Chainguard Image for tensorflow

An Open Source Machine Learning Framework for Everyone

Chainguard Images are regularly-updated, minimal container images with low-to-zero CVEs.

Download this Image

This image is available on cgr.dev:

docker pull cgr.dev/ORGANIZATION/tensorflow:latest

Be sure to replace the ORGANIZATION placeholder with the name used for your organization's private repository within the Chainguard registry.

Compatibility Notes

Chainguard's TensorFlow CPU image is comparable to the CPU accelerated TensorFlow image with Jupyter (tensorflow/tensorflow:latest-jupyter) while the GPU image is comparable to the the tensorflow/tensorflow:latest-gpu-jupyter image.

Chainguard's TensorFlow CPU images have tags ending with -cpu-jupyter and the GPU images have tags ending with -gpu-jupyter. Note that the latest and latest-dev tags for Chainguard's TensorFlow image point to the GPU variant.

Getting Started

CPU

In order to use Chainguard's TensorFlow CPU variant image, run the following command in your project's directory:

docker run --rm --name=tensorflow-cpu -p 8888:8888 -v $(pwd):/tf cgr.dev/ORGANIZATION/tensorflow:2.18.0-cpu

You will need to retrieve the token from the logs of the container to access the Jupyter UI. The logs will look like this:

docker logs tensorflow-cpu
# truncated
[I 2025-01-24 16:20:06.742 ServerApp] Jupyter Server 2.15.0 is running at:
[I 2025-01-24 16:20:06.742 ServerApp] http://b7834ef33453:8888/tree?token=403a50321893cf6c986b0a1f7fd5862ef4f25bb9989d5b87
[I 2025-01-24 16:20:06.742 ServerApp]     http://127.0.0.1:8888/tree?token=403a50321893cf6c986b0a1f7fd5862ef4f25bb9989d5b87
[I 2025-01-24 16:20:06.742 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).

You can then access the Jupyter UI by visiting the given URL. For example: http://127.0.0.1:8888/tree?token=403a50321893cf6c986b0a1f7fd5862ef4f25bb9989d5b87.

To visit the JupyterLab UI, we can click on View followed by Open JupyterLab to access the JupyterLab UI.

GPU

In order to use Chainguard's TensorFlow CPU variant image, run the following command:

docker run --pull always --entrypoint bash -it --rm --gpus all  cgr.dev/ORGANIZATION/tensorflow:latest bash

Then, within the container, run the following commands:

nvidia-smi
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

You can also try running Python scripts. To illustrate, create a file named test.py and add the following content to it:

import tensorflow as tf
from tensorflow.keras import layers, models, datasets
import numpy as np

# Load a smaller subset of CIFAR-10
(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()
x_train, y_train = x_train[:500], y_train[:500]
x_test, y_test = x_test[:100], y_test[:100]

# Normalize pixel values
x_train, x_test = x_train / 255.0, x_test / 255.0

# Convert to NumPy arrays and ensure correct dtype
x_train = np.array(x_train, dtype=np.float32)
x_test = np.array(x_test, dtype=np.float32)
y_train = np.array(y_train, dtype=np.int64).reshape(-1)
y_test = np.array(y_test, dtype=np.int64).reshape(-1)

# Build a very simple model
model = models.Sequential([
    layers.Flatten(input_shape=(32, 32, 3)),
    layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train with a smaller batch size
print("Training the model on GPU...")
with tf.device('/GPU:0'):
    history = model.fit(x_train, y_train, epochs=1, batch_size=16)

# Evaluate
print("\nEvaluating the model on test data...")
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(f"\nTest accuracy: {test_acc * 100:.2f}%")

Then run the following command to execute the Python script:

python test.py

Documentation and Resources

• TensorFlow documentation for CPU-only images.
• TensorFlow documentation for GPU-enabled images.

Contact Support

If you have a Zendesk account (typically set up for you by your Customer Success Manager) you can reach out to Chainguard's Customer Success team through our Zendesk portal.

What are Chainguard Images?

Chainguard Images are a collection of container images designed for security and minimalism.

Many Chainguard Images are distroless; they contain only an open-source application and its runtime dependencies. These images do not even contain a shell or package manager. Chainguard Images are built with Wolfi, our Linux undistro designed to produce container images that meet the requirements of a secure software supply chain.

The main features of Chainguard Images include:

• Minimal design, with no unnecessary software bloat
• Automated nightly builds to ensure Images are completely up-to-date and contain all available security patches
• High quality build-time SBOMs (software bills of materials) attesting the provenance of all artifacts within the Image
• Verifiable signatures provided by Sigstore
• Reproducible builds with Cosign and apko (read more about reproducibility)

-dev Variants

As mentioned previously, Chainguard’s distroless Images have no shell or package manager by default. This is great for security, but sometimes you need these things, especially in builder images. For those cases, most (but not all) Chainguard Images come paired with a -dev variant which does include a shell and package manager.

Although the -dev image variants have similar security features as their distroless versions, such as complete SBOMs and signatures, they feature additional software that is typically not necessary in production environments. The general recommendation is to use the -dev variants only to build the application and then copy all application artifacts into a distroless image, which will result in a final container image that has a minimal attack surface and won’t allow package installations or logins.

That being said, it’s worth noting that -dev variants of Chainguard Images are completely fine to run in production environments. After all, the -dev variants are still more secure than many popular container images based on fully-featured operating systems such as Debian and Ubuntu since they carry less software, follow a more frequent patch cadence, and offer attestations for what they include.

Learn More

To better understand how to work with Chainguard Images, we encourage you to visit Chainguard Academy, our documentation and education platform.