I am having trouble with a Jupyter script that I am using for a class at university. Useful information: I am using a MacBook Air (first time), macOS Monterey 12.0.1, M1 Apple chip, and I am working in a conda virtual environment with conda 4.11.0 and Python 3.9.7. This is the first part of the script:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from tensorflow import keras <--
import tensorflow as tf <--
from sklearn.utils import shuffle
from sklearn import preprocessing
from keras import regularizers <--
import random
from keras.utils.vis_utils import plot_model <--
import time
from IPython.display import Image
When I run it, I get this message:
The kernel appears to have died. It will restart automatically.
I tried commenting each row, and apparently, the problem is due to the ones that I highlighted with the arrow. So there is something wrong with TensorFlow. As suggested by my professor, I went on the terminal, typed ipython, then this:
In [1]: import tensorflow
zsh: illegal hardware instruction ipython
I looked it up on the internet, and I understood that there are some incompatibilities between some Python packages and the M1 Apple chip. I tried to follow this https://github.com/apple/tensorflow_macos, but when I use the command written there, this is what happens
(phdcourse) alessandroruggieri#Alessandros-MacBook-Air ~ % /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/apple/tensorflow_macos/master/scripts/download_and_install.sh)"
ERROR: TensorFlow with ML Compute acceleration is only available on macOS 11.0 and later.
This is pretty weird, considering I have macOS 12.0.1 (as said at the beginning).
To conclude, I have seen some posts about similar issues on the internet, but they all look rather confusing, so I would really appreciate an easy and clear help.
Related
I am currently trying to run a U-Net model in Keras, using PyCharm and Anaconda on Windows. My first attempts kept on working well. However, now I'm getting an error when I try to acces keras:
My imports:
from unet_model import multi_unet_model
from keras.utils import normalize
import os
import glob
import cv2
import numpy as np
from matplotlib import pyplot as plt
The error:
AttributeError: module 'tensorflow.python.client.pywrap_tf_session' has no attribute 'cxx_version'
Maybe one of you encountered the same problem and can give me a hint how to solve it.
The problem occured after trying to move the model from cpu to gpu. Now the model can't be processed at all. Maybe I changed some references without knowing it.
I've installed tensorflow and keras in every possible way I can think of, I have updated pip, used brew, tried a virtualenv but for some reason it won't let me import specific methods from tensorflow.keras (see image). What can I do?
Import issue
from tensorflow.python.keras.models import model_from_json
I am using python 3.8, spaCy 3.2.4, tensorflow 2.8.0, cuda 11.2, tensorflow-gpu 2.8.0. I want to import spacy, but it shows this error
import spacy
Process finished with exit code -1073740791 (0xC0000409)
Then, to fix this issue, I have to use this code
import tensorflow
import spacy
Now, it works. Although I already fixed the issue, I still want to know the reason behind the solution.
Furthermore, another question is when typing
import spacy
It shows:
Backend QtAgg is interactive backend. Turning interactive mode on.
How to always choose Qt5Agg or TkAgg instead of QtAgg? How to always turn on the interactive mode? I am using PyCharm IDE Community Edition 2020.3.5.
I try to do some PCA and TSNE analyses. I want to use sklearn to import the PCA and TSNE package.
Internet says that I should use these imports
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html
However, I get this message when I try to import those.
ImportError: cannot import name 'PCA' from 'sklearn.decomposition' (unknown location)
I did install sklearn and am able to import sklearn.decomposition and sklearn.manifold. However, If I I try to do this:
import sklearn.decomposition as decom
decom.PCA
I get the same ImportError. I tried reinstalling sklearn and updating it, but that doesn't solve the problem. Does one of you have a clue of what I'm doing wrong? I'm using Python 3.7.8 and use jupyter lab with MiniConda.
The documentation of sklearn is version 23.2 and that is the same version as my scikit-learn is.
EDIT: I created a totally new virtual environment and now my dir(decom) is totally different. And those packages for PCA and TSNE are working. Could somebody enlighten me why those are not working in the my current environment? Does it have to do with incompatible packages? I did not get any warning for it. It is sort of solved, yet I'm curious what the reason is and how I can fix it in the future.
Usually, I perform my coding under Linux using VS Code + Anaconda but, sometimes, I have to use a Windows machine. It is easy to sync to github and keep going on Windows on the same VS Code + Anaconda.
I try to use the Visual Studio Intellicode and the default python extensions but on Windows the experience is painfully slow.
To run the code (yes, just the imports) below (the first run) takes 26.509 sec:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
I can see that Microsoft.Python.LanguageServer is eating up to 60% CPU from my i7 notebook and more than 1500 MB RAM (sometimes with intensive I/O activity on my disk).
Anyone facing the same issues?
By the way, I'm using Python 3.7.2 and all packages installed with the default conda channels.