Train Your Machine Learning Models on Google’s GPUs for Free(hackernoon.com)
hackernoon.com
Train Your Machine Learning Models on Google’s GPUs for Free
https://hackernoon.com/train-your-machine-learning-models-on-googles-gpus-for-free-forever-a41bd309d6ad
15 comments
I'm assuming they have an eye on all your data, or will so in the future? What's the catch?
Something makes me think this offer won't be this free for long.
Good to know thanks!
Quick summary: Google Colaboratory (Colab) provides a Jupyter notebook environment with up to 12 hours continuous access to an Nvidia K80 for free.
https://research.google.com/colaboratory/faq.html
Quick summary: Google Colaboratory (Colab) provides a Jupyter notebook environment with up to 12 hours continuous access to an Nvidia K80 for free.
https://research.google.com/colaboratory/faq.html
just so that you appreciate this outstanding gift: the equivalent compute time would quickly be worth several dozen of dollars if not hundreds of dollars on Amazon (plus setup time).
this is truly one of the most remarkable giveaways since … the Internet. of course by doing this they want to find talent and hook you. but I see no harm in this bait and switch scheme.
TL;DR: wow! free supercomputer time!
this is truly one of the most remarkable giveaways since … the Internet. of course by doing this they want to find talent and hook you. but I see no harm in this bait and switch scheme.
TL;DR: wow! free supercomputer time!
what we need now is some peer to peer or community driven learning so that people can collaborate on creating the best speech recognition model etc
This is great - especially for small projects and learning. However, there are a few limitations to be aware of if you’re thinking about using it for real projects.
First, is memory. I’ve seen reports of practical limitations being around 500mb. Call me a spoiled millennial noob but the vast majority of my real-world ML projects use a lot more - especially when working with image data.
Second, is getting data into your notebook. Most ML is data heavy and you want a fast way of working with it. For example, the Google Landmark challenge on Kaggle has nearly half a terabyte of unaugmented images just for the test set. You could easily push several terabytes for that one-off challenge alone if you were not careful.
Note: it is possible to work with much less data in many cases by resizing and preprocessing the images as you pull them down but that has problems too. Also, you can use Google Drive but it’s not ideal for large datasets.
Related:
https://www.reddit.com/r/MachineLearning/comments/84532y/n_g...
https://stackoverflow.com/questions/48750199/google-colabora...
First, is memory. I’ve seen reports of practical limitations being around 500mb. Call me a spoiled millennial noob but the vast majority of my real-world ML projects use a lot more - especially when working with image data.
Second, is getting data into your notebook. Most ML is data heavy and you want a fast way of working with it. For example, the Google Landmark challenge on Kaggle has nearly half a terabyte of unaugmented images just for the test set. You could easily push several terabytes for that one-off challenge alone if you were not careful.
Note: it is possible to work with much less data in many cases by resizing and preprocessing the images as you pull them down but that has problems too. Also, you can use Google Drive but it’s not ideal for large datasets.
Related:
https://www.reddit.com/r/MachineLearning/comments/84532y/n_g...
https://stackoverflow.com/questions/48750199/google-colabora...
The memory limitation is on the host (ya know - the stuff you don't actually need). You get 12GB of GPU memory I think.
You can mount a remote ssh disk as a working directory, and for your actual data stream it in from cloud storage direct from tensorflow.
You can mount a remote ssh disk as a working directory, and for your actual data stream it in from cloud storage direct from tensorflow.
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They give you 12GB of GPU ram.
The 500mb is simply the host ram for the python runtime. As long as you aren't converting everything to numpy arrays, you'll be fine.
The 500mb is simply the host ram for the python runtime. As long as you aren't converting everything to numpy arrays, you'll be fine.
fantastic, thanks!
what prevents ethereum mining on this if its free?
I would assume it's against the TOS and you'd run the risk of losing your Google Account if you got caught.
Do you have a URL for the Google Colaboratory TOS? Maybe I'm blind, but my cursory search failed.
> for Free — Forever
Nothing is ever free forever.
Nothing is ever free forever.
They even allow installing of python modules via pip.
And they already include opencv
After playing some random python module bingo here are some of the most useful libraries
And they already include opencv
import cv2
print(cv2.__version__)
'3.4.0'
After playing some random python module bingo here are some of the most useful libraries
sklearn 0.19.1
skimage 0.13.1
bs4 0.4.6 (BeautifulSoup4)
scipy 0.19.1
seaborn 0.7.1
PIL 4.0.0
keras 2.1.5
nltk 3.2.1
zmq 16.0.4