README.md

MultiTask

Setup Instructions

Create Local Conda Environment and Install Dependencies

1. Create and activate a local Conda environment in the current folder:

   conda create --prefix ./.conda python=3.9 -y
   conda activate ./.conda

2. Install all required dependencies:

   conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
   pip install -r requirements.txt

3. Generate requirements.txt:

   pip freeze > requirements.txt

Requirements

The requirements.txt file contains all the dependencies required for this project. You can install them using:

pip install -r requirements.txt

pip install tensorboard==2.12.0 pip install numpy==1.23.0 tensorboard --logdir="/mnt/pipeline_1/MLT/writer_logs/training_try_stage2_share/" --port=6007

mkdir -p ./mnt/pipeline_1/MLT/Weather/training_try_stage2_share/

Training, Testing, and Inference

Training To train the model, run the training script:

bash train_mult.sh

Testing After training, you can test the model by running the following script:

bash test.sh

Inference For inference on new data, use the following Python script:

python inference.py --model_path <path_to_pretrained_model> --save_path <path_to_save_visualization_results>

--model_path: The path to the pretrained model. This should point to the model file you wish to use for inference. --save_path: The directory where the visualization results will be saved.