"Kernelspec cannot be found" when using Jupyter over EC2 instance - amazon-web-services

Just started an EC2 instance (P3) and am trying to configure Jupyter on it. Followed the instructions here: https://docs.aws.amazon.com/dlami/latest/devguide/setup-jupyter.html
I am able to get the server up and running, but when I try to create a notebook, none of the kernels work since the kernelspecs can't be found. I thought this was odd since earlier in the log, it said that Jupyter "Found new kernels in environments". Here is what I'm seeing: Kernelspec error pic

Related

Jupyter internal API is not active - Vertex-AI jupyterlab error 524

I cannot access Jupyterlab by web interface (error 524). It still works by ssh. I've followed the support documentations, but nothing works.
My best guess is that the main issue is with the opened ports of docker.
The key problem is probably below:
curl http://127.0.0.1:8080/api/kernelspecs
curl: (7) Failed to connect to 127.0.0.1 port 8080: Connection refused
And the following command simply restarts the service without error (but still inaccessible through web interface)
sudo service jupyter restart
Thanks!
EDIT: to clarify, all help from this article which specifically is supposed to fix error 524, doesn't work at all.
The diagnostic tool give this result, and the --repair doesn't work:
And "Verify that the Jupyter internal API is active" is completely useless as it doesn't explain how to fix the error!!
So I know there is a problem with the Jupyter internal API but no idea how to fix that.
EDIT 2:
On the web console, here is a screenshot:
I have gone through the same error, after upgrading the VM problem got solved all the Jupyter API are healthy try upgrading the VM. Before that take a snapshot of disk(upgrading might erase your VM).
How to upgrade the VM
As I mentioned in the comment a work around to resolve the issue can be by create a new instance keeping the old data.For this you can follow below steps:
Step 1: Create a new storage bucket and a new notebook.
Step 2: Copy the data to the newly created bucket by running the following command in the old notebook terminal.
"gsutil cp -R /home/jupyter/* gs://NEW_STORAGE_BUCKET_PATH"
Step 3: From the new managed notebook’s terminal, run the below command to copy the data to this new notebook .
"gsutil cp gs://NEW_STORAGE_BUCKET_PATH* /home/jupyter/"

Restart Jupyter Lab server running in the background

I'm trying to restart a Jupyter Lab server (not just the kernels) running in the background of an AWS SageMaker notebook instance. I have already tried the following:
Killing the server by it's process ID
pgrep doesn't show me the process
pkill can't find the process
ps aux shows the process ID as constantly changing
Stopping the server through jupyter notebook stop
I get an SSL error and nothing happens
The only thing I've been able to do is reboot the entire instance, which isn't a great option as it can take awhile to become available again.
Edit 1:
The main reason I am trying to do this is that after installing the tqdm package and trying to use tqdm.notebook in Jupyter Lab, in order for it to display correctly I need to enable/install notebook and lab extensions. In order for these to take effect the server then needs to be restarted.
Try this:
Left hand navbar, Commands
Navigate to the Help section on the popout menu
Reset Application State
Both classic Jupyter and Jupyter lab live within the same process.
sudo initctl restart jupyter-server --no-wait is what AWS suggest in https://forums.aws.amazon.com/thread.jspa?messageID=917594&#917594
Assuming it runs on port 8888:
jupyter lab stop 8888 && jupyter lab

Setting up the VM server on Google Cloud to run Jupyter notebook

I am following tutorial how to run Jupyter notebook on Google Cloud Platform (https://towardsdatascience.com/running-jupyter-notebook-in-google-cloud-platform-in-15-min-61e16da34d52). I am stuck at "Step 8: Set up the VM server". I have created Jupyter configuration file by typing jupyter notebook
--generate-config
in SSH session. After checking if it was created with
ls ~/.jupyter/jupyter_notebook_config.py
I get message No such file or directory. I really don't understand what is going on. I have never created VM before and I am a biologist (who tries to become a data scientist, lost in IT terminology), all I want to do is merge my dataframes on the cloud as I am lacking memory in my laptop. Can you please help me?

Tensorboαrd not reading log files, how can i erase the existing files?

I am trying to use tensorboard for the very first time and if I run
tensorboαrd --logdir = train_log
it starts up tensorboard 1.9.0 but nothing happens after that. And when I go to the link on port 6006 I see nothing – no scalar, no graphs. I am trying to start all over again but I don't know how to erase the previous logs from tensorboard.
A snapshot of what happens:
I am running tensorboard on google cloud platform Linux, I also tried using my own machine on Ubuntu and still having the same issues.

Openstack dashboard gives error "Error: Unable to retrieve usage information"

I installed OpenStack on an ec2 instance running Ubuntu 14.04 LTS via devstack. When I login into the dashboard I get an error "Error: Unable to retrieve usage information"
When I installed it and logged in for the first time, everything was working fine. But after I stopped my ec2 instance and restarted, I am facing this problem.
What might be causing this error?
I used the stable juno version of devstack.
And the AMI for my ec2 instance is Ubuntu Server 14.04 LTS (HVM), SSD Volume Type.
Does restarting the instance might have caused some problem?
cd to devstack and execute ./rejoin-stack
That solved it. I was trying to reboot nova and other services individually.
But since the installation was done using devstack, you need to run the ./rejoin-stack script.
In addition to [akshay1188] answer, you can re-stack your system. Sometimes, rejoin-stack does not work as expected. In that case, you can unstack (unstack.sh) it and stack (stack.sh) again. *This may take much time.
Another observation of mine says this can be an issue with IP address of the system. Try to keep IP address same after reboot.