llmeter 0.1.2

A lightweight, cross-platform latency and throughput profiler for LLMs

Measuring large language models latency and throughput

LLMeter is a pure-python library for simple latency and throughput testing of large language models (LLMs). It's designed to be lightweight to install; straightforward to run standard tests; and versatile to integrate - whether in notebooks, CI/CD, or other workflows.

🛠️ Installation

LLMeter requires python>=3.10, please make sure your current version of python is compatible.

To install the basic metering functionalities, you can install the minimum package using pip install:

pip install llmeter

LLMeter also offers extra features that require additional dependencies. Currently these extras include:

• plotting: Add methods to generate charts and heatmaps to summarize the results
• openai: Enable testing endpoints offered by OpenAI
• litellm: Enable testing a range of different models through LiteLLM

You can install one or more of these extra options using pip:

pip install llmeter[plotting, openai, litellm]

🚀 Quick-start

At a high level, you'll start by configuring an LLMeter "Endpoint" for whatever type of LLM you're connecting to:

# For example with Amazon Bedrock...
from llmeter.endpoints import BedrockConverse
endpoint = BedrockConverse(model_id="...")

# ...or OpenAI...
from llmeter.endpoints import OpenAIEndpoint
endpoint = OpenAIEndpoint(model_id="...", api_key="...")

# ...or via LiteLLM...
from llmeter.endpoints import LiteLLM
endpoint = LiteLLM("{provider}/{model_id}")

# ...and so on

You can then run the high-level "experiments" offered by LLMeter:

# For example a heatmap of latency by input & output token count:
from llmeter.experiments import LatencyHeatmap
latency_heatmap = LatencyHeatmap(
    endpoint=endpoint,
    clients=10,
    source_file="examples/MaryShelleyFrankenstein.txt",
    ...
)
heatmap_results = await latency_heatmap.run()
latency_heatmap.plot_heatmap()

# ...Or testing how throughput varies with concurrent request count:
from llmeter.experiments import LoadTest
sweep_test = LoadTest(
    endpoint=endpoint,
    payload={...},
    sequence_of_clients=[1, 5, 20, 50, 100, 500],
)
sweep_results = await sweep_test.run()
sweep_test.plot_sweep_results()

Alternatively, you can use the low-level llmeter.runner.Runner class to run and analyze request batches - and build your own custom experiments.

For more details, check out our selection of end-to-end code examples in the examples folder!

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.