# Use a custom Container Image


Inference Endpoints not only allows you to [customize your inference handler](/docs/inference-endpoints/guides/custom_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](https://hub.docker.com/), [AWS ECR](https://aws.amazon.com/ecr/?nc1=h_ls), [Azure ACR](https://azure.microsoft.com/de-de/services/container-registry/), or [Google GCR](https://cloud.google.com/container-registry?hl=de). 

<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/custom_container.png" alt="custom container config" />


The [creation flow](/docs/inference-endpoints/guides/create_endpoint) 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 use` tensorflow/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 \
  --model_base_path="/repository"
```

