gcube-cli 0.1.2

Official command-line tool for the gcube AI GPU cloud platform

gcube CLI

Official command-line tool for the gcube AI GPU cloud platform. Manage GPU workloads, monitor resources, and stream container logs — all from your terminal.

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Overview

gcube <service> <operation> [options]

Feature	Description
Easy install	pip install gcube-cli, Python 3.10+
AWS CLI style	gcube <service> <operation> with --output table|json|yaml
Token auth	Set your token from the gcube web console once, then use all commands
Key dependencies	click · httpx · rich · pyyaml · websockets

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Installation

pip install gcube-cli

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Quick Start

# 1. Set your API token (copy from gcube web console)
gcube configure set --token "eyJ..."

# 2. Register a GPU workload
gcube workload register -f workload.yaml

# 3. Check status
gcube workload list

# 4. Stream logs
gcube workload logs <ser>

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Configuration

Get a Token

1. Log in to the gcube web console
2. Go to API Token menu and copy your token
3. Set it in the CLI:

gcube configure set --token "eyJ..."

Config File

Generated automatically at ~/.gcube/config.yaml:

platform_url: https://api.gcube.ai
ws_url: wss://console.gcube.ai:61443
auth:
  access_token: "eyJ..."
  expires_at: "2026-04-03T10:00:00Z"
output: table          # table | json | yaml

Configure Commands

gcube configure                              # Interactive setup
gcube configure set --token <token>          # Set API token
gcube configure set --platform-url <url>     # Set platform URL
gcube configure set --ws-url <url>           # Set WebSocket URL (for log streaming)
gcube configure set --output <format>        # Set default output format
gcube configure get <key>                    # Get a config value (token, platform-url, ws-url, output)
gcube configure status                       # Show current config and token validity

Environment Variables

Environment variables take precedence over the config file.

Variable	Description
GCUBE_PLATFORM_URL	Platform base URL
GCUBE_ACCESS_TOKEN	Bearer token (useful for CI/CD)
GCUBE_OUTPUT	Default output format (table|json|yaml)

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Command Reference

Workload

Manage the full lifecycle of GPU workloads.

Command	Description
gcube workload register -f <yaml>	Register a workload from a YAML file
gcube workload register --image <img> ...	Register a workload with inline flags
gcube workload update <ser> -f <yaml>	Update a stopped workload
gcube workload list	List workloads
gcube workload describe <ser>	Show workload details
gcube workload start <ser>	Start a workload
gcube workload stop <ser>	Stop a workload
gcube workload delete <ser>	Delete a workload
gcube workload logs <ser>	Stream container logs in real time
gcube workload logs <ser> --pod <idx> --container <idx>	Stream a specific container's logs
gcube workload pods <ser>	List pods

Key Flags

Flag	Description
-f, --file <path>	Workload YAML file path
--image <image>	Container image
--gpu <code>	GPU code (from gcube gpu list CODE column)
--cuda <version>	CUDA version code
--category <type>	Workload type: infer|learn
--owner <email>	Filter by owner (list only)
--pod <idx>	Pod index for log streaming (0-based)
--container <idx>	Container index for log streaming (0-based)
-y, --yes	Skip confirmation prompt (stop/delete)

CUDA Version Codes (format: major×1000 + minor×10)

Code	CUDA
12000	12.0
12020	12.2
12030	12.3
12040	12.4
12050	12.5
12060	12.6
12080	12.8
12090	12.9
13000	13.0

Workload YAML Format (for register -f and update -f)

# workload.yaml
description: "My ML training job"  # required, 2-80 chars
cuda: "12020"                      # optional CUDA version code
sharedMemory: 1                    # GB

containers:
  - containerImage: "pytorch/pytorch:2.0"  # required
    repo: docker.io
    port: 0                        # 0 = auto-detect
    maxConnection: 4
    containerCommand: "python train.py"
    isCredential: false
    containerEnvs:
      - EPOCHS: "100"
      - BATCH_SIZE: "32"

gpuSpecs:
  - gpuCode: "029"                 # required — CODE from 'gcube gpu list'
  # add more entries for multiple replicas:
  # - gpuCode: "029"

Run gcube workload register --skeleton to print a blank template. Run gcube workload update <ser> --skeleton > workload.yaml to export the current config, then fill in gpuCode and pass it with -f.

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GPU

List available GPUs with specs and pricing.

Command	Description
gcube gpu list	Available GPUs (CODE, specs, hourly price range)
gcube gpu list --all	All GPUs including unavailable ones

The CODE column is a zero-padded sequence number (001, 002, ...) used when registering workloads.

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Resource

Monitor CPU, GPU, and memory usage of a workload.

Command	Description
gcube resource workload <ser>	Workload resource usage (time average)

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Point

Check point balance and spending history.

Command	Description
gcube point status	Balance, total charged/spent, low-balance warning
gcube point spending	Daily spending for the current month
gcube point spending --month 2026-03	Daily spending for a specific month
gcube point spending --workload <ser>	Filter by workload

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Credential

Manage container registry credentials.

Command	Description
gcube credential list	List saved credentials
gcube credential create --repo <type> --username <name> --token <token>	Save a credential
gcube credential delete --repo <type>	Delete a credential

Supported --repo types: docker, github, harbor, aws, huggingface, quay

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Output Formats

gcube workload list              # table (default)
gcube -o json workload list      # JSON
gcube -o yaml workload list      # YAML

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Global Options

Flag	Description	Default
-o, --output <format>	Output format: table|json|yaml	table
-V, --version	Show version	—
--help	Show help	—

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Exit Codes

Code	Meaning
0	Success
1	Invalid argument or state error
2	gcube API error
3	Authentication failure or token expired
4	Network error

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Examples

Register and Manage Workloads

# Check available GPUs (note the CODE column)
gcube gpu list

# Register from a YAML file
gcube workload register -f workload.yaml

# Register with inline flags
gcube workload register \
  --description "inference service" \
  --image ollama/ollama:latest \
  --gpu 029

# List and inspect
gcube workload list
gcube workload describe 2212
gcube -o json workload describe 2212

# Start / stop / delete
gcube workload start 2212
gcube workload stop 2212
gcube workload delete 2212

Stream Logs

# Single container — streams immediately
gcube workload logs 2212

# Multi-container — shows selection list first
gcube workload logs 2226
gcube workload logs 2226 --pod 0 --container 1

Resource Monitoring

gcube resource workload 2212
gcube -o json resource workload 2212

CI/CD Pipeline

export GCUBE_ACCESS_TOKEN="eyJ..."
export GCUBE_OUTPUT=json

gcube workload register -f pipeline.yaml
gcube workload describe 2212 | jq '.state'

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License

Apache License 2.0