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import gradio as gr
def get_process_config():
return {
"process.numactl": gr.Checkbox(
value=False,
label="process.numactl",
info="Runs the model with numactl",
),
"process.numactl_kwargs": gr.Textbox(
label="process.numactl_kwargs",
value="{'cpunodebind': 0, 'membind': 0}",
info="Additional python dict of kwargs to pass to numactl",
),
}
def get_inference_config():
return {
"inference.warmup_runs": gr.Slider(
step=1,
value=10,
minimum=0,
maximum=10,
label="inference.warmup_runs",
info="Number of warmup runs",
),
"inference.duration": gr.Slider(
step=1,
value=10,
minimum=0,
maximum=10,
label="inference.duration",
info="Minimum duration of the benchmark in seconds",
),
"inference.iterations": gr.Slider(
step=1,
value=10,
minimum=0,
maximum=10,
label="inference.iterations",
info="Minimum number of iterations of the benchmark",
),
"inference.latency": gr.Checkbox(
value=True,
label="inference.latency",
info="Measures the latency of the model",
),
"inference.memory": gr.Checkbox(
value=False,
label="inference.memory",
info="Measures the peak memory consumption",
),
}
def get_pytorch_config():
return {
"pytorch.torch_dtype": gr.Dropdown(
value="float32",
label="pytorch.torch_dtype",
choices=["bfloat16", "float16", "float32", "auto"],
info="The dtype to use for the model",
),
"pytorch.torch_compile": gr.Checkbox(
value=False,
label="pytorch.torch_compile",
info="Compiles the model with torch.compile",
),
}
def get_onnxruntime_config():
return {
"onnxruntime.export": gr.Checkbox(
value=True,
label="onnxruntime.export",
info="Exports the model to ONNX",
),
"onnxruntime.use_cache": gr.Checkbox(
value=True,
label="onnxruntime.use_cache",
info="Uses cached ONNX model if available",
),
"onnxruntime.use_merged": gr.Checkbox(
value=True,
label="onnxruntime.use_merged",
info="Uses merged ONNX model if available",
),
"onnxruntime.torch_dtype": gr.Dropdown(
value="float32",
label="onnxruntime.torch_dtype",
choices=["bfloat16", "float16", "float32", "auto"],
info="The dtype to use for the model",
),
}
def get_openvino_config():
return {
"openvino.export": gr.Checkbox(
value=True,
label="openvino.export",
info="Exports the model to ONNX",
),
"openvino.use_cache": gr.Checkbox(
value=True,
label="openvino.use_cache",
info="Uses cached ONNX model if available",
),
"openvino.use_merged": gr.Checkbox(
value=True,
label="openvino.use_merged",
info="Uses merged ONNX model if available",
),
"openvino.reshape": gr.Checkbox(
value=False,
label="openvino.reshape",
info="Reshapes the model to the input shape",
),
"openvino.half": gr.Checkbox(
value=False,
label="openvino.half",
info="Converts model to half precision",
),
}
def get_ipex_config():
return {}
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