tensorflow / tensorflow/java

Unary VariantDecodeFn for type_name: tensorflow::data::WrappedDatasetVariant already registered

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Beschreibung

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:

  1. I prepared a TensorFlow model pre-trained Python TensorFlow. The model is stored in a .pb file.
  2. I executed a Python process, which contains:
    1. import neseccary python libraries such as tensorflow and pyjnius.
    2. invoke JNI by using pyjnius which loads the stored model by using SavedModelBundle.load().
  3. 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

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  2. Schreib ins Issue, dass du es übernimmst — das erspart doppelte Arbeit.
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  4. Öffne einen Pull Request, der die Issue-Nummer nennt.

Rechercherichtung

Beginne mit dem verlinkten tf_conflict-Beispiel und test/conflict.py und reproduziere den Fehler, wenn Python TensorFlow tensorflow-java über pyjnius lädt und SavedModelBundle.load() aufruft. Verfolge die Fehler aufgrund doppelter Registrierungen in diesem Setup; die Aufgabe ist abgeschlossen, wenn Python TensorFlow und Java TensorFlow in einem Prozess ausgeführt werden können, ohne abzubrechen oder miteinander in Konflikt stehende Registrierungen zu erzeugen.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Bewertung

Tech-Stack
java, python
Bereich
backend, machine-learning
Issue-Typ
Bug
Schwierigkeit
4/5
Geschätzter Aufwand
3-5 Tage
Aktivitätsstatus
Veraltet
Klarheit
Größtenteils klar
Anfängerfreundlichkeit
32/100

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