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Add goodput tutorial (#46)
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* Add goodput tutorial

* Updated tutorial, goodput is printed inside table
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# Benchmark Goodput with GenAI-Perf

## Context

Goodput is defined as the number of completed requests per second
that meet specified metric constraints, also called service level
objectives.

For example, perhaps you want to measure the user experience of your service
by considering throughput only including requests where the time to first token
is under 50ms and inter-token latency is under 10ms.

GenAI-Perf provides this value as goodput.

## Tutorials

Below you can find tutorials on how to benchmark different models
using goodput.

### Examples

- [LLM Examples](#LLM)

- [Embedding Model Examples](#embeddings)

### Profile LLM Goodput<a id="LLM"></a>

#### Run GPT2 on an OpenAI Chat Completions API-compatible server

```bash
docker run -it --net=host --rm --gpus=all vllm/vllm-openai:latest --model gpt2 --dtype float16 --max-model-len 1024
```

#### Run GenAI-Perf with Goodput Constraints

```bash
genai-perf profile \
-m gpt2 \
--service-kind openai \
--endpoint-type chat \
--measurement-interval 1000 \
--streaming \
--goodput time_to_first_token:4.35 inter_token_latency:1.1
```

Example output:

```
NVIDIA GenAI-Perf | LLM Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃ Statistic ┃ avg ┃ min ┃ max ┃ p99 ┃ p90 ┃ p75 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│ Time to first token (ms) │ 7.46 │ 4.15 │ 18.40 │ 17.89 │ 13.26 │ 5.56 │
│ Inter token latency (ms) │ 1.05 │ 0.98 │ 1.16 │ 1.16 │ 1.12 │ 1.06 │
│ Request latency (ms) │ 632.78 │ 620.90 │ 648.19 │ 647.81 │ 644.38 │ 638.67 │
│ Output sequence length │ 595.60 │ 539.00 │ 638.00 │ 636.80 │ 626.00 │ 608.00 │
│ Input sequence length │ 550.00 │ 550.00 │ 550.00 │ 550.00 │ 550.00 │ 550.00 │
│ Output token throughput (per sec) │ 940.96 │ N/A │ N/A │ N/A │ N/A │ N/A │
│ Request throughput (per sec) │ 1.58 │ N/A │ N/A │ N/A │ N/A │ N/A │
│ Request goodput (per sec) │ 0.32 │ N/A │ N/A │ N/A │ N/A │ N/A │
└───────────────────────────────────┴────────┴────────┴────────┴────────┴────────┴────────┘
```

### Profile Embeddings Models Goodput<a id="embeddings"></a>

#### Create a Sample Embeddings Input File

To create a sample embeddings input file, use the following command:

```bash
echo '{"text": "What was the first car ever driven?"}
{"text": "Who served as the 5th President of the United States of America?"}
{"text": "Is the Sydney Opera House located in Australia?"}
{"text": "In what state did they film Shrek 2?"}' > embeddings.jsonl
```

This will generate a file named embeddings.jsonl with the following content:
```jsonl
{"text": "What was the first car ever driven?"}
{"text": "Who served as the 5th President of the United States of America?"}
{"text": "Is the Sydney Opera House located in Australia?"}
{"text": "In what state did they film Shrek 2?"}
```

#### Start an OpenAI Embeddings-Compatible Server

To start an OpenAI embeddings-compatible server, run the following command:
```bash
docker run -it --net=host --rm --gpus=all vllm/vllm-openai:latest --model intfloat/e5-mistral-7b-instruct --dtype float16 --max-model-len 1024
```

#### Run GenAI-Perf with goodput constraints

```bash
genai-perf profile \
-m intfloat/e5-mistral-7b-instruct \
--service-kind openai \
--endpoint-type embeddings \
--batch-size 2 \
--input-file embeddings.jsonl \
--measurement-interval 1000 \
--goodput request_latency:22.5
```
Example output:

```
NVIDIA GenAI-Perf | Embeddings Metrics
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ Statistic ┃ avg ┃ min ┃ max ┃ p99 ┃ p90 ┃ p75 ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ Request latency (ms) │ 23.23 │ 21.98 │ 147.53 │ 23.74 │ 22.75 │ 22.52 │
│ Request throughput (per sec) │ 42.95 │ N/A │ N/A │ N/A │ N/A │ N/A │
│ Request goodput (per sec) │ 30.40 │ N/A │ N/A │ N/A │ N/A │ N/A │
└──────────────────────────────┴───────┴───────┴────────┴───────┴───────┴───────┘
```

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