A10G --> A100

app.py

@@ -345,7 +345,7 @@ git+https://github.com/huggingface/datasets.git@3f149204a2a5948287adcade5e90707a
     # The subprocess call for autotrain spacerunner
     api = HfApi(token=token)
     username = api.whoami()["name"]
-    subprocess_command = ["autotrain", "spacerunner", "--project-name", slugged_lora_name, "--script-path", spacerunner_folder, "--username", username, "--token", token, "--backend", "spaces-a10g-small", "--env",f"HF_TOKEN={token};HF_HUB_ENABLE_HF_TRANSFER=1", "--args", spacerunner_args]
+    subprocess_command = ["autotrain", "spacerunner", "--project-name", slugged_lora_name, "--script-path", spacerunner_folder, "--username", username, "--token", token, "--backend", "spaces-a100-large", "--env",f"HF_TOKEN={token};HF_HUB_ENABLE_HF_TRANSFER=1", "--args", spacerunner_args]
     outcome = subprocess.run(subprocess_command)
     if(outcome.returncode == 0):
         return f"""# Your training has started. 
@@ -363,7 +363,7 @@ def calculate_price(iterations, with_prior_preservation):
     total_seconds = (iterations * seconds_per_iteration) + 210
     cost_per_second = 1.05/60/60
     cost = round(cost_per_second * total_seconds, 2)
-    return f'''To train this LoRA, we will duplicate the space and hook an A10G GPU under the hood.
+    return f'''To train this LoRA, we will duplicate the space and hook an A100 GPU under the hood.
 ## Estimated to cost <b>< US$ {str(cost)}</b> for {round(int(total_seconds)/60, 2)} minutes with your current train settings <small>({int(iterations)} iterations at {seconds_per_iteration}s/it)</small>
 #### ↓ to continue, grab you <b>write</b> token [here](https://huggingface.co/settings/tokens) and enter it below ↓'''