# LLM API Introduction

The LLM API is a high-level Python API designed to streamline LLM inference workflows.

It supports a broad range of use cases, from single-GPU setups to multi-GPU and multi-node deployments, with built-in support for various parallelism strategies and advanced features. The LLM API integrates seamlessly with the broader inference ecosystem, including NVIDIA [Dynamo](https://github.com/ai-dynamo/dynamo).

While the LLM API simplifies inference workflows with a high-level interface, it is also designed with flexibility in mind. Under the hood, it uses a PyTorch-native and modular backend, making it easy to customize, extend, or experiment with the runtime.


## Quick Start Example
A simple inference example with TinyLlama using the LLM API:

```{literalinclude} ../../../examples/llm-api/quickstart_example.py
    :language: python
    :linenos:
```

For more advanced usage including distributed inference, multimodal, and speculative decoding, please refer to this [README](../../../examples/llm-api/README.md).

## Model Input

The `LLM()` constructor accepts either a Hugging Face model ID or a local model path as input.

### 1. Using a Model from the Hugging Face Hub

To load a model directly from the [Hugging Face Model Hub]((https://huggingface.co/)), simply pass its model ID (i.e., repository name) to the LLM constructor. The model will be automatically downloaded:

```python
llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
```

You can also use [quantized checkpoints](https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4) (FP4, FP8, etc) of popular models provided by NVIDIA in the same way.

### 2. Using a Local Hugging Face Model

To use a model from local storage, first download it manually:

```console
git lfs install
git clone https://huggingface.co/meta-llama/Meta-Llama-3.1-8B
```

Then, load the model by specifying a local directory path:

```python
llm = LLM(model=<local_path_to_model>)
```

> **Note:** Some models require accepting specific [license agreements]((https://ai.meta.com/resources/models-and-libraries/llama-downloads/)). Make sure you have agreed to the terms and authenticated with Hugging Face before downloading.


## Tips and Troubleshooting

The following tips typically assist new LLM API users who are familiar with other APIs that are part of TensorRT-LLM:

### RuntimeError: only rank 0 can start multi-node session, got 1

  There is no need to add an `mpirun` prefix for launching single node multi-GPU inference with the LLM API.

  For example, you can run `python llm_inference_distributed.py` to perform multi-GPU on a single node.

### Hang issue on Slurm Node

  If you experience a hang or other issue on a node managed with Slurm, add prefix `mpirun -n 1 --oversubscribe --allow-run-as-root` to your launch script.

  For example, try `mpirun -n 1 --oversubscribe --allow-run-as-root python llm_inference_distributed.py`.

### MPI_ABORT was invoked on rank 1 in communicator MPI_COMM_WORLD with errorcode 1.

  Because the LLM API relies on the `mpi4py` library, put the LLM class in a function and protect the main entrypoint to the program under the `__main__` namespace to avoid a [recursive spawn](https://mpi4py.readthedocs.io/en/stable/mpi4py.futures.html#mpipoolexecutor) process in `mpi4py`.

  This limitation is applicable for multi-GPU inference only.

### Cannot quit after generation

  The LLM instance manages threads and processes, which may prevent its reference count from reaching zero. To address this issue, there are two common solutions:
  1. Wrap the LLM instance in a function, as demonstrated in the quickstart guide. This will reduce the reference count and trigger the shutdown process.
  2. Use LLM as an contextmanager, with the following code: `with LLM(...) as llm: ...`, the shutdown methed will be invoked automatically once it goes out of the `with`-statement block.

### Single node hanging when using `docker run --net=host`

The root cause may be related to `mpi4py`. There is a [workaround](https://github.com/mpi4py/mpi4py/discussions/491#discussioncomment-12660609) suggesting a change from `--net=host` to `--ipc=host`, or setting the following environment variables:

```bash
export OMPI_MCA_btl_tcp_if_include=lo
export OMPI_MCA_oob_tcp_if_include=lo
```

Another option to improve compatibility with `mpi4py` is to launch the task using:

```bash
mpirun -n 1 --oversubscribe --allow-run-as-root python my_llm_task.py
```

This command can help avoid related runtime issues.