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juglab
Tensorflow-gpu version 2.4.1 is not compatile with Windows
Just to report that the tensorflow gpu version written in the README instructions is not compatible with Windows machines. It is a Linux only version, from what I could check.
Might be a problem for new users when creating new environments in different OS.
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
PackagesNotFoundError: The following packages are not available from current channels:
- tensorflow-gpu=2.4.1
Might be good to test with newer versions of tensorflow-gpu and/or keras. I did not check the Keras version compatibility.
Nuno
Hey,
In my case I used (with CUDA 11.0 on Windows10):
pip install tensorflow-gpu==2.4.0 keras==2.3.1
I think conda does not "support" yet tensorflow-gpu=2.4.0 and keras=2.3.1
I hope it helps.
Carlo
Few more details: On Windows10 conda search results for TF and keras
(base) C:\Users\...>conda search tensorflow
Loading channels: done
# Name Version Build Channel
tensorflow 1.7.0 0 pkgs/main
tensorflow 1.7.0 0 anaconda
tensorflow 1.7.1 0 pkgs/main
tensorflow 1.7.1 0 anaconda
tensorflow 1.8.0 0 pkgs/main
tensorflow 1.8.0 0 anaconda
tensorflow 1.9.0 eigen_py35hb0e21f4_1 pkgs/main
tensorflow 1.9.0 eigen_py35hb0e21f4_1 anaconda
tensorflow 1.9.0 eigen_py36h0b764b7_1 pkgs/main
tensorflow 1.9.0 eigen_py36h0b764b7_1 anaconda
tensorflow 1.9.0 gpu_py35h0075c17_1 pkgs/main
tensorflow 1.9.0 gpu_py35h0075c17_1 anaconda
tensorflow 1.9.0 gpu_py36hfdee9c2_1 pkgs/main
tensorflow 1.9.0 gpu_py36hfdee9c2_1 anaconda
tensorflow 1.10.0 eigen_py35h38c8211_0 pkgs/main
tensorflow 1.10.0 eigen_py35h38c8211_0 anaconda
tensorflow 1.10.0 eigen_py36h849fbd8_0 pkgs/main
tensorflow 1.10.0 eigen_py36h849fbd8_0 anaconda
tensorflow 1.10.0 gpu_py35ha5d5ef7_0 pkgs/main
tensorflow 1.10.0 gpu_py35ha5d5ef7_0 anaconda
tensorflow 1.10.0 gpu_py36h3514669_0 pkgs/main
tensorflow 1.10.0 gpu_py36h3514669_0 anaconda
tensorflow 1.10.0 mkl_py35h4a0f5c2_0 pkgs/main
tensorflow 1.10.0 mkl_py35h4a0f5c2_0 anaconda
tensorflow 1.10.0 mkl_py36hb361250_0 pkgs/main
tensorflow 1.10.0 mkl_py36hb361250_0 anaconda
tensorflow 1.11.0 eigen_py36h346fd36_0 pkgs/main
tensorflow 1.11.0 eigen_py36h346fd36_0 anaconda
tensorflow 1.11.0 gpu_py36h5dc63e2_0 pkgs/main
tensorflow 1.11.0 gpu_py36h5dc63e2_0 anaconda
tensorflow 1.11.0 mkl_py36h41bbc20_0 pkgs/main
tensorflow 1.11.0 mkl_py36h41bbc20_0 anaconda
tensorflow 1.12.0 eigen_py36h67ac661_0 pkgs/main
tensorflow 1.12.0 eigen_py36h67ac661_0 anaconda
tensorflow 1.12.0 gpu_py36ha5f9131_0 pkgs/main
tensorflow 1.12.0 gpu_py36ha5f9131_0 anaconda
tensorflow 1.12.0 mkl_py36h4f00353_0 pkgs/main
tensorflow 1.12.0 mkl_py36h4f00353_0 anaconda
tensorflow 1.13.1 eigen_py36hf0a88a9_0 pkgs/main
tensorflow 1.13.1 eigen_py36hf0a88a9_0 anaconda
tensorflow 1.13.1 eigen_py37h2a8d240_0 pkgs/main
tensorflow 1.13.1 eigen_py37h2a8d240_0 anaconda
tensorflow 1.13.1 gpu_py36h1635174_0 pkgs/main
tensorflow 1.13.1 gpu_py36h1635174_0 anaconda
tensorflow 1.13.1 gpu_py36h9006a92_0 pkgs/main
tensorflow 1.13.1 gpu_py36h9006a92_0 anaconda
tensorflow 1.13.1 gpu_py37h83e5d6a_0 pkgs/main
tensorflow 1.13.1 gpu_py37h83e5d6a_0 anaconda
tensorflow 1.13.1 gpu_py37hbc1a9d5_0 pkgs/main
tensorflow 1.13.1 gpu_py37hbc1a9d5_0 anaconda
tensorflow 1.13.1 mkl_py36hd212fbe_0 pkgs/main
tensorflow 1.13.1 mkl_py36hd212fbe_0 anaconda
tensorflow 1.13.1 mkl_py37h9463c59_0 pkgs/main
tensorflow 1.13.1 mkl_py37h9463c59_0 anaconda
tensorflow 1.14.0 eigen_py36hf4fd08c_0 pkgs/main
tensorflow 1.14.0 eigen_py36hf4fd08c_0 anaconda
tensorflow 1.14.0 eigen_py37hcf3f253_0 pkgs/main
tensorflow 1.14.0 eigen_py37hcf3f253_0 anaconda
tensorflow 1.14.0 gpu_py36h305fd99_0 pkgs/main
tensorflow 1.14.0 gpu_py36h305fd99_0 anaconda
tensorflow 1.14.0 gpu_py36heb2afb7_0 pkgs/main
tensorflow 1.14.0 gpu_py36heb2afb7_0 anaconda
tensorflow 1.14.0 gpu_py37h2fabf85_0 pkgs/main
tensorflow 1.14.0 gpu_py37h2fabf85_0 anaconda
tensorflow 1.14.0 gpu_py37h5512b17_0 pkgs/main
tensorflow 1.14.0 gpu_py37h5512b17_0 anaconda
tensorflow 1.14.0 mkl_py36hb88db5b_0 pkgs/main
tensorflow 1.14.0 mkl_py36hb88db5b_0 anaconda
tensorflow 1.14.0 mkl_py37h7908ca0_0 pkgs/main
tensorflow 1.14.0 mkl_py37h7908ca0_0 anaconda
tensorflow 1.15.0 eigen_py36h932cce6_0 pkgs/main
tensorflow 1.15.0 eigen_py36h932cce6_0 anaconda
tensorflow 1.15.0 eigen_py37h9f89a44_0 pkgs/main
tensorflow 1.15.0 eigen_py37h9f89a44_0 anaconda
tensorflow 1.15.0 gpu_py36h2b26d6b_0 pkgs/main
tensorflow 1.15.0 gpu_py36h2b26d6b_0 anaconda
tensorflow 1.15.0 gpu_py37hc3743a6_0 pkgs/main
tensorflow 1.15.0 gpu_py37hc3743a6_0 anaconda
tensorflow 1.15.0 mkl_py36h997801b_0 pkgs/main
tensorflow 1.15.0 mkl_py36h997801b_0 anaconda
tensorflow 1.15.0 mkl_py37h3789bd0_0 pkgs/main
tensorflow 1.15.0 mkl_py37h3789bd0_0 anaconda
tensorflow 2.0.0 eigen_py36h457aea3_0 pkgs/main
tensorflow 2.0.0 eigen_py36h457aea3_0 anaconda
tensorflow 2.0.0 eigen_py37hbfc5123_0 pkgs/main
tensorflow 2.0.0 eigen_py37hbfc5123_0 anaconda
tensorflow 2.0.0 gpu_py36hfdd5754_0 pkgs/main
tensorflow 2.0.0 gpu_py36hfdd5754_0 anaconda
tensorflow 2.0.0 gpu_py37h57d29ca_0 pkgs/main
tensorflow 2.0.0 gpu_py37h57d29ca_0 anaconda
tensorflow 2.0.0 mkl_py36h781710d_0 pkgs/main
tensorflow 2.0.0 mkl_py36h781710d_0 anaconda
tensorflow 2.0.0 mkl_py37he1bbcac_0 pkgs/main
tensorflow 2.0.0 mkl_py37he1bbcac_0 anaconda
tensorflow 2.1.0 eigen_py36hdbbabfe_0 pkgs/main
tensorflow 2.1.0 eigen_py36hdbbabfe_0 anaconda
tensorflow 2.1.0 eigen_py37hd727fc0_0 pkgs/main
tensorflow 2.1.0 eigen_py37hd727fc0_0 anaconda
tensorflow 2.1.0 gpu_py36h3346743_0 pkgs/main
tensorflow 2.1.0 gpu_py36h3346743_0 anaconda
tensorflow 2.1.0 gpu_py37h7db9008_0 pkgs/main
tensorflow 2.1.0 gpu_py37h7db9008_0 anaconda
tensorflow 2.1.0 mkl_py36h31ad7c1_0 pkgs/main
tensorflow 2.1.0 mkl_py36h31ad7c1_0 anaconda
tensorflow 2.1.0 mkl_py37ha977152_0 pkgs/main
tensorflow 2.1.0 mkl_py37ha977152_0 anaconda
tensorflow 2.3.0 mkl_py37h04bc1aa_0 pkgs/main
tensorflow 2.3.0 mkl_py37h10aaca4_0 pkgs/main
tensorflow 2.3.0 mkl_py37h3bad0a6_0 pkgs/main
tensorflow 2.3.0 mkl_py37h48e11e3_0 pkgs/main
tensorflow 2.3.0 mkl_py37h856240d_0 pkgs/main
tensorflow 2.3.0 mkl_py37h936c3e2_0 pkgs/main
tensorflow 2.3.0 mkl_py37h952ae9f_0 pkgs/main
tensorflow 2.3.0 mkl_py37he40ee82_0 pkgs/main
tensorflow 2.3.0 mkl_py37he70e3f7_0 pkgs/main
tensorflow 2.3.0 mkl_py38h1fcfbd6_0 pkgs/main
tensorflow 2.3.0 mkl_py38h37f7ee5_0 pkgs/main
tensorflow 2.3.0 mkl_py38h3c6dea5_0 pkgs/main
tensorflow 2.3.0 mkl_py38h46e32b0_0 pkgs/main
tensorflow 2.3.0 mkl_py38h637f690_0 pkgs/main
tensorflow 2.3.0 mkl_py38h8557ec7_0 pkgs/main
tensorflow 2.3.0 mkl_py38h8c0d9a2_0 pkgs/main
tensorflow 2.3.0 mkl_py38ha39cb68_0 pkgs/main
tensorflow 2.3.0 mkl_py38hd19cc29_0 pkgs/main
tensorflow 2.5.0 eigen_py37hba85c30_0 pkgs/main
tensorflow 2.5.0 eigen_py38h6b3c56f_0 pkgs/main
tensorflow 2.5.0 eigen_py39hf7bd2bc_0 pkgs/main
tensorflow 2.5.0 gpu_py37h23de114_0 pkgs/main
tensorflow 2.5.0 gpu_py38h8e8c102_0 pkgs/main
tensorflow 2.5.0 gpu_py39h7dc34a2_0 pkgs/main
tensorflow 2.5.0 mkl_py37h99b934d_0 pkgs/main
tensorflow 2.5.0 mkl_py38hbe2df88_0 pkgs/main
tensorflow 2.5.0 mkl_py39h1fa1df6_0 pkgs/main
(base) C:\Users\...>conda search keras
Loading channels: done
# Name Version Build Channel
keras 2.0.8 py35h15001cb_0 pkgs/main
keras 2.0.8 py35h15001cb_0 anaconda
keras 2.0.8 py36h65e7a35_0 pkgs/main
keras 2.0.8 py36h65e7a35_0 anaconda
keras 2.1.2 py35_0 pkgs/main
keras 2.1.2 py35_0 anaconda
keras 2.1.2 py36_0 pkgs/main
keras 2.1.2 py36_0 anaconda
keras 2.1.3 py35_0 pkgs/main
keras 2.1.3 py35_0 anaconda
keras 2.1.3 py36_0 pkgs/main
keras 2.1.3 py36_0 anaconda
keras 2.1.4 py35_0 pkgs/main
keras 2.1.4 py35_0 anaconda
keras 2.1.4 py36_0 pkgs/main
keras 2.1.4 py36_0 anaconda
keras 2.1.5 py35_0 pkgs/main
keras 2.1.5 py35_0 anaconda
keras 2.1.5 py36_0 pkgs/main
keras 2.1.5 py36_0 anaconda
keras 2.1.6 py35_0 pkgs/main
keras 2.1.6 py35_0 anaconda
keras 2.1.6 py36_0 pkgs/main
keras 2.1.6 py36_0 anaconda
keras 2.2.0 0 pkgs/main
keras 2.2.0 0 anaconda
keras 2.2.2 0 pkgs/main
keras 2.2.2 0 anaconda
keras 2.2.4 0 pkgs/main
keras 2.2.4 0 anaconda
keras 2.3.1 0 pkgs/main
keras 2.3.1 0 anaconda
keras 2.4.3 0 pkgs/main
keras 2.4.3 0 anaconda
Hi all,
I was getting the same error (in a fresh conda environment) and I am glad there is already an issue about it here.
Thus, would the developers recommend any specific version for windows with conda? If there is not, maybe a quick warning in the readme.md file would be helpful for windows users :)
I tried with tf-gpu 2.3.0 and 2.5.0 but got this same error:
Pinned packages:
- python 3.7.*
Encountered problems while solving:
- package tensorflow-gpu-2.5.0-h17022bd_0 requires tensorflow 2.5.0, but none of the providers can be installed
I ended up installing with pip.
Best, Marcelo
I ended up installing with pip.
An update on this. I had several conflicts of versions, therefore I could not make it work at all. If anyone knows a way to make it work on Windows, I would be grateful! I would really like to try this package in my local machine.
Which version of Python are you using?
I tried recently Python 3.8 and then conda install tensorflow-gpu, which should install TF 2.4.
But I was working on a Linux machine.
I tried with python 3.7 as in the installation instructions.
I just attempted the same commands in a new conda environment with python 3.8, but these package versions do not seem to work there as well :(
- python 3.8.*
Encountered problems while solving:
- nothing provides requested tensorflow-gpu 2.4.1**
- package keras-2.3.1-py37h21ff451_0 requires python >=3.7,<3.8.0a0, but none of the providers can be installed```
Can you post the errors? Which CUDA version are you using? Please also try nvidia-smi and nvcc -V and post the output.
Hi @cberri ,
Sorry, I should have posted all versions right from the beginning.
These are the packages in my environment with python 3.8
# packages in environment at C:\Miniconda\envs\n2v-env:
#
# Name Version Build Channel
bzip2 1.0.8 h8ffe710_4 conda-forge
ca-certificates 2021.10.8 h5b45459_0 conda-forge
libffi 3.4.2 h8ffe710_5 conda-forge
libzlib 1.2.11 h8ffe710_1014 conda-forge
openssl 3.0.2 h8ffe710_1 conda-forge
pip 22.0.4 pyhd8ed1ab_0 conda-forge
python 3.8.13 hcf16a7b_0_cpython conda-forge
python_abi 3.8 2_cp38 conda-forge
setuptools 62.1.0 py38haa244fe_0 conda-forge
sqlite 3.38.3 h8ffe710_0 conda-forge
tk 8.6.12 h8ffe710_0 conda-forge
ucrt 10.0.20348.0 h57928b3_0 conda-forge
vc 14.2 hb210afc_6 conda-forge
vs2015_runtime 14.29.30037 h902a5da_6 conda-forge
wheel 0.37.1 pyhd8ed1ab_0 conda-forge
xz 5.2.5 h62dcd97_1 conda-forge
This is my cuda version (11.6):
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Tue_Mar__8_18:36:24_Pacific_Standard_Time_2022
Cuda compilation tools, release 11.6, V11.6.124
Build cuda_11.6.r11.6/compiler.31057947_0
Tue May 3 09:29:22 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 511.65 Driver Version: 511.65 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA GeForce ... WDDM | 00000000:01:00.0 Off | N/A |
| N/A 36C P8 8W / N/A | 0MiB / 4096MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
I use mamba to install packages. When I try to mamba install tensorflow-gpu I get this error message:
Looking for: ['tensorflow-gpu']
conda-forge/win-64 Using cache
conda-forge/noarch Using cache
pkgs/msys2/win-64 No change
pkgs/main/win-64 No change
pkgs/main/noarch No change
pkgs/r/win-64 No change
pkgs/r/noarch No change
pkgs/msys2/noarch No change
Pinned packages:
- python 3.8.*
Encountered problems while solving:
- package tensorflow-gpu-1.14.0-h0d30ee6_0 requires tensorflow 1.14.0, but none of the providers can be installed
With conda, I get more specific info (attached file)
tensorflow-gpu_conda_error.txt
If you think this is a tensorflow installation problem unrelated to this repo, that's totally fine. Nevertheless, I was wondering if you have a set of package versions (python, tensorflow, etc) tested on Windows 10 that you could recommend. Then I would try to set up my environment aiming for that :)
Best, Marcelo
Somehow it is trying to install TF 1.14.
Could you try out one of these: https://github.com/CSBDeep/CSBDeep/tree/master/extras#conda-environment
You are using CUDA 11.6. I am not sure if TF can run on such a high CUDA version. At least according to the doc:
https://www.tensorflow.org/install/source_windows
If you scroll down you have a table with the build configurations for Windows 10.
I am running n2v on CUDA 11.0 with TF 2.4.0 installed using pip on Windows 10. Python 3.7.
These are the major steps I followed in case:
-
Create the n2v conda environment
$ conda create -n n2v pip python==3.7 -
Activate the n2v conda environment
$ conda activate n2v -
Install tensorflow and keras
$ pip install tensorflow-gpu==2.4.0 keras==2.3.1Test tensorflow installation
$ python $ import tensorflow as tf $ print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU'))) $ Num GPUs Available: X $ exit() -
Install the last n2v release
$ pip install n2v
Ok thank you @tibuch and @cberri ! I will try these options and get back to you as soon as possible.
Hi guys,
The installation worked! 🎉 I can confirm from my side that following @cberri instructions works! Thank you!
Let me just report a few extra steps I took before following the instructions:
- Downloaded CUDA version 11.0
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Thu_Jun_11_22:26:48_Pacific_Daylight_Time_2020
Cuda compilation tools, release 11.0, V11.0.194
Build cuda_11.0_bu.relgpu_drvr445TC445_37.28540450_0
-
In my case, I noticed I had previous tf and keras versions installed in my base environment (I most likely accidentally installed them there at some point), so I uninstalled Miniconda and also deleted pyhton related folders. Then, reinstalled Miniconda.
-
Followed @cberri steps up to number 3. The
printstatement made me aware I had no cuDNN installed (cudnn64_8.dll not found). If I may, this video was what guided me to a proper cuDNN installation. -
Finally I installed
n2vwithpip.
Thanks again guys! I hope this issue may guide Windows 10 users like me to manage the installation.
Best, Marcelo
Newest N2V version bumped the TF dependency version (>2.7), so this issue should now be obsolete.
