service-catalog/manifests/helm/nemo/25.4.0/charts/nemo-customizer

NeMo Customizer Microservice Helm Chart

For deployment guide, see Admin Setup in the NeMo Microservices documentation.

Values

Key	Type	Default	Description
apiImage	object	This object has the following default values for the NeMo Customizer API only image.	Customizer API only image configuration.
apiImage.imagePullPolicy	string	"IfNotPresent"	Image pull policy for the NeMo Customizer API image.
apiImage.registry	string	"nvcr.io"	Registry for the NeMo Customizer API image.
apiImage.repository	string	"nvidia/nemo-microservices/customizer-api"	Repository for the NeMo Customizer API image.
customizerConfig	object	This object has default values for the following fields.	Configuration for the NeMo Customizer microservice.
customizerConfig.entityStoreURL	string	"http://nemo-gateway.nemo-gateway.svc.cluster.local:8000"	URL for the NeMo Entity Store microservice.
customizerConfig.mlflowURL	string	"http://mlflow-tracking.mlflow-system.svc.cluster.local:80"	URL for the MLflow tracking server.
customizerConfig.models	object	This object has default values for the supported models.	Models configuration.
customizerConfig.models."meta/llama-3.1-70b-instruct"	object	This object has the following default values for the Llama 3.1 70B Instruct model.	Llama 3.1 70B Instruct model configuration.
customizerConfig.models."meta/llama-3.1-70b-instruct".enabled	bool	false	Whether to enable the model.
customizerConfig.models."meta/llama-3.1-70b-instruct".max_seq_length	int	4096	Maximum sequence length for input tokens.
customizerConfig.models."meta/llama-3.1-70b-instruct".micro_batch_size	int	1	Micro batch size for training.
customizerConfig.models."meta/llama-3.1-70b-instruct".model_path	string	"llama-3_1-70b-instruct_0_0_1"	Path where model files are stored.
customizerConfig.models."meta/llama-3.1-70b-instruct".model_uri	string	"ngc://nvidia/nemo/llama-3_1-70b-instruct-nemo:2.0"	NGC model URI for Llama 3.1 70B Instruct model.
customizerConfig.models."meta/llama-3.1-70b-instruct".num_parameters	int	70000000000	Number of model parameters.
customizerConfig.models."meta/llama-3.1-70b-instruct".precision	string	"bf16-mixed"	Model precision format.
customizerConfig.models."meta/llama-3.1-70b-instruct".prompt_template	string	"{prompt} {completion}"	Template for formatting prompts.
customizerConfig.models."meta/llama-3.1-70b-instruct".training_options	list	[{"finetuning_type":"lora","num_gpus":4,"num_nodes":1,"tensor_parallel_size":4,"training_type":"sft"}]	Training options for different fine-tuning methods.
customizerConfig.models."meta/llama-3.1-8b-instruct"	object	This object has the following default values for the Llama 3.1 8B Instruct model.	Llama 3.1 8B Instruct model configuration.
customizerConfig.models."meta/llama-3.1-8b-instruct".enabled	bool	false	Whether to enable the model.
customizerConfig.models."meta/llama-3.1-8b-instruct".max_seq_length	int	4096	Maximum sequence length for input tokens.
customizerConfig.models."meta/llama-3.1-8b-instruct".micro_batch_size	int	1	Micro batch size for training.
customizerConfig.models."meta/llama-3.1-8b-instruct".model_path	string	"llama-3_1-8b-instruct_0_0_1"	Path where model files are stored.
customizerConfig.models."meta/llama-3.1-8b-instruct".model_uri	string	"ngc://nvidia/nemo/llama-3_1-8b-instruct-nemo:2.0"	NGC model URI for Llama 3.1 8B Instruct model.
customizerConfig.models."meta/llama-3.1-8b-instruct".num_parameters	int	8000000000	Number of model parameters.
customizerConfig.models."meta/llama-3.1-8b-instruct".precision	string	"bf16-mixed"	Model precision format.
customizerConfig.models."meta/llama-3.1-8b-instruct".prompt_template	string	"{prompt} {completion}"	Template for formatting prompts.
customizerConfig.models."meta/llama-3.1-8b-instruct".training_options	list	[{"finetuning_type":"lora","num_gpus":1,"training_type":"sft"},{"finetuning_type":"all_weights","num_gpus":8,"num_nodes":1,"tensor_parallel_size":4,"training_type":"sft"}]	Training options for different fine-tuning methods.
customizerConfig.models."meta/llama-3.2-1b"	object	This object has the following default values for the Llama 3.2 1B model.	Llama 3.2 1B model configuration.
customizerConfig.models."meta/llama-3.2-1b".enabled	bool	false	Whether to enable the model.
customizerConfig.models."meta/llama-3.2-1b".max_seq_length	int	4096	Maximum sequence length for input tokens.
customizerConfig.models."meta/llama-3.2-1b".micro_batch_size	int	1	Micro batch size for training.
customizerConfig.models."meta/llama-3.2-1b".model_path	string	"llama32_1b"	Path where model files are stored.
customizerConfig.models."meta/llama-3.2-1b".model_uri	string	"ngc://nvidia/nemo/llama-3_2-1b:2.0"	NGC model URI for Llama 3.2 1B model.
customizerConfig.models."meta/llama-3.2-1b".num_parameters	int	1000000000	Number of model parameters.
customizerConfig.models."meta/llama-3.2-1b".precision	string	"bf16-mixed"	Model precision format.
customizerConfig.models."meta/llama-3.2-1b".prompt_template	string	"{prompt} {completion}"	Template for formatting prompts.
customizerConfig.models."meta/llama-3.2-1b".training_options	list	[{"finetuning_type":"lora","num_gpus":1,"num_nodes":1,"tensor_parallel_size":1,"training_type":"sft"},{"finetuning_type":"all_weights","num_gpus":1,"num_nodes":1,"tensor_parallel_size":1,"training_type":"sft"}]	Training options for different fine-tuning methods.
customizerConfig.models."meta/llama-3.2-1b-instruct"	object	This object has the following default values for the Llama 3.2 1B Instruct model.	Llama 3.2 1B Instruct model configuration.
customizerConfig.models."meta/llama-3.2-1b-instruct".enabled	bool	false	Whether to enable the model.
customizerConfig.models."meta/llama-3.2-1b-instruct".max_seq_length	int	4096	Maximum sequence length for input tokens.
customizerConfig.models."meta/llama-3.2-1b-instruct".micro_batch_size	int	1	Micro batch size for training.
customizerConfig.models."meta/llama-3.2-1b-instruct".model_path	string	"llama32_1b-instruct"	Path where model files are stored.
customizerConfig.models."meta/llama-3.2-1b-instruct".model_uri	string	"ngc://nvidia/nemo/llama-3_2-1b-instruct:2.0"	NGC model URI for Llama 3.2 1B Instruct model.
customizerConfig.models."meta/llama-3.2-1b-instruct".num_parameters	int	1000000000	Number of model parameters.
customizerConfig.models."meta/llama-3.2-1b-instruct".precision	string	"bf16-mixed"	Model precision format.
customizerConfig.models."meta/llama-3.2-1b-instruct".prompt_template	string	"{prompt} {completion}"	Template for formatting prompts.
customizerConfig.models."meta/llama-3.2-1b-instruct".training_options	list	[{"finetuning_type":"lora","num_gpus":1,"num_nodes":1,"tensor_parallel_size":1,"training_type":"sft"},{"finetuning_type":"all_weights","num_gpus":1,"num_nodes":1,"tensor_parallel_size":1,"training_type":"sft"}]	Training options for different fine-tuning methods.
customizerConfig.models."meta/llama-3.2-3b-instruct"	object	This object has the following default values for the Llama 3.2 3B Instruct model.	Llama 3.2 3B Instruct model configuration.
customizerConfig.models."meta/llama-3.2-3b-instruct".enabled	bool	false	Whether to enable the model.
customizerConfig.models."meta/llama-3.2-3b-instruct".max_seq_length	int	4096	Maximum sequence length for input tokens.
customizerConfig.models."meta/llama-3.2-3b-instruct".micro_batch_size	int	1	Micro batch size for training.
customizerConfig.models."meta/llama-3.2-3b-instruct".model_path	string	"llama32_3b-instruct"	Path where model files are stored.
customizerConfig.models."meta/llama-3.2-3b-instruct".model_uri	string	"ngc://nvidia/nemo/llama-3_2-3b-instruct:2.0"	NGC model URI.
customizerConfig.models."meta/llama-3.2-3b-instruct".num_parameters	int	3000000000	Number of model parameters.
customizerConfig.models."meta/llama-3.2-3b-instruct".precision	string	"bf16-mixed"	Model precision format.
customizerConfig.models."meta/llama-3.2-3b-instruct".prompt_template	string	"{prompt} {completion}"	Template for formatting prompts.
customizerConfig.models."meta/llama-3.2-3b-instruct".training_options	list	[{"finetuning_type":"lora","num_gpus":1,"num_nodes":1,"tensor_parallel_size":1,"training_type":"sft"}]	Training options for different fine-tuning methods.
customizerConfig.models."meta/llama-3.3-70b-instruct"	object	This object has the following default values for the Llama 3.3 70B Instruct model.	Llama 3.3 70B Instruct model configuration.
customizerConfig.models."meta/llama-3.3-70b-instruct".enabled	bool	false	Whether to enable the model.
customizerConfig.models."meta/llama-3.3-70b-instruct".max_seq_length	int	4096	Maximum sequence length for input tokens.
customizerConfig.models."meta/llama-3.3-70b-instruct".micro_batch_size	int	1	Micro batch size for training.
customizerConfig.models."meta/llama-3.3-70b-instruct".model_path	string	"llama-3_3-70b-instruct_0_0_1"	Path where model files are stored.
customizerConfig.models."meta/llama-3.3-70b-instruct".model_uri	string	"ngc://nvidia/nemo/llama-3_3-70b-instruct:2.0"	NGC model URI for Llama 3.3 70B Instruct model.
customizerConfig.models."meta/llama-3.3-70b-instruct".num_parameters	int	70000000000	Number of model parameters.
customizerConfig.models."meta/llama-3.3-70b-instruct".precision	string	"bf16-mixed"	Model precision format.
customizerConfig.models."meta/llama-3.3-70b-instruct".prompt_template	string	"{prompt} {completion}"	Template for formatting prompts.
customizerConfig.models."meta/llama-3.3-70b-instruct".training_options	list	[{"finetuning_type":"lora","num_gpus":4,"num_nodes":1,"tensor_parallel_size":4,"training_type":"sft"}]	Training options for different fine-tuning methods.
customizerConfig.models.meta/llama3-70b-instruct	object	This object has the following default values for the Llama 3 70B Instruct model.	Llama 3 70B Instruct model configuration.
customizerConfig.models.meta/llama3-70b-instruct.enabled	bool	false	Whether to enable the model.
customizerConfig.models.meta/llama3-70b-instruct.max_seq_length	int	4096	Maximum sequence length for input tokens.
customizerConfig.models.meta/llama3-70b-instruct.micro_batch_size	int	1	Micro batch size for training.
customizerConfig.models.meta/llama3-70b-instruct.model_path	string	"llama-3-70b-bf16"	Path where model files are stored.
customizerConfig.models.meta/llama3-70b-instruct.model_uri	string	"ngc://nvidia/nemo/llama-3-70b-instruct-nemo:2.0"	NGC model URI for Llama 3 70B Instruct model.
customizerConfig.models.meta/llama3-70b-instruct.num_parameters	int	70000000000	Number of model parameters.
customizerConfig.models.meta/llama3-70b-instruct.precision	string	"bf16-mixed"	Model precision format.
customizerConfig.models.meta/llama3-70b-instruct.prompt_template	string	"{prompt} {completion}"	Template for formatting prompts.
customizerConfig.models.meta/llama3-70b-instruct.training_options	list	[{"finetuning_type":"lora","num_gpus":4,"num_nodes":1,"tensor_parallel_size":4,"training_type":"sft"}]	Training options for different fine-tuning methods.
customizerConfig.models.microsoft/phi-4	object	This object has the following default values for the Phi-4.	Phi-4 model configuration.
customizerConfig.models.microsoft/phi-4.enabled	bool	false	Whether to enable the model.
customizerConfig.models.microsoft/phi-4.max_seq_length	int	4096	Maximum sequence length for input tokens.
customizerConfig.models.microsoft/phi-4.micro_batch_size	int	1	Micro batch size for training.
customizerConfig.models.microsoft/phi-4.model_path	string	"phi-4"	Path where model files are stored.
customizerConfig.models.microsoft/phi-4.model_uri	string	"ngc://nvidia/nemo/phi-4:1.0"	NGC model URI for Phi-4 model.
customizerConfig.models.microsoft/phi-4.num_parameters	int	14659507200	Number of model parameters.
customizerConfig.models.microsoft/phi-4.precision	string	"bf16"	Model precision format.
customizerConfig.models.microsoft/phi-4.prompt_template	string	"{prompt} {completion}"	Template for formatting prompts.
customizerConfig.models.microsoft/phi-4.training_options	list	[{"finetuning_type":"lora","num_gpus":1,"num_nodes":1,"training_type":"sft"},{"finetuning_type":"all_weights","num_gpus":4,"num_nodes":2,"tensor_parallel_size":8,"training_type":"sft"}]	Training options for different fine-tuning methods.
customizerConfig.nemoDataStoreURL	string	"http://nds-datastore-http.nds-v2.svc.cluster.local:3000"	URL for the NeMo Data Store microservice.
customizerConfig.openTelemetry	object	This object has the following default values for the OpenTelemetry settings.	OpenTelemetry settings.
customizerConfig.openTelemetry.enabled	bool	true	Whether to enable OpenTelemetry.
customizerConfig.openTelemetry.exporterOtlpEndpoint	string	""	Endpoint to access a custom OTLP collector listening on port 4317. Example: "http://$(HOST_IP):4317".
customizerConfig.openTelemetry.logsExporter	string	"otlp"	Sets the logs exporter type (otlp, console, none).
customizerConfig.openTelemetry.metricsExporter	string	"otlp"	Sets the metrics exporter type (otlp, console, none).
customizerConfig.openTelemetry.tracesExporter	string	"otlp"	Sets the traces exporter type (otlp, console, none).
customizerConfig.tolerations	list	[]	Tolerations on the customization job pods.
customizerConfig.training	object	This object has the following default values for the training configuration.	Training configuration for customization jobs.
customizerConfig.training.container_defaults	object	{"env":null,"imagePullPolicy":"IfNotPresent"}	Default container configuration for training jobs.
customizerConfig.training.container_defaults.env	string	nil	Environment variables for the training container. Cannot override env variables reserved by NeMo Customizer.
customizerConfig.training.pvc.size	string	"5Gi"	Size of the training job PVC.
customizerConfig.training.pvc.storageClass	string	"local-nfs"	Storage class for the training job PVC.
customizerConfig.training.pvc.volumeAccessMode	string	"ReadWriteMany"	Volume access mode for the training job PVC.
customizerConfig.training.queue	string	"default"	Queue name used by the underlying scheduler of NemoTrainingJob. Maps to "resourceGroup" in NemoTrainingJob.
customizerConfig.training.workspace_dir	string	"/pvc/workspace"	Directory path for training workspace.
customizerConfig.trainingNetworking	list	[{"name":"NCCL_IB_SL","value":0},{"name":"NCCL_IB_TC","value":41},{"name":"NCCL_IB_QPS_PER_CONNECTION","value":4},{"name":"UCX_TLS","value":"TCP"},{"name":"UCX_NET_DEVICES","value":"eth0"},{"name":"HCOLL_ENABLE_MCAST_ALL","value":0},{"name":"NCCL_IB_GID_INDEX","value":3}]	Network configuration for training jobs on Oracle Kubernetes Engine (OKE) on Oracle Cloud Infrastructure (OCI).
customizerConfig.wandb	object	{"entity":null,"project":"nvidia-nemo-customizer"}	Weights and Biases (WandB) Python SDK intialization configuration for logging and monitoring training jobs in WandB.
customizerConfig.wandb.entity	string	nil	Reference: https://docs.wandb.ai/ref/python/init/
env	object	{}	A map of environment variables to inject into the NeMo Customizer app container. Example: {HOST_IP: valueFrom: fieldRef: fieldPath: status.hostIP OTEL_EXPORTER_OTLP_ENDPOINT: "http://$(HOST_IP):4317"}
externalDatabase	object	This object has the following default values for the external PostgreSQL configuration.	External PostgreSQL configuration.
externalDatabase.database	string	"finetuning"	Name of the database to use.
externalDatabase.existingSecret	string	""	Name of an existing secret resource containing the database credentials.
externalDatabase.existingSecretPasswordKey	string	""	Name of an existing secret key containing the database credentials.
externalDatabase.host	string	"localhost"	External database host address.
externalDatabase.password	string	"nemo"	Password for the non-root username for the NeMo Customizer microservice.
externalDatabase.port	int	5432	External database port number.
externalDatabase.user	string	"nemo"	Non-root username for the NeMo Customizer microservice.
fullnameOverride	string	""	String to fully override the chart and release name on resulting objects when deployed.
image	object	This object has the following default values for the NeMo Customizer microservice image.	NeMo Customizer image that supports training and standalone mode.
image.imagePullPolicy	string	"IfNotPresent"	Image pull policy for the NeMo Customizer image.
image.registry	string	"nvcr.io"	Registry for the NeMo Customizer image.
image.repository	string	"nvidia/nemo-microservices/customizer"	Repository for the NeMo Customizer image.
imagePullSecrets	list	[{"name":"nvcrimagepullsecret"}]	Image pull secrets configuration.
ingress	object	This object has the following default values for the Ingress configuration.	Ingress configuration.
ingress.annotations	object	{}	Additional annotations for the Ingress resource.
ingress.className	string	""	Ingress class name.
ingress.enabled	bool	false	Whether to enable the ingress resource.
ingress.hostname	string	""	Hostname for the ingress resource.
ingress.hosts	list	[]	A list of maps, each containing the keys host and paths for the ingress resource. You must specify a list for configuring ingress for the microservice.
ingress.tls	list	[]	TLS configuration for the ingress resource.
logging	object	{"logHealthEndpoints":false,"logLevel":"INFO"}	Logging configuration.
logging.logHealthEndpoints	bool	false	Enable logging for health endpoints.
logging.logLevel	string	"INFO"	Log level for the application.
modelDownloader	object	This object has the following default values for the model downloader.	Download models to PVC model cache configuration.
modelDownloader.pollIntervalSeconds	int	15	Interval in seconds to poll for model download status.
modelDownloader.securityContext	object	{"fsGroup":1000,"runAsGroup":1000,"runAsNonRoot":true,"runAsUser":1000}	Security context for the model downloader.
modelDownloader.ttlSecondsAfterFinished	int	7200	Time to live in seconds after the job finishes.
modelsStorage	object	{"accessModes":["ReadWriteMany"],"enabled":true,"size":"1Ti","storageClassName":""}	Configure the PVC for models mount, where we store the parent/base models.
modelsStorage.accessModes	list	["ReadWriteMany"]	Access modes for the persistent volume.
modelsStorage.enabled	bool	true	Enable persistent volume for model storage.
modelsStorage.size	string	"1Ti"	Size of the persistent volume.
modelsStorage.storageClassName	string	""	Storage class name for the models PVC. Empty string uses the default storage class.
nameOverride	string	""	String to override chart name on resulting objects when deployed.
nemoDataStoreTools	object	This object has the following default values for the NeMo Data Store tools image.	Tools configuration for downloading and uploading entities to NeMo Data Store.
nemoDataStoreTools.imagePullSecret	string	"nvcrimagepullsecret"	Image pull secret for the NeMo Data Store tools image.
nemoDataStoreTools.registry	string	"nvcr.io"	Registry for the NeMo Data Store tools image.
nemoDataStoreTools.repository	string	"nvidia/nemo-microservices/nds-v2-huggingface-cli"	Repository for the NeMo Data Store tools image.
nemoDataStoreTools.tag	string	""	Tag for the NeMo Data Store tools image.
ngcAPISecret	string	"ngc-api"	Secret used for auto hydrating the model cache from NGC for enabled models.
ngcAPISecretKey	string	"NGC_API_KEY"	Key in the NGC API secret containing the API key.
opentelemetry-collector	object	This object has the following default values for the Open Telemetry Collector configuration.	Open Telemetry Collector configuration.
opentelemetry-collector.config	object	{"exporters":{"debug":{"verbosity":"detailed"}},"extensions":{"health_check":{},"zpages":{"endpoint":"0.0.0.0:55679"}},"processors":{"batch":{}},"receivers":{"otlp":{"protocols":{"grpc":{},"http":{"cors":{"allowed_origins":["*"]}}}}},"service":{"extensions":["zpages","health_check"],"pipelines":{"logs":{"exporters":["debug"],"processors":["batch"],"receivers":["otlp"]},"metrics":{"exporters":["debug"],"processors":["batch"],"receivers":["otlp"]},"traces":{"exporters":["debug"],"processors":["batch"],"receivers":["otlp"]}}}}	Base collector configuration for Open Telemetry Collector.
opentelemetry-collector.enabled	bool	true	Switch to enable or disable Open Telemetry Collector.
opentelemetry-collector.image.repository	string	"otel/opentelemetry-collector-k8s"	Repository for Open Telemetry Collector image.
opentelemetry-collector.image.tag	string	"0.102.1"	Overrides the image tag whose default is the chart appVersion.
opentelemetry-collector.mode	string	"deployment"	Deployment mode for Open Telemetry Collector. Valid values are "daemonset", "deployment", and "statefulset".
postgresql	object	This object has the following default values for the PostgreSQL configuration.	PostgreSQL configuration for the NeMo Customizer microservice.
postgresql.architecture	string	"standalone"	PostgreSQL architecture (standalone or replication).
postgresql.auth.database	string	"finetuning"	Name for a custom database to create.
postgresql.auth.enablePostgresUser	bool	true	Whether to assign a password to the "postgres" admin user. Otherwise, remote access will be blocked for this user.
postgresql.auth.existingSecret	string	""	Name of existing secret to use for PostgreSQL credentials.
postgresql.auth.password	string	"nemo"	Password for the custom user to create.
postgresql.auth.username	string	"nemo"	Name for a custom user to create.
postgresql.enabled	bool	true	Whether to enable or disable the PostgreSQL helm chart.
replicaCount	int	1	Number of replicas to deploy.
service	object	{"internalPort":9009,"port":8000,"type":"ClusterIP"}	Service configuration.
service.internalPort	int	9009	Internal port for the service.
service.port	int	8000	External port for the service.
service.type	string	"ClusterIP"	Type of Kubernetes service to create.
serviceAccount	object	{"annotations":{},"automountServiceAccountToken":true,"create":true,"name":""}	Service account configuration.
serviceAccount.annotations	object	{}	Annotations to add to the service account.
serviceAccount.automountServiceAccountToken	bool	true	Automatically mount a ServiceAccount's API credentials.
serviceAccount.create	bool	true	Specifies whether a service account should be created.
serviceAccount.name	string	""	The name of the service account to use. If not set and create is true, a name is generated.
useRunAIExecutor	bool	false	Enable or disable RunAI executor.
wandb	object	This object has the following default values for the WandB configuration.	WandB configuration.
wandb.existingSecret	string	""	Name of an existing Kubernetes secret resource for the WandB encryption secret.
wandb.existingSecretKey	string	""	Name of the key in the existing WandB secret containing the secret value. The secret value must be exactly 32 alphanumeric characters: ^[a-zA-Z0-9]{32}$
wandb.secretValue	string	"ec60d96b639764ccf9859bc10d4363d1"	WandB secret value. Must contain exactly 32 alphanumeric characters. Creates a new Kubernetes secret named "wandb-secret" with key-value pair "encryption_key=<wandb.secretValue>". Ignored if wandb.existingSecret is set.