batching: dynamic
checkpoints:
- name: 0.5ms-D
  url: https://api.ngc.nvidia.com/v2/models/nvidia/dle/gpunet_p0_pyt_ckpt/versions/21.12.0_amp/zip
configurations:
- checkpoint: 0.5ms-D
  parameters:
    backend_accelerator: trt
    checkpoint: 0.5ms-D
    device_kind: gpu
    export_format: onnx
    export_precision: fp16
    format: onnx
    max_batch_size: 64
    number_of_model_instances: 2
    precision: fp16
    tensorrt_capture_cuda_graph: 0
    torch_jit: none
container_version: '21.12'
datasets:
- name: imagenet
datasets_dir: datasets
ensemble_model_name: null
framework: PyTorch
measurement_steps_offline: 8
measurement_steps_online: 32
model_name: GPUnet
performance_tool: model_analyzer
triton_container_image: nvcr.io/nvidia/tritonserver:21.12-py3
triton_custom_operations: null
triton_dockerfile: null
triton_load_model_method: explicit
