filter cuda

app.py

@@ -1,3 +1,4 @@
+import torch
 import random
 import gradio as gr
 from optimum_benchmark.task_utils import (
@@ -14,9 +15,11 @@ from config_store import (
     get_pytorch_config,
 )
 
+cuda_available = torch.cuda.is_available()
+
 BACKENDS = ["pytorch", "onnxruntime", "openvino", "neural-compressor"]
+DEVICES = ["cpu", "cuda"] if cuda_available else ["cpu"]
 BENCHMARKS = ["inference", "training"]
-DEVICES = ["cpu", "cuda"]
 
 
 with gr.Blocks() as demo:
@@ -32,7 +35,7 @@ with gr.Blocks() as demo:
     model = gr.Textbox(
         label="model",
         value="optimum/distilbert-base-uncased-finetuned-sst-2-english",
-        info="Model to run the benchmark on. In the particular case of this space, only models that are hosted on huggingface.co/models can be benchmarked.",
+        info="Model to run the benchmark on. Press enter to infer the task automatically.",
     )
     task = gr.Dropdown(
         label="task",
@@ -48,7 +51,7 @@ with gr.Blocks() as demo:
     )
     experiment = gr.Textbox(
         label="experiment_name",
-        value=f"experiment_{random.getrandbits(16)}",
+        value=f"awesome-experiment-{random.randint(0, 1000)}",
         info="Name of the experiment. Will be used to create a folder where results are stored.",
     )
     model.submit(fn=infer_task_from_model_name_or_path, inputs=model, outputs=task)