My OS is Ubuntu 14.04. I want to install TensorFlow in anaconda2 and I am not using root. I downloaded anaconda2 and installed in /usr/anaconda2. I typed the following command:
sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.8.0-cp27-none-linux_x86_64.whl
but in /usr/anaconda2/bin/python, I cannot import TensorFlow. I found that sudo pip installs the TensorFlow in Ubuntu default python2.7.6, so I tried installing it without sudo, but:
running build_scripts
creating build/scripts.linux-x86_64-2.7
Creating build/scripts.linux-x86_64-2.7/f2py
adding 'build/scripts.linux-x86_64-2.7/f2py' to scripts
changing mode of build/scripts.linux-x86_64-2.7/f2py from 664 to 775
running install_lib
creating /usr/anaconda2/pkgs/lib
error: could not create '/usr/anaconda2/pkgs/lib': Permission denied
----------------------------------------
Command "/home/ds/.conda/envs/tensorflow_env/bin/python -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-THK_wR/numpy/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp/pip-znMmTV-record/install-record.txt --single-version-externally-managed --compile --prefix=/usr/anaconda2/pkgs" failed with error code 1 in /tmp/pip-build-THK_wR/numpy/
I have no Permission to write file in /usr/anaconda2. What should I do?
Anaconda comes with its own package manager called conda,to install a package on Anaconda environment and not the default python do
conda update conda
conda install <package name>
in your case:
conda install -c conda-forge tensor flow
or you could just add the channel first:
conda config --add channels conda-forge
then install by:
conda install tensorflow
Related
On my Mac M1 (Monterey) I have created a new virtual environment with
virtualenv --python=/opt/homebrew/bin/python3.8 ~/.virtualenvs/datascience_env
to link it with specific Python 3.8 version. Now I'm trying to install tensorflow. The following command (issued in the virtual environment):
(datascience_env)% pip install tensorflow
causes the following error:
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow
Just to be sure, I also did:
(datascience_env)% python -c "import sys; print(sys.version)" or python -c "import struct; print(struct.calcsize('P')*8)"
and the output confirms the 3.8 version:
3.8.14 (default, Sep 6 2022, 23:17:06)
[Clang 13.1.6 (clang-1316.0.21.2.5)]
I've finally opted for a simpler solution. I followed here the procedure to install Tensorflow on Mac M1 with miniforge. Works like a charm. In summary the steps are install miniforge, then:
conda config --set auto_activate_base false
conda create --name mytfenv python=3.8
conda activate mytfenv
conda install -c apple tensorflow-deps
pip install tensorflow-macos
pip install tensorflow-metal
conda install -c conda-forge -y pandas jupyter
I have been trying to install TensorFlow on my Macbook Air with a M1 chip.
Using Python 3.9.7.
Originally was on MacOS 11, but subsequently upgraded to 12.01
At first, I tried these instructions [https://towardsdatascience.com/installing-tensorflow-on-the-m1-mac-410bb36b776] but got stuck when trying to execute
pip3 install --upgrade --force --no-dependencies https://github.com/apple/tensorflow_macos/releases/download/v0.1alpha3/tensorflow_addons_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl https://github.com/apple/tensorflow_macos/releases/download/v0.1alpha3/tensorflow_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl
ERROR: tensorflow_addons_macos-0.1a3-cp38-cp38-macosx_11_0_arm64.whl is not a supported wheel on this platform.
So I tried to follow these instructions [https://www.tensorflow.org/install/source#macos_1] to compile TensorFlow, but when I try
bazel build //tensorflow/tools/pip_package:build_pip_package
I get these errors:
ERROR: /Users/scottbrown/tensorflow/tensorflow/lite/python/BUILD:62:10: Target '//tensorflow/lite/python:tflite_convert' depends on toolchain '#local_config_cc//:cc-compiler-darwin', which cannot be found: error loading package '#local_config_cc//': cannot load '#local_config_cc_toolchains//:osx_archs.bzl': no such file'
ERROR: Analysis of target '//tensorflow/tools/pip_package:build_pip_package' failed; build aborted: Analysis failed
When I try
pip3 install tensorflow-macos
I get this error:
Building wheel for h5py (pyproject.toml) ... error
ERROR: Command errored out with exit status 1:
command: /opt/homebrew/opt/python#3.9/bin/python3.9 /opt/homebrew/lib/python3.9/site-packages/pip/_vendor/pep517/in_process/_in_process.py build_wheel /var/folders/gz/28jpdfcd3b3g4pm7zl0wmrkh0000gn/T/tmpz_m057zj
cwd: /private/var/folders/gz/28jpdfcd3b3g4pm7zl0wmrkh0000gn/T/pip-install-kz29fkw2/h5py_0747e63c821445b6944ecb4fc6b2d1e1
I'm basing my answer on the article from Prabhat Kumar Sahu:
How to install Tensorflow on M1 Mac the easy way
Set up environment
Make sure you have homebrew, xcode, and miniforge installed.
create a virtual environment
conda create --name mlp python=3.8
activate environment
conda activate mlp
Install tensorflow for mac-os
(sets up the wheel files etc.)
conda install -c apple tensorflow-deps
pip install tensorflow-macos
pip install tensorflow-metal
That's it. You should have the environment all ready to go. Look at Prabhat's article for a sample Jupyter Notebook test for an example of how to benchmark/test your environment.
Hey guys I had the same issue but I fixed it with the following instructions :
NOTE: If using conda environment built against pre-macOS 11 SDK use:
SYSTEM_VERSION_COMPAT=0 python -m pip install tensorflow-macos
otherwise, you will get errors like: “not a supported wheel on this platform”
STEPS :
OS Requirements macOS 12.0+ (latest beta)
Currently Not Supported
Multi-GPU support
Acceleration for Intel GPUs
V1 TensorFlow Networks
Installation Instructions
Step 1: Environment setup
CPU TYPE x86: AMD
Create virtual environment (recommended):
python3 -m venv ~/tensorflow-metal
source ~/tensorflow-metal/bin/activate
python -m pip install -U pip
NOTE: python version 3.8 required
CPU TYPE : arm64 : Apple Silicon
Download and install Conda env:
chmod +x ~/Downloads/Miniforge3-MacOSX-arm64.sh
sh ~/Downloads/Miniforge3-MacOSX-arm64.sh
source ~/miniforge3/bin/activate
OR
conda env create --file=environment.yml --name tf_m1
and then activate tf_m1
Install the TensorFlow dependencies:
conda install -c apple tensorflow-deps
When upgrading to new base TensorFlow version, we recommend:
uninstall existing tensorflow-macos and tensorflow-metal
python -m pip uninstall tensorflow-macos
python -m pip uninstall tensorflow-metal
Upgrade tensorflow-deps
conda install -c apple tensorflow-deps --force-reinstall
or point to specific conda environment
conda install -c apple tensorflow-deps --force-reinstall -n my_env
tensorflow-deps versions are following base TensorFlow versions so:
For v2.5:
conda install -c apple tensorflow-deps==2.5.0
For v2.6:
conda install -c apple tensorflow-deps==2.6.0
NOTE: Python versions 3.8 and 3.9 supported
Step 2: Install base TensorFlow
python -m pip install tensorflow-macos
NOTE: If using conda environment built against pre-macOS 11 SDK use:
SYSTEM_VERSION_COMPAT=0 python -m pip install tensorflow-macos
otherwise you will get errors like: “not a supported wheel on this
platform
Step 3: Install tensorflow-metal plugin
python -m pip install tensorflow-metal
The current default version of Python running on Google Colab is 3.7, but I need 3.9 for my notebooks to work.
How can I update Google Colab's Python version to 3.9 (or greater)?
In Google Colab you have a Debian-based Linux, and you can do whatever you can on a Debian Linux. Upgrading Python is as easy as upgrading it on your own Linux system.
Detect the current python version in Colab:
!python --version
#Python 3.8.16
Install new python version
Let's first install and upgrade to Python 3.9:
#install python 3.9
!sudo apt-get update -y
!sudo apt-get install python3.9
#change alternatives
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 2
#check python version
!python --version
#3.9.16
Port Colab kernel to the new installed python
As mentioned in the comments, the above commands just add a new python version to your google colab and update the default python for commandline usage. But your runtime packages such as sys are still running on the previous python version. The following commands need to be executed as well, to update the sys version.
# install pip for new python
!sudo apt-get install python3.9-distutils
!wget https://bootstrap.pypa.io/get-pip.py
!python get-pip.py
# credit of these last two commands blongs to #Erik
# install colab's dependencies
!python -m pip install ipython ipython_genutils ipykernel jupyter_console prompt_toolkit httplib2 astor
# link to the old google package
!ln -s /usr/local/lib/python3.8/dist-packages/google \
/usr/local/lib/python3.9/dist-packages/google
Now you can restart runtime and check the sys version. Note that in the new python version you have to install every packages, such as pandas, tensorflow, etc. from scratch.
Also, note that you can see a list of installed Python versions and switch between them at any time with this command:
(If nothing changed after installation, use this command to select python version manually)
!sudo update-alternatives --config python3
#after running, enter the row number of the python version you want.
It's also possible to update the kernel without going through ngrok or conda with some creative package installation.
Raha's answer suggesting making a link between the default google package and the newly installed Python version is the trick that makes this work because, at least with Python 3.9, the version of pandas (0.24.0) that the google package requires fails to build.
Here's the code I used to install and switch my Colab kernel to Python 3.9:
#install python 3.9 and dev utils
#you may not need all the dev libraries, but I haven't tested which aren't necessary.
!sudo apt-get update -y
!sudo apt-get install python3.9 python3.9-dev python3.9-distutils libpython3.9-dev
#change alternatives
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.9 2
#Check that it points at the right location
!python3 --version
# install pip
!curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
!python3 get-pip.py --force-reinstall
#install colab's dependencies
!python3 -m pip install ipython ipython_genutils ipykernel jupyter_console prompt_toolkit httplib2 astor
# link to the old google package
!ln -s /usr/local/lib/python3.8/dist-packages/google \
/usr/local/lib/python3.9/dist-packages/google
# There has got to be a better way to do this...but there's a bad import in some of the colab files
# IPython no longer exposes traitlets like this, it's a separate package now
!sed -i "s/from IPython.utils import traitlets as _traitlets/import traitlets as _traitlets/" /usr/local/lib/python3.9/dist-packages/google/colab/*.py
!sed -i "s/from IPython.utils import traitlets/import traitlets/" /usr/local/lib/python3.9/dist-packages/google/colab/*.py
If Google updates from Python 3.8, you'll have to change the path to the default package.
Then go the Runtime menu and select Restart runtime. It should reconnect and choose the updated version of Python as the default kernel. You can check that it worked with:
#check python version
import sys
print(sys.version)
!python3 --version
!python --version
To use another python version in google colab, you need to:
1- Installing Anaconda.
2- Adding (fake) google colab library.
3- Starting Jupyterlab.
4- Accessing it with ngrok.
# install Anaconda3
!wget -qO ac.sh https://repo.anaconda.com/archive/Anaconda3-2020.07-Linux-x86_64.sh
!bash ./ac.sh -b
# a fake google.colab library
!ln -s /usr/local/lib/python3.6/dist-packages/google \
/root/anaconda3/lib/python3.8/site-packages/google
# start jupyterlab, which now has Python3 = 3.8
!nohup /root/anaconda3/bin/jupyter-lab --ip=0.0.0.0&
# access through ngrok, click the link
!pip install pyngrok -q
from pyngrok import ngrok
print(ngrok.connect(8888))
you can also use:
# Install the python version
!apt-get install python3.9
# Select the version
!python3.9 setup.py
another way is to use a virtual environment with your desired python version:
virtualenv env --python=python3.9
Update 24.12.2022 - Unfortunately, the method does not work anymore.
This worked for me (copied from GitHub), I successfully installed Python 3.10.
#The code below installs 3.10 (assuming you now have 3.8) and restarts environment, so you can run your cells.
import sys #for version checker
import os #for restart routine
if '3.10' in sys.version:
print('You already have 3.10')
else:
#install python 3.10 and dev utils
#you may not need all the dev libraries, but I haven't tested which aren't necessary.
!sudo apt-get update -y
!sudo apt-get install python3.10 python3.10-dev python3.10-distutils libpython3.10-dev
!sudo apt-get install python3.10-venv binfmt-support #recommended in install logs of the command above
#change alternatives
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.10 2
# install pip
!curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10
!python3 get-pip.py --force-reinstall
#install colab's dependencies
!python3 -m pip install setuptools ipython ipython_genutils ipykernel jupyter_console prompt_toolkit httplib2 astor
#minor cleanup
!sudo apt autoremove
#link to the old google package
!ln -s /usr/local/lib/python3.8/dist-packages/google /usr/local/lib/python3.10/dist-packages/google
#this is just to verify if 3.10 folder was indeed created
!ls /usr/local/lib/python3.10/
#restart environment so you don't have to do it manually
os.kill(os.getpid(), 9)
In addition to Kaveh's answer, I added the following code. (This colab python version is python 3.8 and I tried to downgrade to python 3.7)
!pip install google-colab==1.0.0
# install colab's dependencies
!python -m pip install ipython==7.9.0 ipython_genutils==0.2.0 ipykernel==5.3.4 jupyter_console==6.1.0 prompt_toolkit==2.0.10 httplib2==0.17.4 astor==0.8.1 traitlets==5.7.1 google==2.0.3
This way, I solved the crashing runtime error.
Simple as that: -
!wget -O mini.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_4.9.2-Linux-x86_64.sh
!chmod +x mini.sh
!bash ./mini.sh -b -f -p /usr/local
!conda install -q -y jupyter
!conda install -q -y google-colab -c conda-forge
!python -m ipykernel install --name "py39" --user
Source: https://colab.research.google.com/drive/1m47aWKayWTwqJG--x94zJMXolCEcfyPS?usp=sharing#scrollTo=r3sLiMIs8If3
I want to have access to features from TensorFlow federated (tff.python.research) which aren't present with the pip3 install method.
I'm working on a remote server that does not have bazel, thus I cannot build from source. Are there other ways to get and install the latest working version of TFF from its GitHub REPO?
(https://github.com/tensorflow/federated)
To install the latest Tensorflow 2.0 federated, you may follow the steps below.
Install TensorFlow Federated using pip
Install the Python development environment
On Ubuntu:
$ sudo apt update
$ sudo apt install python3-dev python3-pip # Python 3
$ sudo pip3 install --upgrade virtualenv # system-wide install
On macOS:
$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ export PATH="/usr/local/bin:/usr/local/sbin:$PATH"
$ brew update
$ brew install python # Python 3
$ sudo pip3 install --upgrade virtualenv # system-wide install
Create a virtual environment
$ virtualenv --python python3 "venv"
$ source "venv/bin/activate"
(venv) $ pip install --upgrade pip
Note: To exit the virtual environment, run deactivate.
Install the TensorFlow Federated pip package.
(venv) $ pip install --upgrade tensorflow_federated
(Optional) Test Tensorflow Federated.
(venv) $ python -c "import tensorflow_federated as tff; print(tff.federated_computation(lambda: 'Hello World')())"
Build the TensorFlow Federated pip package
Install the Python development environment.
On Ubuntu:
$ sudo apt update
$ sudo apt install python3-dev python3-pip # Python 3
$ sudo pip3 install --upgrade virtualenv # system-wide install
On macOS:
$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ export PATH="/usr/local/bin:/usr/local/sbin:$PATH"
$ brew update
$ brew install python # Python 3
$ sudo pip3 install --upgrade virtualenv # system-wide install
Install Bazel
Install Bazel, the build tool used to compile Tensorflow Federated.
Clone the Tensorflow Federated repository.
$ git clone https://github.com/tensorflow/federated.git
$ cd "federated"
Create a virtual environment.
$ virtualenv --python python3 "venv"
$ source "venv/bin/activate"
(venv) $ pip install --upgrade pip
Note: To exit the virtual environment, run deactivate.
Install Tensorflow Federated dependencies.
(venv) $ pip install --requirement "requirements.txt"
(Optional) Test Tensorflow Federated.
(venv) $ bazel test //tensorflow_federated/...
Create a new project.
$ mkdir "/tmp/project"
$ cd "/tmp/project"
$ virtualenv --python python3 "venv"
$ source "venv/bin/activate"
(venv) $ pip install --upgrade pip
Note: To exit the virtual environment run deactivate.
Install the pip package.
(venv) $ pip install --upgrade "/tmp/tensorflow_federated/tensorflow_federated-"*".whl"
Test Tensorflow Federated.
(venv) $ python -c "import tensorflow_federated as tff; print(tff.federated_computation(lambda: 'Hello World')())"
Reference: https://www.tensorflow.org/federated/install
screen shot
As Python2.7 will be deprecated on 01/01/2020. I was planning to start using python3. So, I tried to install the tensorflow==1.14.0 on the raspberry pi and it was successful, but when I am loading the Tensorflow for further operations then it throws a load error.
Python - 3.7 (Default installed by Raspbian OS)
Any suggestions why am I facing this issue?
Thanks for your time
You can't install later versions of Tensorflow on the Raspberry Pi using pip. You have to install from source. I made a video doing this: https://youtu.be/GNRg2P8Vqqs
Installing Tensorflow requires some extra steps on the Pi's ARM architecture.
This is how I installed tf 2.0 on my Pi 4:
Make your project directory:
cd Desktop
mkdir tf_pi
cd tf_pi
Make a virtual environment:
python3 -m pip install virtualenv
virtualenv env
source env/bin/activate
Run the commands based on https://github.com/PINTO0309/Tensorflow-bin/#usage:
sudo apt-get install -y libhdf5-dev libc-ares-dev libeigen3-dev
python3 -m pip install keras_applications==1.0.8 --no-deps
python3 -m pip install keras_preprocessing==1.1.0 --no-deps
python3 -m pip install h5py==2.9.0
sudo apt-get install -y openmpi-bin libopenmpi-dev
sudo apt-get install -y libatlas-base-dev
python3 -m pip install -U six wheel mock
Pick a tensorflow release from https://github.com/lhelontra/tensorflow-on-arm/releases (I picked 2.0.0). Picking a higher version of Tensorflow (like 2.1.0) requires a higher version of scipy that wasn't compatible with my Raspberry Pi:
wget https://github.com/lhelontra/tensorflow-on-arm/releases/download/v2.0.0/tensorflow-2.0.0-cp37-none-linux_armv7l.whl
python3 -m pip uninstall tensorflow
python3 -m pip install tensorflow-2.0.0-cp37-none-linux_armv7l.whl
RESTART YOUR TERMINAL
Reactivate your virtual environment:
cd Desktop
cd tf_pi
source env/bin/activate
Test:
Open a python interpreter by executing:
python3
import tensorflow
tensor.__version__
This should have no errors and output: 2.0.0
I got the same issue today when trying to run the fresh tf installation on my pi 3+