Command Line Reference

This document provides a comprehensive reference for all command line interfaces in TensorFusion.

Operator & Scheduler CLI

CLI Parameters

Parameter	Description	Default
-enable-http2	Enables HTTP/2 for the metrics and webhook servers	-
-health-probe-bind-address	The address the probe endpoint binds to	:8081
-kubeconfig	Path to a kubeconfig file (only required if out-of-cluster)	-
-leader-elect	Enable leader election for controller manager to ensure only one active instance	-
-metrics-bind-address	The address the metrics endpoint binds to	0 (disabled)
-metrics-secure	Serve metrics endpoint securely via HTTPS (use --metrics-secure=false for HTTP)	-
-zap-devel	Use development mode for logging	true
-zap-encoder	Zap log encoding format (json or console)	-
-zap-log-level	Verbosity level for logging (debug, info, error, or any integer value > 0)	-
-zap-stacktrace-level	Level at which stacktraces are captured (info, error, panic)	-
-zap-time-encoding	Time encoding format (epoch, millis, nano, iso8601, rfc3339, rfc3339nano)	epoch

Environment Variables

Variable	Description	Example
INITIAL_GPU_NODE_LABEL_SELECTOR	Initial label selector for GPU nodes	nvidia.com/gpu.present=true
ENABLE_WEBHOOKS	Enable webhook functionality	true
OPERATOR_NAMESPACE	Namespace for the operator	tensor-fusion-sys
KUBECONFIG	Path to kubeconfig file	<kubeconfig>

Hypervisor CLI

CLI Parameters

Parameter	Description	Default
--sock_path	Worker unix socket path	/tensor-fusion/worker/sock
--gpu_metrics_file	GPU metrics file location	/logs/metrics.log
--scheduler	Scheduling policy for multiple processes on single GPU node (when GPU load is high)	Options: FIFO for simple first-in-first-out, MLFQ for multi-level feedback queue

Node Discovery CLI

CLI Parameters

Parameter	Description	Example
--hostname	Custom hostname for binding current node with GPUNode custom resource	<hostname>
--gpu-info-config	Path to the GPU info configuration file	See example below

GPU Info Config Example

- model: RTX5090
  fullModelName: "NVIDIA GeForce RTX 5090"
  vendor: NVIDIA
  costPerHour: 0.65
  fp16TFlops: 419

Environment Variables

Variable	Description	Example
HOSTNAME	Node hostname	<hostname>
KUBECONFIG	Path to kubeconfig file	<kubeconfig>
NODE_DISCOVERY_REPORT_GPU_NODE	GPU node custom resource name	<gpu-node-custom-resource-name>

Worker CLI

CLI Parameters

Parameter	Description	Default/Notes
-n	Network protocol	Currently only native (native TCP communication)
-p	Worker port	Random value assigned by TensorFusion Operator-Scheduler
-s	Unix socket path folder	Should be /tensor-fusion/worker/sock/ in Kubernetes

Environment Variables

Variable	Description	Value
TF_ENABLE_LOG	Enable logging	1

GPU Client Stub

The GPU Client Stub consists of two libraries that use LD_PRELOAD to run before every process started inside the container or server:

• libadd_path.so: Adds additional library paths for AI application environments (e.g., hooked NVML)
• libcuda.so: Hooks into CUDA runtime

Example configuration in worker template:

env:
- name: LD_PRELOAD
  value: /tensor-fusion/libadd_path.so:/tensor-fusion/libcuda.so

Environment Variables

Variable	Description	Value/Notes
TF_PATH	Appended to PATH environment variable	/tensor-fusion
TF_LD_PRELOAD	Appended to LD_PRELOAD	Varies
TF_LD_LIBRARY_PATH	Appended to LD_LIBRARY_PATH	/tensor-fusion
TF_ENABLE_LOG	Disable/Enable logging, default to disabled	0