I am using Google Colab and I clone some data from a repository. I can do !ls to see that the data is there. However I could not find the listed files anywhere.
Actually a question like this has been asked already here. However, in that question, the answers all responded with methods to *recover * the data , by mounting it. No one actually responded the question whih was where is the data.
So where is the data ?
Related
I have a problem when creating the BigQuery table by CSV file that storge on Google Drive. The old tables still working fine. I try to use the same file but change to the Upload option (instead of Google Drive), it is working without problem.
I have had this problem 5-7 days ago.
This issue has been raised in this issue tracker. We cannot provide an ETA at this moment but you can follow the progress in the issue tracker and you can ‘STAR’ the issue to receive automatic updates and give it traction by referring to this Link.
As a workaround you can disable the Editors tab, and then try creating the table. This works fine.
I'm working with Google colabs with my friend, then in a point of the project we must work at the same time, but sometimes when we will save the project for some of us is asking or telling us that is a error, then we have to save it manually or choose what lines of code should stay or not. Then that is why I'm asking about it, or maybe we have to make some other step like a pull request
thank you so much
I'm not sure if colab have such feature, but how about putting the colab in a github repo? That way you guys can both have your own version of the same notebook on a separate branch and when you need to have you friend's changes, you can just have him push it to the remote repo, then you can pull and merge your branch into his?
I am trying to share the google colab document (that contains my data-visualization project) with friends so they can run the code but not actually see the code, because I don't want them to copy the code. How do I do this?
I want to use google colab. But my data is pretty huge. So I want to access my data directly from the machine in google colab. And I also want to save the files directly in my machine directory. Is there a way I can do that as I can't seem to find any.
Look at how to use local runtime here.
https://research.google.com/colaboratory/local-runtimes.html
Otherwise, you can store your data on GDrive, GCS, or S3. Then, you can just mount it, no need to upload every time.
I mounted my google drive in my colab notebook, and I have a fairly big pandas dataframe and try to mydf.to_feather(path) where path is in my google drive. it is expected to be 100meg big and it is taking forever.
Is this to be expected? it seems the network link between colab and google drive is not great. Anyone know if the servers are in same region/zone?
I may need to change my workflow to avoid this. If you have any best practice or suggestion, pls let me know, anything short of going all GCP (which I expect don't have this kind of latency).
If you find calling df.to_feather("somewhere on your gdrive") from google colab and it is on the order of ~X00mb, you may find sporadic performance. It can take anywhere between a few min to a whole hour to save a file. I can't explain this behavior.
Workaround: First save to /content/, the colab's host machine's local dir. Then copy the file from /content to your gdrive mount dir. This seems to work much more consistently and faster for me. I just can't explain why .to_feather directly to gdrive suffer so much.