Triton Inference Server is a powerful tool developed by NVIDIA that simplifies the deployment of AI models at scale. It supports multiple frameworks such as TensorFlow, PyTorch, and ONNX, allowing for seamless integration and efficient model serving. Triton is designed to handle high-performance inference workloads, making it an essential component in AI-driven applications.
When working with Triton Inference Server, you might encounter the ModelVersionNotFound error. This error typically occurs when you attempt to query a specific version of a model that is not available in the server's model repository. The error message might look something like this:
{
"error": "ModelVersionNotFound: The specified model version is not available."
}
The ModelVersionNotFound error indicates that the server cannot find the requested model version. This could be due to several reasons, such as the model version not being deployed, a typo in the version number, or the model repository not being updated with the latest version. Understanding the root cause is crucial for resolving this issue effectively.
To resolve the ModelVersionNotFound error, follow these steps:
Ensure that the model version you are querying is correctly specified. Check the version number in your request and compare it with the versions available in the model repository. You can list the available versions using the following command:
curl -X GET http://localhost:8000/v2/models/{model_name}/versions
Replace {model_name} with the actual name of your model.
Navigate to your model repository and verify that the desired model version is present. The repository should have a directory structure similar to:
model_repository/
└── {model_name}/
└── {version_number}/
└── model files
If the version is missing, ensure it is correctly deployed.
If the model version is present but still not recognized, check the config.pbtxt file in the model directory. Ensure that the version policy is correctly defined. For example:
version_policy: {
specific: { versions: [1, 2, 3] }
}
Update the policy to include the desired version if necessary.
For more detailed information on managing model versions in Triton Inference Server, refer to the official Triton Inference Server documentation. You can also explore the Model Repository Guide for best practices in organizing and deploying models.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
