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+| Documentation | Paper | + +
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a/docs/assets/wandb_dashboard.png and /dev/null differ diff --git a/docs/conf.py b/docs/conf.py index ae1bf08..746b1d6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -30,3 +30,11 @@ # -- Added configurations ---------------------------------------------------- html_title = "metron" + +html_context = { + "display_github": True, # Integrate GitHub + "github_user": "project-metron", # Username + "github_repo": "metron", # Repo name + "github_version": "main", # Version + "conf_py_path": "/docs/", # Path in the checkout to the docs root +} diff --git a/docs/guides/guide.rst b/docs/guides/guide.rst index e09a5e8..a1e5b34 100644 --- a/docs/guides/guide.rst +++ b/docs/guides/guide.rst @@ -1,7 +1,7 @@ Guides ====== -This section contains additional topics and features that are not covered in the :doc:`../getting_started` section. These guides are meant to provide a deeper understanding of the library and its features, and help in getting the most out of the ``metron``. +This section contains additional topics and features that are not covered in the :doc:`../how_to_use` section. These guides are meant to provide a deeper understanding of the library and its features, and help in getting the most out of the ``metron``. Check out the following guides to learn more: diff --git a/docs/getting_started.rst b/docs/how_to_use.rst similarity index 80% rename from docs/getting_started.rst rename to docs/how_to_use.rst index 73be2a4..c0f70fc 100644 --- a/docs/getting_started.rst +++ b/docs/how_to_use.rst @@ -1,5 +1,5 @@ -Getting Started -=============== +How to use metron +================= .. toctree:: :maxdepth: 2 @@ -7,13 +7,12 @@ Getting Started tutorials/blackbox_evaluation tutorials/measuring_qps - tutorials/metrics ``metron`` can evaluate LLM inference systems as a black-box and also determine the serving capacity of the LLM inference system. ``metron`` provides two evaluation recipes as described below: -- **Black-box Evaluation**: ``metron`` hits LLM inference server exposed through API endpoint with set of requests with different prompt lengths and tracks when each output token is generated. This allows ``metron`` to calculate several metrics like TTFT, TBT, TPOT, and *unified-deadline*. With *unified-deadline* metric, ``metron`` also infers the minimum TBT required to meet target acceptance rate threshold, such as - 99% of requests should have acceptance rate of at least 90%. +- **Black-box Evaluation**: ``metron`` hits LLM inference server exposed through API endpoint with set of requests with different prompt lengths and tracks when each output token is generated. This allows ``metron`` to calculate several metrics like TTFT, TBT, TPOT, and *fluidity-index*. With *fluidity-index* metric, ``metron`` also infers the minimum TBT required to meet target acceptance rate threshold, such as - 99% of requests should have acceptance rate of at least 90%, and obtains *fluid-token-generation-rate* metric. - **Capacity Evaluation**: When deploying LLM inference system, operator needs to know how many requests can be served by the system. This will help operator in determining the configuration of the system, for example, the number of GPUs needed, to meet certain service quality requirements. To help with this process, ``metron`` provides a capacity evaluation module which determines maximum capacity each replica can provide under different request loads while meeting target SLO requirements. diff --git a/docs/index.rst b/docs/index.rst index fdbee97..b2b596f 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -10,7 +10,7 @@ metron Serving large language models (LLMs) in production is very expensive, and this challenge has prompted recent advances in inference system optimizations. As of today, these systems are evaluated through conventional metrics like TTFT (time to first token), TBT (time between tokens), normalized latency, and TPOT (time per output token). However, these metrics fail to capture the nuances of LLM inference leading to incomplete assessment of user-facing performance in real-time applications like chat. -``metron`` is a holistic performance evaluation framework that includes new metric, *unified-deadline*, alongside existing conventional metrics. This new metric reflects the intricacies of LLM inference process and its impact on real-time user experience. +``metron`` is a holistic performance evaluation framework that includes new metrics, :ref:`fluidity-index` and :ref:`fluid-token-generation-rate`, alongside existing conventional metrics. The new metrics reflect the intricacies of LLM inference process and its impact on real-time user experience. ``metron`` is designed to be easy to use, and can be integrated with any LLM inference system. It is built on top of Ray, a popular distributed computing framework, and can be used to benchmark LLM inference systems on a single machine or a cluster. @@ -20,7 +20,8 @@ Check out the following resources to get started: :maxdepth: 2 installation - getting_started + tutorials/metrics + how_to_use guides/guide Citation diff --git a/docs/setup.md b/docs/setup.md index d74f9ea..ad9573b 100644 --- a/docs/setup.md +++ b/docs/setup.md @@ -2,7 +2,7 @@ ## Setup ```bash -pip install sphinx furo +pip install -r requirements.txt ``` ## Run @@ -11,6 +11,3 @@ make html ``` An `index.html` file will be generated under `_build/html` directory. Open it in browser. - -## Website -