Welcome to the Engine Documentation. Engine allows you to easily track and execute machine learning code. In this guide, we will walk you through creating an account and launching your first run.
Create an Account
In order to interact with Engine, create an account and set up your machine.
Install the CLI and Library
Use the following
pip install command to install the Engine command line interface and python library. We recommend
using a fresh virtual environment.
Set up Autocomplete
After you've installed the pip packages, enable autocomplete on the CLI. Autocomplete will allow you to tab-complete
engine commands on your terminal.
Add an SSH Key
Engine operates services that communicate over ssh. These services are secured via the use of ssh keys. If you do not have a public/private key pair, generate one:
Engine only supports unencrypted private keys. Please do not set a password when creating your key.
Copy the public key contents with your favorite text editor or with
pbcopy < ~/.ssh/id_rsa.pub. Paste the contents
into the "Add SSH Key" modal on your settings page.
Generate a CLI Key
Use your new CLI key to log into Engine on your terminal with
engine login. If you have
not already added an ssh key to your account, you will be prompted to do so during the login process.
Launch a Run
Now that you have initialized your account, let's launch our first run.
Create a Project
Navigate via the Engine dashboard and click on
New in the Projects section on the top left corner.
Follow the prompt to create a new project called 'quickstart'.
Engine ML hosts preconfigured examples for training a digit classifier using TensorFlow, Pytorch, and Keras on GitHub.
Clone the repository and use the
vision/mnist/get-data.sh script to download the MNIST dataset:
Engine uses a run configuration file to know how and where to execute your code. Configuration files are defined in YAML
and have different specifications depending on where the code is executed. The most basic run configuration has three
The Engine ML Examples repository comes with run configurations for TensorFlow, Pytorch, and Keras. These configurations
Choose a machine learning framework and update the respective run configuration so that the
repository field points to
your newly created project. Note that
repository's value must take the form
Owner is either your
username or a team's name and
project is your project's name (in this case,
Engine requires that your code is tracked by Git and that any changes are committed. To expediate your workflow,
Engine automatically commits staged and unstaged changes to files that are already in the Git index (i.e. Engine
performs the equivalent of
git add -u && git commit executed in the root of the repository). Note that files that are
not tracked by Git will not be tracked by Engine.
Track Your Progress
- Stream your run's logs with
engine tail -f RUN_ID
- Download your run's output files with
engine get-files RUN_ID PATTERN
- Use tags to organize your run with
engine tag RUN_ID TAG
Congratulations! You have launched your first Engine run. If you have any questions, reach out on our support Slack group.