# Wandb error by usage of mlflow and hydra regarding protobuf lib **URL:** \ **Category:** W&B Help\ **Tags:** wandb\ **Created:** [February 12, 2023, 5:17pm UTC](https://community.wandb.ai/t/wandb-error-by-usage-of-mlflow-and-hydra-regarding-protobuf-lib/3866 "2023-02-12T17:17:38Z")\ **Posts on this page:** 1\ **Showing post:** 1
**Author:** ![ilo](https://avatars.discourse-cdn.com/v4/letter/i/67e7ee/32.png) [@ilo](https://community.wandb.ai/u/ilo)\ **Post date:** [February 12, 2023, 5:17pm UTC](https://community.wandb.ai/t/wandb-error-by-usage-of-mlflow-and-hydra-regarding-protobuf-lib/3866/1 "2023-02-12T17:17:38Z")
Hi all, having an issue and need some help for Python project inside a Udacity course. We shall use Python 3.8, therefore I am using 3.8.16 and having created a virtual env starting from that version and activated virtual env. Using Jupyter Lab I am trying to use a pipeline for mlflow, hydra and wandb I am getting the same error as mentioned e.g. in > > > While Looking up ESPNet using this Tutorial: https://colab.research.google.com/d…rive/1L85G7jdhsI1QKs2o0qCGEbhm5X4QV2zN > > I came across this issue when the Library "Wandb" is imported. > > \`\`\` > \# python3 -m espnet2.bin.asr\_train --collect\_stats true --use\_preprocessor true --bpemodel data/token\_list/bpe\_unigram30/bpe.model --token\_type bpe --token\_list data/token\_list/bpe\_unigram30/tokens.txt --non\_linguistic\_symbols none --cleaner none --g2p none --train\_data\_path\_and\_name\_and\_type dump/raw/train\_nodev/wav.scp,speech,sound --train\_data\_path\_and\_name\_and\_type dump/raw/train\_nodev/text,text,text --valid\_data\_path\_and\_name\_and\_type dump/raw/train\_dev/wav.scp,speech,sound --valid\_data\_path\_and\_name\_and\_type dump/raw/train\_dev/text,text,text --train\_shape\_file exp/asr\_stats\_raw\_bpe30/logdir/train.32.scp --valid\_shape\_file exp/asr\_stats\_raw\_bpe30/logdir/valid.32.scp --output\_dir exp/asr\_stats\_raw\_bpe30/logdir/stats.32 --frontend\_conf fs=16k  > \# Started at Wed Oct 27 16:36:43 EDT 2021 > \`\`\` > > \`\`\` >     from .wandb\_init import \_attach, init  # noqa: F401 >   File "/home/debayan/.local/lib/python3.8/site-packages/wandb/sdk/wandb\_init.py", line 35, in \ >     from .backend.backend import Backend >   File "/home/debayan/.local/lib/python3.8/site-packages/wandb/sdk/backend/backend.py", line 20, in \ >     from ..interface import interface >   File "/home/debayan/.local/lib/python3.8/site-packages/wandb/sdk/interface/interface.py", line 21, in \ >     from wandb.proto import wandb\_internal\_pb2 as pb >   File "/home/debayan/.local/lib/python3.8/site-packages/wandb/proto/wandb\_internal\_pb2.py", line 15, in \ >     from wandb.proto import wandb\_base\_pb2 as wandb\_dot\_proto\_dot\_wandb\_\_base\_\_pb2 >   File "/home/debayan/.local/lib/python3.8/site-packages/wandb/proto/wandb\_base\_pb2.py", line 21, in \ >     create\_key=\_descriptor.\_internal\_create\_key, > AttributeError: module 'google.protobuf.descriptor' has no attribute '\_internal\_create\_key > \`\`\` > > This was in Wandb version 0.12.6 . > > However, when I reverted to Wandb version 0.12.2, it started working as expected. > > Feel free to reach out if any questions! I tried already the mentioned protobuf version 3.20.0 and 3.20.1 and different wandb versions via conda.yml, always getting the same error: ```auto name: download_data channels: - conda-forge - defaults dependencies: - python=3.8 - requests=2.24.0 - pip=20.3.3 - mlflow=2.1.1 - hydra-core=1.3.1 #1.0.6 # - wandb=0.13.10 - pip: #- wandb==0.10.21 - protobuf==3.20.0 - wandb==0.12.2 - hydra-joblib-launcher==1.1.2 ``` Still getting the following stacktrace for the ML model and its param setting: 🌞 ❌ mlflow run . -P hydra\_options=“random\_forest\_pipeline.random\_forest.max\_depth=5” 2023/02/12 17:55:15 INFO mlflow.utils.conda: === Creating conda environment mlflow-8284cfd5101c5c151da499d35f932662f514265c === Collecting package metadata (repodata.json): …working… done Solving environment: …working… done Preparing transaction: …working… done Verifying transaction: …working… done Executing transaction: …working… done Installing pip dependencies: …working… done 2023/02/12 18:02:28 INFO mlflow.projects.utils: === Created directory /tmp/tmpcv9ww8ry for downloading remote URIs passed to arguments of type ‘path’ === 2023/02/12 18:02:28 INFO mlflow.projects.backend.local: === Running command ‘source /home/ilona/miniconda3/bin/…/etc/profile.d/conda.sh && conda activate mlflow-8284cfd5101c5c151da499d35f932662f514265c 1\>&2 && python main.py $(echo random\_forest\_pipeline.random\_forest.max\_depth=5)’ in run with ID ‘a6d323399b9242c1b227bae18109b69d’ === 2023/02/12 18:02:39 INFO mlflow.utils.conda: Conda environment mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f already exists. 2023/02/12 18:02:39 INFO mlflow.projects.utils: === Created directory /tmp/tmpsxd\_dtnx for downloading remote URIs passed to arguments of type ‘path’ === 2023/02/12 18:02:39 INFO mlflow.projects.backend.local: === Running command ‘source /home/ilona/miniconda3/bin/…/etc/profile.d/conda.sh && conda activate mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f 1\>&2 && python run.py --train\_data exercise\_6/data\_train.csv:latest –model\_config /home/ilona/MLOps/nd0821-c2-build-model-workflow-exercises/lesson-4-training-validation-experiment-tracking/exercises/exercise\_11/starter/outputs/2023-02-12/18-02-33/random\_forest\_config.yml’ in run with ID ‘59002ca997e54d1dac9fd30e5a418009’ === Traceback (most recent call last): File “run.py”, line 15, in import wandb File “/home/ilona/miniconda3/envs/mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f/lib/python3.8/site-packages/wandb/ **init**.py”, line 38, in from wandb import sdk as wandb\_sdk File “/home/ilona/miniconda3/envs/mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f/lib/python3.8/site-packages/wandb/sdk/ **init**.py”, line 12, in from .wandb\_init import init # noqa: F401 File “/home/ilona/miniconda3/envs/mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f/lib/python3.8/site-packages/wandb/sdk/wandb\_init.py”, line 29, in from .backend.backend import Backend File “/home/ilona/miniconda3/envs/mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f/lib/python3.8/site-packages/wandb/sdk/backend/backend.py”, line 17, in from …interface import interface File “/home/ilona/miniconda3/envs/mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f/lib/python3.8/site-packages/wandb/sdk/interface/interface.py”, line 18, in from wandb.proto import wandb\_internal\_pb2 as pb File “/home/ilona/miniconda3/envs/mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f/lib/python3.8/site-packages/wandb/proto/wandb\_internal\_pb2.py”, line 15, in from wandb.proto import wandb\_telemetry\_pb2 as wandb\_dot\_proto\_dot\_wandb\_\_telemetry\_\_pb2 File “/home/ilona/miniconda3/envs/mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f/lib/python3.8/site-packages/wandb/proto/wandb\_telemetry\_pb2.py”, line 34, in \_descriptor.FieldDescriptor( File “/home/ilona/miniconda3/envs/mlflow-70e71a7afdc413046f59e7b87abd03dc50d8745f/lib/python3.8/site-packages/google/protobuf/descriptor.py”, line 560, in **new** \_message.Message.\_CheckCalledFromGeneratedFile() TypeError: Descriptors cannot not be created directly. If this call came from a \_pb2.py file, your generated code is out of date and must be regenerated with protoc \>= 3.19.0. If you cannot immediately regenerate your protos, some other possible workarounds are: 1. Downgrade the protobuf package to 3.20.x or lower. 2. Set PROTOCOL\_BUFFERS\_PYTHON\_IMPLEMENTATION=python (but this will use pure-Python parsing and will be much slower). More information: [Changes made on May 6, 2022 | Protocol Buffers Documentation](https://developers.google.com/protocol-buffers/docs/news/2022-05-06#python-updates) … In advance, thank you very much for help and solution proposals, Ilona --- _[View the full topic](https://community.wandb.ai/t/wandb-error-by-usage-of-mlflow-and-hydra-regarding-protobuf-lib/3866)._