NVIDIA NeMo Inference Microservice Deploy Job

Deploy a model artifact from W&B to a NVIDIA NeMo Inference Microservice. To do this, use W&B Launch. W&B Launch converts model artifacts to NVIDIA NeMo Model and deploys to a running NIM/Triton server.

W&B Launch currently accepts the following compatible model types:

1. Llama2
2. StarCoder
3. NV-GPT (coming soon)

info

Deployment time varies by model and machine type. The base Llama2-7b config takes about 1 minute on GCP's a2-ultragpu-1g.

Quickstart

1. Create a launch queue if you don't have one already. See an example queue config below.

   net: host
   gpus: all # can be a specific set of GPUs or `all` to use everything
   runtime: nvidia # also requires nvidia container runtime
   volume:
     - model-store:/model-store/

   

2. Create this job in your project:

   wandb job create -n "deploy-to-nvidia-nemo-inference-microservice" \
      -e $ENTITY \
      -p $PROJECT \
      -E jobs/deploy_to_nvidia_nemo_inference_microservice/job.py \
      -g andrew/nim-updates \
      git https://github.com/wandb/launch-jobs

3. Launch an agent on your GPU machine:

   wandb launch-agent -e $ENTITY -p $PROJECT -q $QUEUE

4. Submit the deployment launch job with your desired configs from the Launch UI

   1. You can also submit via the CLI:

      wandb launch -d gcr.io/playground-111/deploy-to-nemo:latest \
        -e $ENTITY \
        -p $PROJECT \
        -q $QUEUE \
        -c $CONFIG_JSON_FNAME
      

5. You can track the deployment process in the Launch UI.

6. Once complete, you can immediately curl the endpoint to test the model. The model name is always ensemble.

    #!/bin/bash
    curl -X POST "http://0.0.0.0:9999/v1/completions" \
        -H "accept: application/json" \
        -H "Content-Type: application/json" \
        -d '{
            "model": "ensemble",
            "prompt": "Tell me a joke",
            "max_tokens": 256,
            "temperature": 0.5,
            "n": 1,
            "stream": false,
            "stop": "string",
            "frequency_penalty": 0.0
            }'