The recommended way of using the templates is as follows.
First, you clone the repository:
git clone Then, you create a new branch for your solutions:
cd mpv-python-assignment-templates
git checkout -b solutionsAfter that, you can work on your solutions, commiting as necessary.
In order to update the template, commit all your work and execute:
# download the new template version:
git checkout master
git pull
# and update your solutions branch:
git checkout solutions
git merge masterYou can create conda environment with all required packages via the following for CPU:
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-depsAnd following for GPU. You may need to change the cuda version to the actually installed one.
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:
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 |
+-----------------------------------------------------------------------------+
In the example above, the CUDA version is 11.2, so you should use –extra-index-url https://download.pytorch.org/whl/cu112
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-depsFor Apple Silicon devices (M1, M2 family) use:
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-depsKeep in mind that the assignments and the assignment templates will be updated during the semester. Always pull the current template version before starting to work on an assignment!
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