:bug: tensorboard dependency missing, API update

[enryH]

• main class supports logging, but the dependencies are not automatically installed
• update Network and BINNClassifier interface in docs

I left the errors in the notebooks. It's rather a documentation of the errors than an update.

[enryH]

One more thing I noticed: Your git history is ~4GB large, which means probably some old data is still in the history...

[enryH]

I'll try now local installation: Is your pypi package up-to-date?

[enryH]

So yes, this was it indeed. In the shap_example.ipynb I now get the following error, when trying to call trainer.fit

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
c:\Users\user\Documents\repos\BINN\docs\shap_example.ipynb Cell 2 line 1
     14 dataloader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=True)
     16 trainer = Trainer(max_epochs=3, log_every_n_steps=10)
---> 17 trainer.fit(binn, dataloader)

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\trainer\trainer.py:544, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    542 self.state.status = TrainerStatus.RUNNING
    543 self.training = True
--> 544 call._call_and_handle_interrupt(
    545     self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
    546 )

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\trainer\call.py:44, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
     42     if trainer.strategy.launcher is not None:
     43         return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
---> 44     return trainer_fn(*args, **kwargs)
     46 except _TunerExitException:
     47     _call_teardown_hook(trainer)

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\trainer\trainer.py:580, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
    573 assert self.state.fn is not None
    574 ckpt_path = self._checkpoint_connector._select_ckpt_path(
    575     self.state.fn,
    576     ckpt_path,
    577     model_provided=True,
    578     model_connected=self.lightning_module is not None,
    579 )
--> 580 self._run(model, ckpt_path=ckpt_path)
    582 assert self.state.stopped
    583 self.training = False

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\trainer\trainer.py:989, in Trainer._run(self, model, ckpt_path)
    984 self._signal_connector.register_signal_handlers()
    986 # ----------------------------
    987 # RUN THE TRAINER
    988 # ----------------------------
--> 989 results = self._run_stage()
    991 # ----------------------------
    992 # POST-Training CLEAN UP
    993 # ----------------------------
    994 log.debug(f"{self.__class__.__name__}: trainer tearing down")

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\trainer\trainer.py:1035, in Trainer._run_stage(self)
   1033         self._run_sanity_check()
   1034     with torch.autograd.set_detect_anomaly(self._detect_anomaly):
-> 1035         self.fit_loop.run()
   1036     return None
   1037 raise RuntimeError(f"Unexpected state {self.state}")

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\loops\fit_loop.py:202, in _FitLoop.run(self)
    200 try:
    201     self.on_advance_start()
--> 202     self.advance()
    203     self.on_advance_end()
    204     self._restarting = False

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\loops\fit_loop.py:359, in _FitLoop.advance(self)
    357 with self.trainer.profiler.profile("run_training_epoch"):
    358     assert self._data_fetcher is not None
--> 359     self.epoch_loop.run(self._data_fetcher)

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py:136, in _TrainingEpochLoop.run(self, data_fetcher)
    134 while not self.done:
    135     try:
--> 136         self.advance(data_fetcher)
    137         self.on_advance_end(data_fetcher)
    138         self._restarting = False

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\loops\training_epoch_loop.py:240, in _TrainingEpochLoop.advance(self, data_fetcher)
    237 with trainer.profiler.profile("run_training_batch"):
    238     if trainer.lightning_module.automatic_optimization:
    239         # in automatic optimization, there can only be one optimizer
--> 240         batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
    241     else:
    242         batch_output = self.manual_optimization.run(kwargs)

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\loops\optimization\automatic.py:187, in _AutomaticOptimization.run(self, optimizer, batch_idx, kwargs)
    180         closure()
    182 # ------------------------------
    183 # BACKWARD PASS
    184 # ------------------------------
    185 # gradient update with accumulated gradients
    186 else:
--> 187     self._optimizer_step(batch_idx, closure)
    189 result = closure.consume_result()
    190 if result.loss is None:

File c:\Users\user\Anaconda3\envs\binn\Lib\site-packages\lightning\pytorch\loops\optimization\automatic.py:265, in _AutomaticOptimization._optimizer_step(self, batch_idx, train_step_and_backward_closure)
    262     self.optim_progress.optimizer.step.increment_ready()
    264 # model hook
--> 265 call._call_lightning_module_hook(
    266     trainer,
    267     "optimizer_step",
...
   3051 if size_average is not None or reduce is not None:
   3052     reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 3053 return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing)

RuntimeError: expected scalar type Long but found Short

[enryH]

So this was just the type of y, which needs to be int64: torch.tensor(y, dtype=torch.int64, device=binn.device)

[ErikHartman]

One more thing I noticed: Your git history is ~4GB large, which means probably some old data is still in the history...

I'm currently working on reducing the .git history. Oddly, I can't find any large files in the git history... But it might be some old logs from PyTorch (so-called lightning logs). Currently looking into if I could remove these from the git-history. Alternatively, I'll create an orphan branch and make it the main branch, but this is not to be preferred really.

[enryH]

I guess you can keep your history as long as the pip package is up-to-date:) I cloned the repository without the full history, which was fine. Maybe you just put this as the default (with lets say the last 10 commits)