Unary VariantDecodeFn for type_name: tensorflow::data::WrappedDatasetVariant already registered
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Descripción
Please make sure that this is a bug. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:bug_template
System information
- Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
- OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Debian GNU/Linux 9.13 (stretch) (inside Docker container)
- Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
- TensorFlow installed from (source or binary): binary
- TensorFlow version (use command below): TensorFlow 2.3.1 and tensorflow-java 0.2.0
- Python version: 3.6.13
- Bazel version (if compiling from source):
- GCC/Compiler version (if compiling from source):
- CUDA/cuDNN version:
- GPU model and memory:
You can collect some of this information using our environment capture script
You can also obtain the TensorFlow version with
python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)"
Describe the current behavior
I executed Python TensorFlow v2.3.1 and TensorFlow java invoked from JNI by using pyjnius in a Python process. Then, the following error was shown and the Python process was aborted.
2021-02-25 07:57:21.005274: F external/org_tensorflow/tensorflow/core/framework/variant_op_registry.cc:46] Check failed: existing == nullptr (0x56489dc7a258 vs. nullptr)Unary VariantDecodeFn for type_name: tensorflow::data::WrappedDatasetVariant already registered
Aborted
More details:
- I prepared a TensorFlow model pre-trained Python TensorFlow. The model is stored in a
.pbfile. - I executed a Python process, which contains:
- import neseccary python libraries such as
tensorflowandpyjnius. - invoke JNI by using
pyjniuswhich loads the stored model by usingSavedModelBundle.load().
- import neseccary python libraries such as
- The step 2.2 raised the error described above.
Describe the expected behavior
The user should be able to execute both (python and java) from a Python process without any conflict exceptions.
Code to reproduce the issue
Provide a reproducible test case that is the bare minimum necessary to generate the problem.
I pushed a sample code into https://github.com/akiou/tf_conflict. You can reproduce this error by using the repo sample code.
Other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.
The error message is dependeing on the platform OS. The error message in this issue is observed inside a Linux docker container. If you run the sample code in Mac OS, then the following error message is shown instead:
2021-02-25 17:32:28.532107: E tensorflow/core/lib/monitoring/collection_registry.cc:77] Cannot register 2 metrics with the same name: /tensorflow/core/eager_context_created
2021-02-25 17:32:28.532275: F tensorflow/core/framework/op.cc:62] Non-OK-status: RegisterAlreadyLocked(op_data_factory) status: Already exists: Op with name XlaLaunch
[1] 70088 abort python test/conflict.py
Guía de contribución
Primeros pasos
- Lee el issue completo y luego la guía de contribución del proyecto.
- Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
- Haz un fork del repositorio y trabaja en una rama.
- Abre un pull request que haga referencia al número del issue.
Línea de trabajo
Comienza con el ejemplo tf_conflict enlazado y test/conflict.py, y reproduce el fallo cuando Python TensorFlow carga tensorflow-java mediante pyjnius y llama a SavedModelBundle.load(). Rastrea los errores de registro duplicado en esa configuración; se considera terminado cuando Python TensorFlow y Java TensorFlow pueden ejecutarse en un mismo proceso sin abortar ni generar registros en conflicto.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- java, python
- Área
- backend, machine-learning
- Tipo de issue
- Error
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Estancado
- Claridad
- Bastante claro
- Aptitud para principiantes
- 32/100