We can use this topic as study group, may be for meetups too.
great. interested.
Interested.
Great! Interested!
Interested
Nice initiative for Pune city. Go Pune!!!
Most of you would have done earlier fast.ai courses but if you are doing this for this first time, you will need to prepare the environment and install python and packages. Please let us know here if you need any help with that.
How do I get started with it? Is cloud service recommended? Among them what is preferred?
Great! 
https://forums.fast.ai/t/setup-local-aws-and-faq/25298
This will help you.
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Preference is always local if you have a decent machine with atleast 7 GB GPU memory otherwise you will endup waiting a lot for training to finish. Otherwise go for cloud service and there are pretty neat and easy to use cloud services. But you have to pay for that. Check the above link shared by @rohitpatil.
Last time in Part1 2017 we received AWS credit, lets wait if something similar happens now too.
You can also checkout “https://lab.snark.ai/”, “https://www.crestle.com/” “Google Colab”, “paperspace” too.
I personally prefer, Google cloud solution. With pre-built image, I was able to deploy a new server in 5 min.
Also they have 300$ discount for a year.
Checkout this post for installation, https://blog.kovalevskyi.com/google-compute-engine-now-has-images-with-pytorch-1-0-0-and-fastai-1-0-2-57c49efd74bb
With the free credit may be i will try google cloud solution, lets see how it pan’s out
i am in
hey guys…you can also use https://medium.com/@prakash_31206/fastest-way-to-setup-fast-ai-course-notebooks-for-free-using-google-colab-gpu-and-clouderizer-c8a004e1d50d
with this, you can use google colab resource for free
I haven’t used Google colab in a while. But, I had issue with getting GPU for model. It seems your request will go in queue and execute only when GPU was available.
Is it still valid?
I would say you can use Kaggle Kernel if you want to use free GPU. But the performance is not comparable with dedicated server + GPU.
I used colab with the help of clouderizer few days back and was getting around 11gb GPU memory. I used it for small task like going through notebooks and tweaking parameters. But it cuts connection if the notebook is processing for a long time.
So if you want to train model then definitely dedicated server and GPU is always better.
Hi! I’m interested too.
Interested
Hi guys shall we meetup this weekend to know each other n to discuss any thing in universe?. I have created a group for the same.