This is a Guild AI example that defines a CIFAR10 model based on TensorFlow's excellent Convolutional Neural Networks Guide.
- Guild - Guild project file (more info)
- samples.py - Implementation of the samples resource
- single.py - Training script for single GPU
- support.py - Shared code across training scripts
- upstream_single.py - Wrapper to train using the original TensorFlow (upstream) script for single GPU
To work with this project, ensure that you have Guild AI installed along with its requirements.
If you haven't already, clone Guild AI examples:
$ git clone https://github.com/guildai/guild-examples.git
Prepare and train the CIFAR10 model:
$ cd guild-examples/cifar10
$ guild prepare
$ guild trainThe prepare command downloads the CIFAR10 data, which will be used
for subsequent operations.
At any point you can view project run results by in Guild
View by running the
view command in a separate terminal:
$ guild viewGuild View runs on port 6333 by default — to view it,
open http://localhost:6333 in your browser.
To calculate the most recently trained model's final accuracy using
test data, use the evaluate command:
$ guild evaluate --latest-runTo run a model as an HTTP service in Guild Serve run:
$ guild serve RUNwhere RUN is a value returned by list-runs (i.e. a path to the run
directory) or --latest-run to serve the model exported in the last
run.
This example provides a samples resource that can be used to
generate a number of CIFAR10 images and their corresponding JSON
encodings. These file can be used to run ad hoc inference on the model
in Guild View (see the Serve tab) or Guild Serve
(see Serving above).
Images are generated from the CIFAR10 training/test data.
To generate samples, run:
$ guild prepare samplesThis will create a local samples subdirectory containing the samples
images.
For more information about the Guild AI project, see https://guild.ai.