Getting Started#
Prerequisites#
Check the Support Matrix to make sure that you have the supported hardware and software stack.
NGC Authentication#
Generate an API Key#
An NGC API key is required to access NGC resources. You can generate a key at NGC API Keys.
When creating an NGC API Personal key, ensure that at least “NGC Catalog” is selected from the “Services Included” dropdown. More Services can be included if this key is to be reused for other purposes.
Note
Personal keys allow you to configure an expiration date, revoke or delete the key using an action button, and rotate the key as needed. For more information about key types, please refer the NGC User Guide.
Export the API Key#
Pass the value of the API key to the docker run
command in the next section as the NGC_API_KEY
environment variable to download the appropriate models and resources when starting the NIM.
If you are not familiar with how to create the NGC_API_KEY
environment variable, the simplest way is to export it in your terminal:
export NGC_API_KEY=<value>
Run one of the following commands to make the key available at startup:
# If using bash
echo "export NGC_API_KEY=<value>" >> ~/.bashrc
# If using zsh
echo "export NGC_API_KEY=<value>" >> ~/.zshrc
Note
Other, more secure options include saving the value in a file, so that you can retrieve with cat $NGC_API_KEY_FILE
, or using a password manager.
Docker Login to NGC#
To pull the NIM container image from NGC, first authenticate with the NVIDIA Container Registry with the following command:
echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin
Use $oauthtoken
as the username and NGC_API_KEY
as the password. The $oauthtoken
username is a special name that indicates that you will authenticate with an API key and not a user name and password.
Launching the NIM Container#
The following command launches the Eye Contact NIM container with the gRPC service. (For a list of parameters, see Runtime Parameters for the Container.
# Choose manifest profile id based on target architecture.
export MANIFEST_PROFILE_ID=<enter_valid_manifest_profile_id>
# Run the container
docker run -it --rm --name=maxine-eye-contact-nim \
--runtime=nvidia \
--gpus all \
--shm-size=8GB \
-e NGC_API_KEY=$NGC_API_KEY \
-e NIM_MANIFEST_PROFILE=$MANIFEST_PROFILE_ID \
-e NIM_HTTP_API_PORT=8000 \
-e MAXINE_MAX_CONCURRENCY_PER_GPU=1 \
-p 8000:8000 \
-p 8001:8001 \
nvcr.io/nim/nvidia/maxine-eye-contact:latest
There is also an option to cache the model manifest locally. For more details, refer to Model Caching.
Note
The flag --gpus all
assigns all available GPUs to the Docker container.
To assign specific GPUs to the Docker container (in case of multiple GPUs available in your machine), use --gpus '"device=0,1,2..."'
.
Model Manifest Profiles#
The following table lists manifest profile IDs that you can specify in MANIFEST_PROFILE_ID
.
GPU Architecture (compute capability) |
Manifest profile ID |
---|---|
Blackwell (cc 12.0/10.0 ) |
0c1298dbaedef627d76009e13f42a4216ee2cbc31c0208aa014dabf6b59ba107 |
Ada (cc 8.9) |
9120702ed52cc1341bdd7bb9016624eeeb80accbd7698c4e1a99cbe1af2df847 |
Ampere (cc 8.6) |
602a0baa26b47a61a28404f798045b1f67d370a29b370fd61a529aecd89bba41 |
Turing (cc 7.5) |
5f6e14277b184194131ac7de9f324fce9af270d25b923e4f488b895d745c30f0 |
Volta (cc 7.0) |
60670da94d32b223ca4faff2e3716272cb464e3574d2dd2aac21cd9723ad1e26 |
Note
MANIFEST_PROFILE_ID
is an optional parameter. If the manifest profile ID is not supplied, the NIM automatically selects a matching profile ID based on the target hardware architecture.
However, if MANIFEST_PROFILE_ID
is used, ensure that the associated GPU architecture is compatible with the target hardware. If an incorrect manifest profile ID is used, a deserialization error occurs on inference.
If the command runs successfully, you get a response similar to the following.
I1126 09:22:21.048202 31 grpc_server.cc:2558] "Started GRPCInferenceService at 127.0.0.1:9001"
I1126 09:22:21.048377 31 http_server.cc:4704] "Started HTTPService at 127.0.0.1:9000"
I1126 09:22:21.089295 31 http_server.cc:362] "Started Metrics Service at 127.0.0.1:9002"
Maxine GRPC Service: Listening to 0.0.0.0:8001
Note
By default Eye Contact gRPC service is hosted on port 8001
. You will have to use this port for inferencing requests.
Environment Variables#
The following table describes the environment variables that can be passed into a NIM as a -e
argument added to a docker run
command:
ENV |
Required? |
Default |
Notes |
---|---|---|---|
|
Yes |
None |
You must set this variable to the value of your personal NGC API key. |
|
Optional |
|
Location (in container) where the container caches model artifacts. |
|
No |
|
Publish the NIM service to the prescribed port inside the container. Be sure to adjust the port passed to the |
|
Optional |
None |
You must set this model profile to be able to download the specific model type supported on your GPU. For more about |
|
No |
disabled |
Set SSL security on the endpoints to |
|
No |
None |
Set the path to CA root certificate inside the NIM. This is required only when |
|
No |
None |
Set the path to the server’s public SSL certificate inside the NIM. This is required only when an SSL mode is enabled. For example, if the SSL certificates are mounted at |
|
No |
None |
Set the path to the server’s private key inside the NIM. This is required only when an SSL mode is enabled. For example, if the SSL certificates are mounted at |
Runtime Parameters for the Container#
Flags |
Description |
---|---|
|
|
|
Delete the container after it stops (see Docker docs) |
|
Give a name to the NIM container. Use any preferred value. |
|
Ensure NVIDIA drivers are accessible in the container. |
|
Expose NVIDIA GPUs inside the container. If you are running on a host with multiple GPUs, you need to specify which GPU to use, you can also specify multiple GPUs. See GPU Enumeration for further information on for mounting specific GPUs. |
|
Allocate host memory for multi-process communication. |
|
Provide the container with the token necessary to download adequate models and resources from NGC. See above. |
|
Number of concurrent inference requests to be supported by the NIM server per GPU |
|
Ports published by the container are directly accessible on the host port. |
Stopping the Container#
The following commands can be used to stop the container.
docker stop $CONTAINER_NAME
docker rm $CONTAINER_NAME