Inference Endpoints (dedicated) documentation

Use a custom Container Image

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Use a custom Container Image

Inference Endpoints not only allows you to customize your inference handler, but it also allows you to provide a custom container image. Those can be public images like tensorflow/serving:2.7.3 or private Images hosted on Docker Hub, AWS ECR, Azure ACR, or Google GCR.

custom container config

The creation flow of your Image artifacts from a custom image is the same as the base image. This means Inference Endpoints will create a unique image artifact derived from your provided image, including all Model Artifacts.

The Model Artifacts (weights) are stored under /repository. For example, if you usetensorflow/serving as your custom image, then you have to set `model_base_path=“/repository”:

tensorflow_model_server \
  --rest_api_port=5000 \
  --model_name=my_model \
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