I have tried
conda install -c conda-forge gcc
but it only installed gcc. I also need g++.
Narrow Solution
It ships under the package name gxx:
conda install -c conda-forge gxx
Recommended Specification
Generally, I prefer using the Conda Forge compilers package or one of its subpackages (e.g., cxx-compiler), as this specification works across platforms. For example, cxx-compiler will install g++ on Linux, clang++ on OSX, and vc on Windows:
conda install -c conda-forge cxx-compiler
Related
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
I installed Tensorflow-macos and try to install opencv-python
but always fail in this message
ERROR: Command errored out with exit status 1: /Users/sean/Documents/sysvenv/tf24v/bin/python3 /Users/sean/Documents/sysvenv/tf24v/lib/python3.8/site-packages/pip install --ignore-installed --no-user --prefix /private/var/folders/gy/jzs3xnwd1z3203d75y_31nxc0000gn/T/pip-build-env-en64krht/overlay --no-warn-script-location -v --no-binary :none: --only-binary :none: -i https://pypi.org/simple -- setuptools wheel scikit-build cmake pip 'numpy==1.13.3; python_version=='"'"'3.6'"'"'' 'numpy==1.14.5; python_version=='"'"'3.7'"'"'' 'numpy==1.17.3; python_version=='"'"'3.8'"'"'' 'numpy==1.19.3; python_version>='"'"'3.9'"'"'' Check the logs for full command output.
it lookalike the bumpy version problem.
But In Tensorflow-macos it it 1.8.5 and python is 3.8
Does anynoe have the same problem?
thanks
You might want to look at these suggestions:
opencv issues with M1 MAC - OpenCV imshow doesnot work
conda install opencv
I was successful in installing Python 3.9.1 for Apple Silicon and then running conda install opencv. However, the slow time to compute a few functions for the first time might suggest some components are still being translated via Rosetta. Regardless, while I did not test performance the functions I needed seemed to work.
after searching the web I have successfully installed OpenCV on my Mac M1.
Probably you don't have brew installed, so here's how you should install it:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew install wget
brew install miniforge
brew install cmake, you will need cmake for building OpenCV code
Then just follow this blog: https://sayak.dev/install-opencv-m1/#Install-conda
OR, here is the YouTube video which explains the second step: https://youtu.be/x_kAkabk-5o
P.S. If mdfind cv2.cpython returning an empty string then try to delete the build folder and compile opencv again. Otherwise, you have done everything correctly.
In conda the latest version of conda is:
cudnn 7.3.1 cuda10.0_0 anaconda
But i need 7.4.2 for tensorflow-gpu.1.13
How install cuDNN==7.4.2 in conda?
conda update --force conda
conda update conda
conda install -c anaconda cudnn
conda list cudnn
You can install with conda-forge
conda install -c conda-forge cudnn
https://anaconda.org/conda-forge/cudnn
It is more up to date than anaconda channel - for example as of today, latest version of cudnn on anaconda is still 7.6.5, but on conda-forge v8.2.0.53.
Same applies to cudatoolkit package.
You need to uninstall cudnn: conda uninstall cudnn.
Uninstall any tensorflow dependencies: "conda uninstall tensorflow"
Install tensorflow using pip: "pip install tensorflow"
Install CuDNN and Cuda ToolKit following the instructions in here: https://www.tensorflow.org/install/gpu#linux_setup
Use PyCharm or Spyder to run Scripts using tensorflow
The best use is to install both cuda-toolkit and CuDNN using conda environment for the best compatibility. But in some cases people might need the latest version. Moreover sometimes cuda packages are updated in different schedules such as the time being this answer is provided, conda provides cudatoolkit-11.0 but cant provide CuDNN-8.0 at the same time. which happened in my case. There is a workaround for this problem.
install conda-toolkit using conda enviroment and download the latest matching CuDNN version from Nvidia CuDNN page for installed cuda-toolkit. Use tar and unzip the packages and copy the CuDNN files to your anaconda environment.
sudo cp cuda/include/cudnn*.h /anaconda3/envs/<your environment here>/include
sudo cp cuda/lib64/libcudnn* /anaconda3/envs/<your environment here>/lib
sudo chmod a+r /usr/local/cuda/include/cudnn*.h /anaconda3/envs/<your environment here>/lib/libcudnn*
In the given snipped "cuda" path represent the unzipped CuDNN folder. This workaround is tested with tensorflow-2.4 & cudatoolkit-11.0 & CuDNN 8.0.4
This is how i installed cudnn.
1. You can download cudnn tar file of a version which you want from NVIDIA and extract it.
Then, you can see "cuda" folder including cudnn files.
2. Copy and paste the cudnn files to conda envs lib and include folder:
sudo cp cuda/include/cudnn*.h anaconda3/envs/"your_env_name"/include
sudo cp cuda/lib64/libcudnn* anaconda3/envs/"your_env_name"/lib
anaconda3 is your anaconda installation folder.
In my case, it worked.
This was not possible to do it with conda at the time the question was made. That is way it was suggested to try this. However, now it is possible. Follow the other answers
I am trying to run this statement in MacOS.
conda install -c conda-forge tensorflow
It just stuck at the
Solving Environment:
Never finish.
$ conda --version
conda 4.5.12
Nothing worked untill i ran this in conda terminal:
conda upgrade conda
Note that this was for poppler (conda install -c conda-forge poppler)
On win10 I waited about 5-6 minutes but it depends of the number of installed python packages and your internet connection.
Also you can install it via Anaconda Navigator
One can also resolve the "Solving environment" issue by using the mamba package manager.
I installed tensorflow-gpu==2.6.2 on Linux (CentOS Stream 8) using the following commands
conda create --name deeplearning python=3.8
conda activate deeplearning
conda install -c conda-forge mamba
mamba install -c conda-forge tensorflow-gpu
To check the successful usage of GPU, simply run either of the commands
python -c "import tensorflow as tf;print('\n\n\n====================== \n GPU Devices: ',tf.config.list_physical_devices('GPU'), '\n======================')"
python -c "import tensorflow as tf;print('\n\n\n====================== \n', tf.reduce_sum(tf.random.normal([1000, 1000])), '\n======================' )"
References
Conda Forge blog post
mamba install instead of conda install
The same error happens with me .I've tried to install tensorboard with anaconda prompt but it was stuck on the environment solving .So i've added these paths to my environment variables:
C:\Anaconda3
C:\Anaconda3\Library\mingw-w64\bin
C:\Anaconda3\Library\usr\bin
C:\Anaconda3\Library\bin
C:\Anaconda3\Scripts
and it worked well.
Follow the instruction by nekomatic.
I left it running for 1 hour. Yes. it is finally finished.
But now I got the conflicts
Solving environment: failed
UnsatisfiableError: The following specifications were found to be in conflict:
- anaconda==2018.12=py37_0 -> bleach==3.0.2=py37_0
- anaconda==2018.12=py37_0 -> html5lib==1.0.1=py37_0
- anaconda==2018.12=py37_0 -> numexpr==2.6.8=py37h7413580_0
- anaconda==2018.12=py37_0 -> scikit-learn==0.20.1=py37h27c97d8_0
- tensorflow
Use "conda info <package>" to see the dependencies for each package.
Back in the days, installing tensorflow-gpu required to install separately CUDA and cuDNN and add the path to LD_LIBRARY_PATH and CUDA_HOME to the environment.
Now, a simple conda install tensorflow-gpu==1.9 takes care of everything. Removing the CUDA_HOME and LD_LIBRARY_PATH from the environment has no effect whatsoever on tensorflow-gpu.
Question: where is the path to CUDA specified for TensorFlow when installing it with anaconda?
When you install tensorflow-gpu, it installs two other conda packages:
cudatoolkit: 9.0-h13b8566_0
cudnn: 7.1.2-cuda9.0_0
And if you look carefully at the tensorflow dynamic shared object, it uses RPATH to pick up these libraries on Linux:
(tflow) $ ldd $CONDA_PREFIX/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so | grep -E 'cufft|curand'
libcufft.so.9.0 => /tmp/wani.1533146686/tflow/lib/python3.6/site-packages/tensorflow/python/../../../../libcufft.so.9.0 (0x00007fbb9454a000)
libcurand.so.9.0 => /tmp/wani.1533146686/tflow/lib/python3.6/site-packages/tensorflow/python/../../../../libcurand.so.9.0 (0x00007fbb905e4000)
(tflow) $ ldd $CONDA_PREFIX/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow_internal.so | grep cudnn
libcudnn.so.7 => /tmp/wani.1533146686/tflow/lib/python3.6/site-packages/tensorflow/python/../../../../libcudnn.so.7 (0x00007fd73b55d000)
The only thing is required from you is libcuda.so.1 which is usually available in standard list of search directories for libraries, once you install the cuda drivers.
In my case, the following command took care of it automatically:
$ sudo apt install nvidia-cuda-toolkit