Updated devcontainer, .gitignore and README for gutenberg project (#107)

* added ch05/03_bonus_pretraining_on_gutenberg model checkpoints and preprocessing output folders to .gitignore

* removed prettier extension, added github alerts markdown extension

* specified download instructions and fixed code markdown

* Update ch05/03_bonus_pretraining_on_gutenberg/README.md

* Update ch05/03_bonus_pretraining_on_gutenberg/README.md

---------

Co-authored-by: Sebastian Raschka <mail@sebastianraschka.com>

.devcontainer/devcontainer.json

@@ -11,7 +11,7 @@
         "ms-python.python",
         "ms-azuretools.vscode-docker",
         "ms-toolsai.jupyter",
-        "esbenp.prettier-vscode"
+        "yahyabatulu.vscode-markdown-alert"
       ]
     }
   }

.gitignore

@@ -12,7 +12,10 @@ ch05/01_main-chapter-code/gpt2/
 ch05/02_alternative_weight_loading/checkpoints
 ch05/01_main-chapter-code/model.pth
 ch05/01_main-chapter-code/model_and_optimizer.pth
+ch05/03_bonus_pretraining_on_gutenberg/model_checkpoints
 
+# Preprocessing output folders
+ch05/03_bonus_pretraining_on_gutenberg/gutenberg_preprocessed
 
 # Temporary OS-related files
 .DS_Store

ch05/03_bonus_pretraining_on_gutenberg/README.md

@@ -23,16 +23,35 @@ As of this writing, this will require approximately 50 GB of disk space, but it
 
 Linux and macOS users can follow these steps to download the dataset (if you are a Windows user, please see the note below):
 
+Set the `03_bonus_pretraining_on_gutenberg` folder as working directory to clone the `gutenberg` repository locally in this folder (this is necessary to run the provided scripts `prepare_dataset.py` and `pretraining_simple.py`). For instance, when being in the `LLMs-from-scratch` repository's folder, navigate into the *03_bonus_pretraining_on_gutenberg* folder via:
+```bash
+cd ch05/03_bonus_pretraining_on_gutenberg
+```
 
-1. `git clone https://github.com/pgcorpus/gutenberg.git`
-
-2. `cd gutenberg`
+2. Clone the `gutenberg` repository in there:
+```bash
+git clone https://github.com/pgcorpus/gutenberg.git
+```
 
-3. `pip install -r requirements.txt`
+3. Navigate into the locally cloned `gutenberg` repository's folder:
+```bash
+cd gutenberg
+```
 
-4. `python get_data.py`
+4. Install the required packages defined in *requirements.txt* from the `gutenberg` repository's folder:
+```bash
+pip install -r requirements.txt
+```
+ 
+5. Download the data:
+```bash
+python get_data.py
+```
 
-5. `cd ..`
+6. Go back into the `03_bonus_pretraining_on_gutenberg` folder
+```bash
+cd ..
+```
 
  
 #### Special instructions for Windows users
@@ -54,14 +73,14 @@ sudo apt-get install -y rsync && \
 > [!NOTE]
 > Instructions about how to set up Python and installing packages can be found in  [Appendix A: Optional Python Setup Preferences](../../appendix-A/01_optional-python-setup-preferences/README.md) and [Appendix A: Installing Python Libraries](../../appendix-A/02_installing-python-libraries/README.md).
 >
-> Optionally, a Docker image running Ubuntu is provided with this repository. When having cloned the [`pgcorpus/gutenberg`](https://github.com/pgcorpus/gutenberg) GitHub repository, copy the *.devcontainer* folder of this `LLMs-from-scratch` repository and paste it to the locally cloned `gutenberg` repository. Instructions about how to run a container with the provided Docker image can be found in [Appendix A: Optional Docker Environment](../../appendix-A/04_optional-docker-environment/README.md).
+> Optionally, a Docker image running Ubuntu is provided with this repository. Instructions about how to run a container with the provided Docker image can be found in [Appendix A: Optional Docker Environment](../../appendix-A/04_optional-docker-environment/README.md).
 
  
 ### 2) Prepare the dataset
 
 Next, run the `prepare_dataset.py` script, which concatenates the (as of this writing, 60,173) text files into fewer larger files so that they can be more efficiently transferred and accessed:
 
-```
+```bash
 python prepare_dataset.py \
   --data_dir gutenberg/data \
   --max_size_mb 500 \
@@ -90,34 +109,32 @@ python pretraining_simple.py \
 
 The output will be formatted in the following way:
 
-```
-Total files: 3
-Tokenizing file 1 of 3: data_small/combined_1.txt
-Training ...
-Ep 1 (Step 0): Train loss 9.694, Val loss 9.724
-Ep 1 (Step 100): Train loss 6.672, Val loss 6.683
-Ep 1 (Step 200): Train loss 6.543, Val loss 6.434
-Ep 1 (Step 300): Train loss 5.772, Val loss 6.313
-Ep 1 (Step 400): Train loss 5.547, Val loss 6.249
-Ep 1 (Step 500): Train loss 6.182, Val loss 6.155
-Ep 1 (Step 600): Train loss 5.742, Val loss 6.122
-Ep 1 (Step 700): Train loss 6.309, Val loss 5.984
-Ep 1 (Step 800): Train loss 5.435, Val loss 5.975
-Ep 1 (Step 900): Train loss 5.582, Val loss 5.935
-...
-Ep 1 (Step 31900): Train loss 3.664, Val loss 3.946
-Ep 1 (Step 32000): Train loss 3.493, Val loss 3.939
-Ep 1 (Step 32100): Train loss 3.940, Val loss 3.961
-Saved model_checkpoints/model_pg_32188.pth
-Book processed 3h 46m 55s 
-Total time elapsed 3h 46m 55s 
-ETA for remaining books: 7h 33m 50s
-Tokenizing file 2 of 3: data_small/combined_2.txt
-Training ...
-Ep 1 (Step 32200): Train loss 2.982, Val loss 4.094
-Ep 1 (Step 32300): Train loss 3.920, Val loss 4.097
-...
-```
+> Total files: 3  
+> Tokenizing file 1 of 3: data_small/combined_1.txt  
+> Training ...  
+> Ep 1 (Step 0): Train loss 9.694, Val loss 9.724  
+> Ep 1 (Step 100): Train loss 6.672, Val loss 6.683  
+> Ep 1 (Step 200): Train loss 6.543, Val loss 6.434  
+> Ep 1 (Step 300): Train loss 5.772, Val loss 6.313  
+> Ep 1 (Step 400): Train loss 5.547, Val loss 6.249  
+> Ep 1 (Step 500): Train loss 6.182, Val loss 6.155  
+> Ep 1 (Step 600): Train loss 5.742, Val loss 6.122  
+> Ep 1 (Step 700): Train loss 6.309, Val loss 5.984  
+> Ep 1 (Step 800): Train loss 5.435, Val loss 5.975  
+> Ep 1 (Step 900): Train loss 5.582, Val loss 5.935  
+> ...  
+> Ep 1 (Step 31900): Train loss 3.664, Val loss 3.946  
+> Ep 1 (Step 32000): Train loss 3.493, Val loss 3.939  
+> Ep 1 (Step 32100): Train loss 3.940, Val loss 3.961  
+> Saved model_checkpoints/model_pg_32188.pth  
+> Book processed 3h 46m 55s   
+> Total time elapsed 3h 46m 55s   
+> ETA for remaining books: 7h 33m 50s  
+> Tokenizing file 2 of 3: data_small/combined_2.txt  
+> Training ...  
+> Ep 1 (Step 32200): Train loss 2.982, Val loss 4.094  
+> Ep 1 (Step 32300): Train loss 3.920, Val loss 4.097  
+> ...