I am trying to run tombo v 1.5.1 and it needs numpy. I have installed numpy 1.24.1 but when I am running tombo, it is giving following error:
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject
Please help!!!
Installed numpy v 1.24.1
pip show numpy
Name: numpy
Version: 1.24.1
When I am running tombo it is giving following error:
ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject
I uninstalled the previously installed numpy and reinstall lower verion with pip install numpy==1.20.3. Then it is giving me following error:
ERROR: Failed building wheel for numpy
Failed to build numpy
ERROR: Could not build wheels for numpy, which is required to install pyproject.toml-based projects
Note: I am using HPC system to install it.
I am trying to use deep learning in pyspark but it's not working.
here is my code :
featurizer = DeepImageFeaturizer(inputCol="image",
outputCol="features",
modelName="InceptionV3")
lr = LogisticRegression(maxIter=5, regParam=0.03,
elasticNetParam=0.5, labelCol="label")
sparkdn = Pipeline(stages=[featurizer, lr])
spark_model = sparkdn.fit(train)
The last part of The error:
File ~/anaconda3/lib/python3.9/site-packages/sparkdl/transformers/keras_applications.py:45, in KerasApplicationModel.getModelData(self, featurize)
44 def getModelData(self, featurize):
---> 45 sess = tf.Session()
46 with sess.as_default():
47 K.set_learning_phase(0)
AttributeError: module 'tensorflow' has no attribute 'Session'
I tried to uninstall and install tf.
also I am running pyspark with deep-learning package:
pyspark --packages databricks:spark-deep-learning:0.1.0-spark2.1-s_2.11
Looks like you are trying to use the DeepImageFeaturizer transformer from the spark-deep-learning package. This error usually occurs if you have an old version of TensorFlow installed on your system, or if your using a version of TensorFlow that is not compatible with the spark-deep-learning package.
Try the following:
Uninstall the current version of TensorFlow using pip uninstall tensorflow.
Then install the latest version of TensorFlow: pip install tensorflow.
Make sure that the version of TensorFlow that you are using is compatible with the spark-deep-learning package. According to the documentation it requires TensorFlow 1.6 or later.
Try setting the environment variable TF_CPP_MIN_LOG_LEVEL to 3 before running your code. This can help suppress some TensorFlow warnings and errors.
When I try to install Numpy 1.20.2 module with Python Poetry 1.1.4 package manager (poetry add numpy) in a Python 3.9.0 virtual environment, I get:
ERROR: Failed building wheel for numpy
Failed to build numpy
ERROR: Could not build wheels for numpy which use PEP 517 and cannot be installed directly
I read a few threads like this one, but since then it seems the latest Numpy versions are supposed to be able to be built with 3.9 (see this official Numpy release doc, and this answer).
Did I miss something?
EDIT: using pip 21.0.1 (latest)
I am trying to train my own custom object detector using Tensorflow Object-Detection-API
I installed the tensorflow using "pip install tensorflow" in my google compute engine. Then I followed all the instructions on this site: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/training.html
When I try to use train.py I am getting this error message:
Traceback (most recent call last):
File "train.py", line 49, in
from object_detection.builders import dataset_builder
File "/usr/local/lib/python3.6/dist-packages/object_detection-0.1->py3.6.egg/object_detection/builders/dataset_builder.py", line 27, in
from object_detection.data_decoders import tf_example_decoder
File "/usr/local/lib/python3.6/dist-packages/object_detection-0.1-py3.6.egg/object_detection/data_decoders/tf_example_decoder.py", line 27, in
slim_example_decoder = tf.contrib.slim.tfexample_decoder
AttributeError: module 'tensorflow' has no attribute 'contrib'
Also I am getting different results when I try to learn version of tensorflow.
python3 -c 'import tensorflow as tf; print(tf.version)' : 2.0.0-dev20190422
and when I use
pip3 show tensorflow:
Name: tensorflow
Version: 1.13.1
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: opensource#google.com
License: Apache 2.0
Location: /usr/local/lib/python3.6/dist-packages
Requires: gast, astor, absl-py, tensorflow-estimator, keras-preprocessing, grpcio, six, keras-applications, wheel, numpy, tensorboard, protobuf, termcolor
Required-by:
sudo python3 train.py --logtostderr --train_dir=training/ --
pipeline_config_path=training/ssd_inception_v2_coco.config
What should I do to solve this problem? I couldn't find anything about this error message except this: tensorflow 'module' object has no attribute 'contrib'
tf.contrib has moved out of TF starting TF 2.0 alpha.
Take a look at these tf 2.0 release notes https://github.com/tensorflow/tensorflow/releases/tag/v2.0.0-alpha0
You can upgrade your TF 1.x code to TF 2.x using the tf_upgrade_v2 script
https://www.tensorflow.org/alpha/guide/upgrade
This issue might be helpful for you, it explains how to achieve TPUStrategy, a popular functionality of tf.contrib in TF<2.0.
So, in TF 1.X you could do the following:
resolver = tf.contrib.cluster_resolver.TPUClusterResolver('grpc://' + os.environ['COLAB_TPU_ADDR'])
tf.contrib.distribute.initialize_tpu_system(resolver)
strategy = tf.contrib.distribute.TPUStrategy(resolver)
And in TF>2.0, where tf.contrib is deprecated, you achieve the same by:
tf.config.experimental_connect_to_host('grpc://' + os.environ['COLAB_TPU_ADDR'])
resolver = tf.distribute.cluster_resolver.TPUClusterResolver('grpc://' + os.environ['COLAB_TPU_ADDR'])
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
One easy way is you can pass your code written in TensorFlow 1.x to the below code to automatically upgrade it to TensorFlow 2.x.
$tf_upgrade_v2 \
--intree my_project/ \
--outtree my_project_v2/ \
--reportfile report.txt
The above code will replace all the commands which are deprecated in 2.x with the onces that are actually working in 2.x. And then you can run your code in TensorFlow 2.x.
In case if it throws an error and is unable to convert the complete code and then don't panic. Please open the "report.txt" file that is generated by the above code. In this file, you will find commands that are deprecated and their alternative commands that can be used in TensorFlow 2.x.
Taadaa, just replace the commands that are throwing errors with the new ones.
Example:
If the command in TensorFlow 1.x is:
tf.contrib
Then the same command in Tensorflow 2.x is:
tf.compat.v1.estimator
In the above example replace "tf.contrib" with "tf.compat.v1.estimator" and that should solve the problem.
I used google colab to run my models and everything was perfect untill i used inline tesorboard. With tensorboard inline, I had the same issue of "Module 'tensorflow' has no attribute 'contrib'".
It was able to run training when rebuild and reinstall the model using setup.py(research folder) after initialising tensorboard.
I used tensorflow 1.8 to train my model and there is no problem for now. Tensorflow 2.0 alpha is not suitable with object detection API
I'm using Google Colab as well. A comment suggested to put
%tensorflow_version 1.x
in the first (code) cell, and it worked!
If you want to use tf.contrib, you need to now copy and paste the source code from github into your script/notebook. It's annoying and doesn't always work. But that's the only workaround I've found. For example, if you wanted to use tf.contrib.opt.AdamWOptimizer, you have to copy and paste from here. https://github.com/tensorflow/tensorflow/blob/590d6eef7e91a6a7392c8ffffb7b58f2e0c8bc6b/tensorflow/contrib/opt/python/training/weight_decay_optimizers.py#L32
I face the same error and solve it by install python version 3.7 then i can install tensorflow 1.15 and it work.
I used tensorflow==2.9 but tensorflow-probability==0.6.0 so I met this error too. tensorflow-probability==0.6.0 seems to be compatible with tf 1
this is solution: pip install tensorflow_probability==0.12.2
This version of TensorFlow Probability requires TensorFlow version >= 2.3
if there are still some errors pip install tensorflow_probability==0.17.0
For me it worked using the latest release of tensorflow: pip install tensorflow==2.2.0
I am using Jupiter Notebook installed with Anaconda distribution and I want to install Tensorflow.
I wrote
!pip install tensorflow
it installed it, but now when I want to import tensorflow, when I type
import tensorflow as tf
it gives me an error:
ImportError: DLL load failed with error code -1073741795
I searched for answers and questions related to this issue but nothing helped me. The most weird is that I managed to install and import tensorflow on another computer like this. I can`t find out what I am missing.