Triton Inference Server Model version mismatch error encountered during inference request.
Triton Inference Server Model version mismatch error encountered during inference request.
Understanding Triton Inference Server
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.
Identifying the Symptom: Model Version Mismatch
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."
}
Exploring the Issue: What Causes Model Version Mismatch?
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:
- The model version specified in the request is incorrect or does not exist.
- The model repository configuration does not include the requested version.
- The server has not been updated with the latest model versions.
For more details on model versioning, refer to the Triton Model Configuration Documentation.
Steps to Resolve the Model Version Mismatch
Step 1: Verify Available Model Versions
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.
Step 2: Update the Client Request
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"
}
Step 3: Check Model Repository Configuration
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.
Step 4: Restart Triton Inference Server
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.
Conclusion
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.
Master
Triton Inference Server
debugging in Minutes
— Grab the Ultimate Cheatsheet
(Perfect for DevOps & SREs)
Triton Inference Server
Cheatsheet
(Perfect for DevOps & SREs)
MORE ISSUES
Backed by
