Preparation - Set up GPU drivers¶
Follow the instructions in Configure GPU on windows page first.
Install Fastai v2.¶
What did not work:
- The Fastbook install instructions don’t work as the automatic Pip install steps fail. Manually running Pip install also results in conflicts.
conda install -c fastchan fastai will not resolve as conda satsolver takes forever without results.
- Install Anaconda individual edition
- Create a new env:
conda create -n fastaiv2 without any packages. This provides conda a fresh start and makes it easy for the solver
- Then run
conda install -c fastai -c pytorch fastai to install all fastaiv2 and all of its dependencies.
- If you want to run the notebooks, then run
conda install jupyter to install Jupyter
- Optional: Clone the v2 book repo: https://github.com/fastai/fastbook
pip install -U fastbook to install the book’s deps and files on disk.
Verify GPU is picked up¶
From terminal or Notebook, run the following
import torch torch.cuda.is_available()
If you get a
True, you are good to go. Else revisit the config doc page listed above.
Verify FastAI v2 can be imported¶
Run the quickstart example from the book (or copy below):
from fastai.vision.all import * path = untar_data(URLs.PETS)/'images' def is_cat(x): return x.isupper() dls = ImageDataLoaders.from_name_func( path, get_image_files(path), valid_pct=0.2, seed=42, label_func=is_cat, item_tfms=Resize(224)) learn = cnn_learner(dls, resnet34, metrics=error_rate) learn.fine_tune(1)
As you run the above, open the Task Manager and monitor the GPU usage. You should see a spike while the CPU rate seems constant. The training should go much faster. Instead if you notice CPU peaking to 95-100%, then verify if
True. If it does not, then recheck the driver and OS version steps from above.
Congrats! You are all set to use fastaiv2 on Windows OS without needing a dual boot for Linux or installing the Windows subsystem for linux options.