ETRV tensorflow config_error ai_generated true

tensorflow.python.framework.errors_impl.InternalError: TRITONBACKEND_ModelInstanceInitialize: 模型 'resnet50' 版本1的 TensorFlow 运行时版本不受支持。期望 2.12.0，实际为 2.10.0

tensorflow.python.framework.errors_impl.InternalError: TRITONBACKEND_ModelInstanceInitialize: model 'resnet50' version 1 has unsupported TensorFlow runtime version. Expected 2.12.0, got 2.10.0

ID: tensorflow/triton-inference-version-mismatch

其他格式: JSON · Markdown 中文 · English
88%修复率
85%置信度
1证据数
2024-06-20首次发现

版本兼容性

版本状态引入弃用备注
2.10 active — — —
2.12 active — — —
2.13 active — — —

根因分析

Triton 推理服务器后端编译的 TensorFlow 运行时版本与保存模型所需的版本不匹配。

English

The TensorFlow runtime version compiled into the Triton Inference Server backend does not match the version required by the saved model.

generic

官方文档

https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/model_configuration.html

解决方案

  1. Use a Triton Inference Server Docker image that matches the TensorFlow version of your model. Check the tag at https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver and pull the correct version, e.g., nvcr.io/nvidia/tritonserver:23.12-tf2-py3 for TF 2.12.
  2. Export the model using the same TensorFlow version as the Triton backend. Run 'pip install tensorflow==2.12.0' and re-save the model with tf.saved_model.save().

无效尝试

常见但无效的做法:

  1. Reinstall Triton Inference Server with the latest version 50% 失败

    The latest Triton may still bundle a different TensorFlow version; you need to match the exact version.

  2. Convert the model to ONNX format 60% 失败

    ONNX conversion may not support all TF ops, and the error is about runtime version, not model format.