Web Application where people new to Machine Learning can input a dataset and experiment around with basic Pytorch modules through a drag and drop interface
Deployed website: https://datasciencegt-dlp.com/
> GitHub repo: https://github.com/DSGT-DLP/Deep-Learning-Playground
> Owners: See CODEOWNERS
Have the following installed first:
- NodeJS v18 (should come with NPM v9, you must install Yarn v1.22 afterwards using NPM)
- Poetry
- AWS CLI. After installing, type
aws configure
in your terminal and type in the credentials given in Secrets
- GitKraken for helping with Git commands, especially if you're new to Git
- Node Version Manager for managing NodeJS versions
- pyenv for managing python versions
Action | Command |
---|---|
Install (one-time) / Update Frontend Packages | yarn run installf |
Install Backend Packages (one-time) | yarn run installb |
Install AWS-Write Packages (one-time) | yarn run installa |
Update Backend Packages | yarn run updateb |
Update AWS-Write Packages | yarn run updatea |
Get secrets | yarn run secrets |
Running the Frontend | yarn run startf |
Running the Backend | yarn run startb |
Running the AWS-Write Microservice | yarn run starta |
- To start the frontend:
- If you're using Unix, run
REACT_APP_MODE=prod yarn run startf
from the root of the project - If you're using Windows, run
set REACT_APP_MODE=prod && yarn run startf
from the root of the project
- If you're using Unix, run
- Run the backend as usual
- To run the SQS container, run the command in this document
To protect our secrets, we use the GitGuardian ggshield pre-commit check to ensure no keys are being committed. After installing the backend, run
pre-commit install
You should get output like "pre-commit installed at .git/hooks/pre-commit". Login to GitGuardian to activate the pre-commit hook using
ggshield auth login
If this command works properly, you will be redirected to an auth route in the Git Guardian website. Sign in using your Github account. Then, you should be all set!
-
poetry install
in the project root directory (this installs both dev and prod dependencies). Make sure you runpip install poetry
prior to running this command -
Updating dependencies:
poetry update
-
If you encounter any error related to "ChefBuildError", downgrade your poetry version to 1.3.2 by running
pip install poetry==1.3.2
before doingpoetry install
(See related github issue here)
-
Access the VSCode command palette via
Ctrl+Shift+P
. PressPython: Select Interpreter
. -
To select aws_write's python interpreter, select the interpreter with the path
./aws_write/.venv/bin/python
(may need to manually enter interpreter path) -
To select backend's python interpreter, select the interpreter with the path
./backend/.venv/bin/python
(may need to manually enter interpreter path)
-
To install pyenv, a python version management tool, you can use the following command via homebrew:
brew install pyenv
-
Installing python version:
pyenv install 3.9.17
-
Set the global python version:
pyenv global 3.9.17
-
Verify the installation using
pyenv --version
-
If you encounter any issues related to Python versions or missing import modules (no modules named "x"), you can solve by:
export CONFIGURE_OPTS="--with-openssl=$(brew --prefix openssl)"
pyenv install -v 3.9.17
- Open up Windows Powershell as Administrator
- Follow the setup instructions for pyenv here
- Run
pyenv install 3.9.13
- Set global version by running
pyenv global 3.9.13
poetry run python app.py
from the ~/Deep-Learning-Playground/backend
directory
The backend supports training of a deep learning model and/or a classical ML model
See Architecture.md
To see how app.py
is used, see Backend_Examples.md
Note: You will need the
.env
file to get certain pages working. See the commandGet secrets
above to get the.env
file.
-
For complete functionality with the backend, first, start the backend using the instructions above. The backend will be live at http://localhost:8000/
-
Then in a separate terminal, start the frontend development server. After installing the prerequisites above, run the shortcut above or run the following commands:
cd frontend yarn install yarn start
-
Then, go to http://localhost:3000/
-
Install WSL and make sure you get the Ubuntu distro
-
Open the Ubuntu terminal
-
Installing AWS: Type
sudo apt install unzip
. Follow these instructions. Typeaws configure
and enter secrets. -
Navigate to project directory. Type
yarn run installf
. If your NodeJS is outdated, follow these instructions. -
Enter these commands:
yarn run installb
yarn run secrets
- Type
brew install tmux
- Type
./tmux-script.sh
- Visit cheatsheet website
See Architecture.md
Deep Learning Playground is MIT licensed, as found in the LICENSE file.
Deep Learning Playground documentation is Creative Commons licensed, as found in the LICENSE-docs file.