch
from torch import nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = F.log_softmax(x, dim=1)
        return output

Model host

Now it's time to host the model. Convert to the format for deploying the model with torch-model-archiver.

(venv):~/torchserve-examples$ mkdir model_store
(venv):~/torchserve-examples$ torch-model-archiver --model-name densenet161 \
--version 1.0 --model-file serve/examples/image_classifier/densenet_161/model.py \
--serialized-file densenet161-8d451a50.pth \
--export-path model_store \
--extra-files serve/examples/image_classifier/index_to_name.json \
--handler image_classifier
(venv):~/torchserve-examples$ ls model_store/
densenet161.mar

A file called densenet161.mar is created. A description of the options can be found here (https://github.com/pytorch/serve/blob/master/model-archiver/README.md).

(venv):~/torchserve-examples$ torch-model-archiver -h
usage: torch-model-archiver [-h] --model-name MODEL_NAME --serialized-file
                            SERIALIZED_FILE [--model-file MODEL_FILE]
                            --handler HANDLER [--source-vocab SOURCE_VOCAB]
                            [--extra-files EXTRA_FILES]
                            [--runtime {python,python2,python3}]
                            [--export-path EXPORT_PATH]
                            [--archive-format {tgz,no-archive,default}] [-f]
                            -v VERSION

Torch Model Archiver Tool

optional arguments:
  -h, --help            show this help message and exit
  --model-name MODEL_NAME
                        Exported model name. Exported file will be named as
                        model-name.mar and saved in current working directory if no --export-path is
                        specified, else it will be saved under the export path
  --serialized-file SERIALIZED_FILE
                        Path to .pt or .pth file containing state_dict in case of eager mode
                        or an executable ScriptModule in case of TorchScript.
  --model-file MODEL_FILE
                        Path to python file containing model architecture.
                        This parameter is mandatory for eager mode models.
                        The model architecture file must contain only one
                        class definition extended from torch.nn.modules.
  --handler HANDLER     TorchServe's default handler name
                         or handler python file path to handle custom TorchServe inference logic.
  --source-vocab SOURCE_VOCAB
                        Vocab file for source language. Required for text based models.
  --extra-files EXTRA_FILES
                        Comma separated path to extra dependency files.
  --runtime {python,python2,python3}
                        The runtime specifies which language to run your inference code on.
                        The default runtime is "python".
  --export-path EXPORT_PATH
                        Path where the exported .mar file will be saved. This is an optional
                        parameter. If --export-path is not specified, the file will be saved in the
                        current working directory. 
  --archive-format {tgz,no-archive,default}
                        The format in which the model artifacts are archived.
                        "tgz": This creates the model-archive in <model-name>.tar.gz format.
                        If platform hosting TorchServe requires model-artifacts to be in ".tar.gz"
                        use this option.
                        "no-archive": This option creates an non-archived version of model artifacts
                        at "export-path/{model-name}" location. As a result of this choice, 
                        MANIFEST file will be created at "export-path/{model-name}" location
                        without archiving these model files
                        "default": This creates the model-archive in <model-name>.mar format.
                        This is the default archiving format. Models archived in this format
                        will be readily hostable on native TorchServe.
  -f, --force           When the -f or --force flag is specified, an existing .mar file with same
                        name as that provided in --model-name in the path specified by --export-path
                        will overwritten
  -v VERSION, --version VERSION
                        Model's version

The options used this time are as follows.

ʻExtra-files has a rule [ʻindex_to_name.json](https://github.com/pytorch/serve/blob/master/examples/image_classifier/index_to_name.json) that converts the predicted index to a string. It is specified.

• The contents of json are {"0 ": [" n01440764 "," tench "]," 1 ": ["n01443537 "," goldfish "]," 2 ": ["n01484850 "," great_white_shark "] ,. It's .., but I wasn't sure what the first component of the array represented. Also, if you do not specify ʻextra-files`, it seems that a 503 error will occur even if you host it.

You can implement the handler yourself. The handle method is the entry point. The arguments are data and context, data is an array of requests, and the property of context is here ) Can be seen. See the Documentation (https://pytorch.org/serve/custom_service.html) for more information. Examples of MNIST can be found in serve / examples / image_classifier / mnist / mnist_handler.py.

import io
import logging
import numpy as np
import os
import torch
from PIL import Image
from torch.autograd import Variable
from torchvision import transforms

logger = logging.getLogger(__name__)


class MNISTDigitClassifier(object):
    """
    MNISTDigitClassifier handler class. This handler takes a greyscale image
    and returns the digit in that image.
    """

    def __init__(self):
        self.model = None
        self.mapping = None
        self.device = None
        self.initialized = False

    def initialize(self, ctx):
        """First try to load torchscript else load eager mode state_dict based model"""

        properties = ctx.system_properties
        self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
        model_dir = properties.get("model_dir")

        # Read model serialize/pt file
        model_pt_path = os.path.join(model_dir, "mnist_cnn.pt")
        # Read model definition file
        model_def_path = os.path.join(model_dir, "mnist.py")
        if not os.path.isfile(model_def_path):
            raise RuntimeError("Missing the model definition file")

        from mnist import Net
        state_dict = torch.load(model_pt_path, map_location=self.device)
        self.model = Net()
        self.model.load_state_dict(state_dict)
        self.model.to(self.device)
        self.model.eval()

        logger.debug('Model file {0} loaded successfully'.format(model_pt_path))
        self.initialized = True

    def preprocess(self, data):
        """
         Scales, crops, and normalizes a PIL image for a MNIST model,
         returns an Numpy array
        """
        image = data[0].get("data")
        if image is None:
            image = data[0].get("body")

        mnist_transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307,), (0.3081,))
        ])
        image = Image.open(io.BytesIO(image))
        image = mnist_transform(image)
        return image

    def inference(self, img, topk=5):
        ''' Predict the class (or classes) of an image using a trained deep learning model.
        '''
        # Convert 2D image to 1D vector
        img = np.expand_dims(img, 0)
        img = torch.from_numpy(img)

        self.model.eval()
        inputs = Variable(img).to(self.device)
        outputs = self.model.forward(inputs)

        _, y_hat = outputs.max(1)
        predicted_idx = str(y_hat.item())
        return [predicted_idx]

    def postprocess(self, inference_output):
        return inference_output


_service = MNISTDigitClassifier()


def handle(data, context):
    if not _service.initialized:
        _service.initialize(context)

    if data is None:
        return None

    data = _service.preprocess(data)
    data = _service.inference(data)
    data = _service.postprocess(data)

    return data

Host the model. Specify the directory where the .mar file is stored in --model-store. Specify in --models in the format of model name = file path. If there are multiple models, you can specify multiple models separated by commas.

(venv):~/torchserve-examples$ torchserve --start --model-store model_store --models densenet161=densenet161.mar

Try requesting the inference API on the same host. The endpoint will be / predictions / {model name}.

$ curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg
$ curl -X POST http://127.0.0.1:8080/predictions/densenet161 -T kitten.jpg
[
  {
    "tiger_cat": 0.4693354070186615
  },
  {
    "tabby": 0.46338820457458496
  },
  {
    "Egyptian_cat": 0.06456134468317032
  },
  {
    "lynx": 0.0012828148901462555
  },
  {
    "plastic_bag": 0.00023322994820773602
  }
]

Since ʻimage_classifier` is specified in the handler, the top 5 prediction probabilities are returned. tiger_cat is a tabby cat and tabby is a tabby cat (I don't know the difference). You can see that it can be predicted as a cat for the time being.

Model management

The management API is provided on port 8081.

(venv):~/torchserve-examples$ curl "http://127.0.0.1:8081/models"
{
  "models": [
    {
      "modelName": "densenet161",
      "modelUrl": "densenet161.mar"
    }
  ]
}

Suppose you have another model. Let's prepare with the code below.

(venv):~/torchserve-examples$ wget https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth
(venv):~/torchserve-examples$ torch-model-archiver --model-name fastrcnn --version 1.0 \
--model-file serve/examples/object_detector/fast-rcnn/model.py \
--serialized-file fasterrcnn_resnet50_fpn_coco-258fb6c6.pth \
--export-path model_store \
--handler object_detector \
--extra-files serve/examples/object_detector/index_to_name.json

Register the model from the management API.

$ curl -X POST "http://127.0.0.1:8081/models?url=fastrcnn.mar"
{
  "status": "Model \"fastrcnn\" Version: 1.0 registered with 0 initial workers. Use scale workers API to add workers for the model."
}
$ curl "http://127.0.0.1:8081/models"
{
  "models": [
    {
      "modelName": "densenet161",
      "modelUrl": "densenet161.mar"
    },
    {
      "modelName": "fastrcnn",
      "modelUrl": "fastrcnn.mar"
    }
  ]
}

No workers are assigned to the new model, so the following code sets the minimum number of workers.

$ curl -v -X PUT "http://127.0.0.1:8081/models/fastrcnn?min_worker=2"
$ curl "http://localhost:8081/models/fastrcnn"
[
  {
    "modelName": "fastrcnn",
    "modelVersion": "1.0",
    "modelUrl": "fastrcnn.mar",
    "runtime": "python",
    "minWorkers": 2,
    "maxWorkers": 2,
    "batchSize": 1,
    "maxBatchDelay": 100,
    "loadedAtStartup": false,
    "workers": [
      {
        "id": "9001",
        "startTime": "2020-07-15T13:55:11.813Z",
        "status": "READY",
        "gpu": true,
        "memoryUsage": 0
      },
      {
        "id": "9002",
        "startTime": "2020-07-15T13:55:11.813Z",
        "status": "READY",
        "gpu": true,
        "memoryUsage": 0
      }
    ]
  }
]

You can also unregister the model.

$ curl -X DELETE http://localhost:8081/models/fastrcnn/
{
  "status": "Model \"fastrcnn\" unregistered"
}
$ curl "http://127.0.0.1:8081/models"
{
  "models": [
    {
      "modelName": "densenet161",
      "modelUrl": "densenet161.mar"
    }
  ]
}

By default, the API can only be accessed from the local host, so make it accessible from the outside as well. Create config.properties.

(venv):~/torchserve-examples$ touch config.properties

The contents are as follows.

inference_address=http://0.0.0.0:8080

Set config.properties to --ts-config.

(venv):~/torchserve-examples$ torchserve --start --model-store model_store --models densenet161=densenet161.mar --ts-config config.properties

You can access the API from the outside.

$ curl -X POST http://<host ip address>:8080/predictions/densenet161 -T kitten.jpg

Please refer to here for SSL settings and CORS settings.

Deploy with docker

~$ docker --version
Docker version 19.03.11, build 42e35e61f3

Check the version of CUDA. The version was 10.0.

~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2018 NVIDIA Corporation
Built on Sat_Aug_25_21:08:01_CDT_2018
Cuda compilation tools, release 10.0, V10.0.130

Check the version of cuDNN. The version was 7.5.1.

~$ cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 7
#define CUDNN_MINOR 5
#define CUDNN_PATCHLEVEL 1
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)

#include "driver_types.h"

GPU compatible container Only cudnn7 is supported, so first change the CUDA version. How to change is described in here.

~$ sudo rm /usr/local/cuda
~$ sudo ln -s /usr/local/cuda-10.1 /usr/local/cuda
~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243

It is 10.1 properly.

Next, create a docker image. Create a Dockerfile and config.properties packed with the above operations. The working directory is ~ / torchserve-examples2.

torchserve-examples2/
　├ Dockerfile
　└ config.properties

Dockerfile

FROM alpine/git AS build

WORKDIR /work

COPY . .

RUN git clone https://github.com/pytorch/serve.git && \
    wget https://download.pytorch.org/models/densenet161-8d451a50.pth && \
    mkdir model_store

# FROM pytorch/torchserve:0.1.1-cpu
FROM pytorch/torchserve:0.1.1-cuda10.1-cudnn7-runtime

COPY --from=build /work /home/model-server

WORKDIR /home/model-server

RUN torch-model-archiver --model-name densenet161 \
    --version 1.0 --model-file serve/examples/image_classifier/densenet_161/model.py \
    --serialized-file densenet161-8d451a50.pth \
    --export-path /home/model-server/model-store \
    --extra-files serve/examples/image_classifier/index_to_name.json \
    --handler image_classifier

CMD ["torchserve", \
    "--start",\
    "--models", "densenet161=densenet161.mar",\
    "--ts-config", "config.properties"]

config.properties

inference_address=http://0.0.0.0:8080
model_store=/home/model-server/model-store

Here, since git and wget are not included in the torchserve container, I created the build container and the execution container separately.

Run the container with the following command:

~$ docker build -t sample/torchserve:latest .
~$ docker run -d --rm -t -p 8080:8080 -p 8081:8081 sample/torchserve:latest

Make a request to the API from the development machine.

$ curl -X POST http://<<host ip address>>:8080/predictions/densenet161 -T kitten.jpg
[
  {
    "tiger_cat": 0.4693359136581421
  },
  {
    "tabby": 0.4633873701095581
  },
  {
    "Egyptian_cat": 0.06456154584884644
  },
  {
    "lynx": 0.001282821292988956
  },
  {
    "plastic_bag": 0.00023323031200561672
  }
]

Other notes

--There is no authentication function. --By default, TorchServe prints log messages to stderr and stout. TorchServe uses log4j and can customize logging by changing the log4j property.