Releases | Open Source Science

Sapsan - v0.6.5

Changes

Estimators

Added a Physics-Informed CNN estimator for 1D Turbulence
- example in pimlturb1d_example.ipynb
- added a unit test on commit as well
- Gaussian filter layer now supports 1D

Examples

New plotting_examples.py that include tutorials on:
- line_plot
- slice_plot
- pdf_plot
- cdf_plot
- log_plot
- model_graph
Adjusted example formatting to unify them in style
Reduced the size of the example data by 50%

Plotting

Fixed: model_graph() - model visualizations are fully operational again
- it also now supports 1D graphs, as well as 2D and 3D

MLflow

Added new methods
- log_model() - corresponding to mlflow.pytorch.log_model()
- load_model() - corresponding to mlflow.pytorch.load_model()

CLI

Fixed: sapsan get_examples not copying all examples

Training Backend

Slowly transitioning away from Catalyst - it is no longer required for the model save/load routines
Added TorchScript option
Fixed: .next() for iter() in evaluation module
- also get_loader_shape call of the loader's next iteration

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 2 years ago

Sapsan - v0.6.2

Changes

Estimators

Fixed: PIMLturb compatibility with TorchScript for remote deployment
- other estimators should be already compatible

MLflow

Fixed: hanging indefinitely trying to auto-start the mlflow ui
- added a 30s timeout in case the given port is taken, but not with MLflow
- clear error message asking to change the port
Default port is now 5000, as per MLflow's standard defaults
- the previous one had a compatibility issue with WSL
Latest MLflow version is now supported

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 3 years ago

Sapsan - v0.6.1

Changes

Physics

PowerSpectrum now supports velocity input in 1D, 2D, and 3D
Cleaned-up filters code for legibility
Kolmogorov doesn't include '0th' bin: no more warning

GUI

Fixed: Examples.py widget parameter adjustment

Tests

Fixed: test error if resource_path exists

Compatibility

mlflow>=1.20.1 -> mlflow>=1.20.1,<=1.25
- mlflow>=1.26 breaks ui client auto-startup

Other

Examples' output has been updated

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 3 years ago

Sapsan - v0.6.0

Changes

Wiki

Brand New! Created with mkdocs-material: sapsan-wiki.github.io
- Powerful search function
- Redesigned API
- Improved navigation
- Versioning
- Dark mode: automatically adjusts based on system preferences
- Wiki on Github has been depreciated

Estimators

Significant improvements to the PIMLturb estimator
- cleaned up redundancies improving the performance and readability
- generalized the approach to calculate CDF and KS loss
- now works with the 1D CCSN calculations
- should be consistent no matter the scale of the data
- added ks_frac, ks_scale, l1_scale, l1_beta, sigma to be adjusted upon calling the estimator
- learn more at PIMLTurb API
- scientific notation for PIMLturb loss stdout
- Fixed SmoothL1_KSLoss train/valid output
- PIMLturb now logs the model, optimizer, and scheduler parameters through MLflow

GUI

Updated GUI examples, adding compatibility with streamlit=1.12.0
- significant improvements to UI through st.expander
Converted GUI to use st.session_state for all widgets
- that fixed config reloading
- included minor quality of life features
- significantly reduced the complexity of the code
Fixed: editing the model code with jupyter notebooks
Added:
- progress bar
- slice plots
- Dark Mode

CLI

Lighter init, improved CLI speed x3
- Affected syntax:
  - Past: from sapsan import Train, Evaluate
  - New: from sapsan.lib import Train, Evaluate
Fixed: paths with CLI commands

Examples

Examples now include output
Updated sample data for picae
- randomly sampled from a normal distribution
Returned FakeBackend()
- makes it easier to disable logging everywhere in one line
Cleaned up Examples to be up-to-date on comments

Plotting

Beautified colormap bar
- always equals to the size of the plot itself
- slimmed down
Added: dpi parameter to plot functions
- Default: dpi=60 for all to avoid 'ballooning' in small margin jupyter notebooks
Added: cdf_plot(), an exception if value ranges don't overlap, hence KS stat cannot be calculated

MLflow

Train will try to log forward() of your model
- no longer Catalyst exclusive
- won't cause an error with scikit-learn

Compatibility

Added: python=3.9 and 3.10 support
streamlit==0.84.0 -> streamlit>=1.12.0
- major improvements
- not backward compatible

Other

README: added shields.io badges to track sapsan and compatible python versions
Fixed setuptool installation: python setup.py install
Github Workflow updates and improvements. Added tests for PyPI, CLI, python 3.9, 3.10

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 3 years ago

Sapsan - v0.5.0

Changes

Estimators

Added a Physics-Informed CNN estimator used to predict diagonal Reynolds stress tensor terms for further turbulence pressure calculation
- pimlturbdiagonalestimator.py
- the method was published in P.I.Karpov et al, 2022
- wiki page: Physics-Informed CNN for Turbulence Modeling

Examples

Added PIMLTurb data at 17³ resolution & adjusted data path for examples
Added PIMLTurb diagonal example with new torch_modules
- pimlturbdiagonalexample.ipynb

Evaluation

Added full support for 2D and 1D evaluations
- Plots: slices for 3D and 2D, profiles for 1D

Data Loading

Fixed: features_label & target_label loading
- useful when different features are contained in the same .hdf5 file
- if target is not provided, but the target_label is given, the target will still be loaded
Fixed: 1d data axis assignment
input_size now takes lists

Plotting

renamed names -> labels for plots
- now consistent with matplotlib
Added linestyle argument to line_plot()
Added new parameters for log_plot()
- aids flexibility for custom logs
- Review new parameters in the API Plotting under log_plot()

Package Compatibility

Changed requirements:
- protobuf==3.20.*
  - torch misbehaves with the latest protobuf
- numpy>=1.19.0 -> numpy>=1.21.0
- scipy>=1.5.2 -> scipy>=1.7.3
- scikit-learn>=0.23.2 -> scikit-learn>=1.0.2
- scikit0image>=0.17.2 -> scikit-image>=0.19.3
  - Fixed issues with the KRR example

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 3 years ago

Sapsan - v0.4.8

Changes

Import Sapsan

Considerably faster package loading
- Estimators are no longer loaded on __init__: frees up memory

Training

Improved torch_backend.set_device() to assign exact device index
- relays device passed to config
Fixed: cleanup won't cause an error if artifacts are misplaced

Training log plot

Added valid_loss to be plotted in runtime_log plot

Data Loading

Added new parameter to HDF5Dataset: batch_num
- sets the number of batches to be loaded at a time
  - helps with memory
  - loss is averaged over all checkpoints
  - Ex: you have 10 batches to train on, you can load 1 at a time for training
- updated the API in the Wiki on github

MLflow

Fixed: MLflow ui auto-start freeze
- added a check if a port is free: if True - mlflow ui will start, if False - it will try to set an mlflow experiment

Package Compatibility

Locked Catalyst to version >=21.5, <=21.12
- hot fix
- Catalyst 22.0+ changed its conventions, which broke device/engine setup and logging

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 4 years ago

Sapsan - v0.4.7

Changes

Data Loading

Fixed: using multiple checkpoints when batch_num is not specified
- before data would load from all checkpoints, but only the 1st one would be used for training by default

Training log plot

Fixed: if training log fails, the plot was producing and error, with the trained model not being saved
- happened sometimes during the long training sessions
- log plotting no longer affects model output

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] about 4 years ago

Sapsan - v0.4.6

Changes

Filters

Fixed: compatibility with the latest opencv-python ( >=4.5.4)
- 2D box filter is now working correctly

Subgrid Model

Updated: Dynamic Smagorinsky model
- improved initialization
- added 2D support

Evaluation

Removed: a redundant parameter requirement in Evaluate()
- 'flat' condition is now passed along with other input data parameters in data_loader

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] about 4 years ago

Sapsan - v0.4.5

Changes

Distributed Data Parallel (DDP)

Fixed: DistributedDataParallel (DDP)
- engine is no longer overwritten
- will be determined automatically by Catalist if ddp=True
- can always be customized by hand (Parallel GPU Training)
Updated: device in Run Info better reflects if attempting to run in parallel

MLflow

Returned auto-termination of MLflow tracking after Evaluation.run()
- cleans up MLflow logging (was getting messy before, when loading the model to evaluate without training)

Plotting

Updated: default plot formatting
- colormaps are colorblind friendly now (tableau-colorblind10 and viridis)
- log ticks are inward + size adjustment for a cleaner look
- thin dotted default grid
- you can always call sapsan.utils.plot.plot_params(), which returns the full set of default parameters
Updated: spectrum_plot formatting for consistency with other plotting routines
- Renamed: plot_spectrum() -> spectrum_plot()
- Now returns Axes object, as do others

Command Line Interface (CLI)

Renamed: --ddp -> --gtb or --get_torch_backend option for sapsan
- to copy torch_backend.py when creating the project: sapsan create --gtb -n {name}
New Command: sapsan get_torch_backend
- copies torch_backend.py into your working directory
- this allows you to not have to 'create' a project to copy the backend
- you can proceed to edit the Catalyst runner (custom loss, optimizer, DDP config, etc.)

Custom Estimator

Added: a guide on how to go deeper and edit Catalyst runner
Added: a convenient command to copy torch_backend.py in your working directory (see above)

Gradient Model

Fixed: derivative multiplication
Fixed: model calculation consistency

Other

Renamed: tensor() -> ReynoldsStress() to avoid confusion
Documentation updated accordingly

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - 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
  - nested = True by default
- 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
- sapsan/examples/GUI
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

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - v0.4.3

Changes

PyPi Release

./examples directory is now included in the release
- added MANIFEST.in

Command Line Interface (CLI)

Fixed: sapsan get_examples command

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - v0.4.0

Changes

General

Loaders for Train and Evaluate now have the same format
The functions above have an identical interface for both PyTorch and Sklearn

Estimators

Fixed: Model Saving & Loading
- loaded models can continue to be trained
- upon their initialization when loading the model, you can redefine its previous config, such as n_epoch, lr
- optimizer dict state is correctly saved and loaded
- optimizer state is moved to cpu or gpu depending on a setup (catalyst doesn't do it on its own, which caused issues when evaluating a loaded model)
- Added dummy estimators for loading (otherwise all estimators have load and save)
  - load_estimator() for torch
  - load_sklearn_estimator() for sklearn
Reworked how the models are initialized
- upon calling the estimator, ex: estimator = CNN3d(loaders=loaders)
  - before: when training the model, upon estimator.train
- model initialization requires to provide loaders now
All self vars in ModelConfig() get recorded in tracking by default
Added options in ModelConfig()
- lr and min_lr - learning rate parameters are no longer hard-coded
- device - sets a specific device to run the models on, either cpu or cuda
Added sklearn_backend and torch_backend to be used by all estimators
- sklearn-based estimators have a structure close to torch-based
- pytorchestimator -> torchbackend
- cleared up variable name conventions throughout

Evaluation

Evaluate and Data loader accept data without target
- useful when there is no ground truth to compare to
- will still output pdf, cdf, and spatial, without comparison metrics
- Evaluate.run() now output a dict of "pred_cube" and "target_cube" (if the latter is provided)
PDF and CDF plots are now combined under a single figure
- recorded as 'pdf_cdf.png' in MLflow
Fixed: definition of n_output_channel in Evaluate()

Command Line Interface (CLI)

Added new option: sapsan create --ddp option copies torch_backend.py
- gives ability to customize Catalyst Runner
- adjust DDP settings based on the linked Catalyst DDP tutorial in the Wiki
- will be useful when running on HPC
- refer to Parallel GPU Training on the Wiki for more details
Fixed: CLI click initialization

Graphical User Interface (GUI)

Up to date with Streamlit 0.87.0
PDF and CDF plots are now showed as well
Fixed: data loading issue in regards to train_fraction

MLflow

MLflow: evaluate runs will be nested under the recent train run
- significantly aids organization
Added estimator.model.forward() to be recorded by MLflow (if torch is used)

Plotting

Plotting routines return Axes object
All parameters are changed for the Axes instead of plt which allows individual tweaking after return
figsize and ax arguments added to most plotting routines
- useful if you create a figure and subplots outside of the plotting routines
Universal plotting params expanded and were made easily accessible through plot_params()

Other

Edited the examples, tests, and estimator template to reflect model initialization changes
Requirements Updated:
- streamlit >= 0.87.0
- plotly >= 5.2.0
- tornado >= 6.1.0
- notebook >= 6.4.3 (fixes security vulnerabilities)
Added a few data_loader warnings
Cleaned up debug prints throughout the code
Expanded code comments

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - v0.3.0

Changes

Command Line Interface (CLI)

New & Changed Commands
- sapsan create --name {name} or sapsan create -n {name} - creates a custom project template tree
- sapsan test - runs pytest to make sure Sapsan is working correctly
- sapsan get_examples - copies Sapsan's examples into the current working directory for easy access
- sapsan --version - to check the installed version
Updated CLI options and --help

Testing

moved tests to Sapsan's root folder, so they are always accessible when installed via pip
notebook tests don't create a separate folder, but test on the existing example notebooks
sapsan test allows for the user to run pytest tests to check if everything was installed correctly

Custom Estimators

To get started, run: sapsan create -n {name}
- where {name} should be replaced with your custom project name
Significantly simplified the template structure, making it much easier to navigate and get started
- cleaned up by removing all unnecessary templates, leaving a few scripts that allow to customize the estimator (i.e. ML network), Jupyter Notebook interface, and Dockerfile to easily share your project
- pre-filled all templates with the custom project name

Examples

sapsan get_examples: copies examples into ./sapsan_examples
- makes the example jupyter notebooks with various ML algorithms easily accessible

Other

sapsan now has __version__ attribute
Updated the following sections in Documentation to reflect the CLI and template changes
Minor comment updates to example notebooks

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - v0.2.11

Changes

Testing

notebook testing errors trigger pytest
estimator loading testing is the same for both PyTorch and Scikit-learn

MLflow

for evaluation, some model training pars and metrics are also recorded to make it easier to see with what model the prediction was done

PICAE Estimator

removed redundant torch.device pass for PICAE estimator

Estimators

simplified ModelConfig() in both included estimators and custom template
- ModelConfig now only has __init__
- moved load() to backend, removed to_dict()
- added loadconfig() under core Estimator class in models.py to enforce it to be included in custom estimators (most cases won't be affected, as it is part of the backend pytorchestimator.py)
- changes reflected in the estimator template + wiki
- Save config now saves all input parameters instead of only the ones tracked by MLflow
- Loading is now consistent between PyTorch and scikit-learn based models

Other

minor formatting fixes in examples
minor bug fixes

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - v0.2.10

Changes

There are no new features in this update, but numerous quality of life improvements and bug fixes.

Data Loader

fixed multi-checkpoint loading for hdf5 module
- they are added as new batches to the loaded data np.array
fixed splitting by batches
- the rounding error when computing the fraction to split into batches has been corrected
- default batchnum=1, hence batchsize = input_size if it is not specified (no need to specify the batching in the loader)
fixed input data size processing, when sampling is not specified

Backend

FakeBackend is now the default if backend is not specified

Sampling

no longer need to pass original input shape
- it is determined from the data itself
added a warning on dealing with irregular shapes
- the function will try to match the requested shape, but if it can't - a warning is issued

Tensor Calculation

fixed Tensor calculation indices
added "onlyxcomponents" flag
- at times full tensor is too taxing on memory

Requirements

corrected installation requirements under python 3.8.10+

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - v0.2.9

Changes

Parallel GPU

Parallel GPU training is done via Distributed Data Parallel (DDP), through Catalyst
- Besides locally, capable of running on HPC
Fixes in train and evaluation methods to account for DDP
Added a Parallel GPU tutorial on Wiki

Physics

Gradient Model - full 3D tensor is now calculated, fixes to the algorithm
Dynamic Smagorinsky Model - fully operational, outputs full 3D tensor
Added tensor() function to calculate a stress tensor (see API entry)
assertion checks in various functions to make the errors more intelligible

Docker

Minor quality of life simplifications
Added a Docker tutorial on Wiki

MLflow

Updated compatibility with MLflow 18.0
No need to restart kernel when re-running the model in jupyter notebooks
Fixed compatibility with Catalist 21.7 logging
Added MLflow tutorial on Wiki

GUI

Fixed compatibility with Catalist 21.7 - correct logging and plotting

Other

Expanded Custom Estimator tutorial on Wiki
Added Community Guidelines to add new ML models and contribute to Sapsan
Set up a jupyter notebook example on Google Colab to play around with Sapsan
Minor bug fixes

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - v0.2.8

Changes

requirement updates
simplified hierarchy
compatibility with Catalyst 21.5+
removed automatic PyTorch install: user will need to do it manually

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] over 4 years ago

Sapsan - v0.2.7

Changes

fixed PICAE in pypi

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago

Sapsan - v0.2.6

Changes

GUI fixes - brought up to date with the latest backend
minor bug fixes

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago

Sapsan - v0.2.5

Changes

Physics Informed CAE method
- reworked PICAE now adheres Sapsan's interface (sapsan/lib/estimators/picae.py)
- added PICAE example with random data
Data loaders
- loaders are now consistent between pytorch and sklearn
- train() takes in loaders, instead of inputs & targets separately. Those can be either Pytorch.Dataloader or a list of inputs & targets (i.e loaders = [x,y]). This is done to accommodate Pytorch-based and sklearn-based models, which require the input of different formats.
  - data can be loaded as a numpy array by calling load_numpy(), instead of load
  - loaded numpy data can be converted to torch dataloader via convert_to_torch([x, y])
  - alternatively, both of the steps can be combined by just calling load()
- cleaned up datafunctions; added new methods: `flatten, splitbybatch, getloader_shape`
- corrected split into train and valid datasets + enhanced with traintestsplit function from sklearn
- added new params to dataloader: `trainfraction, shuffle`
- support for irregular input data shapes
Examples
- added PICAE example with random data
- cleaned up examples further
Estimators
- cleaned up CNN3d estimator, deleted legacy functions
- further generalized Pytorch estimator to be used as a backend for any PyTorch-based models
Templates
- reflect the changes from above - streamlined
Tests
- added PICAE related tests on push
Misc
- further improvement to backend handling of data transformations
- minor bug fixes

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago

Sapsan - v0.2.4

Changes

estimator template bug fix @pikarpov-LANL (#88)

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago

Sapsan - v0.2.3

Changes

Docker comments
Requirements fixed so jupyter notebook is no longer misbehaving
CLI templates updated @pikarpov-LANL (#86)

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago

Sapsan - v0.2.2

Changes

added a physics model
- power spectrum calculation
- gradient turbulence subgrid model
added data filters,
- spectral filter [2D, 3D]
- box filter [2D]
- gaussian filter [2D, 3D]
updated Docker setup

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago

Sapsan - v0.2.1

Changes

Minor bugs + GUI fixes @pikarpov-LANL (#81)
Cleanup of method parameters @pikarpov-LANL

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago

Sapsan - v0.2.0

Changes

Model graph visualization + GUI @pikarpov-LANL (#76)
- model graph is now much cleaner and more versatile in customization through graphviz
- enhanced my own version of hiddenlayer
  - Graphviz needs to be installed separately to plot model graphs
- GUI interface for model graph has been reworked
- GUI got a customization for train and test checkpoints

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago

Sapsan - v0.1.9

Changes

Upgraded hdf5 loader to load flat and batched data
Upgraded evaluation routine to handle 2D & 3D + flat output
Plotting enhancement
Fixes to KRR example
Expansion of unit tests on push
Automatic mlflow ui start-up for GUI+notebook
Updated readme.md
Minor bug and UI fixes

Scientific Software - Peer-reviewed - Python
Published by github-actions[bot] almost 5 years ago