Bump ubuntu 22.04 + torch 2.5.1 #1666
Merged
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
added 2 commits
November 20, 2024 11:42
j316chuck
approved these changes
Nov 20, 2024
dakinggg
reviewed
Nov 20, 2024
mcli/mcli-1b-eval.yaml
Outdated
| @@ -9,7 +9,7 @@ integrations: | |||
| command: | | |||
| cd llm-foundry/scripts/ | |||
| composer eval/eval.py /mnt/config/parameters.yaml | |||
| image: mosaicml/llm-foundry:2.4.0_cu124-latest | |||
There was a problem hiding this comment.
do not update these, they are pinned to released versions of foundry
Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
j316chuck
force-pushed
the
bump-ubuntu-22.04
branch
from
f0783d7
to
256f13d
Compare
mvpatel2000
approved these changes
Nov 21, 2024
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
As title states.
Testing:
manually verified that all runs were with torch 2.5.1 and that all metrics looked good (memory, eval loss, train loss, and throughput)
for instance: