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updating quickstart link in prnn_tutorial #82

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1d45d72
updating quickstart link in prnn_tutorial
vviggyy Jan 15, 2026
1a592ae
removing dan minigrid fork from prnn tutorial
vviggyy Jan 16, 2026
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8 changes: 3 additions & 5 deletions docs/Resources/prnn_tutorial.md
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Expand Up @@ -9,8 +9,7 @@ There are some extra steps in managing the requirements needed for the [Predicti
3. Clone the `pRNN` package into your `project` folder. This is accessible by typing `cd ~/../../project/levenstein/YOUR_NETID/`. This brings you to the `/gpfs/radev/project/levenstein/YOUR_NETID`.
1. `git clone git@github.com:LevensteinLab/pRNN.git`
2. 👁️ Watch the repository so you can be notified of any updates!
4. Also clone these external repositories into that project directory:
1. Dan's fork of gym-minigrid (`git clone git@github.com:dlevenstein/gym-minigrid.git gym-minigrid-dan-fork`).
4. Also clone the following repository into that project directory:
2. Farama Foundation version of minigrid. This is the most up-to-date version. (`git clone git@github.com:Farama-Foundation/Minigrid.git`)
5. Create a conda environment with Python 3.9.
1. Misha doesn't come with Python 3.9 out of the box, and we need this version for later dependencies.
Expand All @@ -25,8 +24,7 @@ There are some extra steps in managing the requirements needed for the [Predicti
1. `pip install "pip<24.1" setuptools==59.5.0 wheel==0.37.0`
2. `pip install gym==0.21.0 --no-binary gym`
3. `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118 --no-cache-dir`
8. We'll also need to install the external repositories from step 3 into environment.
1. `pip3 install -e ~/../../project/levenstein/YOUR_NETID/gym-minigrid-dan-fork`
8. We'll also need to install the external repository from step 3 into environment.
2. `pip3 install -e ~/../../project/levenstein/YOUR_NETID/Minigrid`
9. Install the `pRNN` package itself into the environment.
1. Navigate to the repository, then `pip install -e .`
Expand All @@ -36,5 +34,5 @@ There are some extra steps in managing the requirements needed for the [Predicti
3. If you run `jupyter kernelspec list`, you should see `prnn_tutorial` as an option.
4. You may need to restart your VSCode or kernel here.
1. If you're on VSCode, you can do this fast with `Cmd + Shift + P` to open the Command Palette and then `Developer: Reload Window`.
11. Your environment should be all set to run example code provided with the package, or to start going through the tutorials. The quickstart tutorial can be found [here](https://github.com/LevensteinLab/pRNN/blob/main/docs/quickstart.rst). This contains an example training run.
11. Your environment should be all set to run example code provided with the package, or to start going through the tutorials. The quickstart tutorial can be found [here](https://prnn.readthedocs.io/en/latest/quickstart.html). This contains an example training run.
1. If you would like to run through quickstart interactively, you will need to copy `pRNN/examples/tutorial.ipynb` to your `prnn_tutorial` folder. This can be done with `rsync -r ../pRNN/examples/ .`