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# Using Perf Analyzer for profiling HuggingFace BART model

## Overview

The example presents profiling of HuggingFace BART model using [Perf
Analyzer](https://github.com/triton-inference-server/client/blob/main/src/c++/perf_analyzer/README.md)

Example consists of following scripts:

- `install.sh` - install additional packages and libraries required to run the example
- `server.py` - start the model with Triton Inference Server
- `client.py` - execute HTTP/gRPC requests to the deployed model

## Requirements

The example requires the `torch` package. It can be installed in your current environment using pip:

```shell
pip install torch
```

Or you can use NVIDIA PyTorch container:

```shell
docker run -it --gpus 1 --shm-size 8gb -v {repository_path}:{repository_path} -w {repository_path} nvcr.io/nvidia/pytorch:23.10-py3 bash
```

If you select to use container we recommend to install
[NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/overview.html).

## Quick Start

The step-by-step guide:

1. Install PyTriton following
   the [installation instruction](../../README.md#installation)
2. Install the additional packages using `install.sh`

```shell
./install.sh
```

3. In current terminal start the model on Triton using `server.py`

```shell
./server.py
```

4. Open new terminal tab (ex. `Ctrl + T` on Ubuntu) or window
5. Go to the example directory
6. Run the `client.sh` to perform queries on model:

```shell
./client.sh
```