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Merge remote-tracking branch 'origin/main' into feature/train-hosted-full-finetune
# Conflicts: # packages/prime/src/prime_cli/api/rl.py # packages/prime/src/prime_cli/commands/rl.py
2 parents 2fe14da + d5d49dc commit 2d15d7a

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‎.codex/environments/environment.toml‎

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# THIS IS AUTOGENERATED. DO NOT EDIT MANUALLY
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version = 1
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name = "prime-cli"
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[setup]
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script = "uv sync"

‎.github/CODEOWNERS‎

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# Default owners for all repository changes.
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* @JannikSt @d42me @kcoopermiller @willccbb @burnpiro @JohannesHa

‎.github/workflows/ci.yml‎

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"packages/prime/src/prime_cli/__init__.py"
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"packages/prime-sandboxes/src/prime_sandboxes/__init__.py"
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"packages/prime-evals/src/prime_evals/__init__.py"
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"packages/prime-mcp-server/src/prime_mcp/__init__.py"
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)
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BUMPED_FILES=""
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ["3.10", "3.11", "3.12"]
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python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
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steps:
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- uses: actions/checkout@v4
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ["3.11", "3.12"]
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python-version: ["3.11", "3.12", "3.13", "3.14"]
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steps:
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- uses: actions/checkout@v4
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‎.github/workflows/release-evals.yml‎

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jobs:
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release:
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runs-on: ubuntu-latest
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environment: pypi-prod
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permissions:
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contents: write
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packages: write
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id-token: write
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steps:
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- uses: actions/checkout@v4
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with:
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run: |
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uv build --out-dir dist
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# Install twine for publishing
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uv venv
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source .venv/bin/activate
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uv pip install twine
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- name: Create tag
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if: steps.check_tag.outputs.exists != 'true'
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run: |
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- name: Publish to PyPI
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if: steps.check_tag.outputs.exists != 'true' || inputs.force_release == 'true'
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working-directory: packages/prime-evals
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
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run: |
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source .venv/bin/activate
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twine upload dist/*
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uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # v1.14.0
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with:
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packages-dir: packages/prime-evals/dist

‎.github/workflows/release-mcp.yml‎

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‎.github/workflows/release-prime.yml‎

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jobs:
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tag-and-release:
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runs-on: ubuntu-latest
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environment: pypi-prod
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permissions:
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contents: write
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packages: write
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id-token: write
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outputs:
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version: ${{ steps.version.outputs.version }}
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release_created: ${{ steps.release_status.outputs.created }}
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run: |
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uv build --out-dir dist
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# Install twine for publishing
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uv venv
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source .venv/bin/activate
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uv pip install twine
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- name: Upload build artifacts
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if: steps.check_tag.outputs.exists != 'true'
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uses: actions/upload-artifact@v4
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files: packages/prime/dist/*
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generate_release_notes: true
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# Publish to PyPI
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# Publish to PyPI via Trusted Publishing (OIDC)
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- name: Publish to PyPI
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if: steps.check_tag.outputs.exists != 'true'
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working-directory: packages/prime
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
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run: |
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source .venv/bin/activate
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twine upload dist/*
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uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # v1.14.0
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with:
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packages-dir: packages/prime/dist
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- name: Notify Slack
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if: steps.check_tag.outputs.exists != 'true'

‎.github/workflows/release-sandboxes.yml‎

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jobs:
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release:
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runs-on: ubuntu-latest
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environment: pypi-prod
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permissions:
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contents: write
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packages: write
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id-token: write
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steps:
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- uses: actions/checkout@v4
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with:
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run: |
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uv build --out-dir dist
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# Install twine for publishing
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uv venv
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source .venv/bin/activate
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uv pip install twine
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- name: Create tag
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if: steps.check_tag.outputs.exists != 'true'
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run: |
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- name: Publish to PyPI
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if: steps.check_tag.outputs.exists != 'true' || inputs.force_release == 'true'
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working-directory: packages/prime-sandboxes
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
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run: |
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source .venv/bin/activate
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twine upload dist/*
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uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # v1.14.0
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with:
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packages-dir: packages/prime-sandboxes/dist

‎.github/workflows/release-tunnel.yml‎

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jobs:
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release:
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runs-on: ubuntu-latest
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environment: pypi-prod
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permissions:
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contents: write
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packages: write
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id-token: write
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steps:
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- uses: actions/checkout@v4
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with:
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run: |
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uv build --out-dir dist
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# Install twine for publishing
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uv venv
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source .venv/bin/activate
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uv pip install twine
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- name: Create tag
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if: steps.check_tag.outputs.exists != 'true'
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run: |
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- name: Publish to PyPI
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if: steps.check_tag.outputs.exists != 'true' || inputs.force_release == 'true'
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working-directory: packages/prime-tunnel
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env:
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TWINE_USERNAME: __token__
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TWINE_PASSWORD: ${{ secrets.PYPI_TOKEN }}
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run: |
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source .venv/bin/activate
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twine upload dist/*
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uses: pypa/gh-action-pypi-publish@cef221092ed1bacb1cc03d23a2d87d1d172e277b # v1.14.0
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with:
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packages-dir: packages/prime-tunnel/dist

‎README.md‎

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[![Python versions](https://img.shields.io/pypi/pyversions/prime?cacheSeconds=60)](https://pypi.org/project/prime/)
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[![Downloads](https://img.shields.io/pypi/dm/prime)](https://pypi.org/project/prime/)
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Command line interface and SDKs for managing Prime Intellect GPU resources, sandboxes, and environments.
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Command line interface and SDKs for Prime Lab, Hosted Training, GPU resources, sandboxes, and environments.
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</div>
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## Quick Start
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# Authenticate
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prime login
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# Set up a Lab workspace for environments, evals, GEPA, and Hosted Training
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prime lab setup
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# See available Hosted Training models, capacity, and pricing
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prime train models
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# Generate and launch a Hosted Training config
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prime train init
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prime train rl.toml
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# Browse verified environments
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prime env list
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## Features
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- **Lab Workspaces** - Set up local verifiers workspaces for environments, evals, GEPA, and training
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- **Hosted Training** - Train models against verifiers environments and inspect runs, logs, metrics, and checkpoints
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- **Environments** - Access hundreds of verified environments on our community hub
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- **Evaluations** - Push and manage evaluation results
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- **GPU Resource Management** - Query and filter available GPU resources
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# View environment details
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prime env info <environment-name>
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# Inspect environment source without downloading the archive
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prime env inspect <environment-name>
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# Install an environment locally
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prime env install <environment-name>
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prime env push my-environment
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```
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### Lab and Hosted Training
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Prime Lab connects verifiers environments to evaluations, GEPA prompt optimization, and Hosted Training. Start with `prime lab setup` to create a local workspace with starter configs, then use `prime train models` to choose a Hosted Training model with current capacity and pricing.
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```bash
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# Set up a Lab workspace
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prime lab setup
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# List trainable models, capacity, and token pricing
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prime train models
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# Generate a Hosted Training config
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prime train init
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# Launch the run from the generated config
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prime train rl.toml
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# Inspect and manage Hosted Training runs
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prime train list
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prime train logs <run-id> -f
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prime train metrics <run-id>
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prime train checkpoints <run-id>
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```
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### GPU Resources
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```bash

‎packages/prime-evals/src/prime_evals/__init__.py‎

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SamplesResponse,
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)
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__version__ = "0.2.0"
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__version__ = "0.2.2"
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__all__ = [
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# Core HTTP Client & Config

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