Chainguard Container for tritonserver-pytorch-backend-fips

The Triton backend for the PyTorch TorchScript models with FIPS support.

Chainguard Containers are regularly-updated, secure-by-default container images.

Download this Container Image

For those with access, this container image is available on cgr.dev:

docker pull cgr.dev/ORGANIZATION/tritonserver-pytorch-backend-fips:latest

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

Compatibility Notes

The Chainguard tritonserver-pytorch-backend-fips image is functionally comparable to the official NVIDIA nvcr.io/nvidia/tritonserver:*-pyt-python-py3 image. This image contains the PyTorch backend (libtriton_pytorch.so) and supporting libraries such as TorchVision and Torch-TensorRT,

This image is optimized for inference only. It does not include utilities for model training or conversion. Use a full PyTorch environment (e.g., the NVIDIA PyTorch image) to prepare and export models.

FIPS Support

The tritonserver-pytorch-backend-fips Chainguard Image ships with a validated redistribution of the OpenSSL's FIPS provider module. For more on FIPS support in Chainguard Images, consult the guide on FIPS-enabled Chainguard Images on Chainguard Academy.

Getting Started

The following instructions serve a TorchScript-exported ResNet-50 model using the Triton Inference Server with the PyTorch backend.

Step 1: Export a TorchScript model using NVIDIA’s PyTorch container

MODEL_PATH=$(pwd)/model_repository/1
mkdir -p "$MODEL_PATH"

docker run --rm -v "$MODEL_PATH:/out" nvcr.io/nvidia/pytorch:25.04-py3   python3 -c '
import torch
import torchvision.models as models
model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model.eval()
torch.jit.script(model).save("/out/model.pt")'

Step 2: Create the model configuration file

cat > model_repository/config.pbtxt <<EOF
name: "resnet50"
platform: "pytorch_libtorch"
max_batch_size: 1
input [
  {
    name: "input"
    data_type: TYPE_FP32
    format: FORMAT_NCHW
    dims: [3, 224, 224]
  }
]
output [
  {
    name: "output"
    data_type: TYPE_FP32
    dims: [1000]
  }
]
EOF

Step 3: Run Triton Inference Server using the Chainguard PyTorch backend image

docker run --rm --name triton -v "$(pwd)/model_repository:/models" -p8000:8000 -p8001:8001 -p8002:8002 cgr.dev/ORGANIZATION/tritonserver-pytorch-backend-fips:latest --model-repository=/models

Step 5: Run inference with dummy input

Once the server becomes ready, create the inference request JSON:

python3 -c '
import json
with open("infer_input.json", "w") as f:
  json.dump({
    "inputs": [{
      "name": "input",
      "shape": [1, 3, 224, 224],
      "datatype": "FP32",
      "data": [0.0] * (1 * 3 * 224 * 224)
    }],
    "outputs": [{"name": "output"}]
  }, f)
'

Send the request:

curl -sf localhost:8000/v2/models/resnet50/infer   -H "Content-Type: application/json"   -d @infer_input.json | jq

You should see a JSON response with output logits of shape [1, 1000] indicating successful inference!

What are Chainguard Containers?

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All other Chainguard Containers are built with Chainguard OS, Chainguard's minimal Linux operating system designed to produce container images that meet the requirements of a more secure software supply chain.

The main features of Chainguard Containers include:

• Minimal design, without unnecessary software bloat
• Daily builds to ensure container images are up-to-date with available security patches
• High quality build-time SBOMs attesting to the provenance of all artifacts within the image
• Verifiable signatures provided by Sigstore
• Reproducible builds with Cosign and apko (read more about reproducibility)

For cases where you need container images with shells and package managers to build or debug, most Chainguard Containers come paired with a development, or -dev, variant.

In all other cases, including Chainguard Containers tagged as :latest or with a specific version number, the container images include only an open-source application and its runtime dependencies. These minimal container images typically do not contain a shell or package manager.

Although the -dev container image variants have similar security features as their more minimal versions, they include additional software that is typically not necessary in production environments. We recommend using multi-stage builds to copy artifacts from the -dev variant into a more minimal production image.

Need additional packages?

To improve security, Chainguard Containers include only essential dependencies. Need more packages? Chainguard customers can use Custom Assembly to add packages, either through the Console, chainctl, or API.

To use Custom Assembly in the Chainguard Console: navigate to the image you'd like to customize in your Organization's list of images, and click on the Customize image button at the top of the page.

Learn More

Refer to our Chainguard Containers documentation on Chainguard Academy. Chainguard also offers VMs and Libraries — contact us for access.

Trademarks

This software listing is packaged by Chainguard. The trademarks set forth in this offering are owned by their respective companies, and use of them does not imply any affiliation, sponsorship, or endorsement by such companies.