I have been working on a GCP AI Notebook for the past couple of weeks when I got '524 error'. I followed the troubleshooting instructions here. I connected to the notebook instance via ssh and restarted the Jupyter service. I am now able to open JupyterLab but I can't find any of my work!! Here is the JupyterLab screenshot. I searched for the files using Terminal in JupyterLab as well as the Cloud Shell but nothing. It looks as if my instance had been wiped clean.
Please help, I lost all my code I have been working on for the past couple of weeks.
Based on the Terminal output, seems to be you are using a Container based instance.
This means that you have a base OS and a Docker instance running JupyterLab service on top. I will be interested in knowing what Docker instance is that you are running. Is this a Deep Learning Container?
By default (If using Deep Learning Containers) files are stored in /home/jupyter and this folder is mapped to local disk so you can see if there is something inside jupyter. Do you have something there?
You can SSH into Jupyter instance and verify which is your container and parameters passed.
sudo docker ps --no-trunc
Related
I am learning to use Azure Machine Learning. it has its Notebooks (which are ok!) and also it allows me to use Jupyter Notebook and VSCode.
However I am wondering if there is a way to efficiently use Spyder with Azure Machine Learing.
eg. I was able to install R-Studio as a custom application using a docker image using steps provided here Stackoverflow link
Spyder supports connecting to a Remote Python kernel, it does, however require SSH.
You can enable SSH on your Compute Instance (see below), but only when you set it up. Also, many companies have policies against enabling SSH, so this might not work for you. If it doesn't, I can highly recommend VSCode.
Little intro:
I have two OS on my pc. Linux and Windows. I need Linux for work, but it freezes on my pc, but windows does not. I've heard that is a common thing for ASRock motherboards.
That's why i want to switch to Windows for work.
So my idea was to create docker image with everything i need for work, such as yarn, make, and a lot of other stuff, and run it on windows for using linux functionality. You got the idea.
I know that docker is designed to only do one thing per image, but i gave this a shot.
But there are problems constantly. For example right now i'm trying to install nvm on my image, but, after building the image, command 'nvm' is not found on bash. It is a known problem and running source ~/.profile adds the command in console, but running it while building the image doesnt affect your console when you run this image. So you need to do that manually every time you use this image.
People suggest putting this in .bashrc which gives segmentation error.
And that's just my problem for today, but i've encountered many more, as i've been trying creating this image for a couple of days already.
So my question is basically this: is it possible to create fully operational OS in one docker image, or maybe one could connect multiple images to create OS, or do i just need to stop that and use a virtual machine like a sensible person?
I would recommend using a virtual machine for your use-case. Since you will be using this for work and modifying settings, and installing new software, these operations are better suited to be in a virtual machine where it is expected that you change the state or configurations.
In contrast, Docker containers are generally meant to be immutable, as in the running instance of the image should not be altered or configured. This is so that others can pull down the image and it works "out-of-the-box." Additionally, most Docker containers available on Docker Hub are made to be lean, with only one or two use cases in mind and not extra (for security purposes and image size), so I expect that you would frequently run into problems trying to essentially set up a Docker image that you would be working on. Lastly, since it is not done frequently, there would be less help available online, and Docker-level virtualization does not really suit your situation.
I'm referring to some docs and tutorials for the Azure DP-100 Exam related to Data Science.
On creating a Compute Instance (STANDARD_DS11_V2), I opened the Jupyter notebook, and cloned a sample repository (https://github.com/microsoftdocs/ml-basics)
After this, I'm not able to load or see the files inside the ml-basics folder on Jupyter.
Nothing happens on clicking on ml-basics folder, apart from the below error message after a long wait -
Timed out attempting to connect to this compute instance.
Check to make sure the compute instance is started. If you just started it, try again in a minute or two.
I'm not able to reproduce this issue, works fine for me. As mentioned in the error message, ensure your instance is up and running. If it is, try restarting it and also ensure there are no firewalls blocking your connection.
I am using a azure instance called NC6 that has a GPU on it. I want to use a tensorflow docker image that can use this GPU that also spins up a jupyter notebook.
I use this command
nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu
When I run this command from within the instance I get
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://localhost:8888/?token=4c495089418941ad470cfe33b002bd6fad67970f84354e29
But when I access the :8888 there is nothing. How can I expose this port to be able to access the notebook from outside the instance?
Although I never used Azure instances I think you have to do the following things:
Open port 8888 of your azure instance to the outside world (this is probably called a service).
Find the IP of you instance (probably defined in this service)
Navigate to http://IP_HERE:8888/?token=4c495089418941ad470cfe33b002bd6fad67970f84354e29
Hope this helps!
Fairly new to cloud computing, so bear with me if question is obvious or silly. With tons of information available on internet, I was able to successfully create an ec2 linux instance and installed R and Rstudio on it. Ran my scripts on it which went really well but took too long (16 hrs) and very expensive as well since I require instances with high memory and vCPUs .
In my programs, I am essentially running the same scripts for different datasets.
My question is, is there any way I can run multiple similar instances of ec2 (with exactly same software installed and my scripts). So, this way, I will be able to run my scripts on every dataset on a separate instance simultaneously in less amount of time.
So what I have tried so far. I have created an AMI image of my existing instance and launched it. But I couldn't SSH it because of its weird username and ip address, something like "root#10.0.0.1". I can see both instances are running (original and the AMI image instance), I can SSH into original but not into the other one. I am able to login to the RStudio for original instance on port 8787.
Another question is how to launch this AMI imaged instance using SSH (Putty) in parallel with original instance. What problem will it cause if I use both of them in the browser (RStudio in this case) simultaneously?
Please help me with this!Thanks!
Problem: For a school project, I was running several machine learning algorithms on pretty large size data which happened to requre 30-35GB of memory and my PC couldn't handle it. I was using R/RStudio. So, I resorted to AWS for my memory limitation problem.
What I did initially: I created an ec2-instance, installed R/RStudio. Everything worked out perfectly and I was able to run my programs on RStudio through browser. I actually, ran my scripts on a very small dataset on this AWS instance to see how things are going. To much of my surprise it took very long for the whole script to run even with this small dataset. Soon enough, I realized that all these algorithms in my programs could be run independently for the same set of features with a little tweak in the scripts.
So, I decided to play with AWS little bit. I recreated the programs such that everything stayed the same except the learning algorithms in each script. In other words, I wanted to simultaneously run a copy of these programs with different algorithms so that I could get everything running simultaneously and produce the results in a smaller amount of time.
Now, my goal was to run multiple copies of this instance (original instance). And I should be able to run RStudio on my browser for each of these instances e.g. 5 ec2 instances will have 5 RStudio running concurrently on browser's different tabs. With that, I would be able to run all RStudio for each instance on my browser.
Then, I created an image (AMI) of this instance and then I created multiple instances from the AMI but I was missing out few points while creating those new instances from AMI which caused the problem I asked in the question above.
I initially suspected that it has something to do with port 8787 and I might not be able to run multiple RStudio for each ec2 instance in the browser. However, that was not the problem at all.
There are few very important things to take care of while you create the new instances from an AMI.
Mistake: While CREATING new instances from this AMI, I was NOT selecting two important things correctly i.e. VPC and Security Group.
Correct method is:
VPC -- On the "Configuration Instance Details" page:
a. Click the "Network" dropdown and select the VPC which was created for the original instance. (Original instance is the one which is used to create the AMI (image))
b. Click the "Auto-assign Public IP" dropdown and select Enable
Security Group -- On the "Configure Security Group" page:
a. for "Assign a security group" option, tick the "Select an existing security group" options
b. If there are more than one security groups in the list then select the one which was created for the original instance (OR create a new Security Group and make sure that it has the same kind of inbound and outbound port accesses)
Once I set this up, as Marc B mentioned in the comments, each instance gets its own IP address, and a local subnet address is assigned as well
IP address of instance looks like : ec2-33-444-22-111.us-west-1.compute.amazonaws.com
subnet looks like: 127.0.0.35
Now, after learning this, I recreated 5 instances from my AMI. So, now I had 5 instances with RStudio on each of them. All of them were running perfectly fine because I was able to SSH into each of them.
Now I thought I should be able to work with these instances in different tabs of the browser and run my scripts in them. But I wasn't able to login to all the RStudio instances in my browser tabs. Only one of them was working fine and the others were just not working in the browser. However, I was able to SSH into all of them from PuTTY. I could have ran my scripts from Linux (SSH) as well but I wanted to run them using RStudio.
After spending a good number of hours on this, I figured out the problem that the RStudio server needs to be started manually for each ec2 instance in the linux except the very first instance.
For one of the ec2-instance (besides the one which was working fine on browser), I did the following to start the RStudio server manually as below:
SSH using PuTTY
Become root: sudo su
Go to this path where RStudio was installed on my Linux instance: cd /usr/lib/rstudio-server/bin
start RServer with this command : rstudio-server start
Now go back to the browser, open another tab and use your ec2-instance address and port number (http://ec2-33-444-22-111.us-west-1.compute.amazonaws.com:8787). And now you should get the login page of RStudio for this instance as well.
Now, with a similar process, I had to manually run RStudio-servers for all other instance in order to be able to access them through the browser. Then I thought, if there is a way to start the RStudio server when Linux starts up every time. Then came up with a solution. To do this, I made a change in one of the configuration files of Linux as follows:
Become root: sudo su
go to this path: cd /etc/rc.d
vi the file rc.local and add the following command:
/usr/lib/rstudio-server/bin/rstudio-server start
save the changes you made.
close the SSH connection
Then, I went back to the AWS console, stopped this instance and created an AMI (image) of it. Now the above changes will be effective for each instance that I create from this AMI i.e. now RStudio server will be started as soon as the instance boots and will be accessible through the browser.
Now I can use multiple RStudio instances using different tabs of my browser. Make sure you are using the correct instance address in the browser. Port number stays same for all i.e. 8787