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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from nemo.collections.llm.api import deploy
# NOTE: This script is an example script to deploy a nemo2 model in-framework (i.e wo converting the model to any
# other model) on PyTriton server by exposing the OpenAI API endpoints (v1/completions and v1/chat/completions).
# The intended use case of this script is to run evaluations with NVIDIA LM-Evaluation-Harness.
def get_parser():
parser = argparse.ArgumentParser(description="NeMo2.0 Deployment")
parser.add_argument(
"--nemo_checkpoint",
type=str,
help="NeMo 2.0 checkpoint to be evaluated",
),
parser.add_argument(
"--ngpus",
type=int,
default=1,
help="Num of gpus per node",
),
parser.add_argument(
"--nnodes",
type=int,
default=1,
help="Num of nodes",
),
parser.add_argument(
"--tensor_parallelism_size",
type=int,
default=1,
help="Tensor parallelism size to deploy the model",
),
parser.add_argument(
"--pipeline_parallelism_size",
type=int,
default=1,
help="Pipeline parallelism size to deploy the model",
)
parser.add_argument(
"--context_parallel_size",
type=int,
default=1,
help="context parallelism size to deploy the model",
)
parser.add_argument(
"--expert_model_parallel_size",
type=int,
default=1,
help="Expert model parallelism size to deploy the model",
)
parser.add_argument(
"--expert_tensor_parallel_size",
type=int,
default=1,
help="Expert tensor parallelism size to deploy the model",
)
parser.add_argument(
"--max_batch_size",
type=int,
default=8,
help="Max batch size for the underlying Triton server",
)
parser.add_argument(
"--max_input_len",
type=int,
default=4096,
help="Max input length for the underlying Triton server",
)
return parser
if __name__ == "__main__":
args = get_parser().parse_args()
deploy(
nemo_checkpoint=args.nemo_checkpoint,
num_gpus=args.ngpus,
num_nodes=args.nnodes,
fastapi_port=8886,
tensor_parallelism_size=args.tensor_parallelism_size,
pipeline_parallelism_size=args.pipeline_parallelism_size,
context_parallel_size=args.context_parallel_size,
expert_model_parallel_size=args.expert_model_parallel_size,
expert_tensor_parallel_size=args.expert_tensor_parallel_size,
max_batch_size=args.max_batch_size,
max_input_len=args.max_input_len,
)
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