feat: Implement slurm scheduler

[garrett4wade]

Description

Implement a slurm scheduler to run single-controller experiments in our cluster.

Type of Change

• Bug fix (non-breaking change that fixes an issue)
• New feature (non-breaking change that adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not
  work as expected)
• Documentation update
• Code refactoring (no functional changes)
• Performance improvement
• Test coverage improvement

Checklist

• I have read the Contributing Guide
• I have run formatting tools (pre-commit or manual)
• I have run relevant unit tests and they pass
• I have added tests for new functionality
• I have updated documentation if needed
• My branch is up to date with main
• This PR introduces breaking changes (if yes, fill out details below)
• If this PR changes documentation, I have built and previewed it locally with
  jb build docs
• No critical issues raised by AI reviewers (/gemini review)

[gemini-code-assist]

Summary of Changes

Hello @garrett4wade, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request significantly extends the system's capabilities by integrating a Slurm scheduler. This allows for the orchestration of single-controller experiments on Slurm-managed clusters, providing robust worker management, resource allocation, and inter-process communication tailored for HPC environments. The changes also refine default resource specifications and enhance the resilience of engine imports and RPC server operations.

Highlights

• Slurm Scheduler Implementation: A new SlurmScheduler class has been added, enabling the system to manage and run experiments on Slurm clusters. This includes functionalities for creating, monitoring, and deleting workers, as well as handling RPC communication within a Slurm environment.
• Resource Specification Defaults: The default CPU and memory requirements in SchedulingSpec have been updated from 0 to 2 CPU cores and 1 GB of memory, respectively, providing more sensible defaults for resource allocation.
• Conditional torch_memory_saver Import: The torch_memory_saver library is now imported conditionally and lazily in fsdp_engine.py, megatron_engine.py, and offload.py. This change prevents import errors if the library is not installed and allows for a mock implementation when unavailable.
• RPC Server Enhancements: The RPC server (rpc_server.py) has been improved with new features, including an endpoint to allocate multiple free ports, better error logging with function names and tracebacks, and dynamic worker index resolution from Slurm environment variables.
• New Slurm Scheduler Tests: A comprehensive test suite (test_slurm_scheduler.py) has been introduced to validate the functionality of the new SlurmScheduler, covering worker lifecycle, RPC communication, and error handling in various scenarios.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request introduces a SlurmScheduler to enable running experiments on a Slurm-managed cluster. The implementation is comprehensive, covering job submission, monitoring, worker lifecycle management, and RPC communication. It also includes a good set of integration tests. I've identified a critical bug in the RPC server's thread initialization logic that needs to be addressed. Additionally, there are a few areas for improvement regarding the Slurm script generation and code duplication that would enhance the robustness and maintainability of the new scheduler. Overall, this is a solid feature addition.

[rchardx]

LGTM