I successfully installed BERT server and client.
I tried to start server by typing following code in a Anaconda Prompt
bert-serving-start -model_dir C:\bert\uncased_L-24_H-1024_A-16 -num_worker=4
It supposed to print some responses if the server started properly as shown in https://github.com/hanxiao/bert-as-service#install.
However, in my case, nothing shows in the prompt.
The following is how I fixed the problem.
uninstall bert-serving-server and bert-serving-client
create new virtual environment
downgrade python version to access lower version of tensorflow
download tensorflow1.15
5.install bert-serving-server and bert-serving-client
6.problem solved!!!
Related
I upgraded to pytohn 3.11 but when i try to run jupyter notebook in local python environment, It throws an error.
I upgraded pip and setuptools, vs code extensions, microsoft c++ distros but still the probelm persist. I tried to follow the error meg thrown and ran the msg in cmd/admin then i get the other error message. I posted the issue in official vs code and ipkernel github, but till date no one has been able to solve this issue even though ipkernel now officially supports python 3.11.
Link to the github post:
https://github.com/microsoft/vscode-jupyter/issues/11820
https://github.com/ipython/ipykernel/issues/1011
Attached are teh two error messages that i get everytime.
Somebody please help. THank You.
I upgraded to Python 3.11 and tried running jupyter notebook cell in local python environment but it throws and error.
I want to use python3.7.13 with anaconda.
From conda documentation
conda install python=3.7.13 should do the job however since all this is taking place at my corporation which is blocked with firewall I get following error:
An HTTP error occurred when trying to retrieve this URL. HTTP errors are ofrten intermittent, and a simple retry will get you on your way. ProxyError(....
I've re-tried multiple times but no luck. After some googling I found that there is a workaround -> from this SO
But it increase risk of attack which I cannot afford.
So I am trying to find out version of Anaconda that comes with python 3.7.13 therefore I could ask my service team to download Anaconda for me.
I have Python3 pre-installed on my personal laptop and now i have installed Jupyter Notebook using PIP but when i create a new notebook using python 3 kernel, it always gives orange highlighted message "Connecting to Kernel" but never connects.
OS - Windows 10, Personal laptop
Network - Wifi and not connecting through any proxy
Anti-virus - Uninstalled
Can someone help what could be the issue ?
I found the answer on stack overflow only in another thread.
Uninstall tornado 6
pip uninstall tornado
and reinstall tornado 5
pip install tornado==5.1.1
I had the same issue,i tried uninstalling tornado and reinstalling it ,i tried running it on different port , none of those worked.but i tried this and worked!!!!!
open anaconda ===> Home ====> find jupyter notebook ====> go to the gear icon (settings) ===> first remove it then you see there is a install button under it,after installation it worked for me!!!!
After having issues with this myself, and looking at the Tornado version, I found this wasn't the problem for me, instead after running the following in the relevant environment after installing ipykernel, this resolved the issue:
conda install -n base nb_conda_kernels
jupyter kernelspec list
Should list all kernels running on your system, make sure that the Python kernel path is accurate.
I would also try to run it over another port, in case if any network restrictions
Whenever I try to import tensorflow in my windows machine, its saying that The kernel appears to have died. It will restart automatically and then its not even working.
The below is the following message given by the jupyter terminal.
Warning! HDF5 library version mismatched error
The HDF5 header files used to compile this application do not match
the version used by the HDF5 library to which this application is linked.
Data corruption or segmentation faults may occur if the application continues.
This can happen when an application was compiled by one version of HDF5 but
linked with a different version of static or shared HDF5 library.
You should recompile the application or check your shared library related
settings such as 'LD_LIBRARY_PATH'.
You can, at your own risk, disable this warning by setting the environment
variable 'HDF5_DISABLE_VERSION_CHECK' to a value of '1'.
Setting it to 2 or higher will suppress the warning messages totally.
Headers are 1.10.1, library is 1.10.2
what could solve this problem.
My version of Python is 3.6.3
and I updated the conda package also.
I have a Windows 10 Machine with 16GB RAM, so it cant be a memory issue also.
I was working with tensorflow previously, but now its not working.
This started to happen like 2 months back! when i was working on my university assignment this happened. The same code was working properly and I ran the code once again on the very same day jupyter notebook it crashed, since then I'm facing this problem.
I also tried to import tensorflow in the command prompt, its still showing the same error.
Has anyone encountered the same problem? What could be the fix?
Message says, Headers are 1.10.1, library is 1.10.2
You need to install 1.10.1 version
conda install -c anaconda hdf5=1.10.1
I'm trying Anaconda/Spyder after using the standard python installation/IDLE for several years. The console (and variable explorer) becomes progressively slower during each work session, with every script and interactive command that I run. Eventually it takes minutes to get a response to simple commands like print, and then it stops responding completely and I shut it down. It's pretty disruptive to my work.
Mac OSX 10.13.6
conda version : 4.5.11
conda-build version : 3.15.1
python version : 3.7.0.final.0
qt 5.9.6
spyder 3.3.1
ipython 7.1.1
I do use matplotlib frequently. Whenever possible I use plt.ioff() at the beginning of a script and/or plt.close('all') at the end. Turning off matplotlib support completely in preferences doesn't help and it's not a permanent fix anyway. Python is not using anywhere near all my CPU or RAM.
As far as I've found out, the problem seems to be with the latest major release of the ipython kernel. Pinning the version of ipykernel to 4.10.0 solved it for me. To do so, add a line containing ipykernel 4.* to the file ~/anaconda3/conda-meta/pinned. Create the file if it does not exist yet. Then conda update ipykernel which will actually downgrade to 4.10.0. Worked for me!
you don't need to downgrade the entire spyder.
For me it worked with downgrading these two:
conda install ipython=6.4.0
conda install ipykernel=4.10.0
So this is not a totally satisfying solution but it seems to be a reasonable temporary fix. If you downgrade your spyder version to 3.1.X you will be able to use the python console rather than the Ipython console, and this seems to work for me. To do this just run:
conda install spyder=3.1
in the terminal while your environment of choice is active. This is not ideal, because the developers removed the python console for a specific reason as mentioned in this question, How to add python console in spyder.
Additionally downgrading this much may have unforeseen effects on other packages your code uses and may cause other issues but so far this is the only solution I was able to come up with.
#Sjoerd Thanks for your answer, this is what worked for me. To be still clearer, here's what I did in terminal on Anaconda 3 on High Sierra:
echo "ipykernel 4.10.0" >> ~/anaconda3/conda-meta/pinned
conda update anaconda
When I tried to update only ipykernel, I would have dependencies errors. By updating anaconda, everything is now up to date but ipykernel which is effectively at version 4.10.0. I didn't experience any hang since then.
#JJR4's answer didn't work for me (seems to work for others). Another workaround is to type %connect_info magic command into the IPython Spyder console which will display the JSON info to connect another console to the kernel.
The output of the magic command explains how to connect, but basically there are three options.
Paste the above JSON into a file, and connect with:
$> jupyter <app> --existing <file>
or, if you are local, you can connect with just:
$> jupyter <app> --existing <kernel>.json
or even just:
$> jupyter <app> --existing
if this is the most recent Jupyter kernel you have started.
I typically use qtconsole as the app. Once you enter your chosen connection method into the command line, a new console will open that is connected to the same kernel as the console in Spyder. For some reason, the new console does not lag even if the Spyder console does.
Enter everything in the new console, even runfile()
Hope this helps!
UPDATE:
Downgrading to ipykernel version 4.10 seems to solve the issue for me. Thanks to Carlos Cordoba - see GitHub conversation in link below.