Google Colab GPU is not available (tensorflow & keras tf-models errors)

Question

Some days ago I wrote a BERT Model for text classification using Google Colab Pro. Everything just worked fine, but since yesterday, I always get the output "GPU is NOT AVAILABLE". I haven't changed anything but noticed that errors occur, when installing tensorflow_hub and keras tf-models. There haven't been any errors before.

! python --version
!pip install tensorflow_hub
!pip install keras tf-models-official pydot graphviz

I get this message:

ERROR: tensorflow 2.5.0 has requirement h5py~=3.1.0, but you'll have h5py 2.10.0 which is incompatible.

ERROR: tf-models-official 2.5.0 has requirement pyyaml>=5.1, but you'll have pyyaml 3.13 which is incompatible.

import os

import numpy as np
import pandas as pd

import tensorflow as tf
import tensorflow_hub as hub

from keras.utils import np_utils

import official.nlp.bert.bert_models
import official.nlp.bert.configs
import official.nlp.bert.run_classifier
import official.nlp.bert.tokenization as tokenization

from official.modeling import tf_utils
from official import nlp
from official.nlp import bert

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

import matplotlib.pyplot as plt
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
  except RuntimeError as e:
    print(e)

print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub version: ", hub.__version__)
print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")

output Version: 2.5.0 Eager mode: True Hub version: 0.12.0 GPU is NOT AVAILABLE

I would really appreciate if someone could help me.

ps.: I already tried to update h5py and PyYAML, but GPU is still not running.

! pip install h5py==3.1.0
! pip install PyYAML==5.1.2

user11530462 · Accepted Answer · 2021-06-01 10:28:30Z

ERROR: tf-models-official 2.5.0 has requirement pyyaml>=5.1, but you'll have pyyaml 3.13 which is incompatible.

I was able to resolved above issue by upgrading pip package before installation of tf-models-official as shown below

!pip install --upgrade pip
!pip install keras tf-models-official pydot graphviz

Working code as shown below

import os

import numpy as np
import pandas as pd

import tensorflow as tf
import tensorflow_hub as hub

from keras.utils import np_utils

import official.nlp.bert.bert_models
import official.nlp.bert.configs
import official.nlp.bert.run_classifier
import official.nlp.bert.tokenization as tokenization

from official.modeling import tf_utils
from official import nlp
from official.nlp import bert

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder

import matplotlib.pyplot as plt
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    for gpu in gpus:
      tf.config.experimental.set_memory_growth(gpu, True)
    logical_gpus = tf.config.experimental.list_logical_devices('GPU')
    print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
  except RuntimeError as e:
    print(e)

print("Version: ", tf.__version__)
print("Eager mode: ", tf.executing_eagerly())
print("Hub version: ", hub.__version__)
print("GPU is", "available" if tf.config.list_physical_devices('GPU') else "NOT AVAILABLE")

Output:

1 Physical GPUs, 1 Logical GPUs
Version:  2.5.0
Eager mode:  True
Hub version:  0.12.0
GPU is available

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