Trove
🔥 Deploy machine learning models in Ruby (and Rails)
Works great with XGBoost, Torch.rb, fastText, and many other gems
Installation
Add this line to your application’s Gemfile:
gem "trove"
And run:
bundle install
trove init
And configure your storage in .trove.yml
.
Storage
Amazon S3
Create a bucket and enable object versioning.
Next, set up your AWS credentials. You can use the AWS CLI:
pip install awscli
aws configure
Or environment variables:
export AWS_ACCESS_KEY_ID=...
export AWS_SECRET_ACCESS_KEY=...
export AWS_REGION=...
IAM users need:
-
s3:GetObject
ands3:GetObjectVersion
to pull files -
s3:PutObject
to push files -
s3:ListBucket
ands3:ListBucketVersions
to list files and versions -
s3:DeleteObject
ands3:DeleteObjectVersion
to delete files
Here’s an example policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Sid": "Trove",
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:GetObjectVersion",
"s3:PutObject",
"s3:ListBucket",
"s3:ListBucketVersions",
"s3:DeleteObject",
"s3:DeleteObjectVersion"
],
"Resource": [
"arn:aws:s3:::my-bucket",
"arn:aws:s3:::my-bucket/trove/*"
]
}
]
}
If your production servers only need to pull files, only give them s3:GetObject
and s3:GetObjectVersion
permissions.
How It Works
Git is great for code, but it’s not ideal for large files like models. Instead, we use an object store like Amazon S3 to store and version them.
Trove creates a trove
directory for you to use as a workspace. Files in this directory are ignored by Git but can be pushed and pulled from the object store. By default, files are tracked in .trove.yml
to make it easy to deploy specific versions with code changes.
Getting Started
Use the trove
directory to save and load models.
# training code
model.save_model("trove/model.bin")
# prediction code
model = FastText.load_model("trove/model.bin")
When a model is ready, push it to the object store with:
trove push model.bin
And commit the changes to .trove.yml
. The model is now ready to be deployed.
Deployment
We recommend pulling files during the build process.
- Heroku and Dokku
- Docker
Make sure your storage credentials are available in the build environment.
Heroku and Dokku
Add to your Rakefile
:
Rake::Task["assets:precompile"].enhance do
Trove.pull
end
This will pull files at the very end of the asset precompile. Check the build output for:
remote: Pulling model.bin...
remote: Asset precompilation completed (30.00s)
Docker
Add to your Dockerfile
:
RUN bundle exec trove pull
Commands
Push a file
trove push model.bin
Pull all files in .trove.yml
trove pull
Pull a specific file (uses the version in .trove.yml
if present)
trove pull model.bin
Pull a specific version of a file
trove pull model.bin --version 123
Delete a file
trove delete model.bin
List files
trove list
List versions
trove versions model.bin
Ruby API
You can use the Ruby API in addition to the CLI.
Trove.push(filename)
Trove.pull
Trove.pull(filename)
Trove.pull(filename, version: version)
Trove.delete(filename)
Trove.list
Trove.versions(filename)
This makes it easy to perform operations from code, iRuby notebooks, and the Rails console.
Automated Training
By default, Trove tracks files in .trove.yml
to make it easy to deploy specific versions with code changes. However, this functionality is entirely optional. Disable it with:
vcs: false
This is useful if you want to automate training or build more complex workflows.
Non-Ruby
Trove can be used in non-Ruby projects as well.
gem install trove
trove init
History
View the changelog
Contributing
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
To get started with development:
git clone https://github.com/ankane/trove.git
cd trove
bundle install
export AWS_ACCESS_KEY_ID=...
export AWS_SECRET_ACCESS_KEY=...
export AWS_REGION=...
export S3_BUCKET=my-bucket
bundle exec rake test