Guild AI supplements your TensorFlow™ operations by collecting a wide range of information about your model's performance, including GPU usage, CPU usage, memory consumption and disk IO. You can view all of this information along with your TensorFlow summary output in realtime using Guild AI view.
Guild is used to measure model performance when running on specific systems. The data you collect can be used to optimize your model for specific applications. For example, you can collect metrics such as:
- GPU memory usage for batch training, single inference, batch inference
- Inference latency and throughput
- Impact of hyper parameter tuning on model accuracy and training time
When your model is trained you can run it using Guild's Google Cloud Machine Learning compatible inference server. This can be used as a local dev/test environment in preparation for cloud deployment, or be run in production within your own environment.
Guild is in a pre-release "alpha" state. All command interfaces, programming interfaces, and data structures may be changed without prior notice. We'll do our best to communicate potentially disruptive changes.
Guild requires the following software for compilation:
- make (available via Linux system package or Command Line Tools via Xcode on OSX)
- Erlang (18 or later)
Guild requires the following software for runtime (i.e. performing model related operations prepare, train, and evaluate).
- Python (2.7 recommended)
- NVIDIA System Management Interface (optional, for GPU stats)
Before building Guild, confirm that you have the required build dependencies installed (see above).
Clone the Guild repository:
$ git clone [email protected]:guildai/guild.git
Change to the Guild directory and run make:
$ cd guild $ make
Please report any compile errors to the Guild issues list on GitHub.
Create a symlink named
guild/scripts/guild-dev that's in
PATH environment. The most convenient location would be
/usr/local/bin (requires root access):
$ sudo ln -s GUILD_REPO/scripts/guild-dev /usr/local/bin/guild
GUILD_REPO is the local Guild repo you cloned above.
Alternatively, create a symlink in a directory in your home directory
~/Bin) and include that directory in your
$ sudo ln -s GUILD_REPO/scripts/guild-dev ~/Bin/guild
Future releases of Guild will provide precompiled packages for Linux and OSX to simplify the process of installing Guild.
Verify that Guild is available by running:
$ guild --help
If you get an error message, verify that you've completed the steps above. If you can't resolve the issue, please open an issue.
The easiest way to start using Guild is to run some of the examples. Clone the example repository:
$ git clone [email protected]:guildai/guild-examples.git
Change to the MNIST example and train the intro model. This model
downloads MNIST images and so requires an initial
before any of the models can be trained.
$ cd guild-examples/mnist $ guild prepare
This operation will take some time to download the MNIST images. When it finished, train the intro model:
$ guild train intro
The intro example corresponds to TensorFlow's MNIST for ML Beginners. It's a very simple model and should train in a few seconds even on a CPU.
Next, run Guild View from the same directory:
$ guild view
Open http://localhost:6333 to view the training result. You should see the results of the intro training, including the model validation accuracy, training accuracy, steps, and time. The view also includes time series charts that plot training loss, accuracy, and CPU/GPU information during the operation. Note the training may not have run long enough in this simple case to collect system stats.
Next, train the expert version of MNIST. You can keep running View
during any Guild operation -- in that case, open another terminal,
guild-examples/mnist and run:
$ guild train expert
This model correspond to TensorFlow's Deep MNIST for Experts example. As it trains a multi-layer convolutional neural network it takes longer to train.
You can view the training progress in real time in Guild View -- select the latest training operation from the dropdown selector in the top left of the View page.
You can compare the performance of multiple runs in Guild View by clicking the Compare tab. When the expert model finishes training, you can compare its validation accuracy to the intro model -- it's significantly more accurate, at the cost of a longer and more computationally expensive training run.
You can train either model using more epochs (rounds of training using the entire MNIST training set) -- this will improve validation accuracy up to a point:
$ guild train expert -F epochs=5
-F sets a model flag that is used by the operation. In this
case we're asking the model to train over 5 epochs. You should see a
slight improvement in validation accuracy -- again, at the cost of
Finally, evaluate the model performance using the MNIST test data:
$ guild evaluate --latest-run
This will evaluate the model trained on the latest and print the test accuracy.
For background on why test is different from validation, see this section in TensorFlow's documentation on network retraining.
Documentation for Guild is in process but not yet available. While lacking in detail, you may benefit from:
- Reading Guild examples source code
guild COMMAND --help
- Guild-enable an existing project by running
guild initand editing the generated