Introduction
In this guide, we will explore how to set up a development environment for a Django app built on PyTorch Image Captioning, along with a blog feed. The environment will run locally on Windows 10 with Python 3.8, NVIDIA CUDA GPU 10.2, cudnn-10.2-windows10-x64-v7.6.5.32.zip, and cuda_10.2.89_441.22_win10.exe.
To get started, follow these steps:
Step-by-Step Guide
1. Extend Path Length
Ensure you have Windows 10 Version 1607 (Anniversary Update) or later.
2. Install Visual Studio 2017 Desktop Express
Download and install Visual Studio 2017 Desktop Express from Microsoft’s website.
3. Install NVIDIA CUDA Toolkit
Visit NVIDIA’s website and download CUDA Toolkit 10.2 for Windows. Follow the installation instructions on NVIDIA’s documentation page.
4. Add CUDA to PATH
Add the following paths to your PATH environment variable:
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\lib\x64
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include5. Install NVIDIA cuDNN
Download cuDNN from NVIDIA’s website and extract the files from cudnn-10.2-windows10-x64-v7.6.5.32.zip. Copy the files to the appropriate locations as instructed on NVIDIA’s website:
Copy cudnn64_7.6.5.32.dll to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\bin.
Copy cudnn.h to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\include.
Copy cudnn.lib to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\lib\x64.6. Verify CUDA_PATH
Ensure the environment variable CUDA_PATH is set to C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2.
7. Install Anaconda
Download and install Anaconda from their website. Select the appropriate version for Windows and follow the installation instructions.
8. Update Anaconda
Open an Anaconda Prompt and run the following commands to update Anaconda and its packages:
conda update conda
conda update anaconda9. Install PyTorch with CUDA 10.2
Run the following command in Anaconda Prompt to install PyTorch and its related packages:
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch10. Install Additional Packages
Install additional packages that may be useful for your Django app and blog:
pip install torchsummary certifi dj-database-url gunicorn psycopg2 Pygments pytz whitenoise11. Create a Django Project
Create a Django project with the following command:
django-admin startproject image_captioner
cd image_captioner12. Create a Django App for the Blog
Create a Django app for the blog with the following commands:
python manage.py startapp blog
python manage.py migrate13. Create a Django App for Image Captioning
Create a Django app for Image Captioning with the following commands:
python manage.py startapp caption
python manage.py makemigrations
python manage.py migrate14. Create Necessary Folders
Create the following folders in the project’s root directory:
media
models
staticfiles15. Download Pre-trained Models
Download the pre-trained models for Image Captioning from Dropbox. Extract the files and place them in the appropriate folders under the models directory:
vocab.pkl
decoder-5-3000.pkl
encoder-5-3000.pkl16. Create Blog Templates and Files
Copy or create the necessary HTML and Python files for the blog and place them in the appropriate directories.
17. Create Admin Account
Create an admin account by running the following command and following the instructions:
python manage.py createsuperuser18. Start the Server
Start the Django web server with the following command:
python manage.py runserverNow you can open your web browser and visit http://127.0.0.1:8000 to see a test page and explore your Django app with Image Captioning and blog.
By following these steps, you will set up a powerful development environment that integrates Django with PyTorch for Image Captioning and includes a blog component. This setup allows you to harness the power of deep learning in a web application, providing a comprehensive and scalable solution for various projects.
