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-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-to-cache
Downloads selected or default model profile(s) to NIM cache. Can be used to pre-cache profiles prior to deployment. Requires
<a href="getting-started.html#export-the-api-key">NGC_API_KEY</a>
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
<a href="#cmdoption-profiles">--profiles</a>
and<a href="#cmdoption-all">--all</a>
arguments are not specified.
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
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
<a href="getting-started.html#export-the-api-key">NGC_API_KEY</a>
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
<a href="#cmdoption-profile">--profile</a>
will be extracted and copied to.
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
- 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
- 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