Releases
v0.4.4
Changes
MLflow
New parameters for Train()
and Evaluate()
run_id
parameter after run()
has been called
allows to resume and record to a specific run at a later time
run_name
to change the recorded run names
by default, they are train
and evaluate
as recorded in MLflow
One can add new metrics/parameters/artifacts after Train or Evaluate have completed
either Sapsan's backend interface or a traditional MLflow interface can be used
Wiki update: MLflow Tracking
Changes to MLflowBackend()
while loop for close_active_run()
to make sure all runs have been closed
new function resume()
which requires to provide the run_id
to resume and record to the run
Wiki update: API Reference: Backend (Tracking)
Plotting
New parameter for cdf_plot
ks
- controls to print Kolmogorov-Smirnov Statistic on the plot itself
also outputs it as ax, ks = cdf_plot(...)
New parameters for Evaluate
pdf_xlim
, pdf_ylim
- x and y limits to control the pdf plot
cdf_xlim
, cdf_ylim
- same for the cdf plot
Fixed: model_graph
no longer sets number of channels to 1
the easiest way to construct the graph is to pass the training loader shape
Wiki update: Model Graph
Graphical User Interface (GUI)
GUI examples are now included in PyPi
The file structure has been simplified
unnecessary files removed
The scripts have been cleaned up, with more comments, and a clearer function organization to aid editing
Brought up to date with the most recent Sapsan version
Core package has been locked to streamlit == 0.84.2
there is a known bug causing pd.DataFrames to not display properly
will update once Streamlit team fixes those issues
Wiki update: GUI Examples
Command Line Interface (CLI)
Changes to sapsan get_examples
GUI examples will be copied as well, found in ./sapsan-examples/GUI
Other
Fixed the exact device ID issue: affected the multi-GPU systems
tensors no longer move only to the default (cuda:0 ), but to a correct device id
Updated the requirements template
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