hi there, just want to share my finding when running 0.7.0 on crestle.
If you have only install fastai (0.7.0) without install torch (0.3.1) before, then it used to using torch 0.3.1 (automatically). but crestle has recently upgraded torch to 0.4.0, and most importantly, the related packages.
so you may need to install torch 0.3.1 and torchtext 0.2.3, if you didn’t install torch before.
@stas, thanks for the hint the pytorch version was correct but
“** torchtext**” version was not compatible it has to be “torchtext==0.2.3.” for pytorch 0.3.1.
Installed fastai v1 first, in a dedicated env (it seems to work). Then I installed fastai 0.7 following the procedure described (which is the usual procedure).
As I try to import fastai modules, it complains what follows:
I am using clouderizer.com Fast.ai project template for the course and to run the template I use google colab like a backend and I am not able to import the library properly. I have already posted my query here and the solution I received was for the case when we run the complete notebook on google colab itself or I may not be able to understand it because these things are new to me. If someone in the forum is using clouderizer and has found a solution, please help!.
Thanks in advance.
Edit: Finally found a solution. Click on edit project, go to the setup section, in the setup script column, add pip install torchtext==0.2.3. Now I am able to run the notebooks without any problem.
thanks stas!
tried that in kaggle kernels but got error: AttributeError: ‘torch.dtype’ object has no attribute ‘type’
but also seems somewhat random, i managed to get it work once, then it showed error, some other people also reported errors.
ML lesson 1 worked with !pip install fastai==0.7.0, however DL v2 lessons are not running on kernels reliably. the more i look forward to v3 course. noticed v3 notebooks started to appear on github repo!
Thank you for the PR, @Cesare.montresor
It looks like everybody has been using environment*yml files, so requirements.txt got out of sync. I synced it to match environment.yaml.
Thanks, really fast!
I use it on Colab for giving classes with my 2 study groups.
As it requires to reinstall dependencies every time, using pip is somehow faster