Stop TensorFlow from printing warning message-CodePudding

I am working on a Kaggle notebook and whenever I run a cell that references the TensorFlow module at all, it prints out a huge warning about some sort of settings but still works. I looked up how to suppress warnings from TensorFlow, and everything I found said to do the following:

import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # Or "3", either one should work and I've tried both

I have tried putting this both before and after importing TensorFlow but to no avail. The message still prints out. This is the message I am getting:

User settings:

   KMP_AFFINITY=granularity=fine,verbose,compact,1,0
   KMP_BLOCKTIME=0
   KMP_SETTINGS=1
   KMP_WARNINGS=0

Effective settings:

   KMP_ABORT_DELAY=0
   KMP_ADAPTIVE_LOCK_PROPS='1,1024'
   KMP_ALIGN_ALLOC=64
   KMP_ALL_THREADPRIVATE=128
   KMP_ATOMIC_MODE=2
   KMP_BLOCKTIME=0
   KMP_CPUINFO_FILE: value is not defined
   KMP_DETERMINISTIC_REDUCTION=false
   KMP_DEVICE_THREAD_LIMIT=2147483647
   KMP_DISP_NUM_BUFFERS=7
   KMP_DUPLICATE_LIB_OK=false
   KMP_ENABLE_TASK_THROTTLING=true
   KMP_FORCE_REDUCTION: value is not defined
   KMP_FOREIGN_THREADS_THREADPRIVATE=true
   KMP_FORKJOIN_BARRIER='2,2'
   KMP_FORKJOIN_BARRIER_PATTERN='hyper,hyper'
   KMP_GTID_MODE=3
   KMP_HANDLE_SIGNALS=false
   KMP_HOT_TEAMS_MAX_LEVEL=1
   KMP_HOT_TEAMS_MODE=0
   KMP_INIT_AT_FORK=true
   KMP_LIBRARY=throughput
   KMP_LOCK_KIND=queuing
   KMP_MALLOC_POOL_INCR=1M
   KMP_NUM_LOCKS_IN_BLOCK=1
   KMP_PLAIN_BARRIER='2,2'
   KMP_PLAIN_BARRIER_PATTERN='hyper,hyper'
   KMP_REDUCTION_BARRIER='1,1'
   KMP_REDUCTION_BARRIER_PATTERN='hyper,hyper'
   KMP_SCHEDULE='static,balanced;guided,iterative'
   KMP_SETTINGS=true
   KMP_SPIN_BACKOFF_PARAMS='4096,100'
   KMP_STACKOFFSET=64
   KMP_STACKPAD=0
   KMP_STACKSIZE=8M
   KMP_STORAGE_MAP=false
   KMP_TASKING=2
   KMP_TASKLOOP_MIN_TASKS=0
   KMP_TASK_STEALING_CONSTRAINT=1
   KMP_TEAMS_THREAD_LIMIT=4
   KMP_TOPOLOGY_METHOD=all
   KMP_USE_YIELD=1
   KMP_VERSION=false
   KMP_WARNINGS=false
   OMP_AFFINITY_FORMAT='OMP: pid %P tid %i thread %n bound to OS proc set {%A}'
   OMP_ALLOCATOR=omp_default_mem_alloc
   OMP_CANCELLATION=false
   OMP_DEFAULT_DEVICE=0
   OMP_DISPLAY_AFFINITY=false
   OMP_DISPLAY_ENV=false
   OMP_DYNAMIC=false
   OMP_MAX_ACTIVE_LEVELS=1
   OMP_MAX_TASK_PRIORITY=0
   OMP_NESTED: deprecated; max-active-levels-var=1
   OMP_NUM_THREADS: value is not defined
   OMP_PLACES: value is not defined
   OMP_PROC_BIND='intel'
   OMP_SCHEDULE='static'
   OMP_STACKSIZE=8M
   OMP_TARGET_OFFLOAD=DEFAULT
   OMP_THREAD_LIMIT=2147483647
   OMP_WAIT_POLICY=PASSIVE
   KMP_AFFINITY='verbose,warnings,respect,granularity=fine,compact,1,0'

Is there any way I can stop this from printing?

EDIT: Code to reproduce this message:

import tensorflow as tf
tf.constant(())

EDIT: Output of env:

{'SHELL': '/bin/bash',
 'KMP_WARNINGS': '0',
 'DL_ANACONDA_HOME': '/opt/conda',
 'KAGGLE_DATA_PROXY_TOKEN': '<hidden>',
 'KAGGLE_URL_BASE': 'https://www.kaggle.com',
 'KAGGLE_KERNEL_INTEGRATIONS': '',
 'CONTAINER_NAME': 'tf2-cpu/2-6',
 'PWD': '/kaggle/working',
 'TESSERACT_PATH': '/usr/bin/tesseract',
 'TENSORFLOW_VERSION': '2.6.0',
 'HOME': '/root',
 'LANG': 'C.UTF-8',
 'KMP_SETTINGS': '1',
 'JAX_VERSION': '0.2.19',
 'CONTAINER_URL': 'gcr.io/deeplearning-platform-release/tf-cpu.2-6:nightly-2021-11-17',
 'ANACONDA_PYTHON_VERSION': '3.7',
 'PYTHONPATH': '/kaggle/lib/kagglegym:/kaggle/lib:/kaggle/input/tensorflow-great-barrier-reef',
 'KMP_BLOCKTIME': '0',
 'KAGGLE_DATA_PROXY_PROJECT': 'kaggle-161607',
 'KAGGLE_USER_SECRETS_TOKEN': '<hidden>',
 'SHLVL': '1',
 'KAGGLE_KERNEL_RUN_TYPE': 'Interactive',
 'PROJ_LIB': '/opt/conda/share/proj',
 'MPLBACKEND': 'agg',
 'LD_LIBRARY_PATH': '/usr/local/cuda/lib64:/usr/local/cuda/lib:/usr/local/lib/x86_64-linux-gnu:/usr/local/nvidia/lib:/usr/local/nvidia/lib64:',
 'KMP_AFFINITY': 'granularity=fine,verbose,compact,1,0',
 'MKL_THREADING_LAYER': 'GNU',
 'LC_ALL': 'C.UTF-8',
 'PATH': '/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin',
 'PYTHONUSERBASE': '/root/.local',
 'KAGGLE_DATA_PROXY_URL': 'https://dp.kaggle.net',
 '_': '/opt/conda/bin/jupyter',
 'GIT_PYTHON_REFRESH': 'quiet',
 'PYDEVD_USE_FRAME_EVAL': 'NO',
 'JPY_PARENT_PID': '9',
 'TERM': 'xterm-color',
 'CLICOLOR': '1',
 'PAGER': 'cat',
 'GIT_PAGER': 'cat',
 'TF_CPP_MIN_LOG_LEVEL': '2',
 'TF2_BEHAVIOR': '1'}

CodePudding user response：

So I managed to fix the problem with the following line:

os.environ["KMP_SETTINGS"] = "false"