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Highlights of Guild AI

Real time training updates

Guild track your TensorFlow experiments in real time and gives you up-to-the-second feedback. Guild uses intelligent polling and a high performance embedded database to minimize the impact on your system.

At-a-glance run comparisons

Guild summarizes important run results and presents them in a sortable, filterable table for quick access. Results are updated in real time to keep you updated on your current experiment while comparing it to previous runs.

Compare model training and system statistics

Guild lets you dig deeper into run results by comparing TensorFlow scalars and system statistics. Guild has integrated TensorBoard’s charting component which lets you compare series data by global step, relative time, and wall time. It also supports series smoothing and logarithmic Y axis.

Capture more data

In addition to your TensorFlow event logs, which contain your training statistics and other summaries, Guild captures a range of other essential build artifacts including script output, command flags, system attributes and stats such as GPU and CPU utilization. This information is indispensable for answering certain questions, particularly those concerning operational performance.

TensorBoard integration

When you need to drill into even more detail, Guild seamlessly integrates TensorBoard into your project view. There’s no need to start a separate TensorBoard process — Guild handles this in the background when you run the view command. TensorBoard lets you view event log summaries including scalars, images, audio, variable distributions and histograms, and an interactive view of the model graph.

Simplified training workflow

Guild workflow consists of simple commands that you run for a project: prepare, train, evaluate, and serve. Guild fills in the details for each command using information from the Guild project file, letting you run complex operations typing long, complex commands.

Self documenting project structure

Guild projects provide instructions for performing the prepare, train, and evaluate operations. Project are plain text files that are easy for humans to read. They are useful not only to Guild for running commands but as model interface specifications.


name              = MNIST
description       = Guild MNIST example

[model "expert"]

prepare           = expert --prepare
train             = expert
train_requires    = ./data
evaluate          = expert --test


datadir           = ./data
rundir            = $RUNDIR
batch_size        = 100
epochs            = 10
train_dir         = $RUNDIR

Integrated inference server

Guild provides an integrated HTTP server that you can use to test your trained models before deploying them to TensorFlow serving.