From 1d45d7206e65663d9605297cf40789f4e066ea87 Mon Sep 17 00:00:00 2001 From: vviggyy Date: Thu, 15 Jan 2026 14:00:28 -0500 Subject: [PATCH 1/2] updating quickstart link in prnn_tutorial --- docs/Resources/prnn_tutorial.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/Resources/prnn_tutorial.md b/docs/Resources/prnn_tutorial.md index 7e19ec6..d3f7214 100644 --- a/docs/Resources/prnn_tutorial.md +++ b/docs/Resources/prnn_tutorial.md @@ -36,5 +36,5 @@ 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/ .` From 1a592aefbc193db5bcfcb12919b6530959785928 Mon Sep 17 00:00:00 2001 From: vviggyy Date: Fri, 16 Jan 2026 13:35:41 -0500 Subject: [PATCH 2/2] removing dan minigrid fork from prnn tutorial --- docs/Resources/prnn_tutorial.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/docs/Resources/prnn_tutorial.md b/docs/Resources/prnn_tutorial.md index d3f7214..b8f1b0f 100644 --- a/docs/Resources/prnn_tutorial.md +++ b/docs/Resources/prnn_tutorial.md @@ -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. @@ -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 .`