Skip to content
GitLab
Projects Groups Snippets
  • /
  • Help
    • Help
    • Support
    • Community forum
    • Submit feedback
    • Contribute to GitLab
  • Sign in / Register
  • M metaseq
  • Project information
    • Project information
    • Activity
    • Labels
    • Members
  • Repository
    • Repository
    • Files
    • Commits
    • Branches
    • Tags
    • Contributors
    • Graph
    • Compare
  • Issues 95
    • Issues 95
    • List
    • Boards
    • Service Desk
    • Milestones
  • Merge requests 41
    • Merge requests 41
  • CI/CD
    • CI/CD
    • Pipelines
    • Jobs
    • Schedules
  • Deployments
    • Deployments
    • Environments
    • Releases
  • Packages and registries
    • Packages and registries
    • Package Registry
    • Infrastructure Registry
  • Monitor
    • Monitor
    • Incidents
  • Analytics
    • Analytics
    • Value stream
    • CI/CD
    • Repository
  • Wiki
    • Wiki
  • Snippets
    • Snippets
  • Activity
  • Graph
  • Create a new issue
  • Jobs
  • Commits
  • Issue Boards
Collapse sidebar
  • Administrator
  • metaseq
  • Merge requests
  • !218

added memory profiling for training and finetuning runs

  • Review changes

  • Download
  • Email patches
  • Plain diff
Merged Administrator requested to merge kchakrabarty/memprofiling into main Jul 12, 2022
  • Overview 6
  • Commits 2
  • Pipelines 0
  • Changes 1

Created by: KUNAL1612

Patch Description Added the ability to generate memory profiling information and save it in the checkpoint directory. Chose to go with PyTorch profiler over other alternatives because of its ability to offer better stack traces.

Testing steps Ran training for 8m and 125m models to generate traces and observed these traces.

Assignee
Assign to
Reviewers
Request review from
Time tracking
Source branch: kchakrabarty/memprofiling