Important

NeMo 2.0 is an experimental feature and currently released in the dev container only: nvcr.io/nvidia/nemo:dev. Please refer to the Migration Guide for information on getting started.

Deploy NeMo Multimodal Models

Optimized Inference for Multimodal Models using TensorRT

For scenarios requiring optimized performance, NeMo multimodal models can leverage TensorRT. This process involves converting NeMo models into a format compatible with TensorRT using the nemo.export module.

Supported GPUs

TensorRT-LLM supports NVIDIA DGX H100 and NVIDIA H100 GPUs based on the NVIDIA Hopper, NVIDIA Ada Lovelace, NVIDIA Ampere, and NVIDIA Turing architectures.

Supported Models

The following table shows the supported models.

Model Name	NeMo Precision	TensorRT Precision
Neva	bfloat16	bfloat16
Video Neva	bfloat16	bfloat16
LITA/VITA	bfloat16	bfloat16
VILA	bfloat16	bfloat16
SALM	bfloat16	bfloat16

Access the Models with a Hugging Face Token

If you want to run inference using the LLama3 model, you’ll need to generate a Hugging Face token that has access to these models. Visit Hugging Face Hugging Face for more information. After you have the token, perform one of the following steps.

Log in to Hugging Face:
huggingface-cli login
Or, set the HF_TOKEN environment variable:
export HF_TOKEN=your_token_here

Export and Deploy a NeMo Multimodal Checkpoint to TensorRT

This section provides an example of how to quickly and easily deploy a NeMo checkpoint to TensorRT. Neva will be used as an example model. Please consult the table above for a complete list of supported models.

Run the Docker container image using the command shown below. Change the :vr tag to the version of the container you want to use:

docker pull nvcr.io/nvidia/nemo:vr

docker run --gpus all -it --rm --shm-size=4g -p 8000:8000 -v /path/to/nemo_neva.nemo:/opt/checkpoints/nemo_neva.nemo -w /opt/NeMo nvcr.io/nvidia/nemo:vr

Run the following deployment script to verify that everything is working correctly. The script exports the downloaded NeMo checkpoint to TensorRT-LLM and subsequently serves it on the Triton server:
```
python scripts/deploy/multimodal/deploy_triton.py --visual_checkpoint /opt/checkpoints/nemo_neva.nemo --model_type neva --llm_model_type llama --triton_model_name neva --modality vision
```
If you only want to export the NeMo checkpoint to TensorRT, use the examples/multimodal/multimodal_llm/neva/neva_export.py.
If the test yields a shared memory-related error, change the shared memory size using --shm-size.
In a separate terminal, run the following command to get the container ID of the running container. Please find the nvcr.io/nvidia/nemo:24.vr image to get the container ID.
```
docker ps
```
Access the running container and replace container_id with the actual container ID as follows:
```
docker exec -it container_id bash
```

To send a query to the Triton server, run the following script:

python scripts/deploy/multimodal/query.py -mn neva -mt=neva -int="What is in this image?" -im=/path/to/image.jpg

To export and deploy a different model, such as Video Neva, change the model_type and modality in the scripts/deploy/multimodal/deploy_triton.py script.

Use a Script to Run Inference on a Triton Server

You can deploy a multimodal model from a NeMo checkpoint on Triton using the provided script. This deployment uses TensorRT to achieve optimized inference.

Export and Deploy a Multimodal Model to TensorRT

After executing the script, it will export the model to TensorRT and then initiate the service on Triton.

Start the container using the steps described in the previous section.
To begin serving the model, run the following script:
```
python scripts/deploy/multimodal/deploy_triton.py --visual_checkpoint /opt/checkpoints/nemo_neva.nemo --model_type neva --llm_model_type llama --triton_model_name neva
```
The following parameters are defined in the deploy_triton.py script:
- modality - modality of the model. choices=[“vision”, “audio”]. By default, it is set to “vision”.
- visual_checkpoint - path of the .nemo of visual model or the path to perception model checkpoint for SALM model
- llm_checkpoint - path of .nemo of LLM. Would be set as visual_checkpoint if not provided
- model_type - type of the model. choices=[“neva”, “video-neva”, “lita”, “vila”, “vita”, “salm”].
- llm_model_type - type of LLM. choices=[“gptnext”, “gpt”, “llama”, “falcon”, “starcoder”, “mixtral”, “gemma”].
- triton_model_name name of the model on Triton.
- triton_model_version - version of the model. Default is 1.
- triton_port - port for the Triton server to listen for requests. Default is 8000.
- triton_http_address - HTTP address for the Triton server. Default is 0.0.0.0
- triton_model_repository - TensorRT temp folder. Default is /tmp/trt_model_dir/.
- num_gpus - number of GPUs to use for inference. Large models require multi-gpu export.
- dtype - data type of the model on TensorRT-LLM. Default is “bfloat16”. Currently only “bfloat16” is supported.
- max_input_len - maximum input length of the model.
- max_output_len - maximum output length of the model.
- max_batch_size - maximum batch size of the model.
- max_multimodal_len - maximum lenghth of multimodal input
- vision_max_batch_size - maximum batch size for input images for vision encoder. Default is 1. For models like LITA and VITA on video inference, this should be set to 256.
Note

The parameters described here are generalized and should be compatible with any NeMo checkpoint. It is important; however, that you check the Multimodal model table above for optimized inference model compatibility. We are actively working on extending support to other checkpoints.

Whenever the script is executed, it initiates the service by exporting the NeMo checkpoint to the TensorRT. If you want to skip the exporting step in the optimized inference option, you can specify an empty directory.
To export and deploy a different model, such as Video Neva, change the model_type and modality in the scripts/deploy/multimodal/deploy_triton.py script. Please see the table below to learn more about which model_type and modality is used for a Multimodal model.

Model Name

model_type

modality

Neva

neva

vision

Video Neva

video-neva

vision

LITA

lita

vision

VILA

vila

vision

VITA

vita

vision

SALM

salm

audio

Stop the running container and then run the following command to specify an empty directory:

mkdir tmp_triton_model_repository

docker run --gpus all -it --rm --shm-size=4g -p 8000:8000 -v ${PWD}:/opt/checkpoints/ -w /opt/NeMo nvcr.io/nvidia/nemo:vr

python scripts/deploy/multimodal/deploy_triton.py --visual_checkpoint /opt/checkpoints/nemo_neva.nemo --model_type neva --llm_model_type llama --triton_model_name neva --triton_model_repository /opt/checkpoints/tmp_triton_model_repository --modality vision

The checkpoint will be exported to the specified folder after executing the script mentioned above.

To load the exported model directly, run the following script within the container:

python scripts/deploy/multimodal/deploy_triton.py --triton_model_name neva --triton_model_repository /opt/checkpoints/tmp_triton_model_repository --model_type neva --llm_model_type llama --modality vision

Send a Query

After starting the service using the provided scripts from the previous section, it will wait for incoming requests. You can send a query to this service in several ways.

Use the Query Script: Execute the query script within the currently running container.
PyTriton: Utilize PyTriton to send requests directly.
HTTP Requests: Make HTTP requests using various tools or libraries.

The following example shows how to execute the query script within the currently running container.

To use a query script, run the following command. For VILA/LITA/VITA models, the input_text should add <image>\n before the actual text, such as <image>\n What is in this image?:

python scripts/deploy/multimodal/query.py --url "http://localhost:8000" --model_name neva --model_type neva --input_text "What is in this image?" --input_media /path/to/image.jpg

Change the url and the model_name based on your server and the model name of your service. The code in the script can be used as a basis for your client code as well. input_media is the path to the image or audio file you want to use as input.

Use NeMo Export and Deploy Module APIs to Run Inference

Up until now, we’ve used scripts for exporting and deploying Multimodal models. However, NeMo’s Deploy and Export modules offer straightforward APIs for deploying models to Triton and exporting NeMo checkpoints to TensorRT.

Export a Multimodal Model to TensorRT

You can use the APIs in the export module to export a NeMo checkpoint to TensorRT-LLM. The following code example assumes the nemo_neva.nemo checkpoint has already mounted to the /opt/checkpoints/ path. Additionally, the /opt/data/image.jpg is also assumed to exist.

Run the following command:

from nemo.export.tensorrt_mm_exporter import TensorRTMMExporter

exporter = TensorRTMMExporter(model_dir="/opt/checkpoints/tmp_triton_model_repository/", modality="vision")
exporter.export(visual_checkpoint_path="/opt/checkpoints/nemo_neva.nemo", model_type="neva", llm_model_type="llama", tensor_parallel_size=1)
output = exporter.forward("What is in this image?", "/opt/data/image.jpg", max_output_token=30, top_k=1, top_p=0.0, temperature=1.0)
print("output: ", output)

Be sure to check the TensorRTMMExporter class docstrings for details.

Deploy a Multimodal Model to TensorRT

You can use the APIs in the deploy module to deploy a TensorRT-LLM model to Triton. The following code example assumes the nemo_neva.nemo checkpoint has already mounted to the /opt/checkpoints/ path.

Run the following command:

from nemo.export.tensorrt_mm_exporter import TensorRTMMExporter
from nemo.deploy import DeployPyTriton

exporter = TensorRTMMExporter(model_dir="/opt/checkpoints/tmp_triton_model_repository/", modality="vision")
exporter.export(visual_checkpoint_path="/opt/checkpoints/nemo_neva.nemo", model_type="neva", llm_model_type="llama", tensor_parallel_size=1)

nm = DeployPyTriton(model=exporter, triton_model_name="neva", port=8000)
nm.deploy()
nm.serve()

Send a Query

The NeMo Framework provides NemoQueryMultimodal APIs to send a query to the Triton server for convenience. These APIs are only accessible from the NeMo Framework container.

To run the request example using NeMo APIs, run the following command:

from nemo.deploy.multimodal import NemoQueryMultimodal

nq = NemoQueryMultimodal(url="localhost:8000", model_name="neva", model_type="neva")
output = nq.query(input_text="What is in this image?", input_media="/opt/data/image.jpg", max_output_token=30, top_k=1, top_p=0.0, temperature=1.0)
print(output)

Change the url and the model_name based on your server and the model name of your service. Please check the NemoQueryMultimodal docstrings for details.

Other Examples

For a complete guide of exporting speech language model such as SALM (to get perception model and merge lora weights), please refer to the document here.