Metadata-Version: 2.2
Name: ml-flashpoint
Version: 0.0.9
Summary: A memory-first, lightning fast, easy-to-use ML checkpointing library.
License: 
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Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.10
Requires-Dist: typing-extensions==4.15.0
Provides-Extra: pytorch
Requires-Dist: torch==2.8.0; extra == "pytorch"
Provides-Extra: megatron
Requires-Dist: megatron_core==0.13.1; extra == "megatron"
Provides-Extra: nemo
Requires-Dist: lightning==2.4.0; extra == "nemo"
Requires-Dist: ml_flashpoint[pytorch]; extra == "nemo"
Requires-Dist: nemo_toolkit[all]==2.4.0; extra == "nemo"
Provides-Extra: docs
Requires-Dist: mkdocs-material==9.7.0; extra == "docs"
Requires-Dist: mkdocstrings-python==2.0.1; extra == "docs"
Provides-Extra: dev-base
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Requires-Dist: ruff==0.12.11; extra == "dev-base"
Provides-Extra: dev-nemo
Requires-Dist: ml-flashpoint[dev-base,docs,megatron,nemo,pytorch]; extra == "dev-nemo"
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Requires-Dist: ml-flashpoint[dev-nemo]; extra == "dev-nemo-rl"
Provides-Extra: dev
Requires-Dist: ml-flashpoint[dev-nemo]; extra == "dev"
Description-Content-Type: text/markdown

<div align="center">

# ML Flashpoint

[![Build and Test](https://github.com/google/ml-flashpoint/actions/workflows/build-and-test.yml/badge.svg)](https://github.com/google/ml-flashpoint/actions/workflows/build-and-test.yml)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/release/python-3100/)

[User Documentation](https://google.github.io/ml-flashpoint/) | [Performance](https://google.github.io/ml-flashpoint/#performance) | [CodeWiki](https://codewiki.google/github.com/google/ml-flashpoint) | [Contributing](./CONTRIBUTING.md)
</div>

## Overview

A memory-first, lightning-fast, ready-to-use ML checkpointing library.

Adapters for PyTorch DCP, Megatron-LM and NeMo 2.0 are readily available for seamless integration.
They are built on top of the core checkpointing APIs, which can also be used directly for custom integrations.

If interested in a native integration with another framework, please let us know by creating a [feature request](https://github.com/google/ml-flashpoint/issues/new?template=feature_request.md) or upvoting an [existing one](https://github.com/google/ml-flashpoint/issues?q=is%3Aissue%20state%3Aopen%20label%3Aenhancement)!

For learning more about using the library and its performance, check out the [user documentation](https://google.github.io/ml-flashpoint). 
Below you will find development instructions for contributors.

## Installation

This library defines core dependencies, as well as additional _optional_ dependencies for specific adapters, to avoid polluting consumers with unnecessary dependencies.
See the adapters installation commands below for examples of the available options, and the [`pyproject.toml`](./pyproject.toml) as the source of truth for all available adapters.

### Core Library
```bash
pip install -e .
```

To avoid building C++ tests (and pulling test dependencies), such as when using for production:

```bash
pip install -e . --config-settings=cmake.define.BUILD_TESTING=OFF
```

NOTE: Currently C++ binaries are expected to be in the package alongside the code, so editable mode (`-e`) is used.

### With Adapters
```bash
# PyTorch
pip install -e .[pytorch]

# Megatron-LM
pip install -e .[megatron]

# Multiple
pip install -e .[pytorch,megatron]
```

## Development

Check out our [CodeWiki](https://codewiki.google/github.com/google/ml-flashpoint) for AI-generated documentation of the code structure and implementation.

### Python version

Ensure you have the correct Python version.
As of this writing, the project uses Python 3.10, due to limitations in NeMo's dependencies.

To confirm, see which versions of python come up when tab-completing `python` in your shell.

You could install `pyenv` to manage different Python versions: https://github.com/pyenv/pyenv?tab=readme-ov-file#installation.

And then install the desired Python version with it e.g. `pyenv install 3.10`.

### Build and Installation

NOTE: If you already have a `.venv` for a different version in this repository, run `rm -rf .venv` first.

To set up the development environment, run (at the project root):

```bash
# Create and activate a virtual environment (e.g., using venv) in your local env (only needed once, but is safe to rerun)
python3.10 -m venv .venv
source .venv/bin/activate

# Install the package in editable mode with development dependencies
pip install -e .[dev]
```

### Linting and Testing

All code changes **must** be accompanied by comprehensive unit tests, and integration tests where feasible.
With AI coding tools, there's no good reason to cut corners or omit tests.
You can prompt your coding tool to "create a comprehensive test plan for X, covering edge cases and corner cases" that you can review.

*   **Build C++ Components:** The C++ components are built automatically when you run one of the `pip install` commands from above.
*   **Python Format:** To apply automated fixes, run (with caution):
    NOTE: This may also modify lines that _do not_ violate the lint rules, so use cautiously!
    ```bash
    ruff check --fix .
    ruff format .
    ```

*   **Python Lint:** To check for code style violations, run:
    ```bash
    ruff check .
    ```

*   **C++ Format:** To apply automated fixes, run:

    ```bash
    # install clang-format-18
    sudo apt-get update && sudo apt-get install -y clang-format-18

    # format all C++ files
    find src -name '*.cpp' -o -name '*.h' | xargs clang-format-18 -i
    ```

*   **C++ Lint:** Check for style violations, run:
    ```bash
    find src -name '*.cpp' -o -name '*.h' | xargs clang-format-18 --dry-run --Werror
    ```

*   **GitHub Actions Lint:** To pin action versions, first install [`ratchet`](https://github.com/sethvargo/ratchet?tab=readme-ov-file#installation) - one way is via `go install`:
    ```bash
    go install github.com/sethvargo/ratchet@latest
    ```
    Then run it on the workflow yaml file:
    ```bash
    ratchet pin .github/workflows/build-and-test.yml
    # Or if installed to a specific location not in your path, something like:
    ~/go/bin/ratchet pin ./.github/workflows/build-and-test.yml
    ```

*   **Test:** To run all tests (Python and C++), run:
    ```bash
    pytest
    ```
    * Python tests should be in the `tests` directory, in a package matching the subject-under-test, and the test files should start with `test_`.
    * C++ tests should be in a `test` directory next to the subject-under-test (so within the `src` directory), and should _end_ with `_test.cpp`.

#### Code Coverage

To calculate code coverage, run `./run_coverage.sh` from the project root.
It will activate the venv located at `.venv`, remove build files, re-install the project, and produce coverage reports.

### Conventional Commits

This project uses [conventional commits](https://www.conventionalcommits.org/), and the commit message should complete the sentence: "This change will...".
Specifying the scope for commits is optional, but highly recommended.
Typically, the scope will match the package the change relates to, and can use `/` for sub-packages, e.g.:

```
chore(replication): add the ReplicationManager skeleton class

feat(adapter/nemo): implement the callback to trigger MLFlashpoint checkpoints
```

### Logging

* Keep useful, low-frequency progress logs and major state changes or important function entries at `INFO` level. 
High level important performance metrics can be `INFO` as well (see the note below on `DEBUG` logging).
* Warnings and things that should be notified to the user, but not necessarily halt immediately, should be `WARNING` level.
This includes things that are potentially unexpected, or could lead to unwanted behavior/performance.
* All errors and exceptions (whether swallowed/handled or not) should be logged at `ERROR` level - these should always be noteworthy and worth trying to address.
If it is not worth fixing, make it `WARNING`.
* Everything else, such as low-level details on branch logic, progress within a function, and more specific performance details should be `DEBUG`.

Here is a guideline for VLOG levels to use in C++.

NOTE: The rule of thumb is to use `VLOG(3)` for `DEBUG` logging in C++, equivalent to when you'd use debug logging in other languages.

| Level | Usage Case | Frequency |
| :--- | :--- | :--- |
| **`VLOG(2)`** | **Detailed events:** Per-request or per-connection logic (e.g., "Connection X added to active set"). | Medium |
| **`VLOG(3)`** | **Deep tracing:** Logic branches inside loops or complex conditionals. | High |
| **`VLOG(4)+`** | **Extremely noisy:** Byte-level data, heartbeats, or internal mutex locking/unlocking details. | Very High |

## Releases

We use release tags of the form `vX.Y.Z` for production releases, following [semver](https://semver.org/), starting with [zerover](https://0ver.org/).

Releases should be created as GitHub Releases, which can be done [here](https://github.com/google/ml-flashpoint/releases/new).

The helper script `create_release.py` will generate release notes that can be added to the Release.

Command: `./scripts/create_release.py`.
Add `-h` for help.

Requirements:

* These release tags **MUST** be immutable - they cannot be modified or deleted after they are created.
* These release tags **MUST** be created from an approved and merged commit, typically from the `main` branch.
* They **MUST NOT** be created from unapproved, unmerged commits, such as a feature branch or patchset.
The commit used to create the release tag must always be accessible and not temporary.

## User Documentation Site

User documentation is all maintained in the `docs/` directory, and is generated using [mkdocs-material](https://squidfunk.github.io/mkdocs-material/getting-started/).
See the `.example-syntax.md` file for guidance on certain supported syntax.

When making changes, you can view them locally via `mkdocs serve`.

Once changes are merged to `main`, they are automatically deployed to the documentation site, available at https://google.github.io/ml-flashpoint.
