I'm using MacOS catalina and try to install pandas by
pip3 install pandas
But while I tried to import pandas, I got this error
python3 -c "import pandas"
This is error:
RuntimeError: Polyfit sanity test emitted a warning, most likely due to using a buggy Accelerate backend. If you compiled yourself, see site.cfg.example for information. Otherwise report this to the vendor that provided NumPy.
RankWarning: Polyfit may be poorly conditioned
I've been facing the same issue. There quite a bit of info here:
https://github.com/numpy/numpy/issues/15947
As I understand, its because your python version is detecting issues with the 'buggy Accelerate backend' in NumPy (NumPy is installed with Pandas).
I was running Python 3.9.0 and I was not able to fix. However I have been able to bypass the issue by using version Python 3.8.6. I used this guide to install 3.8.6
https://opensource.com/article/19/5/python-3-default-mac
See the part about
pyenv install
which I modified to:
pyenv install 3.8.6
After I restart, the debug in visual code now shows 3.8.6. I added all required modules again inluding NumPy and its working for me.
good luck
Related
When I try to install tensorflow on python 3.9 I get following error:
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow
Is not there any tensorflow for 3.9?
What do you guys recommend?
Can I install other version of python beside the existing version?
Right now tensorflow does not have a build for python3.9
The latest one is for python3.8
You can check the build files at PyPI
https://pypi.org/project/tensorflow/#files
yes, you can install another version of python.
The original poster did not mention what type of computer or operating system he was using while attempting to install TensorFlow alongside Python 3.9. The error could be linked to working on a 64-bit Mac with the M1 chip (I recently experienced the same error described above while working on a Mac M1 in a Miniconda environment with Python 3.9.13). I solved the error by running
python3 -m pip install tensorflow-macos
from Terminal (in the Miniconda environment). TensorFlow installed normally alongside Python 3.9.13.
I do recommend installing Miniconda (or Anaconda as others have suggested), because it will allow you to easily create development environments with whatever version of Python modules or dependencies you require at the moment. See https://docs.conda.io/en/latest/miniconda.html. The larger Anaconda comes with a user-friendly 'Navigator' GUI which enables you to choose which environment is used to open a Jupyter notebook or other development environment, several of which come with Anaconda. See https://docs.anaconda.com/anaconda/install/
This is terrible with newer versions of Python that are not compatible with the machine learning module package.
So my approach is to keep the existing version 3.9 and the computer is using Anaconda to install a virtual environment with 3.7. When using vscode or pycharm, just remember to set it to that 3.7 Python environment.
I am trying to install numpy into a pypy3 virtualenv, but I'm stuck with that error (at importing) :
venv_pypy/site-packages/numpy-1.16.0.dev0+1d38e41-py3.5-linux-x86_64.egg/numpy/core/_multiarray_umath.pypy3-60-x86_64-linux-gnu.so: undefined symbol: cblas_sgemm
I am on an up to date archlinux, numpy works fine with CPython, but I have a project using pandas (which depends on numpy) that I need to test on pypy.
I first tried the recommended method (pip install numpy in the venv) but didn't work. (install is fine, but still the same error at execution).
I then, I tried what is suggested https://stackoverflow.com/a/14391693/1745291 (linked from Numpy multiarray.so: undefined symbol: cblas_sgemm ), since I didn't installed ATLAS (aur package on arch I don't want to install), to try building with OpenBias. But still not working (same error, and the method could be outdated since it's from 2013)
...And finally, I tried a build with no accelerations (at least, that is claimed) following : https://docs.scipy.org/doc/numpy-1.15.0/user/building.html#disabling-atlas-and-other-accelerated-libraries
...But still the same result...
What am i doing wrong ?
You can try uninstall it from pip and install from apt (if you are using ubuntu etc.)
This approach solved my problem
pip3 uninstall numpy
sudo apt-get install python3-numpy
Jupyter Notebook kernel dies when importing tensorflow 1.5.0
I have read a lot of posts relating to this but they have all had higher version numbers of tensorflow and have solved it by downgrading to 1.5.0. I also had higher version number and followed the advice to downgrade but I still have the problem.
Does anyone know what to try next?
pip install h5py==2.8.0
worked for me
When trying using the command prompt I got an error message not related to the tensorflow issue (I think);
"Warning! HDF5 library version mismatched error"
The key information from that message body was "Headers are 1.10.1, library is 1.10.2" so I downgraded hdf5 library by "conda install -c anaconda hdf5=1.10.1" and now the error message is gone and the kernel does not die when importing tensorflow.
I got similar problems, any tensorflow or tensorflow related packages (e.g. keras) made my kernel to die when loading, from any interface (jupyter, spyder, console....)
For those having this kind of problems, try running python from the console with verbose mode (python -v) then import tensorflow and look for errors.
I spot errors related to h5py, similar to the reply of #DBSE. I just upgraded the h5py package then everything was solved !
If you are using a conda environment, then the easiest method for fixing this issue is to just create a new environment and install tensorflow with just a single command. I had the same issue, I have tried a lot on most of the version of python and tensorflow. But at the last I have successfully configured it with just a single steps.
Run this command for installing GPU version
conda create --name tf_gpu tensorflow-gpu
The above line of code will automatically install that version of python and tf which is comaptible with your GPU or CPU.
For CPU, Run this command
conda create --name tf_env tensorflow
Both of these command work 100 % with my system for GPU and CPU access and will download the latest version which are compatible with system. It will resolved/fixed "Illegal Instruction (code dumps)" error.
pip install h5py==3.1.0
This is the most updated version which worked for me.
Try using import numpy before Keras and Tensorflow.
Here is mine small research about how-to install numpy for pypy3 on windows 7 with mingw32 compiler.
At first,tried to make it simple:
pypy setup.py install build --compiler=mingw32
But, here i got this error. Fortunately, i found this solution. Now, i'm trying again:
pypy setup.py install build --compiler=mingw32
And the error is:
data=DATA_RE.findall(nm_output)
TypeError:can't use a bytes pattern in string-like object
This error occurs in file pypy_numpy\numpy\distutils\lib2def.py.
So,the question is about how to handle this?
I installed Theano on my machine, but the nosetests break with a Numpy/Fortran related error message. For me it looks like Numpy was compiled with a different Fortran version than Theano. I already reinstalled Theano (sudo pip uninstall theano + sudo pip install --upgrade --no-deps theano) and Numpy / Scipy (apt-get install --reinstall python-numpy python-scipy), but this did not help.
What steps would you recommend?
Complete error message:
ImportError: ('/home/Nick/.theano/compiledir_Linux-2.6.35-31-generic-x86_64-with-Ubuntu-10.10-maverick--2.6.6/tmpIhWJaI/0c99c52c82f7ddc775109a06ca04b360.so: undefined symbol: _gfortran_st_write_done'
My research:
The Installing SciPy / BuildingGeneral page about the undefined symbol: _gfortran_st_write_done' error:
If you see an error message
ImportError: /usr/lib/atlas/libblas.so.3gf: undefined symbol: _gfortran_st_write_done
when building SciPy, it means that NumPy picked up the wrong Fortran compiler during build (e.g. ifort).
Recompile NumPy using:
python setup.py build --fcompiler=gnu95
or whichever is appropriate (see python setup.py build --help-fcompiler).
But:
Nick#some-serv2:/usr/local/lib/python2.6/dist-packages/numpy$ python setup.py build --help-fcompiler
This is the wrong setup.py file to run
Used software versions:
scipy 0.10.1 (scipy.test() works)
NumPy 1.6.2 (numpy.test() works)
theano 0.5.0 (several tests fails with undefined symbol: _gfortran_st_write_done')
python 2.6.6
Ubuntu 10.10
[UPDATE]
So I removed numpy and scipy from my system with apt-get remove and using find -name XXX -delete of what was left.
Than I installed numpy and scipy from the github sources with sudo python setpy.py install.
Afterwards I entered again sudo pip uninstall theano and sudo pip install --upgrade --no-deps theano.
Error persists :/
I also tried the apt-get source ... + apt-get build-dep ... approach, but for my old Ubuntu (10.10) it installs too old version of numpy and scipy for theano: ValueError: numpy >= 1.4 is required (detected 1.3.0 from /usr/local/lib/python2.6/dist-packages/numpy/__init__.pyc)
I had the same problem, and after reviewing the source code, user212658's answer seemed like it would work (I have not tried it). I then looked for a way to deploy user212658's hack without modifying the source code.
Put these lines in your theanorc file:
[blas]
ldflags = -lblas -lgfortran
This worked for me.
Have you tried to recompile NumPy from the sources?
I'm not familiar with the Ubuntu package system, so I can't check what's in your dist-packages/numpy. With a clean archive of the NumPy sources, you should have a setup.py at the same level as the directories numpy, tools and benchmarks (among others). I'm pretty sure that's the one you want to use for a python setup.py build.
[EDIT]
Now that you have recompiled numpy with the proper --fcompiler option, perhaps could you try to do the same with Theano, that is, compiling directly from sources without relying on a apt-get or even pip. You should have a better control on the build process that way, which will make debugging/trying to find a solution easier.
I had the same problem. The solution I found is to add a hack in theano/gof/cmodule.py to link against gfortran whenever 'blas' is in the libs. That fixed it.
class GCC_compiler(object):
...
#staticmethod
def compile_str(module_name, src_code, location=None,
include_dirs=None, lib_dirs=None, libs=None,
preargs=None):
...
cmd.extend(['-l%s' % l for l in libs])
if 'blas' in libs:
cmd.append('-lgfortran')
A better fix is to remove atlas and install openblas. openblas is faster then atlas. Also, openblas don't request gfortran and is the one numpy was linked with. So it will work out of the box.