From 95d5ff1b6615cbf46909c50835869d299d9df856 Mon Sep 17 00:00:00 2001 From: CharlesCNorton <135471798+CharlesCNorton@users.noreply.github.com> Date: Thu, 8 Aug 2024 12:15:41 -0400 Subject: [PATCH] fix typos in documentation Corrected the following typos in the documentation: - "onetimes" to "sometimes" - "assest" to "assets" - "its" to "it's" --- docs/markdowns/model_versioning.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/markdowns/model_versioning.md b/docs/markdowns/model_versioning.md index b846afe..00c052f 100644 --- a/docs/markdowns/model_versioning.md +++ b/docs/markdowns/model_versioning.md @@ -10,7 +10,7 @@ Use-cases: Currently, many organizations manage their experiments by saving snapshots of the data, the logs, the metrics and the models. The best performing models are copied to a different location for deployment to dev and production environments, -which are ometimes managed by a database, or with docker-image registry. Often, another third-party tool is used to +which are sometimes managed by a database, or with docker-image registry. Often, another third-party tool is used to manage experiments, which then needs to be integrated with the rest of the development stack. *An example:* @@ -76,7 +76,7 @@ And this doesn't even cover other common use cases like sharing data or models w ## ML experimentation with pyxet and XetHub -With XetHub, we can use Git to manage every part of your ML experiments by storing models and assest alongside your code. +With XetHub, we can use Git to manage every part of your ML experiments by storing models and assets alongside your code. Optionally, use [Git submodules](https://git-scm.com/book/en/v2/Git-Tools-Submodules) to manage only large data on XetHub. @@ -115,8 +115,8 @@ We can always reproduce results and compare models by checking out branches and Start a new experiment branch from any existing one, pull new data with `git submodule update data` and run the training again. XetHub will overwrite the model, metrics, and checkpoints in the new branch, and if the pull request review is -successful, merge the changes back to prod. This ensures that the model in prod is always up-to-date, and that its -always stored alongside its metrics, inference code and relevant logs. +successful, merge the changes back to prod. This ensures that the model in prod is always up-to-date, and that it's +always stored alongside its metrics, inference code and relevant logs. Everything **this** model needs is saved in the repo. If changes to the app are needed, they are managed together.