Trustii
During the competition, the organizers gave access to a JupyterHub instance. Here are the steps to run the project on the platform. The commands are different because it was a Debian VM.
To run on the Trustii platform, follow these steps:
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Open a terminal
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Clone the repo
git clone ... cd digilut
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Create a Conda env to force the python version
conda create -n env-digitut python=3.12 conda init source ~/.bashrc conda activate env_digilut
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Install poetry
pip install poetry -q
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Install FAST deps:
Install deps (enter yes if asked):
sudo apt install libgl1 libopengl0 libopenslide0 libusb-1.0-0 libxcb-xinerama0
If errors encountered, run the following commands (enter yes if asked), then try again the previous command.
sudo apt update sudo apt --fix-broken install sudo apt install libpocl2
The last dependency of FAST is OpenCL. It depends on your CPU. Check your CPU specs:
cat /proc/cpuinfo
Then download the relevant driver. More doc here (for me it was Intel on Trustii).
# Intel driver opencl. Other option is to go manual: https://github.com/intel/compute-runtime/releases (not sure if sudo apt-get install intel-gmmlib is needed, I ran it) sudo apt-get install intel-opencl-icd
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Finally, install package deps
poetry install --with dev,docs
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Test your installation:
source $(poetry env info --path)/bin/activate digilut --help
You should be prompted something like:
Usage: digilut [OPTIONS] COMMAND [ARGS]... Entrypoint of Digilut's main CLI. ╭─ Options ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ --install-completion Install completion for the current shell. │ │ --show-completion Show completion for the current shell, to copy it or customize the installation. │ │ --help Show this message and exit. │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯ ╭─ Commands ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮ │ clean-bbox Remove obvious labelling mistakes from the input bounding box csv and save a new cleaned dataset csv. │ │ credits Print credits with style. │ │ pyfast Pyfast processing commands to patchify .tif WSI images. │ │ tiles Commands for tiles. │ │ undersample Undersample patches to solve class imbalance │ ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯