README.md

Contents of this Document

• Prerequisites
• Create TorchServe docker image
• Create torch-model-archiver from container
• Running TorchServe docker image in production

Prerequisites

• docker - Refer to the official docker installation guide

• git - Refer to the official git set-up guide

• For base Ubuntu with GPU, install following nvidia container toolkit and driver-

  • Nvidia container toolkit
  • Nvidia driver

• NOTE - Dockerfiles have not been tested on windows native platform.

First things first

If you have not cloned TorchServe source then:

git clone https://github.com/pytorch/serve.git
cd serve/docker

Create TorchServe docker image

Use build_image.sh script to build the docker images. The script builds the production, dev and codebuild docker images.

Parameter	Desciption
-h, --help	Show script help
-b, --branch_name	Specify a branch name to use. Default: master
-g, --gpu	Build image with GPU based ubuntu base image
-bt, --buildtype	Which type of docker image to build. Can be one of : production, dev, codebuild
-t, --tag	Tag name for image. If not specified, script uses torchserve default tag names.
-cv, --cudaversion	Specify to cuda version to use. Supported values cu92, cu101, cu102, cu111, cu113. Default cu102
--codebuild	Set if you need AWS CodeBuild

PRODUCTION ENVIRONMENT IMAGES

Creates a docker image with publicly available torchserve and torch-model-archiver binaries installed.

• To create a CPU based image

./build_image.sh

• To create a GPU based image with cuda 10.2. Options are cu92, cu101, cu102, cu111, cu113

./build_image.sh -g -cv cu102

• To create an image with a custom tag

./build_image.sh -t torchserve:1.0

DEVELOPER ENVIRONMENT IMAGES

Creates a docker image with torchserve and torch-model-archiver installed from source.

• For creating CPU based image :

./build_image.sh -bt dev

• For creating CPU based image with a different branch:

./build_image.sh -bt dev -b my_branch

• For creating GPU based image with cuda version 11.3:

./build_image.sh -bt dev -g -cv cu113

• For creating GPU based image with cuda version 11.1:

./build_image.sh -bt dev -g -cv cu111

• For creating GPU based image with cuda version 10.2:

./build_image.sh -bt dev -g -cv cu102

• For creating GPU based image with cuda version 10.1:

./build_image.sh -bt dev -g -cv cu101

• For creating GPU based image with cuda version 9.2:

./build_image.sh -bt dev -g -cv cu92

• For creating GPU based image with a different branch:

./build_image.sh -bt dev -g -cv cu113 -b my_branch

./build_image.sh -bt dev -g -cv cu111 -b my_branch

• For creating image with a custom tag:

./build_image.sh -bt dev -t torchserve-dev:1.0

CODEBUILD ENVIRONMENT IMAGES

Creates a docker image for codebuild environment

• For creating CPU based image :

./build_image.sh -bt codebuild

• For creating GPU based image with cuda version 11.1:

./build_image.sh -bt codebuild -g -cv cu113

./build_image.sh -bt codebuild -g -cv cu111

• For creating GPU based image with cuda version 10.1:

./build_image.sh -bt codebuild -g -cv cu101

• For creating GPU based image with cuda version 9.2:

./build_image.sh -bt codebuild -g -cv cu92

• For creating image with a custom tag:

./build_image.sh -bt codebuild -t torchserve-codebuild:1.0

Start a container with a TorchServe image

The following examples will start the container with 8080/81/82 and 7070/71 port exposed to outer-world/localhost.

Start CPU container

For the latest version, you can use the latest tag:

docker run --rm -it -p 8080:8080 -p 8081:8081 -p 8082:8082 -p 7070:7070 -p 7071:7071 pytorch/torchserve:latest

For specific versions you can pass in the specific tag to use (ex: pytorch/torchserve:0.1.1-cpu):

docker run --rm -it -p 8080:8080 -p 8081:8081 -p 8082:8082 -p 7070:7070 -p 7071:7071  pytorch/torchserve:0.1.1-cpu

Start GPU container

For GPU latest image with gpu devices 1 and 2:

docker run --rm -it --gpus '"device=1,2"' -p 8080:8080 -p 8081:8081 -p 8082:8082 -p 7070:7070 -p 7071:7071 pytorch/torchserve:latest-gpu

For specific versions you can pass in the specific tag to use (ex: 0.1.1-cuda10.1-cudnn7-runtime):

docker run --rm -it --gpus all -p 8080:8080 -p 8081:8081 -p 8082:8082 -p 7070:7070 -p 7071:7071 pytorch/torchserve:0.1.1-cuda10.1-cudnn7-runtime

For the latest version, you can use the latest-gpu tag:

docker run --rm -it --gpus all -p 8080:8080 -p 8081:8081 -p 8082:8082 -p 7070:7070 -p 7071:7071 pytorch/torchserve:latest-gpu

Accessing TorchServe APIs inside container

The TorchServe's inference and management APIs can be accessed on localhost over 8080 and 8081 ports respectively. Example :

curl http://localhost:8080/ping

Create torch-model-archiver from container

To create mar [model archive] file for TorchServe deployment, you can use following steps

1. Start container by sharing your local model-store/any directory containing custom/example mar contents as well as model-store directory (if not there, create it)

docker run --rm -it -p 8080:8080 -p 8081:8081 --name mar -v $(pwd)/model-store:/home/model-server/model-store -v $(pwd)/examples:/home/model-server/examples  pytorch/torchserve:latest

1. List your container or skip this if you know container name

docker ps

2. Bind and get the bash prompt of running container

docker exec -it <container_name> /bin/bash

You will be landing at /home/model-server/.

3. Download the model weights if you have not done so already (they are not part of the repo)

curl -o /home/model-server/examples/image_classifier/densenet161-8d451a50.pth https://download.pytorch.org/models/densenet161-8d451a50.pth

4. Now Execute torch-model-archiver command e.g.

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

Refer torch-model-archiver for details.

5. desnet161.mar file should be present at /home/model-server/model-store

Running TorchServe in a Production Docker Environment.

You may want to consider the following aspects / docker options when deploying torchserve in Production with Docker.

• Shared Memory Size

  • shm-size - The shm-size parameter allows you to specify the shared memory that a container can use. It enables memory-intensive containers to run faster by giving more access to allocated memory.

• User Limits for System Resources

  • --ulimit memlock=-1 : Maximum locked-in-memory address space.
  • --ulimit stack : Linux stack size

  The current ulimit values can be viewed by executing ulimit -a. A more exhaustive set of options for resource constraining can be found in the Docker Documentation here, here and here

• Exposing specific ports / volumes between the host & docker env.

  • -p8080:8080 -p8081:8081 -p 8082:8082 -p 7070:7070 -p 7071:7071 TorchServe uses default ports 8080 / 8081 / 8082 for REST based inference, management & metrics APIs and 7070 / 7071 for gRPC APIs. You may want to expose these ports to the host for HTTP & gRPC Requests between Docker & Host.
  • The model store is passed to torchserve with the --model-store option. You may want to consider using a shared volume if you prefer pre populating models in model-store directory.

For example,

docker run --rm --shm-size=1g \
        --ulimit memlock=-1 \
        --ulimit stack=67108864 \
        -p8080:8080 \
        -p8081:8081 \
        -p8082:8082 \
        -p7070:7070 \
        -p7071:7071 \
        --mount type=bind,source=/path/to/model/store,target=/tmp/models <container> torchserve --model-store=/tmp/models