CLI for Middleman GPU compute platform
Project description
Middleman CLI
Command-line interface for the Middleman GPU compute platform.
Installation
pip install middleman-cli
Quick Start
# Authenticate with your API key
middleman login
# Submit a training job
middleman run train.py --gpu a100
# Check job status
middleman jobs list
middleman jobs status <job-id>
# View logs
middleman jobs logs <job-id> --follow
# Check credit balance
middleman credits
Commands
Authentication
# Login with API key
middleman login
# Show current user
middleman whoami
# Logout
middleman logout
Running Jobs
# Basic job submission
middleman run train.py --gpu t4
# Advanced options
middleman run train.py \
--gpu a100 \
--name "my-experiment" \
--framework pytorch \
--max-runtime 8 \
--env WANDB_API_KEY=xxx \
--requirements requirements.txt \
--wait
# GPU options: t4, v100, a100, a100-80gb, h100
# Framework options: pytorch, tensorflow, jax, custom
Managing Jobs
# List jobs
middleman jobs list
middleman jobs list --status running
middleman jobs list --all
# Get job status
middleman jobs status <job-id>
middleman jobs status <job-id> --watch
# View logs
middleman jobs logs <job-id>
middleman jobs logs <job-id> --tail 200
middleman jobs logs <job-id> --follow
# Cancel job
middleman jobs cancel <job-id>
Credits
# Show credit balance
middleman credits
Configuration
# Show config
middleman configure --show
# Set custom API URL
middleman configure --api-url https://custom.api.url
# Reset config
middleman configure --reset
Configuration
Configuration is stored in ~/.middleman/config.yaml:
api_key: mdlm_xxxxx
api_url: https://api.middleman.run/api/v1
Environment Variables
MIDDLEMAN_API_KEY- API key (overrides config file)MIDDLEMAN_API_URL- API URL (overrides config file)
Get Your API Key
- Sign up at https://middleman.run
- Go to https://app.middleman.run/dashboard/api-keys
- Create a new API key
- Run
middleman login
Support
- Documentation: https://docs.middleman.run
- Email: support@middleman.run
- Dashboard: https://app.middleman.run
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