I'm running the following Google Colab, but when I run the following command, I get the below error:
!pip install -U tfx
--
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
multiprocess 0.70.11.1 requires dill>=0.3.3, but you have dill 0.3.1.1 which is incompatible.
jupyter-console 5.2.0 requires prompt-toolkit<2.0.0,>=1.0.0, but you have prompt-toolkit 3.0.18 which is incompatible.
google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.24.0 which is incompatible.
google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.25.1 which is incompatible.
datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.
I did face similar problem with !pip install fiftyone but when I did repeat the syntax the problem gone.
Related
New to this. I'm trying to install manim in Google Colab following the instructions from the Manim Community site.
Ran:
!sudo apt update
!sudo apt install libcairo2-dev ffmpeg texlive texlive-latex-extra texlive-fonts-extra texlive-latex-recommended texlive-science tipa libpango1.0-dev
!pip install -U manim==0.3.0
!pip install IPython --upgrade
returned the following error;
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-colab 1.0.0 requires ipython~=7.9.0, but you have ipython 8.9.0 which is incompatible.
I wasn't aware that I had any version of Python installed
I do not know what to do as I have no experience in this area.
I am seeing following error while trying to install pip3 install -r requirements.txt, which downgrade my tf version from 2.10 to 2.7, and dependencies. I assume I can pip3 uninstall tensorflow-serving-api and the rests manually one by one, then rerun the installation. Not sure if this will potentially cause issues, wonder if there this a better automatic way ?
WARNING: Ignoring invalid distribution -olorlog (/usr/local/lib/python3.7/site-packages)
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow-serving-api 2.9.1 requires tensorflow<3,>=2.9.1, but you have tensorflow 2.7.1 which is incompatible.
tensorflow-metadata 1.10.0 requires absl-py<2.0.0,>=0.9, but you have absl-py 0.8.1 which is incompatible.
tensorflow-metadata 1.10.0 requires protobuf<4,>=3.13, but you have protobuf 3.10.0 which is incompatible.
tensorflow-datasets 4.6.0 requires protobuf>=3.12.2, but you have protobuf 3.10.0 which is incompatible.
googleapis-common-protos 1.56.0 requires protobuf>=3.12.0, but you have protobuf 3.10.0 which is incompatible.
google-cloud-storage 2.2.1 requires google-auth<3.0dev,>=1.25.0, but you have google-auth 1.13.1 which is incompatible.
google-api-core 2.7.1 requires google-auth<3.0dev,>=1.25.0, but you have google-auth 1.13.1 which is incompatible.
google-api-core 2.7.1 requires protobuf>=3.12.0, but you have protobuf 3.10.0 which is incompatible.
I am new to Federated Learning, and I am trying to get started with TensorFlow Federated. While working on the tutorial "Federated Learning for Image Classification" on Colab, I tried to install TensorFlow Federated, but was met with these errors:
ERROR: tensorflow 2.5.0 requires tensorboard~=2.5, which is not installed.
ERROR: tensorflow 2.5.0 has requirement grpcio~=1.34.0, but you'll have grpcio 1.37.1 which is incompatible.
ERROR: tensorflow 2.5.0 has requirement keras-nightly~=2.5.0.dev, but you'll have keras-nightly 2.6.0.dev2021062500 which is incompatible.
ERROR: spacy 2.2.4 has requirement tqdm<5.0.0,>=4.38.0, but you'll have tqdm 4.28.1 which is incompatible.
ERROR: pymc3 3.11.2 has requirement cachetools>=4.2.1, but you'll have cachetools 3.1.1 which is incompatible.
ERROR: fbprophet 0.7.1 has requirement tqdm>=4.36.1, but you'll have tqdm 4.28.1 which is incompatible.
ERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.
ERROR: tensorflow-privacy 0.6.1 has requirement attrs>=21.2.0, but you'll have attrs 19.3.0 which is incompatible.
After installing the versions of the libraries mentioned here, I found that there still exist some internal conflicts with the installed libraries. Has anyone else faced this issue? Would be great to get some pointers on this!
You may try the following commands to reset the environment:
!pip uninstall tensorflow
!pip uninstall tf-nightly
!pip uninstall tensorflow-federated
!pip install tensorflow
!pip install tensorflow-federated
!pip install tensorflow-federated-nightly
when I use pip install tensorflow command that error appear :-
'''
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tf-nightly 2.6.0.dev20210601 requires gast==0.4.0, but you have gast 0.3.3 which is incompatible.
tf-nightly 2.6.0.dev20210601 requires h5py~=3.1.0, but you have h5py 2.10.0 which is incompatible.
tf-nightly 2.6.0.dev20210601 requires numpy~=1.19.2, but you have numpy 1.18.5 which is incompatible.'''
Looks like you are trying to install Tensorflow and tf-nightly on the same environment. Since Tensorflow and tf-nightly uses same code, you should never install both in same environment. Latest version overrides the most of the packages, thats how pip works. It recommended to use virtual environment each tensorflow version.
Tf-nightly require gast==0.4.0 and numpy==1.19.2.
Follow the below steps
python -m tf_nightly --system-site-packages .\venv
.\venv\Scripts\activate
pip install --upgrade pip
pip install tf-nightly
Datalab currently seems to be running 0.6.0. I wanted to update to version 0.8.0
I did:
!pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64
I got:
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:581)
Storing debug log for failure in /root/.pip/pip.log
How can I fix this?
It is not recommended to update packages which are installed in Datalab by default. This is to ensure that you do not break a working Datalab environment.
Please try one of the following solutions:
If you deployed Datalab using https://datalab.cloud.google.com/ , visit the Datalab GitHub Issues page and submit an issue to have a new version of datalab published. In the Datalab source code on github, tensorflow is at version 0.8.0)
If you have installed Datalab locally, or on GCE, then simply rebuild the Datalab image to get tensorflow 0.8.0 . See the Datalab Getting Started Wiki page for more information.
If you want to temporarily install a newer version into your existing environment for testing purposes (although this isn't recommended) , then you could try installing tensorflow with the no dependencies option (--no-deps) in order to reduce the chance of breaking the working datalab environment.
%%bash
wget https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl && pip install --ignore-installed --no-deps tensorflow-0.8.0-cp27-none-linux_x86_64.whl
After running the above command, I can see tensorflow is at version 0.8.0.
>> import tensorflow
>> tensorflow.__version__
'0.8.0'
>>!pip show tensorflow
---
---
Metadata-Version: 2.0
Name: tensorflow
Version: 0.8.0
Summary: TensorFlow helps the tensors flow
Home-page: http://tensorflow.org/
Author: Google Inc.
Author-email: opensource#google.com
Installer: pip
License: Apache 2.0
Location: /usr/local/lib/python2.7/dist-packages
Requires: six, protobuf, wheel, numpy
Please keep an eye out for any anomalies now that you have updated a package used by datalab. For example, certain sample notebooks may not work. Also, please note that this setup may not be supported. For example, you may encounter an issue which is directly related to updating a package used by datalab. In that case, the solution may be to revert the updated package and see if that resolves your issue.