refactor: streamline installation and setup instructions in README.md

Author: kshitijrajsharma

backend/README.md

@@ -8,136 +8,16 @@ This project was bootstrapped with [Geodjango Template](https://github.com/itsks
 
 **Note:** Depending upon your OS and Env installation will vary, This project tightly depends on [Tensorflow](https://www.tensorflow.org/install/pip) with GPU support so accordingly build your development environment
 
-### Install Python3, pip and virtualenv first
-
-##### Skip this, step if you already have one
-
-    sudo apt-get install python3
-    sudo apt-get install -y python3-pip
-    sudo apt install python3-virtualenv
-
-##### Create your virtual env
-
-    virtualenv env
-    source ./env/bin/activate
-
-##### Setup Basemodels (Ramp Supported Currently)
-
-- Install git lfs
+### Install UV
 
 ```bash
-sudo apt-get install git-lfs
-```
-
-- Clone Ramp Basemodel
-
-```
-git clone https://github.com/radiantearth/model_ramp_baseline.git
-```
-
-- Clone Ramp - Code
-  Note: This clone location will be your RAMP_HOME
-
-```
-git clone https://github.com/kshitijrajsharma/ramp-code-fAIr.git ramp-code
-```
-
-- Copy Basemodel checkpoint to ramp-code
-
-```
-cp -r model_ramp_baseline/data/input/checkpoint.tf ramp-code/ramp/checkpoint.tf
-```
-
-- Remove basemodel repo we don't need it anymore
-
-```
-rm -rf model_ramp_baseline
-```
-
-- Install numpy
-  Numpy needs to be installed before gdal
-
-```
-pip install numpy==1.23.5
-```
-
-- Install gdal and rasetrio
-  Based on your env : You can either use conda / setup manually on your os
-  for eg on ubuntu :
-
-```
-sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update
-sudo apt-get install gdal-bin
-sudo apt-get install libgdal-dev
-export CPLUS_INCLUDE_PATH=/usr/include/gdal
-export C_INCLUDE_PATH=/usr/include/gdal
-pip install --global-option=build_ext --global-option="-I/usr/include/gdal" GDAL==`gdal-config --version`
-```
-
-- Install Ramp - Dependecies
-
-```
-cd ramp-code && cd colab && make install
-```
-
-- For Conda users : You may need to install rtree, gdal , rasterio & imagecodecs separately
-
-```
-conda install -c conda-forge rtree
-conda install -c conda-forge gdal
-conda install -c conda-forge rasterio
-conda install -c conda-forge imagecodecs
+curl -LsSf https://astral.sh/uv/install.sh | sh
 ```
 
-##### Install necessary libraries for fAIr
+### Sync proj
 
-- Install Tensorflow from [here] (https://www.tensorflow.org/install/pip) According to your os (Tested Versions : 2.9.2, 2.8.0)
-
-- Upgrade your setuptools before installing fair-utilities
-
-```
-pip install --upgrade setuptools
-```
-
-- Install fAIr Utilities
-
-```
-pip install hot-fair-utilities==1.0.41
-```
-
-**Remember In order to run fAIr , You need to configure your PC with tensorflow - GPU Support**
-
-You can check your GPU by :
-
-```
-import tensorflow as tf
-print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
-```
-
-- Install psycopg2
-  Again based on your os/env you can do manual installation
-  for eg : on ubuntu :
-
-```
-sudo apt-get install python3-psycopg2
-```
-
-- Install redis server on your pc
-
-```
-sudo apt install redis
-```
-
-- Install pdm for dependency management
-
-```
-pip install pdm
-```
-
-- Finally install project dependencies
-
-```
-pdm install
+```bash
+uv sync
 ```
 
 ### Make sure you have postgresql installed with postgis extension enabled
@@ -155,9 +35,9 @@ pdm install
 
 #### Now change your username, password and db name in settings.py accordingly to your database
 
-    python manage.py makemigrations login core
-    python manage.py migrate
-    python manage.py runserver
+    uv run python manage.py makemigrations login core
+    uv run python manage.py migrate
+    uv run python manage.py runserver
 
 ### Now server will be available in your 8000 port on web, you can check out your localhost:8000/admin for admin panel
 
@@ -179,40 +59,30 @@ fAIr uses oauth2.0 Authentication using [osm-login-python](https://github.com/ks
 
 ## Start celery workers
 
-- Start celery workers
+- Sync workers env
 
-```
-celery -A fairproject worker --loglevel=debug -n my_worker -Q ramp_training,yolo_training
+```bash
+uv sync --group base-workers
 ```
 
-- Monitor using flower
-  if you are using redis as result backend, api supports both options django / redis
-  You can start flower to start monitoring your tasks
+- Start celery workers
 
 ```
-celery -A fairproject  --broker=redis://127.0.0.1:6379/0 flower
+uv run celery -A fairproject worker --loglevel=debug -n my_worker -Q ramp_training,yolo_training
 ```
 
 ## Start background tasks
 
 ```bash
-python manage.py qcluster
+uv run python manage.py qcluster
 ```
 
 ## Run Tests
 
 ```
-python manage.py test
+uv run python manage.py test
 ```
 
 # Build fAIr with Docker for Development
 
-- Install all the required drivers for your graphics to access it from containers, and check your graphics and drivers with `nvidia-smi` . Up to now only nvidia is Supported
-- Follow docker_sample_env to create `.env` file in your dir
-- Build the Image
-
-```
-docker-compose up -d --build
-```
-
-- Once the image is build, Open the API container terminal and run the migrations
+Follow docker setup instructions [../SETUP.md](../SETUP.md)