I am running a deep learning model in google colab and it works fine with colab notebook. The problem is as the training of deep learning model progresses in the cloud colab notebook, my own computers cpu and memory usage also starts to go up. The RAM usage for the colab notebook browser window alone is more than 500 MB and it keeps climbing as the training progresses.
In google colab we have to keep open our running notebooks to train the model else we will lose all our previous work and it stops training. Can I run google colab from my terminal window instead of browser? Is there any way to force google colab to run in the cloud alone, that is, to run the notebook without opening the computer?
Instructions for local execution are available here:
http://research.google.com/colaboratory/local-runtimes.html
If you combine these with an SSH tunnel, you can run the backend on your own GCE VMs, AWS, or anywhere else you like.
Related
I am running a google colab notebook using my local GPU therefore using the colab feature of connecting to a local runtime.
I was wondering if, when running colab this way, it will remain with the same issues of disconnecting the runtime after a few hours being idle while training a model.
Thanks in advance,
Pedro
I have found by experience that when google colab is connected to a local runtime (i.e. GPU on your own machine as an example) it will never disconnect.
The 12h limit only applies when using google resources, since in this way they are not being used, it does not apply.
Notebooks run by connecting to virtual machines that have maximum lifetimes that can be as much as 12 hours. Notebooks will also disconnect from VMs when left idle for too long. However maximum VM lifetime and idle timeout behavior may vary over time, or based on your usage because this is necessary for Colab to be able to offer computational resources free of charge
Lets be Honest, Google Colab's Terminal gave me a Lot of Power and Potential. But there is one thing that I cannot and How can I Install Tor Network in Google Colab Although, I can Install and run both tor and torghost in terminal but there is a Problem, it says IP Tables not set and I have researched on this for Months but could not find any answers. If anyone can Pull this off you are a Legend.
So, here is a Quick Summary to the testing I have done so Far,
IP Tables are not in Google Colab.
We need to somehow set the IP Tables in a way that we both get connected to the runtime and its IP Dosent change for Google Colab Services but the traffic comming and going out should be through Tor.
HELP NEEDED
I am using google-cloud-shell which is basically a shell that allows us to use online cloud shell (for developing apps etc.) and provides 5gb of free storage (only for home directory).
It is a very cool thing because i don't have PC but google-cloud-shell allows me to run gradle, java, python, etc. without any issues except one issue and i.e typing response. Although it is a very good platform for learning coding but typing is insane.
If i type a character it takes about a second to be displayed on screen and it really really sucks. Now what i want is to connect this shell to termux (which is an app just like terminal in linux) with ssh or any other thing that can connect it.
NOTE: I am not using paid version of google-cloud-storage I am just using it cloud shell which is free to use.
You can use the following gcloud command to SSH into your cloud-shell from local terminal.
gcloud alpha cloud-shell ssh
You can find more details here
I'm trying to learn ML on GCP. Some of the Qwiklabs and Tutorials start with Cloud Shell to setup things like env variables and install Python packages, while others start by opening an SSH terminal into a VM to do those preliminary steps.
I can't really tell the difference between the two approaches, other than the fact that in the second case a VM needs to be provisioned first. Presumably, when you use Cloud Shell some sort of VM instance is being provisioned for you behind the scenes anyway.
So how are the two approaches different?
Cloud Shell is a product that is designed to give a large number of preconfigured tools that are kept updated, as well as being quick to start, accessable from the UI, and free. Basically, its a quick way to get an interactive shell. You can learn more about this environment from its documentation.
There are also limits to Cloud Shell -- you can only use it for 60 hours a week, if you go idle your session is terminated, and there is only 5GB of storage. It is also only an f1-micro instance, IIRC. So while it is provisioned for you (and free!), it isn't really useful for anything other than an interactive shell.
On the other hand, SSHing into a VM places you directly in a terminal on that VM, much like you would on any specific host -- you only have whatever tools that the image installed onto that VM provides (and many VMs come pretty bare bones, it depends on the image). But, you're now in a terminal on the host that is likely executing the code you want to work with, and it has as much CPU and RAM as you provisioned in that instance.
As far as guides pointing you to one or the other -- thats really up to them, but I suspect they'd point client / tool type work to the cloud shell (since its easy and a reasonably standard environment, which can even be scripted with tutorials), while they'd probably point how to install necessary software for use in production to a "real" VM.
Novice here
So yesterday I downloaded Anaconda, and from there I clicked on launch Jupyter. That opened the web browser where I could see all the files in my computer. That is a concern for me as I figure that other people can also access that.
I managed to shut down the server via the terminal by using a command control-x or something along those lines. My questions are:
Does Jupyter somehow save that data somewhere?
Is that data still accessible to somebody else even though I shut down the server?
Thank you
Per default a Jupyter Notebook server runs on localhost, which is only reachable from the local machine. In addition, it generates a unique token that you need to connect. So nobody else from a different machine can see your data.