I used to multi-gpu system in tensorflow.
however, from someday, the following code used CPU only.
tf.debugging.set_log_device_placement(True)
strategy = tf.distribute.MirroredStrategy()
Moreover, the return of physical device check function is empty
tf.config.list_physical_devices('GPU')
The return of nvidia-smi correctly show as following picture
Environment
NVIDIA_SMI: 418.87.00
Driver ver: 418.87.00
CUDA ver: 10.1
Tensorflow: 2.4.1
CuDNN:
How do I handle this issue?
Tensorflow 2.4 is compatible with cudnn v8.0 and cuda 11.
So, upgrade cuddn and cuda.
If you are not using Anaconda, update the system paths and ensure they aren't any previous version.
e.g.,
/usr/local/cuda/bin/nvcc --version
Conda install:
# conda update --force conda ## if needed
# conda update conda ## if needed
conda activate <env>
conda install cudatoolkit
conda install -c anaconda cudnn
conda list cuda
conda list cudnn
Here is a script for manual install / you'll probably need even if using conda:
wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
sudo apt install ./libnvinfer7_7.1.3-1+cuda11.0_amd64.deb
sudo apt-get update
# Install development and runtime libraries (~4GB)
sudo apt-get install --no-install-recommends \
cuda-11-0 \
libcudnn8=8.0.4.30-1+cuda11.0 \
libcudnn8-dev=8.0.4.30-1+cuda11.0
# Install TensorRT. Requires that libcudnn8 is installed above.
sudo apt-get install -y --no-install-recommends libnvinfer7=7.1.3-1+cuda11.0 \
libnvinfer-dev=7.1.3-1+cuda11.0 \
libnvinfer-plugin7=7.1.3-1+cuda11.0
Have you changed anything in eco system.
I would suggest you to install cuda 11 and cudnn 8.0 with tensorflow 2.4.0 and above.
Then give it a try. Hope your problem will be resolved.
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
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
When I run :
pip install --upgrade tensorflow
This message pops up:
could not find the version that satisfies the requirement tensorflow
what should I do?
This is probably happening because you are using a pip version below 8.3.
In that case, you can install tensorflow using
For CPU version - pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.7.0-cp27-none-linux_x86_64.whl
For GPU version - pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.7.0-cp27-none-linux_x86_64.whl
These binaries are for version 1.7 and Python 2.7. You can get the latest wheel URLs from the official installation guide.
This worked for me
conda install pip
python -m pip install --upgrade pip
pip install --ignore-installed --upgrade tensorflow
This is what worked for me on Windows 10. Currently, Tensorflow only works with 64-bit windows, not 32-bit. So, you could create a new 64-bit environment and install tensorflow in it:
set CONDA_FORCE_32BIT=
conda create --name name_of_your_created_environment python=3.5
activate name_of_your_created_environment
conda install -c conda-forge tensorflow
Note:
CONDA_FORCE_32BIT=1 sets to a 32-bit environment whilst CONDA_FORCE_32BIT= sets to a 64-bit environment.
I am facing a problem while installing Tensorflow in Ubuntu 16.04. I was trying to install Tensorfilow CPU supportable version using Anaconda. This is the error I got
'tensorflow-1.3.0-cp34-cp34m-linux_x86_64.whl is not a supported wheel
on this platform.'
Can any one help me to solve this problem.
Try following steps:
First (if you haven't already) install Pip
For Python 2.X
sudo apt-get install python-pip python-dev python-virtualenv
For Python 3.X
sudo apt-get install python3-pip python3-dev python-virtualenv
Switch to your Conda environment:
source activate your_environment_name
And install TensorFlow using Pip:
For Python 2.X
pip install tensorflow
For Python 3.X
pip3 install tensorflow