(I'm moving between meetings so can iterate on this more later, so excuse the very brief Issue for now).

At the moment the project has an upper bound on python_requires

https://github.com/saforem2/l2hmc-qcd/blob/2eb6ee63cc0c53b187e6d716f4c12f418c8b8515/setup.py#L165

Assuming that you're intending l2hmc to be a library and not an application, then I would highly recommend removing this for the reasons summarized in Henry's detailed blog post on the subject.

Congrats on getting l2hmc up on PyPI though! :snake: :rocket:

Pull upstream alpha branch into main

Major changes

• new src/ hierarchical module organization
• Contains skeleton implementation of 4D SU(3) lattice gauge model
  • src/l2hmc/lattice/gauge/lattice.py
• Framework independent configuration
  • Unified configuration system simplifies logic, same configs used for both tensorflow and pytorch experiments
  • Plan to be able to specify which backend to use through config option
• Unified (and framework independent) configurations between tensorflow and pytorch implementations
  • Definitions can be found in l2hmc-qcd/src/l2hmc/configs.py

Note: This is still very much a WIP. Many existing features still need to be re-implemented / updated into new code in src/.

Todo

• [ ] Write unit tests
• [ ] Use simple configs for end-to-end workflow test + integrate into CI
• [ ] dynamic learning rate scheduling
• [ ] Test 4D SU(3) numpy code
• [ ] Write tensorflow and pytorch implementations of LatticeSU3 objects
• [ ] Improved / simplified ( / trainable?) annealing schedule
• [ ] Distributed training support
  • [ ] horovod
  • [ ] DDP for pytorch implementation
  • [ ] DeepSpeed from Microsoft??
• [ ] Testing / inference logic
• [ ] Automatic checkpointing
• [ ] Metric logging
  • [ ] Tensorboard?
  • [ ] Sacred?
  • [ ] build custom dashboard? plot.ly?
• [ ] Setup packaging / distribution through pip
• [ ] Resolve issue

• Major upgrades to how training is initialized in l2hmc-qcd/utils/training_utils.py, particularly when trying to restore a model from an existing checkpoint.
• Significant upgrades to logging mechanics in l2hmc-qcd/utils/logger.py and l2hmc-qcd/utils/logger_config.py which now use a RichHandler to nicely format log messages characterized by severity, including automatic file rotation, etc.
• Improvements to test suite in l2hmc-qcd/tests/test_training.py, more robust tests on larger set of possible cases
  • TODO: Automate using github actions for CI
• Improvements to l2hmc-qcd/dynamics/gauge_dynamics.py but still a WIP

General improvements, rewrote logging methods to use Rich for better formatting.

• Adds dynamic (trainable) step size eps for each separate x and v updates, seems to generally increase the total energy towards the middle of the trajectory but it remains unclear if this corresponds to an improvement in the tunneling rate
• Adds methods for calculating autocorrelations of the topological charge, as well as notebooks for generating the plots
• Updates to the writeup in doc/main.pdf
• Will likely be last changes to writeup before public release of official draft

• Updates to README

• Ability to load network with new training instance

• Updates to doc/, removes old sections related to debugging the bias in the plaquette

Complete rewrite of dynamics.xnet and dynamics.vnet models to use tf.keras.functional Models.

Additional changes include:

• Non-Compact Projection update for gauge fields
• Ability to specify convolution structure to be prepended at beginning of gauge network

Removes models/gauge_model.py entirely.

Instead, a base dynamics class is implemented in dynamics/dynamics.py, and an example subclass is provided in dynamics/gauge_dynamics.py.

Major rewrite of existing codebase.

This pull request updates everything to be compatible with tensorflow >= 2.2 and removes a bunch of redundant legacy code.

• Dynamics object is now compatible with tf >= 2.0
• Running inference on trained model with tensorflow now creates identical graphs and summary files to numpy inference code
• Inference with numpy now uses object oriented structure
• Adds LaTeX + PDF documentation in doc/

Adds new GaugeNetwork architecture as the default for training GaugeModel

Additionally, replaces pickle with joblib for saving data as .z compressed files (as opposed to .pkl files).

Implemented nnehmc_loss calculation for an alternative loss function using the approach suggested in https://infoscience.epfl.ch/record/264887/files/robust_parameter_estimation.pdf.

This modified loss function can be chosen (instead of the standard loss described in the original paper) by passing --use_nnehmc_loss as a command line argument.

As you've made a library and are using it as such:

# snippet from toy_distributions.ipynb

# append parent directory to `sys.path`
# to load from modules in `../l2hmc-qcd/`
module_path = os.path.join('..')
if module_path not in sys.path:
    sys.path.append(module_path)

# Local imports
from utils.attr_dict import AttrDict
from utils.training_utils import train_dynamics
from dynamics.config import DynamicsConfig
from dynamics.base_dynamics import BaseDynamics
from dynamics.generic_dynamics import GenericDynamics
from network.config import LearningRateConfig
from config import (State, NetWeights, MonteCarloStates,
                    BASE_DIR, BIN_DIR, TF_FLOAT)

from utils.distributions import (plot_samples2D, contour_potential,
                                 two_moons_potential, sin_potential,
                                 sin_potential1, sin_potential2)

do you have any plans and/or interest in packaging it as a Python library so it can either be pip installed from GitHub or be distributed on PyPI?