Actually to DL you do need a proper machine with a recent GPU(10xx+) else it’s a bit slow and backward compatibility is taken slightly bit by bit…
(I also have a 960MX at my disposal but it sucks…)
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Hello, will the pip version of fastai.1.x be updated as frequently as that of the git repo? Then perhaps it will be easier to work on Google Colab
I have ubuntu 16 with fastai 0.7 which I will keep in its own separate conda env. I hope later study fastai part 2 v2 which still needs the older fastai after finishing part1 v3
Do you still recommend upgrading to ubuntu 18 if I want to keep using the older fastai too?
I followed all the steps in this guide for a local install on Ubuntu 18.04 and everything went smoothly. All the diagnostic steps provide the correct outputs, as expected. However, when I try to launch the Jupyter Notebook for dogs_cats.ipynb in the fastai examples folder, I get the following error when running the second cell:
NameError: name 'untar_data' is not defined
Not sure why imports aren’t picking up the function above. For the record, I conda installed fastai and the version is 1.0.5 with the Python version being 3.7. When I try manually importing fastai and mucking around, it looks like untar_data isn’t anywhere to be found in the installed lib. Any suggestions?
Try a developer install (see the readme).
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Thanks @jeremy! Developer install did the trick. I guess the current builds on conda/PyPi have an issue that is causing this problem?
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Just pushed a new version that’ll work fine.
I already have a Paperspace account with a machine built using their fast.ai template (v0.7). I submitted a Paperspace help request a few days ago to ask them if they would have a v1.0 template anytime soon, but they haven’t responded. Anyone have any extra information? Shall I assume that I’ll just need to create a new machine and install fast.ai v1.0 myself? Thanks.
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Hey venkat, don’t be afraid 
, I am using exactly your XPS15 laptop and have installed dual boot with no problem. But yes, you will have to disable secure boot functionality. I followed mainly this guide (but did not implement everything like making windows harddrive run as a vm in linux): https://github.com/rcasero/doc/wiki/Ubuntu-linux-on-Dell-XPS-15-(9560).
The other alternative, if you are not afraid of docker is to use that. So if you are planning on continuing to use windows (which I don’t really, even if it’s still there, switching around is a hassle…), that might be the better alternative, but has it’s own “learning overhead”. But docker on windows works quite fine these days…
There are posts in the forums and dockerfiles and docker containers images with fastai you can use out of the box…
Hello community, does fast.ai v1.x.x has support for running on kaggle competitions so that we can make submissions to particular competition ?
Thanks for the encouragement. 
How is the battery management with regard to juggling the on-board and dedicated GPUs?
Bad. I get hours less out of the battery with ubuntu than with windows. I first tried to do something about it, but if you research this a bit, this seems to be generally the case with ubuntu/linux, also on macs, because for windows the parties involved spend a lot of time finetuning all the drivers which seems to lack on linux. So, running any linux on a windows-intended machine or on a mac seems to increase battery drainage. This is independent of gpu/non gpu. It is definitely advisable to use the onboard graphics for x-windows and the gpu only for DL, there is a thread regarding this and my explicit way of doing it here. That gives you back some battery time (because nvidias power management seems to be better on windows than on linux too…). You can now also go directly for headless nvidia drivers (disclaimer: haven’t tested this myself)
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Thank you. I’ll have a look at the options.
I’m using fastai 0.7 and 1.0 with ubuntu 18. Everything is working okay.
I can’t believe I’m saying this, but ubuntu 18 has a “friendly UI” or atleast compared to 16.
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@bholmer It’s just as easy as a pip install.
Please check the steps for AWS in the wiki. You’ll just need to follow those.
If you’re going through the ML mooc, you could build a 2nd environment to install fastai_v1
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Is there a stable docker image for the course? I’ve tried to install everything from scratch on PC, but failed miserable and wasted two weekends in the process, but I know how to use docker 
During class, do we know if it’s better to stick to the development version of fast.ai (in git repo) or use the released version?
I had problems running the examples until I switched to the development version, and I am fine with that, but I think it would be good to know what the recommended approach is.
We’ll be using conda for the class, not development version. But until Monday you might find dev version best if you’re trying to follow along.
Setup Fastai v1 on Paperspace
Hello everyone!
I’ve create a small step-by-step guido to setup and configure the new fast.ai v1 version on the already available fast.ai paperspace template.
Please check the guide on https://gist.github.com/tcvieira/d29d38068a6cd2c455baaaf0d183534b
I’d appreciate all feedbacks!
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