Triton Inference Server is a powerful open-source tool developed by NVIDIA that simplifies the deployment of AI models at scale. It supports multiple frameworks, such as TensorFlow, PyTorch, and ONNX, allowing developers to serve models efficiently in production environments. Triton provides features like model versioning, dynamic batching, and multi-model support, making it a versatile choice for AI inference.
When working with Triton Inference Server, you might encounter an error indicating a ModelVersionMismatch. This error typically manifests when a client request specifies a model version that is not available on the server. The error message might look like this:
{
"error": "ModelVersionMismatch: The model version specified does not match the available versions."
}
The ModelVersionMismatch error occurs when the version of the model requested by the client does not align with the versions currently loaded on the Triton Inference Server. This can happen due to several reasons:
For more details on model versioning, refer to the Triton Model Configuration Documentation.
First, ensure that the model version you are requesting is available on the server. You can list the available versions by checking the model repository. Use the following command to list the models and their versions:
ls /path/to/model/repository/model_name/
Ensure that the version you are requesting is listed.
Once you have verified the available versions, update your client request to specify a valid model version. For example, if you are using the Triton HTTP API, ensure that the model_version parameter in your request matches one of the available versions:
{
"model_name": "your_model_name",
"model_version": "1"
}
Ensure that your model repository configuration is correct and includes the desired model versions. You can configure version policies in the config.pbtxt file. For example:
version_policy: {
specific: {
versions: [1, 2]
}
}
For more information on configuring model versions, visit the Triton Model Configuration Guide.
If you have made changes to the model repository or configuration, restart the Triton Inference Server to apply the changes:
docker restart triton_server_container_name
Ensure that the server is running with the updated configuration and model versions.
By following these steps, you should be able to resolve the ModelVersionMismatch error in Triton Inference Server. Always ensure that your client requests are aligned with the available model versions on the server. For further assistance, consider visiting the Triton Inference Server GitHub Repository for more resources and community support.
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