Keepsake Version control for machine learning

You can store your experiment data in the cloud on Google Cloud Storage or Amazon S3.

This means you store results from multiple training machines in one place and collaborate with other people.

When you list experiments on your local machine, it will list all of the experiments that have run anywhere by anyone, so you can easily compare between them and download the results.

Log in to Google Cloud

To store data on Google Cloud Storage, you first need to install the Google Cloud SDK if you haven't already. Then, run gcloud init and follow its instructions:

  • Log in to your Google account when prompted.
  • If it asks to choose a project, pick the default or first option.
  • If it asks to pick a default region, hit enter and select 1 (you want a default region, but it doesn't matter which one).

Next, run this, because Cloud SDK needs you to log in a second time to let other applications use your Google Cloud account:

gcloud auth application-default login

Point Keepsake at Google Cloud

Then, create keepsake.yaml with this content, replacing [your model name] with your model's name, or some other unique string:

repository: "gs://keepsake-repository-[your model name]"

Log in to Amazon Web Services

First, you need to install and configure the Amazon Web Services CLI if you haven't already. The easiest way to do this on macOS is to install it with Homebrew:

  1. If you haven't already, install Homebrew.
  2. Run brew install awscli
  3. If you haven't already got an Amazon Web Services account, sign up for Amazon Web Services.
  4. Run aws configure and follow these instructions to configure it. You will need to get an access key ID and secret access key and set the region to us-east-1 (unless you want to use a particular region). You don't need to set an output format, or profile.

If you're on another platform, follow these instructions to install the Amazon Web Services CLI.

Point Keepsake at Amazon S3

Then, create keepsake.yaml with this content, replacing [your model name] with your model's name, or some other unique string:

repository: "s3://keepsake-repository-[your model name]"

Now, when you run your training script, calling experiment.checkpoint() will upload all your working directory and checkpoint metadata to this bucket.

If you're following along from the tutorial, run python again. This time it will save your experiments to cloud storage. (It takes a second to save each checkpoint, so press Ctrl-C after a few epochs if you don't want to wait.)

Now, when you run keepsake ls, it will list experiments from the bucket.

Migrate data

If you switch from local file storage to remote cloud storage, you will not see your local experiments any longer. Unlike Git which stores data both locally and remotely, Keepsake only stores data in one location.

It is easy to migrate locations, if you ever need to, because Keepsake just stores its data as plain files. To migrate from local file storage to the cloud, just copy the .keepsake directory, or wherever you store your data, to your Amazon S3 or Google Cloud Storage bucket.

Store data in a Colab notebook

You can store data in Google Cloud Storage directly from a Colab notebook.

You first need to get your project ID, which is explained in the Google Cloud documentation. Then, run this in a cell in your Colab notebook, replacing the first two variables with your project ID and a name for your Keepsake project:

project_id = '[your Cloud Platform project ID]'
bucket_name = 'keepsake-[your repository name]'
from google.colab import auth
!gcloud config set project {project_id}
!echo 'repository: "gs://{bucket_name}"' > keepsake.yaml

For a real example, expand the "Setup" section in this notebook.

What's next

You might want to take a look at:

Let’s build this together

Everyone uses version control for software, but it’s much less common in machine learning.

This causes all sorts of problems: people are manually keeping track of things in spreadsheets, model weights are scattered on S3, and nothing is reproducible. It's hard enough getting your own model from a month ago running, let alone somebody else's.

So why isn’t everyone using Git? Git doesn’t work well with machine learning. It can’t handle large files, it can’t handle key/value metadata like metrics, and it can’t record information automatically from inside a training script. There are some solutions for these things, but they feel like band-aids.

We want to make a small, lightweight, native version control system for ML. Something that does one thing well and combines with other tools to produce the system you need.

We need your help to make this a reality. If you’ve built this for yourself, or are just interested in this problem, join us to help build a better system for everyone.

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