reactor model register

reactor model register [flags]

Description

Register a new AI model. The model is private by default. Provide a model name with --name, or supply a model spec file via --model-file (YAML or JSON). When neither is given, the CLI auto-detects a reactor.yaml by walking up from the current directory (stopping at the enclosing repository root) and loads it as if it had been passed via --model-file. With neither flag set and no enclosing reactor.yaml to find, the existing required-flag error fires. Precedence (highest to lowest):

CLI flags (--name, --description, --gpu, …) explicitly set on the command line ALWAYS win, even when --model-file is also supplied.
--model-file values (whether passed explicitly or auto-detected) fill in fields not set on the command line.

Model names are lowercase, ECR-safe identifiers. Use a short per-account name such as vision-model, or a canonical org/name identifier when the org is your account slug. Accelerators: Models that need accelerator hardware (NVIDIA / AMD GPUs, AWS Trainium / Inferentia) declare the device with --gpu plus --gpu-type. The flag name is historical; --gpu-type also accepts the Neuron families (AWS_TRAINIUM_2/3, AWS_INF_2) and the model is scheduled onto the right hardware automatically. For Trainium/Inferentia, --gpu-type alone is enough - the CLI infers --gpu so partners don’t have to type a misleading flag. Examples:

reactor model register                                        # auto-detect enclosing reactor.yaml
reactor model register --name vision-model
reactor model register --name vendor/awesome-model --gpu --gpu-type NVIDIA_H100
reactor model register --name vendor/rolling-model --gpu-type AWS_TRAINIUM_2
reactor model register --model-file model.yaml
reactor model register --model-file model.json --name override-name

Example model.yaml:

model:
  name: my-model              # required
  description: |
    Free-form description.
  public: false
  resources:
    gpu:
      # GPU types: NVIDIA_H100, NVIDIA_B200, AMD_MI300X, ...
      # Neuron types: AWS_TRAINIUM_2, AWS_TRAINIUM_3, AWS_INF_2
      type: NVIDIA_H100
      count: 1
    cpu:
      request: 500m
      limit: "2"
    memory:
      request: 4Gi
      limit: 32Gi
  extra-args:
    - --batch-size
    - "4"

Options

Flag	Description
`--description string`	Model description
`--extra-arg stringArray`	Extra CLI argument for the model container (repeatable)
`--gpu`	Model requires an accelerator (GPU, Trainium, or Inferentia)
`--gpu-count int`	Number of accelerators per instance (requires `--gpu` or a Neuron `--gpu-type`; defaults to 1)
`--gpu-type string`	Accelerator type (NVIDIA_/AMD_/AWS_TRAINIUM_/AWS_INF_); implies `--gpu` for Neuron types
`--model-file string`	Path to a YAML or JSON file describing the model
`--name string`	Model name: <name> or <org>/<name> (lowercase letters, digits, ’.’, ’_’, ’-‘)
`--public`	Make the model publicly accessible

Global options

Flag	Description
`--server string`	Reactor API server URL (default “https://api.reactor.inc”)
`-v, --verbose`	Enable verbose output

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reactor model register

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