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Multi-Model Quick Start

The steps below will guide you through using Model Analyzer in Docker mode to profile and analyze two models concurrently: add_sub & resnet50_python.

Step 1: Download the models

1. Create a new directory and enter it

mkdir <new_dir> && cd <new_dir>

2. Start a git repository

git init && git remote add -f origin https://github.com/triton-inference-server/model_analyzer.git

3. Enable sparse checkout, and download the examples directory, which contains the add_sub & resnet50_python models

git config core.sparseCheckout true && \
echo 'examples' >> .git/info/sparse-checkout && \
git pull origin main

Step 2: Pull and Run the SDK Container

1. Pull the SDK container:

docker pull nvcr.io/nvidia/tritonserver:24.11-py3-sdk

2. Run the SDK container

docker run -it --gpus all \
      -v /var/run/docker.sock:/var/run/docker.sock \
      -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
      --net=host nvcr.io/nvidia/tritonserver:24.11-py3-sdk

Step 3: Profile both models concurrently

The examples/quick-start directory is an example Triton Model Repository that contains two libtorch models: add_sub & resnet50_python

Run the Model Analyzer profile subcommand inside the container with:

model-analyzer profile \
    --model-repository <path-to-examples-quick-start> \
    --profile-models add_sub,resnet50_python --triton-launch-mode=docker \
    --output-model-repository-path <path-to-output-model-repo>/<output_dir> \
    --export-path profile_results \
    --run-config-profile-models-concurrently-enable

Important: You must specify an <output_dir> subdirectory. You cannot have --output-model-repository-path point directly to <path-to-output-model-repo>

Important: If you already ran this earlier in the container, you can use the --override-output-model-repository option to overwrite the earlier results.

Important: All models must be in the same repository

The --run-config-profile-models-concurrently-enable option tells Model Analyzer to load and optimize both models concurrently using the Quick Search algorithm.

This will profile both models concurrently finding the maximal throughput gain for both models by iterating across instance group counts and batch sizes. By default, the algorithm is attempting to find the best balance of gains for each model, not the best combined total throughput.

After the quick search completes, Model Analyzer will then sweep concurrencies for the top three configurations and then create a summary report and CSV outputs.

Here is an example result summary, run on a TITAN RTX GPU:

Result Summary Top Result Summary Table

You will note that the top model configuration has a lower total combined throughput than the third configuration. However, it does a better job at increasing the total percentage throughput gain versus the other configurations.

For example, comparing the top config [add_sub_config_10, resnet50_python_config_12] to the third best [add_sub_config_5, resnet50_python_config_3], you will see that while the decrease in add_sub throughput is large (~4600 infer/sec), it represents just a 12.5% decrease in throughput. This is offset by an increase in resnet50_python throughput (~46 infer/sec), which represents an 20% increase in throughput. Therefore, the [add_sub_config_10, resnet_python_config_12] is deemed to be a better configuration by Model Analyzer.

The measured data and summary report will be placed inside the ./profile_results directory. The directory will be structured as follows.

$HOME
  |--- model_analyzer
              |--- profile_results
              .       |--- plots
              .       |      |--- simple
              .       |      |      |--- add_sub,resnet50_python
                      |      |              |--- gpu_mem_v_latency.png
                      |      |              |--- throughput_v_latency.png
                      |
                      |--- results
                      |       |--- metrics-model-inference.csv
                      |       |--- metrics-model-gpu.csv
                      |       |--- metrics-server-only.csv
                      |
                      |--- reports
                              |--- summaries
                              .        |--- add_sub,resnet50_python
                              .                |--- result_summary.pdf

Optional: Add constraints

In the above results you can see a large (651%) throughput increase, but this comes at the expense of a large increase in latency. By creating a YAML config file we can specify per model latency constraints:

profile_models:
  add_sub:
    constraints:
      perf_latency_p99:
        max: 5
  resnet50_python:
    constraints:
      perf_latency_p99:
        max: 50

And then re-run with the following command:

model-analyzer profile \
    --model-repository <path-to-examples-quick-start> \
    --config-file <path-to-config-file> --triton-launch-mode=docker \
    --output-model-repository-path <path-to-output-model-repo>/<output_dir> \
    --export-path profile_results \
    --run-config-profile-models-concurrently-enable

Here is an example result summary with the above constraints, run on a TITAN RTX GPU: Result Summary Constraint Top Result Summary Constraint Table

Again, you will observe that the top configuration here has a lower total combined throughput than the second or third best configurations. However the total percentage gain is higher due to the large decrease in throughput for the resnet50_python model in the other configurations.