Spaces:
Sleeping
Sleeping
File size: 5,238 Bytes
78e33ca 66a06cc 78e33ca 66a06cc d58e19e 66a06cc d58e19e 66a06cc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
# Astra Project Setup Instructions
## Prerequisites
Make sure you have the following installed before proceeding:
- Python 3.12.4
- Git
- Git Large File Storage (LFS)
## Step 1: Install Git LFS
Git LFS (Large File Storage) is required for managing large files in the Astra project. Follow these steps to install Git LFS:
### Windows
1. Download the Git LFS installer from [Git LFS Releases](https://git-lfs.github.com/).
2. Run the installer and follow the setup instructions.
3. Open a terminal (Command Prompt or PowerShell) and run:
```sh
git lfs install
```
### macOS
1. Install Git LFS using Homebrew:
```sh
brew install git-lfs
```
2. Initialize Git LFS:
```sh
git lfs install
```
### Linux
1. Install Git LFS using your package manager:
- Debian/Ubuntu:
```sh
sudo apt install git-lfs
```
- Fedora:
```sh
sudo dnf install git-lfs
```
- Arch Linux:
```sh
sudo pacman -S git-lfs
```
2. Initialize Git LFS:
```sh
git lfs install
```
## Step 2: Install Python (Alternative: pyenv)
While Python 3.12.4 is required, it is recommended to use `pyenv` if you want to work with multiple Python versions or if you encounter errors while installing dependencies.
### Installing pyenv
#### macOS & Linux:
```sh
curl https://pyenv.run | bash
```
After installation, restart your terminal and install Python:
```sh
pyenv install 3.12.4
pyenv global 3.12.4
```
#### Windows:
Use [pyenv-win](https://github.com/pyenv-win/pyenv-win):
```sh
git clone https://github.com/pyenv-win/pyenv-win.git ~/.pyenv
setx PYENV "%USERPROFILE%\.pyenv"
setx PATH "%PYENV%\bin;%PYENV%\shims;%PATH%"
pyenv install 3.12.4
pyenv global 3.12.4
```
## Step 3: Clone the Repository
Clone the Astra project repository using Git:
```sh
git clone <repository_url>
cd astra
```
## Step 4: Install Dependencies
Install all required dependencies from the `requirements.txt` file:
```sh
pip install -r requirements.txt
```
## Step 5: Verify Installation
Ensure all dependencies are installed correctly by running:
```sh
python --version
pip list
```
## Step 6: Run the Application or Test the Model
You have two options to proceed:
### Option 1: Run the Gradio App
To open the Gradio app in your web browser and interact with the application, run:
```sh
python app.py
```
### Option 2: Test the Model with a Sample File
To test the fine-tuned model using a sample file, navigate to the root folder of the project and run the following command:
```sh
cd <root_folder>
python new_test_saved_finetuned_model.py \
-workspace_name "ratio_proportion_change3_2223/sch_largest_100-coded" \
-finetune_task "<finetune_task>" \
-test_dataset_path "../../../../fileHandler/selected_rows.txt" \
-finetuned_bert_classifier_checkpoint "ratio_proportion_change3_2223/sch_largest_100-coded/output/highGRschool10/bert_fine_tuned.model.ep42" \
-e 1 \
-b 1000
```
Replace `<finetune_task>` with the actual fine-tuning task value.
### Arguments
**`-workspace_name`**
- Description: The folder/workspace name where the project, dataset, and model outputs are organized.
- Example: `"ratio_proportion_change3_2223/sch_largest_100-coded"`
**`-finetune_task`**
- Description: Specifies which fine-tuning strategy was applied to the model.
- Options:
- **ASTRA-FT-HGR** → Fine-tuned with 10% data from schools that have a **High Graduation Rate (HGR)**.
- **ASTRA-FT-FIRST10-WSKILLS**
- Checkpoint: `first10/bert_fine_tuned.model.first10%.wskills.ep24`
- Description: Fine-tuned with 10% of initial problems from both **HGR + LGR schools**, with **Prior Skills encoded** using **Bayesian Knowledge Tracing (BKT)**.
- **ASTRA-FT-FIRST10-WTIME**
- Checkpoint: `first10/bert_fine_tuned.model.first10%.wfaopttime.wttime.wttopttime.wttnoopttime.ep23`
- Description: Fine-tuned with 10% of initial problems from both **HGR + LGR schools**, using **temporal features** measuring student engagement in MATHia.
- **ASTRA-FT-FIRST10-WSKILLS_WTIME**
- Checkpoint: `first10/bert_fine_tuned.model.first10%.wskills.wfaopttime.wttime.wttopttime.wttnoopttime.ep40`
- Description: Fine-tuned with 10% of initial problems from both **HGR + LGR schools**, combining **Prior Skills (BKT) + temporal features**.
**`-test_dataset_path`**
- Description: Path to the test dataset file that you want to use for evaluation.
- Example: `"../../../../fileHandler/selected_rows.txt"`
**`-finetuned_bert_classifier_checkpoint`**
- Description: The path to the saved fine-tuned BERT model checkpoint (specific `.model.epXX` file).
- Example:
`"ratio_proportion_change3_2223/sch_largest_100-coded/output/highGRschool10/bert_fine_tuned.model.ep42"`
- Note: `ep42` means the checkpoint from **epoch 42** during training.
**`-e`**
- Description: Number of epochs to run during testing (or evaluation).
- Example: `-e 1` → run evaluation once.
**`-b`**
- Description: Batch size for testing — determines how many test samples are processed together in each forward pass.
- Example: `-b 1000` → each batch will contain **1000 examples**.
---
✅ Your Astra project should now be fully set up and ready to use!
|