Utilities - NVIDIA Docs

NIM includes a set of utility scripts to assist with NIM operation.

Utilities can be launched by adding the name of the desired utility to the docker run command. For example, you can execute the list-model-profiles utility with the following command:

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            docker run --rm --runtime=nvidia --gpus=all $IMG_NAME list-model-profiles

You can get more information about each utility with the -h flag:

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            docker run --rm --runtime=nvidia --gpus=all $IMG_NAME download-to-cache -h

List available model profiles

list-model-profiles: Prints to the console the system information detected by NIM, and the list of all profiles for the chosen NIM. Profiles are categorized by whether or not they are compatible with the current system, based on the system information detected.

Example

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            docker run -it --rm --gpus all $IMG_NAME list-model-profiles

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            SYSTEM INFO
- Free GPUs:
  -  [20b2:10de] (0) NVIDIA A100-SXM4-80GB (A100 80GB) [current utilization: 0%]
  -  [20b2:10de] (1) NVIDIA A100-SXM4-80GB (A100 80GB) [current utilization: 0%]
  -  [20b2:10de] (2) NVIDIA A100-SXM4-80GB (A100 80GB) [current utilization: 0%]
  -  [20b2:10de] (3) NVIDIA A100-SXM4-80GB (A100 80GB) [current utilization: 0%]
  -  [20b2:10de] (4) NVIDIA A100-SXM4-80GB (A100 80GB) [current utilization: 0%]
  -  [20b2:10de] (5) NVIDIA A100-SXM4-80GB (A100 80GB) [current utilization: 0%]
  -  [20b2:10de] (6) NVIDIA A100-SXM4-80GB (A100 80GB) [current utilization: 0%]
  -  [20b2:10de] (7) NVIDIA A100-SXM4-80GB (A100 80GB) [current utilization: 0%]
MODEL PROFILES
- Compatible with system and runnable:
  - d86754a6413430bf502ece62fdcc8137d4ed24d6062e93c23c1090f0623d535f (tensorrt_llm-a100-bf16-tp8-latency)
  - 6f437946f8efbca34997428528d69b08974197de157460cbe36c34939dc99edb (tensorrt_llm-a100-bf16-tp4-throughput)
  - 7283d5adcddeeab03996f61a33c51552d9bcff16c38e4a52f1204210caeb393c (vllm-fp16-tp8)
  - cdcbc486dd076bc287cca6262c59fe90057d76ae18a407882075f65a99f5f038 (vllm-fp16-tp4)
  - With LoRA support:
    - 4cac7d500b9ed35bc51cb7845e637288c682f4a644f0b4e6a4f71d3b8b188101 (tensorrt_llm-a100-bf16-tp4-throughput-lora)
    - 7096ab12e70abc4ac0e125a90a8e40b296891603fad45d2b208d655ac1dea9d8 (vllm-fp16-tp8-lora)
    - d4bc4be4167c103b45d9375c9a907c11339f59235dfc5de321a9e13d8132aba6 (vllm-fp16-tp4-lora)
- Incompatible with system:
  - 5296eed82c6309b64b13da03fbb843d99c3276effd6a0c51e28ad5bb29f56017 (tensorrt_llm-h100-fp8-tp8-latency)
  - 4e0aeeefd4dfeae46ad40f16238bbde8858850ce0cf56c26449f447a02a9ac8f (tensorrt_llm-h100-fp8-tp4-throughput)
  - ...

Download model profiles to NIM cache

download-to-cache

Downloads selected or default model profile(s) to NIM cache. Can be used to pre-cache profiles prior to deployment. Requires NGC_API_KEY in environment.

--profiles[PROFILES ...],-p[PROFILES ...]: Profile hashes to download. If none are provided, the optimal profile is downloaded. Multiple profiles can be specified separated by spaces.

--all: Set to download all profiles to cache

--lora: Set this to download default lora profile. This expects --profiles and --all arguments are not specified.

Example

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            docker run -it --rm --gpus all -e NGC_API_KEY -v $LOCAL_NIM_CACHE:/opt/nim/.cache \
  $IMG_NAME download-to-cache -p 6f437946f8efbca34997428528d69b08974197de157460cbe36c34939dc99edb

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            INFO 08-12 18:44:07.810 pre_download.py:80] Fetching contents for profile 6f437946f8efbca34997428528d69b08974197de157460cbe36c34939dc99edb
INFO 08-12 18:44:07.810 pre_download.py:86] {
"feat_lora": "false",
"gpu": "A100",
"gpu_device": "20b2:10de",
"llm_engine": "tensorrt_llm",
"pp": "1",
"precision": "bf16",
"profile": "throughput",
"tp": "4"
}
...

Extracts files from a cached model profile and creates a properly formatted directory. If the profile is not already cached, it will be downloaded to the model cache. Downloading the profile requires NGC_API_KEY in environment.

--profile<PROFILE>,-p<PROFILE>: Profile hash to create a model directory of. Will be downloaded if not present.

--model-store<MODEL_STORE>,-m<MODEL_STORE>: Directory path where model --profile will be extracted and copied to.

Example

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             docker run -it --rm --gpus all -e NGC_API_KEY -v $LOCAL_NIM_CACHE:/opt/nim/.cache $IMG_NAME create-model-store -p 6f437946f8efbca34997428528d69b08974197de157460cbe36c34939dc99edb -m /tmp

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            INFO 08-12 19:49:47.629 pre_download.py:128] Fetching contents for profile 6f437946f8efbca34997428528d69b08974197de157460cbe36c34939dc99edb
INFO 08-12 19:49:47.629 pre_download.py:135] Copying contents for profile 6f437946f8efbca34997428528d69b08974197de157460cbe36c34939dc99edb to /tmp

Check NIM cache

nim-llm-check-cache-env: Checks if the NIM cache directory is present and can be written to.

Example

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            docker run -it --rm --gpus all -v /bad_path:/opt/nim/.cache $IMG_NAME nim-llm-check-cache-env

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            WARNING 08-12 19:54:06.347 caches.py:30] /opt/nim/.cache is read-only, application may fail if model is not already present in cache

Set cache environment variables

nim-llm-set-cache-env: Prints commands for setting cache environment variables to console.

Example

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            docker run -it --rm --gpus all -v $LOCAL_NIM_CACHE:/opt/nim/.cache $IMG_NAME nim-llm-set-cache-env

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            export NUMBA_CACHE_DIR=/tmp/numba
export NGC_HOME=/opt/nim/.cache/ngc
export HF_HOME=/opt/nim/.cache/huggingface
export VLLM_CONFIG_ROOT=/opt/nim/.cache/vllm/config
export VLLM_CACHE_ROOT=/opt/nim/.cache/vllm/cache