On day to day basis, is it enough to run :
conda update -c fastai fastai
conda update -c fastai torchvision-nightly
to update ones local install? If so perhaps this should be added to the install instructions.
On day to day basis, is it enough to run :
conda update -c fastai fastai
conda update -c fastai torchvision-nightly
to update ones local install? If so perhaps this should be added to the install instructions.
Great! Thanks. Iâll try it out tonight.
BTW, will v0.7 still be usable after installing v1.0?
(I noticed that @init_27 says he has both 0.7 and 1.0 working).
you need to set up different conda environments for the 2 versions, then it is no problem.
you cannot have 2 versions of fastai in the same env at the same time. you have to switch between environments using conda activate fastai
and conda activate fastaiv1
(just examples)
Hello, I just updated the wiki guide for implementing Fastai v1 in Google Colab
Please checkout, everything is working fine!
LIke @marcmuc said, you can have both on the same machine but in differente enviroments. Thatâs why in my step by step Iâve created another env for v1.
The only problem Iâve found was that when switching env, sometimes, my Jupyter config gets messy. So, Iâve got to reconfigure it.
Update on my help request to Paperspace - they promise to have a fast.ai v1.0 template ready by the start of class. That will be useful for people new to Paperspace.
Please, feel free to add a note to the wiki. Note, anyone can edit the above wiki. I generally do some policing to reduce redundant points from being added.
Will do!
Binary Updates. Iâve installed the binary version 1.0 on my local UBUNTU 16.04 LTS using conda and âconda install âŠâ and it seems to be working fine, I used the binary instructions and had only one minor problem with the source path. My bash was looking in the wrong place so âsource activateâ wasnât working. This was easily fixed by editing my .bashrc file and commenting out the added export PATH line. In my last class last iteration (Part 2,V2), I would just do a git pull, and get new source. Since Iâm now using Conda, my questions are:
Thanks
Some of you may have a MSDN account and will have access to an Azure subscription (like me). You can use Azure to setup the VM. I found a nice guide to set this up -
Make sure to select Linux in place of Windows.
In case you didnât get an answer:
I configured the Azure Ubuntu 16.04 Deep Learning Virtual Machine for FastAI v1.0 with no issues - just followed the guidance for Part 1 v3 config.
@Interogativ think the updates should slightly become passive once weâre into the course.
For the dev version-they move fast! Fast isnât just fast, its FastAI-fast!
I think a conda update should suffice.
@arjundg If youâve tested these and they are working nicely, please feel free to add these to the wiki.
Thanks.
thanks. the instructions for AWS worked perfectly for me.
I have tested this Dockerfile by building a Docker image and then run the CUDA container using NVIDIA-Docker 2.0 runtime on Google Cloud Engine using the Deep Learning image (Debian GNU/Linux 9.5 (stretch)). Docker CE comes preinstalled. I have not experience the error. Note, I am using Python 3.6 and fastai 1.0.5. However, the Docker image takes up a huge 10 GB of disk space, understandably because I think you have not optimize the Dockerfile layers for minimal footprint.
Be careful, pulling the latest FloydHub PyTorch Docker image doesnât give you torchvision-nightly
package from fast.ai. See FloydHubâs Dockerfile below:
I got dependency errors when I tried to update my drivers. I solved the issue by running âsudo apt-get purge nvidia*â and starting over.
Added note to the wiki.
Iâve installed FastAI 1.0 on two different computers (desktop/laptop) using the latest Linux Mint distribution. I did that as Ubuntu didnât support my laptop hardware, and Mint did. It was really simple following the above instructions.