I am using the Google AI platform which provides jupyterlab notebooks. I have 2 notebook instances set up to run R of which only one notebook now opens. The first notebook will not open regardless of the number of stops and resets I performed. The notebook overview can be seen in this image and circled is a difference (it is 'started'):
The only reason I can imagine for this difficulty is that I changed the machine type for the notebook where I decreased the number of CPUs from 4 to 2 and the RAM from 15 to 7.5. Now I cannot open it and it has a blank for where the environment should say R3.6. I would not mind deleting it and starting over if there was not nonbacked-up work on it.
What can be done to bring the notebook back to operation and if it cannot be done, how can I download it or extract some key files?
As it was commented before, there are two ways to inspect the Notebook Instance using Cloud Console:
GCP Console > AI Platform > Notebooks
GCP Console > Compute Engine > VM Instances. The name of the GCE VM Instance will be the same as the Notebook Instance name.
It looks like you were able to connect to your Notebook instance via SSH button. Additionally you can use gcloud command to connect to instances via SSH that you have permission to access by following:
gcloud compute ssh --project <PROJECT> --zone <ZONE> <INSTANCE>
After you connect, use the terminal to run commands to verify the status of your jupyter service and the service logs by running:
sudo service jupyter status
sudo journalctl -u jupyter.service --no-pager
You can restart the jupyter service to try to recover it:
sudo service jupyter restart
If you want to use other methods or third parties to create a SSH connection to your Notebook instance, you can follow this.
If you were not able to recover your jupyter service, you can copy your files from your VM by click the gear icon in the upper right of the SSH from the Browser window and select Download file.
As it was mentioned before, the gsutil cp command allows you to copy data between your local file system and the cloud, within the cloud, and between cloud storage providers. For example, to upload all files from the local directory to a bucket, you can run:
gsutil cp -r dir gs://my-bucket
Use the -r option to copy an entire directory tree
Related
I have setup a Google Compute Engine (GCE) instance and I want to mount a Google Cloud Bucket to it. Basically, I have uploaded my data to Google Cloud and I want to make it available for use in the R Studio-server I have installed in my instance. It seems my mounting was successful, but I cannot see the data on R (or in the shell).
I want the bucket to be mounted in /home/roberto/remote. I have run chmod 777 /home/roberto/remote and then gcsfuse my-project /home/roberto/remote. I got the following output:
2023/01/28 22:49:01.004683 Start gcsfuse/0.41.12 (Go version go1.18.4) for app "" using mount point: /home/roberto/remote
2023/01/28 22:49:01.022553 Opening GCS connection...
2023/01/28 22:49:01.172583 Mounting file system "my-project"...
2023/01/28 22:49:01.176837 File system has been successfully mounted.
However, I can't see anything inside /home/roberto/remote when I run ls or when I look inside of it from R Studio-server (see image below). What should I do?
UPDATE: I had uploaded my folders to google cloud, but when I uploaded an individual file, it suddenly showed up! This makes me think the issue has something to do with implicit directories. Supposedly, if I run the same command as before with the --implicit-dirs flag that would be enough (something like this: gcsfuse --implicit-dirs my-project /home/roberto/remote). However, this is returning an error message and I am not sure how to deal with it.
Error message:
2023/01/29 01:33:15.428752 Start gcsfuse/0.41.12 (Go version go1.18.4) for app "" using mount point: /home/roberto/remote
2023/01/29 01:33:15.446696 Opening GCS connection...
2023/01/29 01:33:15.548211 Mounting file system "my-project"...
daemonize.Run: readFromProcess: sub-process: mountWithArgs: mountWithConn: Mount: mount: running /usr/bin/fusermount3: exit status 1
Try to edit the VM Cloud API access scopes of Storage to Full.
Follow the steps below:
Click/Select the VM instance
Stop the VM instance, then edit the VM instance.
Scroll down to Access scopes and select "Set access for each API"
Change the Storage from Read Only to Full.
Save and start your VM instance.
Then SSH to your VM instance and try to ls /home/roberto/remote
Other answers here could be useful depending on your issue. In my case, what solved it was indeed running the command gcsfuse --implicit-dirs my-project /home/roberto/remote. The error I was getting in the edit for my question was due to the fact that I had previously mounted the bucket and was trying to mount it again without unmounting it first (here is the official documentation on how to unmount the bucket). For more details on the importance of the --implicit-dirs flag take a look at the official documentation here. There are very similar solutions using, for instance, the /etc/fstab file. For that, take a look at this discussion in the official github page of gcsfuse.
Try running gcsfuse mount command with debug flags which will help in knowing why the mount failed.
Eg: gcsfuse --implicit-dirs --debug_fuse --debug_gcs --debug_http my-project /home/roberto/remote
I had created a Google compute engine (virtual machine) instance with RStudio Server being unaware that RStudio Server is a licensed software. Now, my trial license for RStudio has expired, and I cannot login to my R sessions anymore.
However, I had written some code which I need to recover. How do I download the files?
I have SSH-ed into my virtual machine but cannot find the relevant files or a way to download them.
I had a similar issue and I was able to recover the files by performing the following steps:
SSH to the virtual machine
Once you are in the virtual machine run the following command: cd ../rstudio-user/
Now ls there you will see the file structure you used to see in the RStudio Server interface}
Navigate using cd and ls between the folders to get to the desired file
Once you are in the desired location (where with an ls you can see the files you want to recover) run the following command: pwd
Click on the Engine and go to download file
Enter the full path of the file you want to download, it will be something like: /home/rstudio-user/FILENAME.R
Click on Download
You can do this for each of the files you want to recover.
In case you want to recover a full folder its easier to compress to a zip file and then to download it.
I have tried the run spring boot jar file using putty. but the problem is after closed the putty session service was stopped.
then i tried up the jar file with following command. its working fine .
**nohup java -jar /web/server.jar **
You should avoid using nohup as it will just disassociate your terminal and the process. Instead, use the following command to run your process as a service.
sudo ln -s /path/to/your-spring-boot-app.jar /etc/init.d/your-spring-boot-app
This command creates a symbolic link to your JAR file. Which then, you can run as a service using the command sudo service your-spring-boot-app start. This will write console log to /var/log/your-spring-boot-app.log
Moreover, you can configure spring-boot/application.properties to write console logs at your specified location using logging.path=path-to-your-log-directoryor logging.file=path-to-your-log-file.txt. Also, it may be worth noting that logging.file takes priority over logging.path
I have a Google Compute Engine VM instance with a Asterisk Server running on it. I get this message when I try to run sudo:
sudo: parse error in /etc/sudoers near line 21
sudo: no valid sudoers sources found, quitting
sudo: unable to initialize policy plugin
Is there a password for root so I can try to change it there? Any suggestions on this?
It looks like you have manually edited the /etc/sudoers file so while you would normally have sudo access, due to the parse error, you won't be able to do this directly.
Here's how to fix this situation.
1. Save the current boot disk
go to to the instance view in Developers Console
find your VM instance and click on its name; you should now be looking at a URL such as
https://console.cloud.google.com/project/[PROJECT]/compute/instancesDetail/zones/[ZONE]/instances/[VM-NAME]
stop the instance
detach the boot disk from the instance
2. Fix the /etc/sudoers on the boot disk
create a new VM instance with its own boot disk; you should have sudo access here
attach the disk saved above as a separate persistent disk
mount the disk you just attached
fix the /etc/sudoers file on the disk
unmount the second disk
detach the second disk from the VM
delete the new VM instance (let it delete its boot disk, you won't need it)
3. Restore the original VM instance
re-attach the boot disk to the original VM
restart the original VM with its original boot disk, with fixed config
How to avoid this in the future
Always use the command visudo rather just any text editor directly to edit the /etc/sudoers file which will validate the contents of the file prior to saving it.
I ran into this issue as well and had the same issue Nakilon was reporting when trying the gcloud workaround.
What we ended up doing was configure a startup script that removed the broken sudoers file.
So in your metadata put something like:
#/bin/sh
rm "/etc/sudoers.d/broken-config-file"
echo "ok" > /tmp/ok.log
https://cloud.google.com/compute/docs/startupscript
As you probably figured out this requires the /etc/sudoers file to be fixed. As nobody has root access to the instance, you will not be able to do this from inside the instance.
The best way to solve this is to edit the disk from another instance. The basic steps to do this are:
Take a snapshot of your disk as a backup (!)
Shutdown your instance, taking care not to delete the boot disk.
Start a new "debugger" instance from one of the stock GCE images.
Attach the old boot disk to the new instance.
In the debugger instance, mount the disk.
In the debugger instance, fix the sudoers file on the mounted disk.
In the debugger instance, unmount the disk
Shutdown the debugger instance.
Create a new instance with the same specs as your original instance using the fixed disk as the boot disk.
The new disk will then have the fixed sudoers file.
Since i bumped into this issue too, if you have another instance or any place where you can run with gcloud privileges, you can run:
gcloud compute --project "<project id>" ssh --zone "europe-west1-b" "<servername>"
I ran this on a server which had gcloud as root, so you login to the other box as root too! Then fix your issue. (if you don't have a box, just spin a micro up with the correct gcloud privileges) saves the hassle of disk stuff etc.
As mentioned in above comments, I am getting the same error like below in gcp VM.
sudo: parse error in /etc/sudoers near line 21
sudo: no valid sudoers sources found, quitting
sudo: unable to initialize policy plugin
To solve this
I have ssh to another vm and become root then I ran gcloud ssh command to our main vm (where you are getting the sudo error.)
gcloud compute --project "<project id>" ssh --zone "europe-west1-b "<servername>"
And BOOM!, now are login as root in the VM.
Now you can access/change the /etc/sudoers file accordingly.
I found this hack better than recreating vm/disks.
Hope this helps to someone!
It is possible to connect to a VM as root from your developers console Google Cloud Shell. Make sure the VM is running, start the shell and use this command:
gcloud compute ssh root#<instance-name> --zone <zone> [--project <project-id>]
where instance-name is found in the Compute Engine VM Instances screen. project-id is optional but required if you are connecting to an instance in a different project from the project where you started the shell.
You can then fix this and other issues that may prevent you from using sudo.
I got a Permission denied error when trying to ssh to the problem instance via gcloud. Using a startup script as mentioned above by #Jorick works. Instructions for it are here. You will have to stop and restart the VM instance for the startup script to get executed. I modified the script slightly:
rm -f /etc/sudoers.d/google_sudoers >& /tmp/startup.log
After the restart, launch an SSH session from the cloud console and check that you are able to view the file contents (with sudo more /etc/sudoers.d/google_sudoers for example). If that works your problem has been solved.
I am a novice as far as using cloud computing but I get the concept and am pretty good at following instructions. I'd like to do some simulations on my data and each step takes several minutes. Given the hierarchy in my data, it takes several hours for each set. I'd like to speed this up by running it on Amazon's EC2 cloud.
After reading this, I know how to launch an AMI, connect to it via the shell, and launch R at the command prompt.
What I'd like help on is being able to copy data (.rdata files) and a script and just source it at the R command prompt. Then, once all the results are written to new .rdata files, I'd like to copy them back to my local machine.
How do I do this?
I don't know much about R, but I do similar things with other languages. What I suggest would probably give you some ideas.
Setup a FTP server on your local machine.
Create a "startup-script" that you launch with your instance.
Let the startup script download the R files from your local machine, initialize R and do the calculations, then the upload the new files to your machine.
Start up script:
#!/bin/bash
set -e -x
apt-get update && apt-get install curl + "any packages you need"
wget ftp://yourlocalmachine:21/r_files > /mnt/data_old.R
R CMD BATCH data_old.R -> /mnt/data_new.R
/usr/bin/curl -T /mnt/data_new.r -u user:pass ftp://yourlocalmachine:21/new_r_files
Start instance with a startup script
ec2-run-instances --key KEYPAIR --user-data-file my_start_up_script ami-xxxxxx
first id use amazon S3 for storing the filesboth from your local machine and back from the instance
as stated before, you can create start up scripts, or even bundle your own customized AMI with all the needed settings and run your instances from it
so download the files from a bucket in S3, execute and process, finally upload the results back to the same/different bucket in S3
assuming the data is small (how big scripts can be) than S3 cost/usability would be very effective