Triton serving demo

A Jupyter notebook that compares the performance of Triton with TF to Triton with Modular.

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The Modular AI Engine works as a drop-in backend for popular model inference servers, including NVIDIA’s Triton Inference Server and TensorFlow Serving, as described in the Server integration overview.

This page includes a rendered version of the Jupyter notebook presented by Nick Kreeger in our launch keynote video, in which he shows how you can use our AI Engine as a drop-in replacement for TensorFlow inference workloads on a Triton Inference Server instance. We’re sharing this executed version of the notebook so you can look closely at the code from the video.


The code below sends inference requests to two Triton Inference Server instances running the same BERT large language model:

  • The first server is using the default TensorFlow backend for Triton.
  • The second server is using the Modular AI Engine backend for Triton.

As shown in the video, as soon as we call get_inference_latencies() to get results from the Modular backend, the latency numbers in the line plot (at the bottom of this page) drop dramatically.

A keen viewer will notice differences in the code here, compared to the video. We’re not trying to trick you; this is just to make the line plot render for this web page. In the video, we used jupyterplot to render the inference results in real-time. Unfortunately, this kind of plot doesn’t create an image visible in the notebook output, so we swapped that out for matplotlib. That’s the only difference.

You can also see that we’re using some APIs from a package called modular.serving. This is just a concise higher-level RPC API we are developing to interact with the Triton Inference Server. We plan to share this in future, but rest assured that this client API is not required for integration with Triton or TensorFlow Serving. You can use existing client code as-is with no changes—for an example, see the Server integration overview.

Notebook code

from modular.serving.model import ModelOptions
from modular.serving.tensor import RequestedTensor, Tensor
from modular.serving.client import InferenceGrpcClient

# Runtime parameters
host = "localhost"
modular_port = 8001
tensorflow_port = 9001
tensorflow_url = "{}:{}".format(host, tensorflow_port)
modular_url = "{}:{}".format(host, modular_port)
%matplotlib inline
import time
import numpy as np
from matplotlib import pyplot as plt

# Model-specific parameters
model_options = ModelOptions("bert-large")
requested = RequestedTensor.from_names(["end_logits", "start_logits"]) # <- bert-large
inputs = [
    Tensor("attention_mask", np.ones(shape=(1, 256), dtype=np.int32)),
    Tensor("input_ids", np.ones(shape=(1, 256), dtype=np.int32)),
    Tensor("token_type_ids", np.ones(shape=(1, 256), dtype=np.int32)),

# Set up a plotter to run a series of inference requests against a provided triton backend
def get_inference_latencies(triton_client):
    latencies = []
    for x in range(30):
        # Time the request latency
        start = time.time_ns()
        triton_client.infer(model_options, inputs, requested)
        latency_ms = (time.time_ns() - start) / 1_000_000
    return latencies

def plot_latencies(latencies):
    plt.ylabel("Inference Latency")
    plt.xlabel("Request #")

Start with Triton running the stock TensorFlow backend

tf_triton_client = InferenceGrpcClient(url=tensorflow_url)

# Run 30 requests, store latency for each request
tf_latencies = get_inference_latencies(tf_triton_client)

Now switch to sending requests to the Modular backend

modular_triton_client = InferenceGrpcClient(url=modular_url)

# Run 30 requests, store latency for each request
modular_latencies = get_inference_latencies(modular_triton_client)

# Plot tensorflow and modular latencies in the same plot
plot_latencies(tf_latencies + modular_latencies)