Fixing typos

* Fix typos in README, index and from guide2 to tutorial2

* Update Optimization_and_Initialization.ipynb

* Reducing diff

* Clean README and index file

Fixed a few typos.

Co-authored-by: phlippe <[email protected]>

README.md

@@ -28,11 +28,11 @@ How to run the notebooks
 
 On this website, you will find the notebooks exported into a HTML format so that you can read them from whatever device you prefer. However, we suggest that you also give them a try and run them yourself. There are three main ways of running the notebooks we recommend:
 
-- **Locally on CPU**: All notebooks are stored on the github repository that also builds this website. You can find them here: https://github.com/phlippe/uvadlc_notebooks/tree/master/docs/tutorial_notebooks. The notebooks are designed so that you can execute them on common laptops without the necessity of a GPU. We provide pretrained models that are automatically downloaded when running the notebooks, or can manually be downloaoded from this [Google Drive](https://drive.google.com/drive/folders/1SevzqrkhHPAifKEHo-gi7J-dVxifvs4c?usp=sharing). The required disk space for the pretrained models and datasets is less than 1GB. To ensure that you have all the right python packages installed, we provide a conda environment in the [same repository](https://github.com/phlippe/uvadlc_notebooks/blob/master/) (choose the CPU or GPU version depending on your system). 
+- **Locally on CPU**: All notebooks are stored on the github repository that also builds this website. You can find them here: https://github.com/phlippe/uvadlc_notebooks/tree/master/docs/tutorial_notebooks. The notebooks are designed so that you can execute them on common laptops without the necessity of a GPU. We provide pretrained models that are automatically downloaded when running the notebooks, or can manually be downloaded from this [Google Drive](https://drive.google.com/drive/folders/1SevzqrkhHPAifKEHo-gi7J-dVxifvs4c?usp=sharing). The required disk space for the pretrained models and datasets is less than 1GB. To ensure that you have all the right python packages installed, we provide a conda environment in the [same repository](https://github.com/phlippe/uvadlc_notebooks/blob/master/) (choose the CPU or GPU version depending on your system). 
 
 - **Google Colab**: If you prefer to run the notebooks on a different platform than your own computer, or want to experiment with GPU support, we recommend using [Google Colab](https://colab.research.google.com/notebooks/intro.ipynb#recent=true). Each notebook on this documentation website has a badge with a link to open it on Google Colab. Remember to enable GPU support before running the notebook (`Runtime -> Change runtime type`). Each notebook can be executed independently, and doesn't require you to connect your Google Drive or similar. However, when closing the session, changes might be lost if you don't save it to your local computer or have copied the notebook to your Google Drive beforehand.
 
-- **Lisa cluster**: If you want to train your own (larger) neural networks based on the notebooks, you can make use of the Lisa cluster. However, this is only suggested if you really want to train a new model, and use the other two options to go through the discussion and analysis of the models. Lisa might not allow you with your student account to run jupyter notebooks directly on the gpu_shared partition. Instead, you can first convert the notebooks to a script using `jupyter nbconvert --to script ...ipynb`, and then start a job on Lisa for running the script. A few advices when running on Lisa:
+- **Lisa cluster**: If you want to train your own (larger) neural networks based on the notebooks, you can make use of the Lisa cluster. However, this is only suggested if you really want to train a new model, and use the other two options to go through the discussion and analysis of the models. Lisa might not allow you with your student account to run Jupyter notebooks directly on the gpu_shared partition. Instead, you can first convert the notebooks to a script using `jupyter nbconvert --to script ...ipynb`, and then start a job on Lisa for running the script. A few advices when running on Lisa:
    - Disable the tqdm statements in the notebook. Otherwise your slurm output file might overflow and be several MB large. In PyTorch Lightning, you can do this by setting `progress_bar_refresh_rate=0` in the trainer.
    - Comment out the matplotlib plotting statements, or change :code:`plt.show()` to `plt.savefig(...)`.
 

docs/index.rst

@@ -59,11 +59,11 @@ How to run the notebooks
 On this website, you will find the notebooks exported into a HTML format so that you can read them from whatever device you prefer.
 However, we suggest that you also give them a try and run them yourself. There are three main ways of running the notebooks we recommend:
 
-- **Locally on CPU**: All notebooks are stored on the github repository that also builds this website. You can find them here: https://github.com/phlippe/uvadlc_notebooks/tree/master/docs/tutorial_notebooks. The notebooks are designed so that you can execute them on common laptops without the necessity of a GPU. We provide pretrained models that are automatically downloaded when running the notebooks, or can manually be downloaoded from this `Google Drive <https://drive.google.com/drive/folders/1SevzqrkhHPAifKEHo-gi7J-dVxifvs4c?usp=sharing>`_. The required disk space for the pretrained models and datasets is less than 1GB. To ensure that you have all the right python packages installed, we provide a conda environment in the `same repository <https://github.com/uvadlc/uvadlc_practicals_2020/blob/master/environment.yml>`_ (choose the CPU or GPU version depending on your system).
+- **Locally on CPU**: All notebooks are stored on the github repository that also builds this website. You can find them here: https://github.com/phlippe/uvadlc_notebooks/tree/master/docs/tutorial_notebooks. The notebooks are designed so that you can execute them on common laptops without the necessity of a GPU. We provide pretrained models that are automatically downloaded when running the notebooks, or can manually be downloaded from this `Google Drive <https://drive.google.com/drive/folders/1SevzqrkhHPAifKEHo-gi7J-dVxifvs4c?usp=sharing>`_. The required disk space for the pretrained models and datasets is less than 1GB. To ensure that you have all the right python packages installed, we provide a conda environment in the `same repository <https://github.com/uvadlc/uvadlc_practicals_2020/blob/master/environment.yml>`_ (choose the CPU or GPU version depending on your system).
 
 - **Google Colab**: If you prefer to run the notebooks on a different platform than your own computer, or want to experiment with GPU support, we recommend using `Google Colab <https://colab.research.google.com/notebooks/intro.ipynb#recent=true>`_. Each notebook on this documentation website has a badge with a link to open it on Google Colab. Remember to enable GPU support before running the notebook (:code:`Runtime -> Change runtime type`). Each notebook can be executed independently, and doesn't require you to connect your Google Drive or similar. However, when closing the session, changes might be lost if you don't save it to your local computer or have copied the notebook to your Google Drive beforehand.
 
-- **Lisa cluster**: If you want to train your own (larger) neural networks based on the notebooks, you can make use of the Lisa cluster. However, this is only suggested if you really want to train a new model, and use the other two options to go through the discussion and analysis of the models. Lisa might not allow you with your student account to run jupyter notebooks directly on the gpu_shared partition. Instead, you can first convert the notebooks to a script using :code:`jupyter nbconvert --to script ...ipynb`, and then start a job on Lisa for running the script. A few advices when running on Lisa:
+- **Lisa cluster**: If you want to train your own (larger) neural networks based on the notebooks, you can make use of the Lisa cluster. However, this is only suggested if you really want to train a new model, and use the other two options to go through the discussion and analysis of the models. Lisa might not allow you with your student account to run Jupyter notebooks directly on the gpu_shared partition. Instead, you can first convert the notebooks to a script using :code:`jupyter nbconvert --to script ...ipynb`, and then start a job on Lisa for running the script. A few advices when running on Lisa:
 
    - Disable the tqdm statements in the notebook. Otherwise your slurm output file might overflow and be several MB large. In PyTorch Lightning, you can do this by setting :code:`progress_bar_refresh_rate=0` in the trainer.
    - Comment out the matplotlib plotting statements, or change :code:`plt.show()` to :code:`plt.savefig(...)`.