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# Planner Benchmark Example

This guide shows an example of benchmarking `LocalPlanner` performance with synthetic data. In this example, we focus on 8x H100 SXM GPU and `deepseek-ai/DeepSeek-R1-Distill-Llama-8B` model with TP1 prefill and decode engine.

## Synthetic Data Generation

We first generate synthetic data with varying request rate from 0.75 to 3 using the provided `generate_synthetic_data.py` script.

```bash
python sin_synth.py \
    --time-duration 600 \
    --request-rate-min 5 \
    --request-rate-max 20 \
    --request-rate-period 150 \
    --isl1 3000 \
    --osl1 150 \
    --isl2 3000 \
    --osl2 150
```

This will generate a [mooncake style trace](https://github.com/kvcache-ai/Mooncake) with
* duration = 600 seconds
* isl/osl = 3000/150
* request rate varies sinusoidally from 0.75 to 3 requests with a period of 150 seconds

For other models and GPU SKUs, adjust the request rate ranges accordingly to match the load.

## Run the Benchmark

To measure the performance of dynamo with planner, we start from a 1p1d deployment and set planner to make adjustments every 10 seconds:

```bash
cd examples/llm
dynamo serve graphs.disagg:Frontend -f <path to disagg_1p1d.yml in this folder> --enable-local-planner

# in terminal 2
PYTHONPATH=/workspace/examples/llm python components/planner.py \
    --metric-pulling-interval 1 \
    --adjustment-interval 10 \
    --prefill-queue-scale-down-threshold 0.2 \
    --prefill-queue-scale-up-threshold 10 \
    --decode-kv-scale-down-threshold 0.3 \
    --decode-kv-scale-up-threshold 0.6 \
    --log-dir log/planner

# in terminal 3
genai-perf profile \
    --tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
    -m deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
    --service-kind openai \
    --endpoint-type chat \
    --url http://localhost:8000 \
    --streaming \
    --input-file payload:sin_b512_t600_rr5.0-20.0-150.0_io3000150-3000150-0.2-0.8-10.jsonl
```

To view the performance metrics and planner decisions, launch tensorboard with

```bash
tensorboard --logdir log
```

and open `http://localhost:6006` in your browser. The following metrics are available:

* `average_kv_load`: the average KV load in decode workers
* `prefill_queue_size`: the size of the prefill queue
* `num_queued_request`: the number of requests queued in decode workers
* `num_prefill_workers`: the number of prefill workers
* `num_decode_workers`: the number of decode workers
* `num_gpu`: the total number of GPUs used

The benchmark results will be printed out in terminal 3 that runs the `genai-perf` command.

In this example, we use a fixed 2p2d engine as baseline. Planner provides a `--no-operation` flag to watch and log the metrics without making any adjustments:

```bash
# in terminal 1
dynamo serve --enable-local-planner graphs.disagg:Frontend -f disagg_2p2d.yml

# in terminal 2 (optional)
PYTHONPATH=/workspace/examples/llm python components/planner.py --no-operation --log-dir log/2p2d

# in terminal 3
genai-perf profile --tokenizer deepseek-ai/DeepSeek-R1-Distill-Llama-8B -m deepseek-ai/DeepSeek-R1-Distill-Llama-8B --service-kind openai --endpoint-type chat --url http://localhost:8000 --streaming --input-file payload:sin_b512_t600_rr5.0-20.0-150.0_io3000150-3000150-0.2-0.8-10.jsonl
```

## Results

The below two figures show the performance comparison between planner and the baseline 2p2d deployment. Planner achieves 1.5x speedup while using 7.4% less GPU resources.

![Planner Performance Comparison](./images/planner_perf.png)

![Planner Tensorboard](./images/planner_tensorboard.png)

