Unverified Commit eb4308c4 authored by Arsalan's avatar Arsalan Committed by GitHub
Browse files

adding the triton docker build minimal example (#242)

parent b2eb0805
FROM nvcr.io/nvidia/tritonserver:24.01-py3
WORKDIR /opt
RUN git clone https://github.com/sgl-project/sglang.git
WORKDIR /opt/sglang
RUN pip install --upgrade pip && \
pip install -e "python[all]" && \
pip install datasets
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# sglang_triton
Build the docker image:
```
docker build -t sglang-triton .
```
Then do:
```
docker run -ti --gpus=all --network=host --name sglang-triton -v ./models:/mnt/models sglang-triton
```
inside the docker container:
```
cd sglang
python3 -m sglang.launch_server --model-path mistralai/Mistral-7B-Instruct-v0.2 --port 30000 --mem-fraction-static 0.9
```
with another shell, inside the docker container:
```
docker exec -ti sglang-triton /bin/bash
cd /mnt
tritonserver --model-repository=/mnt/models
```
Send request to the server:
```
curl -X POST http://localhost:8000/v2/models/character_generation/generate \
-H "Content-Type: application/json" \
-d '{
"inputs": [
{
"name": "INPUT_TEXT",
"datatype": "STRING",
"shape": [1],
"data": ["Name1"]
}
]
}'
```
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import triton_python_backend_utils as pb_utils
import numpy
import sglang as sgl
from sglang import function, set_default_backend
from sglang.srt.constrained import build_regex_from_object
from pydantic import BaseModel
sgl.set_default_backend(sgl.RuntimeEndpoint("http://localhost:30000"))
class Character(BaseModel):
name: str
eye_color: str
house: str
@function
def character_gen(s, name):
s += (
name
+ " is a character in Harry Potter. Please fill in the following information about this character.\n"
)
s += sgl.gen("json_output", max_tokens=256, regex=build_regex_from_object(Character))
class TritonPythonModel:
def initialize(self, args):
print("Initialized.")
def execute(self, requests):
responses = []
for request in requests:
tensor_in = pb_utils.get_input_tensor_by_name(request, "INPUT_TEXT")
if tensor_in is None:
return pb_utils.InferenceResponse(output_tensors=[])
input_list_names = [i.decode('utf-8') if isinstance(i, bytes) else i for i in tensor_in.as_numpy().tolist()]
input_list_dicts = [{"name":i} for i in input_list_names]
states = character_gen.run_batch(input_list_dicts)
character_strs = [state.text() for state in states]
tensor_out = pb_utils.Tensor("OUTPUT_TEXT", numpy.array(character_strs, dtype=object))
responses.append(pb_utils.InferenceResponse(output_tensors = [tensor_out]))
return responses
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name: "character_generation"
backend: "python"
input [
{
name: "INPUT_TEXT"
data_type: TYPE_STRING
dims: [ -1 ]
}
]
output [
{
name: "OUTPUT_TEXT"
data_type: TYPE_STRING
dims: [ -1 ]
}
]
instance_group [
{
count: 1
kind: KIND_GPU
gpus: [ 0 ]
}
]
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