so i’m embarking on fast.ai course (fingers crossed!!)
and the first thing is, they need a GPU for the course (duh)
guess who uses a Mac and is unwilling to set up GCP even though I still have free credits shudders (i exaggerate, i’m just lazy), and am too poor for AWS (sad).
they recommended something (crestle)? again, i don’t want to pay.
so… Google Colab is my new best friend!!!
FREE. slightly painful to set up (still better than GCP), and slower than a legit GPU but still a GPU nonetheless.
i’ve set it up before, but here it is again, for people who wonder - how do I actually get my data files up onto Google Colab? I’m not talking about one excel file or two (you can see instructions for that here)
but what if i have like a thousand images (or more) for my dataset?
the easiest way is GOOGLE DRIVE.
copy this bunch of code below and run it - basically it will provide you with two links, one to sign in to Google, and one to allow it access to your Google Drive.
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 > /dev/null
!apt-get -y install -qq google-drive-ocamlfuse fuse
from google.colab import auth
auth.authenticate_user()
from oauth2client.client import GoogleCredentials
creds = GoogleCredentials.get_application_default()
import getpass
!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL
vcode = getpass.getpass()
!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}
once that’s done, put this in the next cell
!mkdir -p drive
!google-drive-ocamlfuse drive
basically if you do a !ls now, likely you will see datalab drive, and if you do a !ls drive you can see all the contents of your Google Drive.
so for example if i save my file called abc.txt in a folder called ColabNotebooks, i can basically access it via a path drive/ColabNotebooks/abc.txt
tada!!!!!
have fun guys.
if you need to install keras, use this:
!pip install -q keras