tensorboard¶
Monitor/profile the training of a model using Tensorboard.
Additional Documentation¶
Usage¶
Usage: mltk tensorboard [OPTIONS] <model>
Start Tensorboard for the given model
In machine learning, to improve something you often need to be able to
measure it.
TensorBoard is a tool for providing the measurements and visualizations
needed during the machine learning workflow.
It enables tracking experiment metrics like loss and accuracy, visualizing
the model graph,
projecting embeddings to a lower dimensional space, and much more.
For more details, see:
https://www.tensorflow.org/tensorboard/get_started
https://siliconlabs.github.io/mltk/docs/guides/model_training_monitor
NOTE: The model must be trained (or actively being trained) before using this
command.
Additionally, the trained model must have the 'tensorboard' property
configured.
e.g.:
my_model.tensorboard = dict(
histogram_freq=1, # frequency (in epochs) at which to compute
activation and weight histograms
# for the layers of the model. If set to 0,
histograms won't be computed.
# Validation data (or split) must be specified
for histogram visualizations.
write_graph=True, # whether to visualize the graph in TensorBoard.
The log file can become quite large when write_graph is set to True.
write_images=False, # whether to write model weights to visualize as
image in TensorBoard.
update_freq="epoch", # 'batch' or 'epoch' or integer. When using
'batch', writes the losses and metrics
# to TensorBoard after each batch. The same
applies for 'epoch'.
# If using an integer, let's say 1000, the
callback will write the metrics and losses
# to TensorBoard every 1000 batches. Note that
writing too frequently to
# TensorBoard can slow down your training.
profile_batch=2, # Profile the batch(es) to sample compute
characteristics.
# profile_batch must be a non-negative integer or
a tuple of integers.
# A pair of positive integers signify a range of
batches to profile.
# By default, it will profile the second batch.
Set profile_batch=0 to disable profiling.
)
----------
Examples
----------
# Start tensorboard for the previously trained keyword_spotting_on_ff_v2
model
# This will open a webpage to the local Tensorboard GUI
mltk tensorboard keyword_spotting_on_ff_v2
# Start training the audio_example1 model
mltk train audio_example1
# In a separate terminal, start the tensorboard GUI
mltk tensorboard audio_example1
Arguments
* model <model> One of the following:
- Name of MLTK model
- Path to MLTK model's python script
[default: None]
[required]
Options
--host -h <host> Local interface to start HTTP server
[default: localhost]
--port -p <port> Listen port of HTTP server used to
view graph
[default: 6002]
--launch --no-launch Automatically open a webbrowser to
the Tensorboard GUI
[default: launch]
--verbose -v Enable verbose console logs
--help Show this message and exit.