Utilities#

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:

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

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

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#

docker run -it --rm --gpus all $IMG_NAME list-model-profiles  \
  -e NGC_API_KEY=$NGC_API_KEY

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 the default LoRA profile. You cannot use this option with either the --profiles or the --all option.

Example#

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

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"
}
...

Create model store#

create-model-store

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>#

The directory path where the model --profile is extracted and copied.

Example#

 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

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#

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

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#

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

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
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