I’ve added this to the topic -
Please update before running and if you still need help, please share the details so that we can help debug.
This happened to me as well. When I ran python -c 'import fastai; fastai.show_install(0)' there seemed to be some issue with torch detecting my GPU. Realized it was possibly either due to running all of this in old environment / jupyter version running on different environment (or with old packages). I have also realized that I was running the whole setup on old 390 drivers (which is not the case with you).
I have then updated drivers to 410 version, reinstalled every package again in a fresh environment (everything including pytorch-nightly, cuda and others listed in main README of the repo) and made sure torch is working with cuda and cudnn.
torch.cuda.is_available() and torch.backends.cudnn.enabled both should return True when run in the notebook. This time the notebook ran fine without any errors.
Here is my current output for the working config:
=== Software ===
python version : 3.6.6
fastai version : 1.0.14
torch version : 1.0.0.dev20181027
nvidia driver : 410.66
torch cuda ver : 9.2.148
torch cuda is : available
torch cudnn ver : 7104
torch cudnn is : enabled
=== Hardware ===
nvidia gpus : 1
torch available : 1
- gpu0 : 11175MB | GeForce GTX 1080 Ti
=== Environment ===
platform : Linux-4.18.0-10-generic-x86_64-with-debian-buster-sid
distro : #11-Ubuntu SMP Thu Oct 11 15:13:55 UTC 2018
conda env : fastai
python : /home/bharadwaj/anaconda3/envs/fastai/bin/python
sys.path :
/home/bharadwaj/anaconda3/envs/fastai/lib/python36.zip
/home/bharadwaj/anaconda3/envs/fastai/lib/python3.6
/home/bharadwaj/anaconda3/envs/fastai/lib/python3.6/lib-dynload
/home/bharadwaj/anaconda3/envs/fastai/lib/python3.6/site-packages
/home/bharadwaj/anaconda3/envs/fastai/lib/python3.6/site-packages/IPython/extensions
Thank you for this useful thread! Your screenshot for AWS setup shows the AMI named “Deep Learning AMI (Ubuntu) Version 15.0” and the official fast.ai AWS EC2 course material references 16.0, but when I am setting up my machine I have the option for 17.0. For this 2018 USF Course, should I use 16.0 or should we always use the “latest and greatest” at the time of setup?
Hi all,
I’m new to Python frameworks and services but have had no trouble so far making a local ubuntu 18 installation which has worked just fine. But while installing Starlette for lesson 2 I ran into trouble installing the ASGI server uvicorn with the following error:
conda install -c conda-forge uvicorn
Solving environment: failed
UnsatisfiableError: The following specifications were found to be in conflict:
- pytorch-nightly -> *[track_features=cuda92]
- uvicorn
Use "conda info <package>" to see the dependencies for each package.
But conda info gave no information on either package. Grateful for any tips on where to start looking for a solution.
Is it possible to use pip to install in combination with conda or should I keep to just the one?
This is a good and clean set of instructions. Thank you!
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So this was my second time trying to go through the course – first time using a raw paperspace instance and the second time with gradient. I was getting consistently tired of messing with remote compute instances while also trying to… learn… deep learning. I needed a new machine anyways, and it was blackfriday so I got a new laptop with a 1070 (hopefully 8gig vram doesn’t hobble me) that is beefy enough to crank through normal ML loads via cpu as well. Took a little while to do, but this thread let me get everything up and running 
GPU load confirms everything is working right
For some context on performance – this is the 8 epoch cell in the first notebook on resnet-50. GPU is a 1070 max-q.
Resnet50 fine tuning cell resulted in blackscreen first time I tried to run it (just banging through everything) – backed off on my undervolt to -.175 instead of -.185 and it ran fine. Everything is maxxed when doing this – all 6 cores and the 1070.
Tried doing this and I’m getting errors. Would be great to see instructions for those who had set up a p2.xlarge on AWS for the v2 version of the course and just want to clone the v3 repo onto this existing AMI. Since AWS permits only one p2.xlarge instance at a time, we will have to terminate the existing instance (and risk losing nbs with work) before signing up for the latest DL AMI.
I am just starting to get my personal system setup for Part 1 (2019) and hit something that others may hit. Installing conda install -c pytorch pytorch-nightly cuda92 does install pytorch-nightly-1.0.0.dev20190127-py3.7_cuda9.0.176_cudnn7.4.1_0, but then when I did conda install -c pytorch torchvision that installed pytorch 0.4.1(!) over the 1.0 install. I found that I needed to --force_reinstall the first command to get 1.0 to show up for me. Hope this helps some others…
UPDATE: before doing what I suggest here see my post to Lesson 1 Discussion ✅
I found the following conda formula worked better for setting up on a personal machine:
conda install -c pytorch -c fastai fastai pytorch torchvision cudaxxx
Where cudaxxx matches the version of CUDA you have installed.
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Thank you @neuradai, indeed your instructions work for me, too. They provide fastai=1.0.42 and pytorch=1.0.0. (I hadn’t realized that Pytorch 1.0 had been released)
The instructions at the top of this thread and in the AWS instructions are out of date, it would seem. I’ve only just restarted, so I’m not confident enough to edit the instructions above. Perhaps someone with more experience would look over the bits where they install pytorch-nightly & update per the prior post instead?
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@rogerallen I just submitted a PR for the conda formula change to the AWS instructions via GitHub. If they agree, maybe the fastai maintainers will propagate it to other instructions, as well…
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I ended up creating a GCP instance for the V3 version only. I guess there are posts on how others have used AWS to create both versions together.
Hi all,
Apologies for not keeping this post updated. I had created this however, I didn’t update it with the latest instructions over time.
I’ll update it soon this week and re-comment. Sorry for the issues that everyone had to face because of this.
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This formula worked great. Thanks for posting.
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I found that if I wanted to update fastai with conda it would include pillow as an update which would overwrite my compiled pillow-simd
Here is what you can do to avoid this. Write a file call ‘pinned’ in your conda-meta directory. You will find this in your
~/anaconda3/envs/fastaiv3/conda-meta
The file ‘pinned’ I create as such
vim ~/anaconda3/envs/fastaiv3/conda-meta/pinned
and placed the text as follows
pillow-simd ==5.0.3.post0
There maybe other files from the build of pillow-simd I need to add to this.
Search the conda online docs for ‘pinned’
Please comment
I have built my own box and these instructions helped immensely, thanks. I have followed them as written, cuda is successfully installed etc. I get as far as
conda install -c fastai torchvision-nightly
and get the following error output
Collecting package metadata: done
Solving environment: failed
PackagesNotFoundError: The following packages are not available from current channels:
- torchvision-nightly
Current channels:
- https://conda.anaconda.org/fastai/linux-64
- https://conda.anaconda.org/fastai/noarch
- https://repo.anaconda.com/pkgs/main/linux-64
- https://repo.anaconda.com/pkgs/main/noarch
- https://repo.anaconda.com/pkgs/free/linux-64
- https://repo.anaconda.com/pkgs/free/noarch
- https://repo.anaconda.com/pkgs/r/linux-64
- https://repo.anaconda.com/pkgs/r/noarch
To search for alternate channels that may provide the conda package you're
looking for, navigate to
https://anaconda.org
and use the search bar at the top of the page.
how should I resolve this?
thank you