Thanks Bob: changing the environment.yml and settings.ini seems to be the best way.
final edit: Actually now I’ve noticed that !pip install -Uqq fastbook has snuck in a copy of torch 1.7.1 again and not from the IBM repository where it is not available, ugh. I’m going to go back to training on CPU as the newer AMD ZEN 2 or ZEN 3 cpus are beasts and good enough for scholastic training, at least compared to laptop NVIDIA 1660ti max-q or lower parts.
full procedure:
I’m running “checkout master” on both fastai and fastbook
-with the exception of edits to the two files as above
fastai installed with NOT using dev (see first post)
pip install -e "fastai"
fastbook installed with the following command to avoid the graphviz missing error
conda install -c fastai fastbook
fastbook must AGAIN be installed with the following command or there will be some error
!pip install -Uqq fastbook
import fastbook
fastbook.setup_book()
the final error is: fastai.callback.all was missing on importing fastbook, and this final forum post was quite helpful in solving that (close and halt the notebook and reopen and comment out “#!pip install -Uqq fastbook”)
$ diff settings.ini settings.ini.bak
16,17c16,18
< requirements = fastcore>=1.3.8,<1.4 torchvision==0.6,<0.9 matplotlib pandas requests pyyaml fastprogress>=0.2.4 pillow>6.0.0 scikit-learn scipy spacy<3
< conda_requirements = pytorch>=1.5
---
> requirements = fastcore>=1.3.8,<1.4 torchvision>=0.8.2,<0.9 matplotlib pandas requests pyyaml fastprogress>=0.2.4 pillow>6.0.0 scikit-learn scipy spacy<3
> pip_requirements = torch>=1.7.0,<1.8
> conda_requirements = pytorch>=1.7.0,<1.8
$ diff environment.yml environment.yml.bak
8c8
< - torchvision==0.6
---
> - torchvision>=0.8
18c18
< - pytorch>=1.5.0
---
> - pytorch>=1.7.0
“”"
original attempt
I tried one more thing before doing the advanced approach because you told me to check settings.ini : turns out that torchvision torchvision>=0.5 is listed (in the checkout which requires pytorch 1.3.)
However only torchvision 0.4.2 is available in the WML standard channel above, torchvision 0.6 is available in the early-access channel. So I made a new environment with IBM LMS pytorch 1.5 from the early access channel, now I’m able to install the checkout 11603b239c2bd03c0d28c4e883e59a35e94bb86f without a hard error.
Although there was no hard error at the fastai install stage there was further difficulty with matching the fastbook version to fastai, so now the top of this post is the much prefered solution.
If running an old checkout version of fastai: I was never able to find a version of fastbook that did not use “Tuple” (out of the old fastcore version) , but also did not use fastai.vision.all instead of fastai2.vision.all (fastai2 is actually the old naming scheme as fastai is now effectively version 2)