@fastino-ai/pioneer-cli
v0.3.0
Published
Pioneer CLI - AI training platform client (auth, datasets, jobs, models)
Readme
Pioneer CLI
Command-line interface for the Pioneer AI training platform.
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╚═╝ ╚═╝ ╚═════╝ ╚═╝ ╚═══╝╚══════╝╚══════╝╚═╝ ╚═╝What's new in 0.3.0 (security release)
The pioneer agent interactive chat command and its WebSocket runtime have
been removed in this release for security reasons. The agent shipped with
local tool execution (shell, filesystem write/read, URL fetching) and a sandbox
surface that we are no longer comfortable shipping in a CLI binary.
If you need conversational workflows for training jobs, datasets, or model deployment, use the Pioneer web app at https://app.pioneer.ai/ instead. All non-agent commands in this CLI (auth, dataset, job, model, telemetry) continue to work unchanged.
Running pioneer agent (or any agent subcommand) now prints a clear
deprecation message and exits.
Installation
npm
npm install -g @fastino-ai/pioneer-cli
Manual Install (requires Bun)
# Install Bun if needed
curl -fsSL https://bun.sh/install | bash
# Clone and install
git clone https://github.com/fastino-ai/pioneer-cli.git
cd pioneer-cli/
bun install
# Run directly
bun run src/index.tsx --helpQuick Install (coming soon)
curl -fsSL https://pioneer.ai/install.sh | shUsage
# Show help
pioneer --help
# Authentication
pioneer auth login # Enter API key interactively
pioneer auth logout # Clear stored API key
pioneer auth status # Check if logged in
# Training Jobs
pioneer job list
pioneer job get <id>
pioneer job logs <id>
pioneer job delete <id>
# To create a training job, use the Pioneer web app at https://app.pioneer.ai/.
# Models
pioneer model base-models # List available base models (tabular)
pioneer model endpoints list # List model catalog entries
pioneer model endpoints create # Create a model entry (interactive by default)
pioneer model endpoints create --model "qwen/Qwen3-8B-Instruct" --repo https://github.com/fastino-ai/Pioneer
pioneer model endpoints get <model-id> # Endpoint details (incl. attached dataset count)
pioneer model endpoints update <model-id> --description "Updated model metadata"
pioneer model endpoints delete <model-id>
pioneer model endpoints quality-metrics <model-id>
pioneer model endpoints deploy <model-id> [--job <training-job-id>] [--reason "..." ] [--all]
# If --job is omitted, pick from a list of deployable jobs interactively.
# The list is filtered to jobs whose base model matches the endpoint; pass --all to bypass that filter.
pioneer model endpoints rollback <model-id> <deployment-id>
pioneer model artifacts list # List trained and deployed artifacts
pioneer model artifacts trained # List trained artifacts
pioneer model artifacts deployed # List deployed artifacts
pioneer model artifacts download <job-id> # Get a signed download URL for the artifact
pioneer model artifacts upload <job-id> --to hf --repo username/model # Push trained artifact to HF
# Datasets
pioneer dataset list # List remote + local datasets (tabular)
pioneer dataset get <name[:version]> # Dataset details (key/value layout)
# Most read commands also accept --json for the raw JSON payload.
# Aliases
pioneer model_endpoints list
pioneer model_artifacts listEnd-to-End Example
Step 1: Create a Training Job
Use the Pioneer web app at https://app.pioneer.ai/ to create a training job. The web app will guide you through picking a base model, selecting datasets, and configuring hyperparameters.
Step 2: Monitor Training Progress
# List all training jobs
pioneer job list
# Check job status
pioneer job get <job-id>
# View training logs
pioneer job logs <job-id>Step 3: Use Your Trained Model
Once training completes:
# List trained models
pioneer model artifacts trained
# Download the trained model
pioneer model artifacts download <job-id>Configuration
The CLI stores configuration in ~/.pioneer/config.json.
Configuration File
The CLI stores your API key in ~/.pioneer/config.json after running pioneer auth login.
{
"apiKey": "your-api-key",
"hfToken": "your-hf-token"
}Authentication
Pioneer API
To use the CLI, you'll need a Pioneer API key. Get one at https://app.pioneer.ai/api-keys.
By default, the CLI points at production (https://api.pioneer.ai).
For dev/staging and local debugging, set PIONEER_API_URL explicitly (for example: https://api-dev.pioneer.ai or http://localhost:5001).
# Login with your API key
pioneer auth login
# Check authentication status
pioneer auth status
# Logout (clears saved key)
pioneer auth logoutHugging Face (Optional)
For uploading model artifacts to Hugging Face, you'll need an HF token. Get one at https://huggingface.co/settings/tokens .
# Set your Hugging Face token
pioneer auth hf
# Check token status
pioneer auth hf status
# Clear token
pioneer auth hf logoutNote: Hugging Face-backed dataset import/export is currently behind the hidden dataset workflow, so the main recommended HF flow today is model artifact upload via model artifacts upload --to hf.
Development & Testing
Building & Running
cd pioneer-cli
bun install
bun run dev # Hot reload
bun run typecheck # Type checkingRelease
Before publishing, bump package.json to a version that is not already on npm:
npm version patch --no-git-tag-versionThen verify the npm package tarball locally:
bun run release:check-version
bun run release:packrelease:pack writes the verified package to release-artifacts/*.tgz and checks that the tarball contains only:
README.md
dist/index.js
dist/yoga.wasm
package.jsonTo publish, run the Publish Pioneer CLI npm Package GitHub Actions workflow from main. Leave dry_run=true to build and upload the tarball as a GitHub Actions artifact without publishing. Set dry_run=false to publish the verified tarball to npm and attach the same .tgz to a vX.Y.Z GitHub Release.
Tech Stack
License
MIT