Upload app.py with huggingface_hub

app.py

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+
+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 = f"60.0"
+subset = f"frac-1over8"
+experiment_name = f"ImageNetTraining60.0-frac-1over8"
+experiment_repo = f"datacomp/ImageNetTraining60.0-frac-1over8"
+
+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"datacomp/ImageNetTraining60.0-frac-1over8", repo_type="dataset",)
+        os.system(f"echo 'Created results output repository datacomp/ImageNetTraining60.0-frac-1over8'")
+    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-60.0-frac-1over8 --log-wandb --wandb-project ImageNetTraining60.0-frac-1over8 --experiment ImageNetTraining60.0-frac-1over8 --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"datacomp/ImageNetTraining60.0-frac-1over8", repo_type="dataset",)
+    API.pause_space(experiment_repo)
+
+def run():
+    with gr.Blocks() as app:
+        gr.Markdown(f"Randomization: 60.0")
+        gr.Markdown(f"Subset: frac-1over8")
+        start = gr.Button("Start")
+        start.click(start_train)
+    app.launch(server_name="0.0.0.0", server_port=7860)
+    
+if __name__ == '__main__':
+    run()