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‎.github/workflows/docs.yml‎

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name: Docs
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on:
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push:
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branches:
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- main
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pull_request:
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workflow_dispatch:
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jobs:
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build-docs:
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name: Build Documentation
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runs-on: ubuntu-latest
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timeout-minutes: 30
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steps:
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- name: Checkout
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uses: actions/checkout@v4
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with:
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fetch-depth: 0
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- name: Setup conda env
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uses: conda-incubator/setup-miniconda@v2
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with:
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auto-update-conda: true
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miniconda-version: "latest"
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activate-environment: test
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python-version: '3.10'
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auto-activate-base: false
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- name: Verify conda environment
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shell: bash -l {0}
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run: |
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conda info
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which python
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which conda
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- name: Update pip
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shell: bash -l {0}
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run: python -m pip install --upgrade pip
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- name: Install pytorch
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shell: bash -l {0}
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run: python -m pip install torch==2.9.0.dev20250826 --extra-index-url https://download.pytorch.org/whl/nightly/cpu
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- name: Install monarch
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shell: bash -l {0}
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run: python -m pip install monarch-no-torch==0.1.0.dev20250826 --find-links assets/ci
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- name: Install torchforge
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shell: bash -l {0}
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env:
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GH_TOKEN: ${{ github.token }}
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run: ./scripts/install.sh
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- name: Install docs dependencies
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shell: bash -l {0}
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run: python -m pip install -r docs/requirements.txt
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- name: Build docs
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shell: bash -l {0}
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working-directory: docs
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run: make html --keep-going SPHINXOPTS='-W'
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- name: Upload docs artifact
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uses: actions/upload-artifact@v4
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with:
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name: docs
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path: docs/build/html/
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# doc-preview:
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# runs-on: [ubuntu-latest]
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# needs: build-docs
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# if: ${{ github.event_name == 'pull_request' }}
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# steps:
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# - name: Checkout
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# uses: actions/checkout@v4
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# - name: Download artifact
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# uses: actions/download-artifact@v4
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# with:
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# name: docs
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# path: docs
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# - name: Add noindex to preview docs
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# run: |
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# echo "Adding noindex meta tag to prevent search engine indexing of preview docs"
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# find docs -name "*.html" -print0 | xargs -0 sed -i 's/<head>/<head>\n <meta name="robots" content="noindex">/'
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# - name: Upload docs preview
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# uses: seemethere/upload-artifact-s3@v5
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# if: ${{ github.event_name == 'pull_request' }}
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# with:
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# retention-days: 14
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# s3-bucket: doc-previews
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# if-no-files-found: error
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# path: docs
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# s3-prefix: meta-pytorch/forge/${{ github.event.pull_request.number }}
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deploy-docs:
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needs: build-docs
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if: github.ref == 'refs/heads/main'
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permissions:
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pages: write
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id-token: write
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environment:
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name: github-pages
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url: ${{ steps.deployment.outputs.page_url }}
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runs-on: ubuntu-latest
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steps:
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- name: Download build artifact
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uses: actions/download-artifact@v4
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with:
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name: docs
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path: .
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- name: Upload Pages artifact
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uses: actions/upload-pages-artifact@v3
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with:
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path: .
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- name: Deploy to GitHub Pages
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id: deployment
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uses: actions/deploy-pages@v4

‎.github/workflows/unit_test.yaml‎

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- name: Install monarch
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run: python -m pip install monarch-no-torch==0.1.0.dev20250826 --find-links assets/ci
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- name: Install torchstore
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run: |
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eval "$(ssh-agent -s)"
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ssh-add - <<< '${{ secrets.FORGE_GITHUB_CI_FOR_TORCHSTORE }}'
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python -m pip install git+ssh://[email protected]/meta-pytorch/torchstore.git
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run: pip install assets/wheels/torchstore-0.1.0-py3-none-any.whl
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- name: Install torchtitan
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run: |
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pip install --pre torchtitan==0.1.0.dev20250826+cpu --extra-index-url https://download.pytorch.org/whl/nightly/cpu
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pip install assets/wheels/torchtitan-0.1.0-py3-none-any.whl
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pip install tyro
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- name: Install dependencies
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run: python -m pip install --no-build-isolation -e ".[dev]"

‎.gitignore‎

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*.pt
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*.pth
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*.ckpt
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*.distcp
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.metadata
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# mypy
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.mypy_cache/
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# DCP artifacts
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model_state_dict/
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forge_dcp_tmp/

‎README.md‎

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# forge
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# <img width="35" height="35" alt="image" src="https://github.com/user-attachments/assets/2700a971-e5d6-4036-b03f-2f89c9791609" /> Forge
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#### A PyTorch native platform for post-training generative AI models
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#### A PyTorch native agentic library for RL post-training and agentic development
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## Overview
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Forge was built with one core principle in mind: researchers should write algorithms, not infrastructure. Forge introduces a “service”-centric architecture that provides the right abstractions for distributed complexity. When you need fine-grained control over placement, fault handling or communication patterns, the primitives are there. When you don’t, you can focus purely on your RL algorithm.
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Key features:
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- Usability for rapid research (isolating the RL loop from infrastructure)
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- Hackability for power users (all parts of the RL loop can be easily modified without interacting with infrastructure)
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- Scalability (ability so shift between async and syncronous training and across thousands of GPUs)
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> ⚠️ **Early Development Warning** Forge is currently in an experimental
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> stage. You should expect bugs, incomplete features, and APIs that may change
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> in future versions. The project welcomes bugfixes, but to make sure things are
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> well coordinated you should discuss any significant change before starting the
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> work. It's recommended that you signal your intention to contribute in the
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> issue tracker, either by filing a new issue or by claiming an existing one.
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## 📖 Documentation
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View Forge's hosted documentation [at this link](https://meta-pytorch.org/forge/).
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## Installation
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