app.py

import os
from fastapi import FastAPI
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)

EXPERIMENT = "imagenet-1k-random-20.0-frac-1over2"
# Input dataset
INPUT = f"datacomp/{EXPERIMENT}"
# Output for files and Space ID
OUTPUT = f"datacomp/ImagenetTraining-{EXPERIMENT}"

app = FastAPI()

@app.get("/")
def start_train():
    os.system("echo 'Space started!'")
    os.system("echo pwd")
    os.system("pwd")
    os.system("echo ls")
    os.system("ls")
    os.system("echo 'creating dataset for output files if it doesn't exist...'")
    try:
        API.create_repo(repo_id=OUTPUT, repo_type="dataset",)
    except:
        pass
    #space_variables = API.get_space_variables(repo_id=SPACE_ID)
    #if 'STATUS' not in space_variables or space_variables['STATUS'] != 'COMPUTING':
    os.system("echo 'Beginning processing.'")
    # API.add_space_variable(repo_id=SPACE_ID, key='STATUS', value='COMPUTING')
    # Handles CUDA OOM errors.
    os.system(f"export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True")
    # Prints more informative CUDA errors (I think? I've forgotten now.)
    os.system("export CUDA_LAUNCH_BLOCKING=1")
    os.system("echo 'Okay, trying training.'")
    os.system(f"cd pytorch-image-models; ./train.sh 4 --dataset hfds/{INPUT} --log-wandb --experiment {EXPERIMENT} --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 ls")
    os.system("ls")
    os.system("echo 'trying to upload...'")
    API.upload_large_folder(folder_path="/app", repo_id=OUTPUT, repo_type="dataset",)
    #API.add_space_variable(repo_id=SPACE_ID, key='STATUS', value='NOT_COMPUTING')
    #API.pause_space(SPACE_ID)
    return {"Completed": "!"}