Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

docs: fix the header of the scaling test table #4507

Merged
merged 1 commit into from
Dec 26, 2024

Conversation

njzjz
Copy link
Member

@njzjz njzjz commented Dec 25, 2024

Fix #4494.

Summary by CodeRabbit

  • Documentation
    • Updated the parallel training documentation for TensorFlow and PyTorch to enhance clarity.
    • Expanded explanations on parallel training processes and data loading utilities.
    • Introduced a flowchart to illustrate data flow and modified the scaling tests table format for better understanding.

@Copilot Copilot bot review requested due to automatic review settings December 25, 2024 19:20

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Copilot reviewed 1 out of 1 changed files in this pull request and generated no comments.

@njzjz njzjz linked an issue Dec 25, 2024 that may be closed by this pull request
@github-actions github-actions bot added the Docs label Dec 25, 2024
Copy link
Contributor

coderabbitai bot commented Dec 25, 2024

📝 Walkthrough

Walkthrough

The pull request updates the documentation for parallel training in the doc/train/parallel-training.md file, focusing on improving the clarity of explanations for TensorFlow and PyTorch backends. The changes include more detailed descriptions of parallel training processes, expanded information on learning rate scaling, batch size specifications, and data loading mechanisms. A new flowchart has been added to visualize the data flow, and instructions for launching training sessions have been refined.

Changes

File Change Summary
doc/train/parallel-training.md - Enhanced TensorFlow section with detailed Horovod parallel training explanation
- Added table for batch size and GPU scaling details
- Refined PyTorch data loading process description
- Included flowchart for data flow visualization
- Clarified torchrun multi-node training instructions

Assessment against linked issues

Objective Addressed Explanation
Understand multi-card training performance [#4494] Documentation improved, but does not directly address wall time discrepancy
View training results No specific guidance on viewing training results provided

Possibly related PRs

Suggested labels

Docs

Suggested reviewers

  • iProzd

Thank you for using CodeRabbit. We offer it for free to the OSS community and would appreciate your support in helping us grow. If you find it useful, would you consider giving us a shout-out on your favorite social media?

❤️ Share
🪧 Tips

Chat

There are 3 ways to chat with CodeRabbit:

  • Review comments: Directly reply to a review comment made by CodeRabbit. Example:
    • I pushed a fix in commit <commit_id>, please review it.
    • Generate unit testing code for this file.
    • Open a follow-up GitHub issue for this discussion.
  • Files and specific lines of code (under the "Files changed" tab): Tag @coderabbitai in a new review comment at the desired location with your query. Examples:
    • @coderabbitai generate unit testing code for this file.
    • @coderabbitai modularize this function.
  • PR comments: Tag @coderabbitai in a new PR comment to ask questions about the PR branch. For the best results, please provide a very specific query, as very limited context is provided in this mode. Examples:
    • @coderabbitai gather interesting stats about this repository and render them as a table. Additionally, render a pie chart showing the language distribution in the codebase.
    • @coderabbitai read src/utils.ts and generate unit testing code.
    • @coderabbitai read the files in the src/scheduler package and generate a class diagram using mermaid and a README in the markdown format.
    • @coderabbitai help me debug CodeRabbit configuration file.

Note: Be mindful of the bot's finite context window. It's strongly recommended to break down tasks such as reading entire modules into smaller chunks. For a focused discussion, use review comments to chat about specific files and their changes, instead of using the PR comments.

CodeRabbit Commands (Invoked using PR comments)

  • @coderabbitai pause to pause the reviews on a PR.
  • @coderabbitai resume to resume the paused reviews.
  • @coderabbitai review to trigger an incremental review. This is useful when automatic reviews are disabled for the repository.
  • @coderabbitai full review to do a full review from scratch and review all the files again.
  • @coderabbitai summary to regenerate the summary of the PR.
  • @coderabbitai generate docstrings to generate docstrings for this PR. (Beta)
  • @coderabbitai resolve resolve all the CodeRabbit review comments.
  • @coderabbitai configuration to show the current CodeRabbit configuration for the repository.
  • @coderabbitai help to get help.

Other keywords and placeholders

  • Add @coderabbitai ignore anywhere in the PR description to prevent this PR from being reviewed.
  • Add @coderabbitai summary to generate the high-level summary at a specific location in the PR description.
  • Add @coderabbitai anywhere in the PR title to generate the title automatically.

CodeRabbit Configuration File (.coderabbit.yaml)

  • You can programmatically configure CodeRabbit by adding a .coderabbit.yaml file to the root of your repository.
  • Please see the configuration documentation for more information.
  • If your editor has YAML language server enabled, you can add the path at the top of this file to enable auto-completion and validation: # yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json

Documentation and Community

  • Visit our Documentation for detailed information on how to use CodeRabbit.
  • Join our Discord Community to get help, request features, and share feedback.
  • Follow us on X/Twitter for updates and announcements.

Copy link
Contributor

@coderabbitai coderabbitai bot left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Actionable comments posted: 0

🧹 Nitpick comments (1)
doc/train/parallel-training.md (1)

32-37: Consider standardizing decimal places in the results.

The table structure and headers are now clear and well-organized. However, for better readability, consider standardizing the decimal places:

| Num of GPU cards | Samples per batch | Seconds every 100 batches | Samples per second | Speed up |
| ---------------- | ----------------- | ------------------------- | ------------------ | -------- |
-| 1                | 1                 | 1.4515                    | 68.89              | 1.00     |
-| 2                | 2                 | 1.5962                    | 62.65\*2           | 1.82     |
-| 4                | 4                 | 1.7635                    | 56.71\*4           | 3.29     |
-| 8                | 8                 | 1.7267                    | 57.91\*8           | 6.72     |
+| 1                | 1                 | 1.452                     | 68.89              | 1.00     |
+| 2                | 2                 | 1.596                     | 62.65\*2           | 1.82     |
+| 4                | 4                 | 1.764                     | 56.71\*4           | 3.29     |
+| 8                | 8                 | 1.727                     | 57.91\*8           | 6.72     |
📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between beeb3d9 and 48ff960.

📒 Files selected for processing (1)
  • doc/train/parallel-training.md (1 hunks)
🔇 Additional comments (1)
doc/train/parallel-training.md (1)

30-31: LGTM! Clear explanation of batch size configuration.

The added context about batch size per GPU card improves clarity and helps readers understand the scaling test results better.

@njzjz njzjz requested a review from wanghan-iapcm December 25, 2024 19:28
Copy link

codecov bot commented Dec 25, 2024

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 84.58%. Comparing base (beeb3d9) to head (48ff960).
Report is 3 commits behind head on devel.

Additional details and impacted files
@@            Coverage Diff             @@
##            devel    #4507      +/-   ##
==========================================
- Coverage   84.59%   84.58%   -0.01%     
==========================================
  Files         675      675              
  Lines       63574    63575       +1     
  Branches     3490     3486       -4     
==========================================
  Hits        53778    53778              
- Misses       8670     8671       +1     
  Partials     1126     1126              

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

@wanghan-iapcm wanghan-iapcm added this pull request to the merge queue Dec 26, 2024
Merged via the queue into deepmodeling:devel with commit bd2395c Dec 26, 2024
60 checks passed
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
Projects
None yet
Development

Successfully merging this pull request may close these issues.

Train result analysis
2 participants