Add files using upload-large-folder tool

app.py

@@ -9,7 +9,7 @@ API = HfApi(token=TOKEN)
 wandb_api_key = os.environ.get('wandb_api_key')
 wandb.login(key=wandb_api_key)
 
-REPO_ID = f"datacomp/imagenet-1k-random-20.0-frac-1over2"
+REPO_ID = f"imagenet-1k-random-20.0-frac-1over2"
 app = FastAPI()
 
 @app.get("/")
@@ -27,11 +27,11 @@ def start_train():
     os.system(f"export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True")
     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/{REPO_ID} --log-wandb --experiment {REPO_ID} --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(f"cd pytorch-image-models; ./train.sh 4 --dataset hfds/datacomp/{REPO_ID} --log-wandb --experiment {REPO_ID} --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_folder(folder_path="/app", repo_id=f"{REPO_ID}", repo_type="dataset",)
+    API.upload_large_folder(folder_path="/app", repo_id=f"datacomp/{REPO_ID}", repo_type="dataset",)
     #    API.add_space_variable(repo_id=REPO_ID, key='STATUS', value='NOT_COMPUTING')
     #API.pause_space(f"{REPO_ID}")
     return {"Completed": "!"}