app.py

import os
import gradio as gr

import wandb
from huggingface_hub import HfApi

TOKEN = os.environ.get("DATACOMP_TOKEN")
API = HfApi(token=TOKEN)
wandb_api_key = os.environ.get('wandb_api_key')
wandb.login(key=wandb_api_key)

random_num = '30.0'
subset = 'frac-1over16'
experiment_name = f"ImageNetTraining{random_num}-{subset}"
experiment_repo = f"datacomp/{experiment_name}"

def start_train():
    os.system("echo '#### pwd'")
    os.system("pwd")
    os.system("echo '#### ls'")
    os.system("ls")
    # Create a place to put the output.
    os.system("echo 'Creating results output repository in case it does not exist yet...'")
    try:
        API.create_repo(repo_id=f"{experiment_repo}", repo_type="dataset",)
        os.system(f"echo 'Created results output repository {experiment_repo}'")
    except:
        os.system("echo 'Already there; skipping.'")
        pass
    os.system("echo 'Beginning processing.'")
    # Handles CUDA OOM errors.
    os.system(f"export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True")
    os.system("echo 'Okay, trying training.'")
    os.system(f"cd pytorch-image-models; ./train.sh 4 --dataset hfds/datacomp/imagenet-1k-random-{random_num}-{subset} --log-wandb --wandb-project {experiment_name} --experiment ImageNetTraining{random_num}-{subset} --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4")
    os.system("echo 'Done'.")
    os.system("ls")
    # Upload output to repository
    os.system("echo 'trying to upload...'")
    API.upload_folder(folder_path="/app", repo_id=f"{experiment_repo}", repo_type="dataset",)
    API.pause_space(experiment_repo)

def run():
    with gr.Blocks() as app:
        gr.Markdown(f"Randomization: {random_num}")
        gr.Markdown(f"Subset: {subset}")
        start = gr.Button("Start")
        start.click(start_train)
    app.launch(server_name="0.0.0.0", server_port=7860)
    
if __name__ == '__main__':
    run()