Update app.py

app.py

@@ -11,9 +11,9 @@ wandb_api_key = os.environ.get('wandb_api_key')
 wandb.login(key=wandb_api_key)
 
 random_num = f"50.0"
-subset = f"frac-1over8"
-experiment_name = f"ImageNetTraining50.0-frac-1over8"
-experiment_repo = f"datacomp/ImageNetTraining50.0-frac-1over8"
+subset = f"frac-1over4"
+experiment_name = f"ImageNetTraining50.0-frac-1over4"
+experiment_repo = f"datacomp/ImageNetTraining50.0-frac-1over4"
 
 def start_train():
     os.system("echo '#### pwd'")
@@ -23,8 +23,8 @@ def start_train():
     # 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/ImageNetTraining50.0-frac-1over8", repo_type="dataset",)
-        os.system(f"echo 'Created results output repository datacomp/ImageNetTraining50.0-frac-1over8'")
+        API.create_repo(repo_id=f"datacomp/ImageNetTraining50.0-frac-1over4", repo_type="dataset",)
+        os.system(f"echo 'Created results output repository datacomp/ImageNetTraining50.0-frac-1over4'")
     except:
         os.system("echo 'Already there; skipping.'")
         pass
@@ -32,18 +32,18 @@ def start_train():
     # 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-50.0-frac-1over8 --log-wandb --wandb-project ImageNetTraining50.0-frac-1over8 --experiment ImageNetTraining50.0-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(f"cd pytorch-image-models; ./train.sh 4 --dataset hfds/datacomp/imagenet-1k-random-50.0-frac-1over4 --log-wandb --wandb-project ImageNetTraining50.0-frac-1over4 --experiment ImageNetTraining50.0-frac-1over4 --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/ImageNetTraining50.0-frac-1over8", repo_type="dataset",)
+    API.upload_folder(folder_path="/app", repo_id=f"datacomp/ImageNetTraining50.0-frac-1over4", repo_type="dataset",)
     API.pause_space(experiment_repo)
 
 def run():
     with gr.Blocks() as app:
         gr.Markdown(f"Randomization: 50.0")
-        gr.Markdown(f"Subset: frac-1over8")
+        gr.Markdown(f"Subset: frac-1over4")
         start = gr.Button("Start")
         start.click(start_train)
     app.launch(server_name="0.0.0.0", server_port=7860)