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This page is based on the one of RPZ course.
The evaluation server has the following packages installed:
This setup is recommended by Dmytro Mishkin 1. Install Miniconda, Python version 3.X. 2. Run conda CLI. 3. Run
git clone https://gitlab.fel.cvut.cz/mishkdmy/mpv-python-assignment-templates conda create --name mpv-assignments-cpu-only python=3.10 conda activate mpv-assignments-cpu-only pip3 install torch==1.12.1+cpu torchvision==0.13.1+cpu torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cpu pip3 install kornia==0.6.10 tqdm notebook matplotlib opencv-contrib-python==4.7.0.68 seaborn tensorboard tensorboardX ipywidgets widgetsnbextension pip3 install kornia_moons --no-deps
For MacOS on Apple Silicon run
git clone https://gitlab.fel.cvut.cz/mishkdmy/mpv-python-assignment-templates conda create --name mpv-assignments-cpu-only python=3.10 conda activate mpv-assignments-cpu-only conda install -c apple tensorflow-deps pip3 install tensorflow-macos tensorflow-metal pip3 install torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cpu pip3 install kornia==0.6.10 tqdm notebook matplotlib opencv-contrib-python==4.7.0.68 seaborn tensorboard tensorboardX ipywidgets widgetsnbextension pip3 install kornia_moons --no-deps
For the GPU setup, if you have CUDA-capable GPU (if needed - change CUDA version in command). To find out your CUDA version, run nvidia-smi. You will see something like:
nvidia-smi
Mon Feb 20 16:49:46 2023 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 460.39 Driver Version: 460.39 CUDA Version: 11.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |===============================+======================+======================| | 0 GeForce GTX 108... On | 00000000:01:00.0 Off | N/A | | 25% 44C P8 18W / 250W | 1MiB / 11178MiB | 0% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ | 1 GeForce GTX 108... On | 00000000:06:00.0 Off | N/A | |ERR! 54C P0 ERR! / 250W | 1MiB / 11178MiB | 75% Default | | | | N/A | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+
–extra-index-url https://download.pytorch.org/whl/cu112
git clone https://gitlab.fel.cvut.cz/mishkdmy/mpv-python-assignment-templates conda create --name mpv-assignments-gpu python=3.10 conda activate mpv-assignments-gpu pip3 install torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu112 pip3 install kornia==0.6.10 tqdm notebook matplotlib opencv-contrib-python==4.7.0.68 seaborn tensorboard tensorboardX ipywidgets widgetsnbextension pip3 install kornia_moons --no-deps
4. a) Activate the environment with
conda activate mpv-assignments-cpu-only
PyCharm jupyter environment is buggy and not recommended. You still can use IDE to edit .py files though. It's best to use jupyter-notebook, google colab or jupyter-lab to work with jupyter notebooks, especially interactive ones (as in Correspondences and Tracking blocks.
It is possible to do all the work in cloud Google Colab without installing anything. You would need to have Google account though.
!pip install kornia==0.6.10