fil_backend_config

configpb.txt

USE_GPU = True
FIL_MODEL_DIR = "./model_repository/fil"

# Maximum size in bytes for input and output arrays. If you are
# using Triton 21.11 or higher, all memory allocations will make
# use of Triton's memory pool, which has a default size of
# 67_108_864 bytes
MAX_MEMORY_BYTES = 60_000_000
NUM_FEATURES = 15
NUM_CLASSES = 2
bytes_per_sample = (NUM_FEATURES + NUM_CLASSES) * 4
max_batch_size = MAX_MEMORY_BYTES // bytes_per_sample

IS_CLASSIFIER = True
model_format = "xgboost_json"

# Select deployment hardware (GPU or CPU)
if USE_GPU:
    instance_kind = "KIND_GPU"
else:
    instance_kind = "KIND_CPU"

# whether the model is doing classification or regression
if IS_CLASSIFIER:
    classifier_string = "true"
else:
    classifier_string = "false"

# whether to predict probabilites or not
predict_proba = False

if predict_proba:
    predict_proba_string = "true"
else:
    predict_proba_string = "false"

config_text = f"""backend: "fil"
max_batch_size: {max_batch_size}
input [                                 
 {{  
    name: "input__0"
    data_type: TYPE_FP32
    dims: [ {NUM_FEATURES} ]                    
  }} 
]
output [
 {{
    name: "output__0"
    data_type: TYPE_FP32
    dims: [ 1 ]
  }}
]
instance_group [{{ kind: {instance_kind} }}]
parameters [
  {{
    key: "model_type"
    value: {{ string_value: "{model_format}" }}
  }},
  {{
    key: "predict_proba"
    value: {{ string_value: "{predict_proba_string}" }}
  }},
  {{
    key: "output_class"
    value: {{ string_value: "{classifier_string}" }}
  }},
  {{
    key: "threshold"
    value: {{ string_value: "0.5" }}
  }},
  {{
    key: "storage_type"
    value: {{ string_value: "AUTO" }}
  }}
]

dynamic_batching {{}}"""

config_path = os.path.join(FIL_MODEL_DIR, "config.pbtxt")
with open(config_path, "w") as file_:
    file_.write(config_text)