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Containers / tensorflow-resnet: README

Last updated on Aug 05, 2025

Bitnami package for TensorFlow ResNet

What is TensorFlow ResNet?

TensorFlow ResNet is a client utility for use with TensorFlow Serving and ResNet models.

Overview of TensorFlow ResNet Trademarks: This software listing is packaged by Bitnami. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

TL;DR

Before running the docker image you first need to download the ResNet model training checkpoint so it will be available for the TensorFlow Serving server.

mkdir -p /tmp/model-data/1
cd /tmp/model-data
curl -o resnet_50_classification_1.tar.gz https://storage.googleapis.com/tfhub-modules/tensorflow/resnet_50/classification/1.tar.gz
tar xzf resnet_50_classification_1.tar.gz -C 1

Why use Bitnami Images?

  • Bitnami closely tracks upstream source changes and promptly publishes new versions of this image using our automated systems.
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  • Bitnami container images are released on a regular basis with the latest distribution packages available.

Looking to use TensorFlow ResNet in production? Try VMware Tanzu Application Catalog, the commercial edition of the Bitnami catalog.

Why use a non-root container?

Non-root container images add an extra layer of security and are generally recommended for production environments. However, because they run as a non-root user, privileged tasks are typically off-limits. Learn more about non-root containers in our docs.

Supported tags and respective Dockerfile links

Learn more about the Bitnami tagging policy and the difference between rolling tags and immutable tags in our documentation page.

You can see the equivalence between the different tags by taking a look at the tags-info.yaml file present in the branch folder, i.e bitnami/ASSET/BRANCH/DISTRO/tags-info.yaml.

Subscribe to project updates by watching the bitnami/containers GitHub repo.

Prerequisites

To run this application you need Docker Engine 1.10.0.

How to use this image

Run TensorFlow ResNet client with TensorFlow Serving

Running TensorFlow ResNet client with the TensorFlow Serving server is the recommended way.

Run the application manually

  1. Create a new network for the application and the database:

    docker network create tensorflow-tier

  2. Start a Tensorflow Serving server in the network generated:

    docker run -d -v /tmp/model-data:/bitnami/model-data -e TENSORFLOW_SERVING_MODEL_NAME=resnet -p 8500:8500 -p 8501:8501 --name tensorflow-serving --net tensorflow-tier bitnami/tensorflow-serving:latest

    Note: You need to give the container a name in order to TensorFlow ResNet client to resolve the host

  3. Run the TensorFlow ResNet client container:

    docker run -d -v /tmp/model-data:/bitnami/model-data --name tensorflow-resnet --net tensorflow-tier bitnami/tensorflow-resnet:latest

Upgrade this application

Bitnami provides up-to-date versions of Tensorflow-Serving and TensorFlow ResNet client, including security patches, soon after they are made upstream. We recommend that you follow these steps to upgrade your container. We will cover here the upgrade of the TensorFlow ResNet client container. For the Tensorflow-Serving upgrade see https://github.com/bitnami/containers/tree/main/bitnami/tensorflow-serving#user-content-upgrade-this-image

  1. Get the updated images:

    docker pull bitnami/tensorflow-resnet:latest

  2. Stop your container

    • $ docker stop tensorflow-resnet

  3. Take a snapshot of the application state

    rsync -a tensorflow-resnet-persistence tensorflow-resnet-persistence.bkp.$(date +%Y%m%d-%H.%M.%S)

Additionally, snapshot the TensorFlow Serving data

You can use these snapshots to restore the application state should the upgrade fail.

  1. Remove the currently running container

    • $ docker rm tensorflow-resnet

  2. Run the new image

    • Mount the directories if needed: docker run --name tensorflow-resnet bitnami/tensorflow-resnet:latest

Configuration

Predict an image

Once you have deployed both the TensorFlow Serving and TensorFlow ResNet containers you can use the resnet_client_cc utility to predict images. To do that follow the next steps:

  1. Exec into the TensorFlow ResNet container.

  2. Download an image:

    curl -L --output cat.jpeg https://tensorflow.org/images/blogs/serving/cat.jpg

  3. Send the image to the TensorFlow Serving server.

    resnet_client_cc --server_port=tensorflow-serving:8500 --image_file=./cat.jpg

  4. The model says the image belongs to the category 286. You can check the imagenet classes index to see how the category 286 correspond to a cougar.

    calling predict using file: cat.jpg  ...
call predict ok
outputs size is 2
the result tensor[0] is:
[2.41628254e-06 1.90121955e-06 2.72477027e-05 4.4263885e-07 8.98362089e-07 6.84422412e-06 1.66555201e-05 3.4298439e-06 5.25692e-06 2.66782135e-05...]...
the result tensor[1] is:
286
Done.

Environment variables

Tensorflow Resnet can be customized by specifying environment variables on the first run. The following environment values are provided to custom Tensorflow:

Customizable environment variables

Name Description Default Value
TF_RESNET_SERVING_PORT_NUMBER Tensorflow serving port number 8500
TF_RESNET_SERVING_HOST Tensorflow serving host name tensorflow-serving

Read-only environment variables

Notable Changes

Starting January 16, 2024

  • The docker-compose.yaml file has been removed, as it was solely intended for internal testing purposes.

2.4.1-debian-10-r87

  • The container initialization logic is now using bash.

Contributing

We'd love for you to contribute to this Docker image. You can request new features by creating an issue or submitting a pull request with your contribution.

Issues

If you encountered a problem running this container, you can file an issue. For us to provide better support, be sure to fill the issue template.

License

Copyright © 2024 Broadcom. The term "Broadcom" refers to Broadcom Inc. and/or its subsidiaries.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.