Datasets:

Rami

/

multi-label-class-github-issues-text-classification

like 0

Support multiple loggers at once

🚀 Feature Users might want to use more than 1 loggers at once

Checkpointing Names

📚 Documentation The names I am getting from checkpointing seem different than this doc page: https://pytorch-lightning.readthedocs.io/en/latest/checkpointing.html Filepath seems to do something different. (directory?)

global_step advanced between accumulations if gradient_accumulation > 1

🐛 Bug If gradient_accumulation is > 1 and a custom scheduler is used that updated the LR based on steps (instead of default epochs) than global step is incorrect since it is advancing at every batch part (depending on gradient_accumulation value) instead only after all parts of the batch times gradient_accumulation. To fix this: Trainer global_step is advanced only if global_step % gradient_accumulation == 0. it has no effect if gradient_accumulation == 1 (global step is advancing as currently implemented) To Reproduce Steps to reproduce the behavior: Run any model with gradient_accumulation > 1 and verify with trainer.global_step Expected behavior For example: (batch size=1, accumulation=2) b_idx = 0, global_step = 1 b_idx = 1, global_step = 2 backprop b_idx = 2, global_step = 3 b_idx = 3, global_step = 4 backprop correct flow: b_idx = 0, global_step = 1 b_idx = 1, global_step = 1 backprop b_idx = 2, global_step = 2 b_idx = 3, global_step = 2 backprop Environment Collecting environment information... PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.1 OS: Ubuntu 16.04.6 LTS GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609 CMake version: version 3.5.1 Python version: 3.7 Is CUDA available: Yes CUDA runtime version: 10.1.243 GPU models and configuration: GPU 0: TITAN Xp GPU 1: TITAN Xp GPU 2: TITAN Xp GPU 3: TITAN Xp Nvidia driver version: 418.87.01 cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.4.2 /usr/local/cuda-10.0/targets/x86_64-linux/lib/libcudnn.so.7.4.2 /usr/local/cuda-8.0/targets/x86_64-linux/lib/libcudnn.so.6.0.20 Versions of relevant libraries: [pip] numpy==1.18.1 [pip] pytorch-ignite==0.2.1 [pip] pytorch-lightning==0.6.0 [pip] torch==1.4.0 [pip] torchprof==1.0.0 [pip] torchvision==0.5.0 [conda] blas 1.0 mkl [conda] mkl 2019.4 243 [conda] mkl-service 2.3.0 py37he904b0f_0 [conda] mkl_fft 1.0.15 py37ha843d7b_0 [conda] mkl_random 1.1.0 py37hd6b4f25_0 [conda] pytorch 1.4.0 py3.7_cuda10.1.243_cudnn7.6.3_0 pytorch [conda] pytorch-ignite 0.2.1 pypi_0 pypi [conda] pytorch-lightning 0.6.0 dev_0 [conda] torchprof 1.0.0 pypi_0 pypi [conda] torchvision 0.5.0 py37_cu101 pytorch

Demo Notebook Error:

🐛 Bug trainer.fit(gan_model) ValueError: Your LightningModule defines 2 optimizers but training_step is missing the "optimizer_idx" argument. To Reproduce Steps to reproduce the behavior: Go to this link. Run the cell Scroll down . See error PyTorch Version (e.g., 1.0): 1.4.0 OS (e.g., Linux): Linux How you installed PyTorch (conda, pip, source): pip Build command you used (if compiling from source): Python version: 3.6 CUDA/cuDNN version: GPU models and configuration: Any other relevant information: Additional context This is the exact demo notebook.

Tensorboard logging should use `num_grad_updates` not `batch_idx`

When accumulate_grad_batches > 1, the x-axis in tensorboard should be number of gradient updates, not number of batches that have been processed.

advanced profiler description fails for python 3.6

🐛 Bug Python 3.6 doesn't have the pstats.SortKey.CUMULATIVE enum so the profiler description breaks. To Reproduce Steps to reproduce the behavior: Use Python 3.6, pass in the AdvancedProfiler, get report at end of a training run. profiler = AdvancedProfiler(line_count_restriction=10) trainer = Trainer(profiler=profiler) trainer.fit(model) Stack trace: 164 for action_name, pr in self.profiled_actions.items(): 165 s = io.StringIO() --> 166 sortby = pstats.SortKey.CUMULATIVE 167 ps = pstats.Stats(pr, stream=s).strip_dirs().sort_stats(sortby) 168 ps.print_stats(self.line_count_restriction) AttributeError: module 'pstats' has no attribute 'SortKey' Code sample from pytorch_lightning import Trainer from pytorch_lightning.profiler import AdvancedProfiler from argparse import Namespace from pl_examples.basic_examples.lightning_module_template import LightningTemplateModel # define model hparams = { "batch_size": 128, "in_features": 784, "hidden_dim": 512, "drop_prob": 0.0, "out_features": 10, "learning_rate": 5e-3, "data_root": "data" } hparams = Namespace(**hparams) model = LightningTemplateModel(hparams) # overfit on small batch profiler = AdvancedProfiler(line_count_restriction=10) trainer = Trainer(profiler=profiler, overfit_pct=0.05, min_epochs=10) trainer.fit(model) Expected behavior Environment Collecting environment information... PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.1 OS: Ubuntu 18.04.3 LTS GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0 CMake version: version 3.12.0 Python version: 3.6 Is CUDA available: Yes CUDA runtime version: 10.0.130 GPU models and configuration: GPU 0: Tesla P100-PCIE-16GB Nvidia driver version: 418.67 cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5 Versions of relevant libraries: [pip3] numpy==1.17.5 [pip3] pytorch-lightning==0.6.1.dev0 [pip3] torch==1.4.0 [pip3] torchsummary==1.5.1 [pip3] torchtext==0.3.1 [pip3] torchvision==0.5.0 [conda] Could not collect

Cross validation feature

🚀 Feature Cross-Validation is a crucial model validation techniques for assessing how the model generalizes on new data. Motivation Research papers usually require cross-validation. From my point of view, this kind of feature would simplify the work of researches. Pitch I want to pass a parameter to the Trainer object to specify that I want to train the model on K-folds. In the case that nobody wants to make a PR, I can start working on that.

Handle abstract loader that doesn't have a dataset member

Feature / Bug (?) I have an abstract loader that chains multiple pytorch loaders when training on video sequences. Each small loader contains one sequence and the chained loader just use itertools.chain to chain them together. I cannot put all data in a single loader because it does not make sense to read images from two different sequences. I cannot use an IterableDataset because that would still have the same problem. pytorch-lightning/pytorch_lightning/trainer/data_loading.py Line 58 in 06242c2 if EXIST_ITER_DATASET and isinstance(self.get_train_dataloader().dataset, IterableDataset): This assumes that a loader must have a dataset field, which I felt is too restrictive. I suggest putting a check to see if the loader has a dataset member before doing this check. My workaround, for now, is just to declare a dataset member in my ChainedLoader to be None. Fix Since this kind of abstract loader does not have an explicit handle to a dataset, it should belong to the concept of an IterableDataset, which the user specifies the number of batches rather than a percentage. So a simple fix would be to put a check of hasattr somewhere in the same line.

Epoch end checkpoint restarts previous epoch

🐛 Bug If restarting the training and reloading the model, the epoch that the checkpoint had just completed is restarted rather than beginning the next. Expected behavior When a checkpoint upon epoch end is saved, restarting it should resume its state and start the next epoch.

Model checkpointing to just save the latest model

🚀 Feature There is currently no automated option of always only keeping the latest saved model - there is always a metric benchmark used that needs to be exceeded. This is fine as a default but there should be a 'keep-it-simple' option of always just saving whatever is the latest. Motivation The concept of top-k is sound but when testing the capacity of new architectures, the most important is just to be resume the latest version. This can probably be implemented by using max(self.global_step) as the metric - but this seems overly complex. Alternatives An option to always save the latest model and no others - e.g. by setting save_top_k=-1 (instead of disabling saving altogether which is better done by not having a ModelCheckpoint).

[Pyright] Cannot access member 'X' for type 'None'

Pyright raises 89 errors on master about Cannot access member 'X' for type 'None'. Here is an example in evaluation_loop.py: # track outputs for collation dl_outputs.append(output) # batch done if test: self.test_progress_bar.update(1) # PYRIGHT ERROR else: self.val_progress_bar.update(1) # PYRIGHT ERROR self.main_progress_bar.update(1) # PYRIGHT ERROR outputs.append(dl_outputs) eval_results = {} One way to fix this is to indicate the type of those variables: self.test_progress_bar: Any = None Unless you see a more elegant way?

test fails with `_lazy_train_dataloader`

🐛 Bug The tests are time-to-time randomly failing across all platforms (but mainly macOS and Win) with following message: _____________________________ test_lbfgs_cpu_model _____________________________ self = LightningTestModel( (c_d1): Linear(in_features=784, out_features=1000, bias=True) (c_d1_bn): BatchNorm1d(1000, eps...ats=True) (c_d1_drop): Dropout(p=0.2, inplace=False) (c_d2): Linear(in_features=1000, out_features=10, bias=True) ) def _get_data_loader(self): try: > value = getattr(self, attr_name) pytorch_lightning/core/decorators.py:16: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = LightningTestModel( (c_d1): Linear(in_features=784, out_features=1000, bias=True) (c_d1_bn): BatchNorm1d(1000, eps...ats=True) (c_d1_drop): Dropout(p=0.2, inplace=False) (c_d2): Linear(in_features=1000, out_features=10, bias=True) ) name = '_lazy_train_dataloader' def __getattr__(self, name): if '_parameters' in self.__dict__: _parameters = self.__dict__['_parameters'] if name in _parameters: return _parameters[name] if '_buffers' in self.__dict__: _buffers = self.__dict__['_buffers'] if name in _buffers: return _buffers[name] if '_modules' in self.__dict__: modules = self.__dict__['_modules'] if name in modules: return modules[name] raise AttributeError("'{}' object has no attribute '{}'".format( > type(self).__name__, name)) E AttributeError: 'LightningTestModel' object has no attribute '_lazy_train_dataloader' To Reproduce https://github.com/Borda/pytorch-lightning/runs/448238658

About fine-tuning and explaining the result

❓ Questions and Help Hi, Thank you for the contribution and framework. Would you mind if you lead me how to freeze all the layers except classification layer during the fine tuning? And also would you help me how to explain the result of the fine tuned model such as which words of the "sentence" made it labelled as positive? Thank you.

Incorrect Signature of `training_end`, `validation_end`, `test_end` in `Experimet Reporing`

📚 Documentation Documentation here defines the signature of the functions as follows: training_end(self, outputs) test_end(self, outputs) validation_end(self, outputs) While in the Experiment Reporting section of docs, the signature of the functions has been misreported as: training_end(self, batch, batch_idx) test_end(self, batch, batch_idx) validation_end(self, batch, batch_idx)

How do I test before any training?

❓ Questions and Help I am now migrating some of my previous works into lightning. I wish to see if it is able to reproduce my previous results or not. But the doc implies that all the testing has to be performed after training or after loading the previous lightning training state, which I do not have either. So How can I test before training? Code trainer = Trainer(logger=logger, max_epochs=5, gpus=[3], distributed_backend=None) hparams = HParams(fold=fold, model=model_name, batch_size=8, num_workers=16) system = MySYS(hparams, trainer) system.model.load_state_dict(torch.load(state_dict)) trainer.test() It cannot work since the trainer does not initialize at all. What have you tried? I found inside the code for testing: def test(self, model=None): r""" Separates from fit to make sure you never run on your test set until you want to. Args: model (LightningModule): The model to test. Example:: # Option 1 # run test after fitting trainer = Trainer() model = LightningModule() trainer.fit() trainer.test() # Option 2 # run test from a loaded model model = LightningModule.load_from_checkpoint('path/to/checkpoint.ckpt') trainer = Trainer() trainer.test(model) """ self.testing = True if model is not None: self.fit(model) else: self.run_evaluation(test=True) Which requires to fit the model that I do not understand at all. Why a fitting is required inside training code? If for the purpose of initialization, can't we just put some init code here?

Example testing

🚀 Feature Find a way how to test examples in CI Motivation Due to some API modification, our examples may become invalid and we would not notice it until a bug report... Pitch The best way is running examples as a part of CI, on the other hand, it may be quite a time consuming

suggestion

not needed

simplification over all kinds of "path" settings

I'm lost in all kinds of "path" settings, could it be more simpler? Across the source code and examples, I find that there are many types of "path" config, such as the path in ModelCheckpoint, logger, and default_save_path in trainer. Could you please explain these path configs in more detail? For example, if we set default_save_path, how does it influence other path configs?

Fix .test() on ddp

This might be broken on notebooks only. #875 solves a few problems with .test() However, ddp + .test might be broken on notebooks because of the "spawn" option. (likely #747).

Unify usage of multiple callbacks

🚀 Feature Simplified API, with callbacks... as e.g. Keras did, pass just list of callbacks to be executed and Trainer will call then when needed instead of having them specified pytorch-lightning/pytorch_lightning/trainer/trainer.py Lines 65 to 66 in b104052 checkpoint_callback=True, early_stop_callback=None, mentioned also in #825 (comment)

MisconfigurationException GPUs on updating lightning

Not sure what's going on here. I updated to the latest pytorch version: Can anyone help? I also included my environment packages. Traceback (most recent call last): File "train_gpu.py", line 232, in <module> main_local(hparam_trial) File "train_gpu.py", line 106, in main_local trainer = Trainer(logger = tt_logger , use_amp=False, gpus=1 ,min_nb_epochs=1002, max_nb_epochs=1003) # amp_level='O2' , File "/home/bruce/anaconda3/envs/ln2/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 530, in __init__ self.data_parallel_device_ids = parse_gpu_ids(gpus) File "/home/bruce/anaconda3/envs/ln2/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 563, in parse_gpu_ids gpus = sanitize_gpu_ids(gpus) File "/home/bruce/anaconda3/envs/ln2/lib/python3.7/site-packages/pytorch_lightning/trainer/distrib_parts.py", line 533, in sanitize_gpu_ids raise MisconfigurationException(message) pytorch_lightning.utilities.debugging.MisconfigurationException: You requested GPUs: [0] But your machine only has: [] # Name Version Build Channel _libgcc_mutex 0.1 main _pytorch_select 0.1 cpu_0 absl-py 0.9.0 pypi_0 pypi blas 1.0 mkl ca-certificates 2020.1.1 0 cachetools 4.0.0 pypi_0 pypi certifi 2019.11.28 py37_0 cffi 1.14.0 py37h2e261b9_0 chardet 3.0.4 pypi_0 pypi cudatoolkit 10.1.243 h6bb024c_0 freetype 2.9.1 h8a8886c_1 future 0.18.2 pypi_0 pypi google-auth 1.11.2 pypi_0 pypi google-auth-oauthlib 0.4.1 pypi_0 pypi grpcio 1.27.2 pypi_0 pypi idna 2.8 pypi_0 pypi imageio 2.8.0 pypi_0 pypi intel-openmp 2020.0 166 joblib 0.14.1 pypi_0 pypi jpeg 9c h14c3975_1001 conda-forge ld_impl_linux-64 2.33.1 h53a641e_7 libedit 3.1.20181209 hc058e9b_0 libffi 3.2.1 hd88cf55_4 libgcc-ng 9.1.0 hdf63c60_0 libgfortran-ng 7.3.0 hdf63c60_0 libpng 1.6.37 hbc83047_0 libstdcxx-ng 9.1.0 hdf63c60_0 libtiff 4.1.0 h2733197_0 markdown 3.2.1 pypi_0 pypi mkl 2020.0 166 mkl-service 2.3.0 py37he904b0f_0 mkl_fft 1.0.15 py37ha843d7b_0 mkl_random 1.1.0 py37hd6b4f25_0 ncurses 6.1 he6710b0_1 ninja 1.9.0 py37hfd86e86_0 numpy 1.18.1 py37h4f9e942_0 numpy-base 1.18.1 py37hde5b4d6_1 oauthlib 3.1.0 pypi_0 pypi olefile 0.46 py37_0 openssl 1.1.1d h7b6447c_4 pandas 1.0.1 pypi_0 pypi pillow 6.2.1 py37hd70f55b_1 conda-forge pip 20.0.2 py37_1 protobuf 3.11.3 pypi_0 pypi pyasn1 0.4.8 pypi_0 pypi pyasn1-modules 0.2.8 pypi_0 pypi pycparser 2.19 py37_0 python 3.7.6 h0371630_2 python-dateutil 2.8.1 pypi_0 pypi pytorch 1.3.1 cpu_py37h62f834f_0 pytorch-lightning 0.6.0 pypi_0 pypi pytz 2019.3 pypi_0 pypi readline 7.0 h7b6447c_5 requests 2.22.0 pypi_0 pypi requests-oauthlib 1.3.0 pypi_0 pypi rsa 4.0 pypi_0 pypi scikit-learn 0.22.1 pypi_0 pypi scipy 1.4.1 pypi_0 pypi setuptools 45.2.0 py37_0 six 1.14.0 py37_0 sqlite 3.31.1 h7b6447c_0 tensorboard 2.1.0 pypi_0 pypi test-tube 0.7.5 pypi_0 pypi tk 8.6.10 hed695b0_0 conda-forge torchvision 0.4.2 cpu_py37h9ec355b_0 tqdm 4.43.0 pypi_0 pypi urllib3 1.25.8 pypi_0 pypi werkzeug 1.0.0 pypi_0 pypi wheel 0.34.2 py37_0 xz 5.2.4 h14c3975_4 zlib 1.2.11 h7b6447c_3 zstd 1.3.7 h0b5b093_0

[dp/ddp mode]Enable checking which process I'm in

🚀 Feature Motivation The motivation is that in dp/ddp mode, one print statement in training_step ends up with multiple printed lines (4 lines because I'm using 4 GPUs) Pitch I hope that there's a self.rank to let the user check which process they're in. So they may choose to print for only rank 0, or print the rank to screen with the msg to avoid confusion. Alternatives @williamFalcon suggests make it automatically handled with a self.print Additional context I'd prefer a self.rank because horovod has this ranking. I'm not sure if there are similar things in pytorch.

Update torchvision to 0.5.0

🚀 Feature Update torchvision to 0.5.0 (pip install downgrades to 0.4.2) ERROR: torchvision 0.4.2 has requirement torch==1.3.1, but you'll have torch 1.4.0 which is incompatible. Installing collected packages: tqdm, torchvision, oauthlib, requests-oauthlib, pyasn1, rsa, pyasn1-modules, cachetools, google-auth, google-auth-oauthlib, markdown, grpcio, absl-py, werkzeug, protobuf, tensorboard, pytorch-lightning Attempting uninstall: torchvision Found existing installation: torchvision 0.5.0 Uninstalling torchvision-0.5.0: Successfully uninstalled torchvision-0.5.0 Successfully installed absl-py-0.9.0 cachetools-4.0.0 google-auth-1.11.2 google-auth-oauthlib-0.4.1 grpcio-1.27.2 markdown-3.2.1 oauthlib-3.1.0 protobuf-3.11.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 pytorch-lightning-0.6.0 requests-oauthlib-1.3.0 rsa-4.0 tensorboard-2.1.0 torchvision-0.4.2 tqdm-4.43.0 werkzeug-1.0.0 Upgrade dependency so that the above doesn't happen. Motivation I recently conda installed pytorch and it came with torchvision-0.5.0. However, after pip installing lightning, it downgraded to torchvision-0.4.2. Pitch Support the latest torchvision.

Improve wandb tests

The tests of the wandb-logger are not as comprehensive as for the rest of the loggers. Currently, they just test if constructing a logger works. Testing wandb in the same way as the other loggers works: def test_wandb_logger(tmpdir): """Verify that basic functionality of wandb logger works.""" tutils.reset_seed() wandb_dir = os.path.join(tmpdir, "wandb") logger = WandbLogger(save_dir=wandb_dir, anonymous=True, offline=True) hparams = tutils.get_hparams() model = LightningTestModel(hparams) trainer_options = dict( default_save_path=tmpdir, max_epochs=1, train_percent_check=0.05, logger=logger ) trainer = Trainer(**trainer_options) result = trainer.fit(model) print('result finished') assert result == 1, "Training failed" def test_wandb_pickle(tmpdir): """Verify that pickling trainer with wandb logger works.""" tutils.reset_seed() wandb_dir = str(tmpdir) logger = WandbLogger(save_dir=wandb_dir, anonymous=True) trainer_options = dict( default_save_path=tmpdir, max_epochs=1, logger=logger ) trainer = Trainer(**trainer_options) pkl_bytes = pickle.dumps(trainer) trainer2 = pickle.loads(pkl_bytes) trainer2.logger.log_metrics({"acc": 1.0}) But the CI tests on github for windows fail. The tests for Ubuntu and Mac work. Seems to be some problem related to file-access that I cannot recreate locally

Relax hparams in model saving/loading

I've managed to train a model using pl.fit(model) and have the .ckpt file. Now, I'm trying to load the .ckpt file so that I can do inference on a single image: model = CoolSystem() to_infer = torch.load('checkpoints/try_ckpt_epoch_1_v0.ckpt') model.load_from_checkpoint(to_infer) # ------------- error is thrown at this line However, upon loading the .ckpt file, the following error is thrown: AttributeError: 'dict' object has no attribute 'seek'. You can only torch.load from a file that is seekable. Please pre-load the data into a buffer like io.BytesIO and try to load from it instead. Am I doing something wrong when using PyTorch Lightning for inference? For reference, this is my system: import pytorch_lightning as pl import os import matplotlib.pyplot as plt import torch import torchvision import torchvision.transforms as transforms import torch.nn.functional as F class CoolSystem(pl.LightningModule): def __init__(self): super(CoolSystem, self).__init__() # self.hparams = hparams self.data_dir = '/content/hymenoptera_data' self.model = torchvision.models.resnet18(pretrained=True) # final layer is of size [bs, 1000] num_ftrs = self.model.fc.in_features self.model.fc = torch.nn.Linear(num_ftrs, 2) # change final layer to be of size [bs, 2] def forward(self, x): x = self.model(x) return x def configure_optimizers(self): # Observe that all parameters are being optimized optimizer = torch.optim.SGD(self.model.parameters(), lr=0.001, momentum=0.9) # Decay LR by a factor of 0.1 every 7 epochs exp_lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1) return [optimizer], [exp_lr_scheduler] def training_step(self, batch, batch_idx): # REQUIRED x, y = batch y_hat = self.forward(x) loss = F.cross_entropy(y_hat, y) tensorboard_logs = {'train_loss': loss} return {'loss': loss, 'log': tensorboard_logs} def validation_step(self, batch, batch_idx): # OPTIONAL x, y = batch y_hat = self.forward(x) return {'val_loss': F.cross_entropy(y_hat, y)} def validation_end(self, outputs): # OPTIONAL avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean() tensorboard_logs = {'val_loss': avg_loss} return {'avg_val_loss': avg_loss, 'log': tensorboard_logs} @pl.data_loader def train_dataloader(self): # REQUIRED transform = transforms.Compose([ transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) train_set = torchvision.datasets.ImageFolder(os.path.join(self.data_dir, 'train'), transform) train_loader = torch.utils.data.DataLoader(train_set, batch_size=32, shuffle=True, num_workers=4) return train_loader @pl.data_loader def val_dataloader(self): transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) val_set = torchvision.datasets.ImageFolder(os.path.join(self.data_dir, 'val'), transform) val_loader = torch.utils.data.DataLoader(val_set, batch_size=32, shuffle=True, num_workers=4) return val_loader And I'm training it this way: model = CoolSystem() import os checkpoint_callback = pl.callbacks.ModelCheckpoint( filepath=os.path.join(os.getcwd(), 'checkpoints'), verbose=True, monitor='val_loss', mode='min', prefix='try', save_top_k=-1, period=1 # check val_loss every n periods, and saves the checkpoint if it is better than the val_loss at the previous period ) trainer = pl.Trainer( max_epochs=2, checkpoint_callback=checkpoint_callback) trainer.fit(model)

Test pass shouldn't require both test_step and test_end

🐛 Bug trainer.test(...) requires implementation of both test_step and test_end, but the warning says you only need to implement either or both. pytorch-lightning/pytorch_lightning/trainer/evaluation_loop.py Line 291 in 56dddf9 if test and not (self.is_overriden('test_step') and self.is_overriden('test_end')): To Reproduce Run .test() on any LightningModule with only test_step or test_end implemented. Code sample import torch from torch.nn import functional as F from torch.utils.data import DataLoader from torchvision.datasets import MNIST import pytorch_lightning as pl class CoolSystem(pl.LightningModule): def __init__(self): super(CoolSystem, self).__init__() self.l1 = torch.nn.Linear(28 * 28, 10) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) def training_step(self, batch, batch_nb): x, y = batch y_hat = self.forward(x) loss = F.cross_entropy(y_hat, y) return {'loss': loss} def test_step(self, batch, batch_idx): x, y = batch y_hat = self.forward(x) return {'test_loss': F.cross_entropy(y_hat, y)} def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) @pl.data_loader def train_dataloader(self): return DataLoader(MNIST('./', train=True, download=True), batch_size=32) @pl.data_loader def test_dataloader(self): return DataLoader(MNIST('./', train=False, download=True), batch_size=32) model = CoolSystem() trainer = pl.Trainer(max_epochs=2, val_percent_check=1) trainer.test(model) Expected behaviour Test pass runs should run with either implemented or when at least test_step is.

Log training metrics for each epoch

Currently, I am able to log training metrics to Tensorboard using: import pytorch_lightning as pl from pytorch_lightning.loggers import TensorBoardLogger logger = TensorBoardLogger(save_dir=save_dir, name="my_model") [...] trainer = pl.Trainer(logger=logger) This logs training metrics (loss, for instance) after each batch. I would like to be able to average these metrics across all batches and log them to TensorBoard only once, at the end of each epoch. This is what the validation_end method does in your example: pytorch-lightning/pl_examples/basic_examples/lightning_module_template.py Line 145 in 446a1e2 def validation_end(self, outputs): . I first thought about writing my own training_end method. But this method is called after each batch instead of being called at the end of an epoch (as I would have thought). The method on_epoch_end seems interesting but does not receive an outputs argument as training_end does. Basically, in my model, I would like to write something like: self.logger.experiment.add_scalar('training_loss', train_loss_mean, global_step=self.current_epoch), but I do not know where to put this line. OS: Debian GNU/Linux 9.11 (stretch) Packaging: PIP Version 0.6.1.dev0

Unrecognized `val_loss` metric

RuntimeWarnings due to unrecognized val_loss metric pytorch_lightning callbacks are unable to recognize val_loss from validation_step() To Reproduce Run CoolModel from Steps to reproduce the behavior: Go to Minimal-Example Run upto trainer.fit() Scroll down to the end of an epoch See error - `/opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/pt_callbacks.py:314: RuntimeWarning: Can save best model only with val_loss available, skipping. ' skipping.', RuntimeWarning) /opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/pt_callbacks.py:144: RuntimeWarning: Early stopping conditioned on metric val_loss which is not available. Available metrics are: loss,avg_val_loss RuntimeWarning)` Code sample Just the Minimal Example import os import torch from torch.nn import functional as F from torch.utils.data import DataLoader from torchvision.datasets import MNIST import torchvision.transforms as transforms import pytorch_lightning as pl class CoolModel(pl.LightningModule): def __init__(self): super(CoolModel, self).__init__() # not the best model... self.l1 = torch.nn.Linear(28 * 28, 10) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) def training_step(self, batch, batch_nb): # REQUIRED x, y = batch y_hat = self.forward(x) return {'loss': F.cross_entropy(y_hat, y)} def validation_step(self, batch, batch_nb): # OPTIONAL x, y = batch y_hat = self.forward(x) return {'val_loss': F.cross_entropy(y_hat, y)} def validation_end(self, outputs): # OPTIONAL avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean() return {'avg_val_loss': avg_loss} def test_step(self, batch, batch_nb): # OPTIONAL x, y = batch y_hat = self.forward(x) return {'test_loss': F.cross_entropy(y_hat, y)} def test_end(self, outputs): # OPTIONAL avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean() return {'avg_test_loss': avg_loss} def configure_optimizers(self): # REQUIRED return torch.optim.Adam(self.parameters(), lr=0.02) @pl.data_loader def train_dataloader(self): return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32) @pl.data_loader def val_dataloader(self): # OPTIONAL # can also return a list of val dataloaders return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32) @pl.data_loader def test_dataloader(self): # OPTIONAL # can also return a list of test dataloaders return DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor()), batch_size=32) from pytorch_lightning import Trainer model = CoolModel() trainer = Trainer() trainer.fit(model) Output Excluding the print from pip install and training tqdm prints Epoch 1: 100%|██████████| 3750/3750 [00:22<00:00, 163.69batch/s, batch_idx=1874, loss=1.530, v_num=0] /opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/pt_callbacks.py:314: RuntimeWarning: Can save best model only with val_loss available, skipping. ' skipping.', RuntimeWarning) /opt/conda/lib/python3.6/site-packages/pytorch_lightning/callbacks/pt_callbacks.py:144: RuntimeWarning: Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,avg_val_loss RuntimeWarning) 1 Expected behavior The 'val_loss' metric should be recognized when a dic with key val_loss is returned by validation_step() Environment Collecting environment information... PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: None OS: Debian GNU/Linux 9 (stretch) GCC version: (Debian 6.3.0-18+deb9u1) 6.3.0 20170516 CMake version: version 3.7.2 Python version: 3.6 Is CUDA available: No CUDA runtime version: No CUDA GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA Versions of relevant libraries: [pip] msgpack-numpy==0.4.4.3 [pip] numpy==1.18.1 [pip] numpydoc==0.9.2 [pip] pytorch-ignite==0.3.0 [pip] pytorch-lightning==0.6.0 [pip] pytorch-pretrained-bert==0.6.2 [pip] pytorch-transformers==1.1.0 [pip] torch==1.4.0 [pip] torchaudio==0.4.0a0+719bcc7 [pip] torchtext==0.5.0 [pip] torchvision==0.4.2 [conda] blas 1.0 mkl [conda] cpuonly 1.0 0 pytorch [conda] mkl 2019.3 199 [conda] mkl-service 2.0.2 py36h7b6447c_0 [conda] mkl_fft 1.0.12 py36ha843d7b_0 [conda] mkl_random 1.0.2 py36hd81dba3_0 [conda] pytorch 1.4.0 py3.6_cpu_0 [cpuonly] pytorch [conda] pytorch-ignite 0.3.0 pypi_0 pypi [conda] pytorch-lightning 0.6.0 pypi_0 pypi [conda] pytorch-pretrained-bert 0.6.2 pypi_0 pypi [conda] pytorch-transformers 1.1.0 pypi_0 pypi [conda] torchaudio 0.4.0 py36 pytorch [conda] torchtext 0.5.0 pypi_0 pypi [conda] torchvision 0.4.2 pypi_0 pypi Additional context Something interesting is you cannot reproduce this when you run CoolSystem. You won't get any warning because validation_end returns tensorboard logs with val_loss as a key. If you remove this log you will able to catch the bug. For what its worth, I was trying to make a PyTorch Lightning based Kernel in Kaggle and WandB as logger. After trying "a lot" to find a mistake in my code, I realized that this warning is not shown every time. Once you run and get the warning and re-run the block (I was using Kaggle Kernel Notebook) it disappears. I assume that it is reading from some cache? Since the val_loss is something for the first epoch(maybe something from last epoch or log) and stay 0 for the rest. I am not familiar with the internal working of PL but I suspect there is some mix up between metrics that are logged and metrics returned by lightning methods.

How to properly load checkpoint for testing?

I've trained a system as follows: model = CoolSystem(hparams) checkpoint_callback = pl.callbacks.ModelCheckpoint( filepath=os.path.join(os.getcwd(), 'checkpoints'), verbose=True, monitor='val_acc', mode='max', prefix='try', save_top_k=-1, period=1 ) trainer = pl.Trainer( max_epochs=hparams.epochs, checkpoint_callback=checkpoint_callback) trainer.fit(model) trainer.test() And with the above, the test accuracy is 0.7975 However, when I load the checkpoints separately instead: model_test = CoolSystem(hyperparams) model_test.load_from_checkpoint('checkpoints/try_ckpt_epoch_1.ckpt') trainer = pl.Trainer() trainer.test(model_test) The accuracy returned is 0.5705 Am I loading the checkpoints wrongly?

Add "epoch" options to basic templates

🚀 Feature Add "epochs" option to parser of 'basic_examples/lightning_module_template.py' Motivation Thanks to 'basic_examples/lightning_module_template.py', I could build my deep learning model. Some beginners like me might build their model from this basic template. However, there are no options to manipulate epochs. I just thought that what people use often should be included in the basic template, so I uploaded my issue. Pitch I suggest that the basic template includes "epoch" option in the basic template. Alternatives Add "epoch" options to parser of 'basic_examples/lightning_module_template.py' parser.add_argument('--epochs', default=10, type=int, metavar='N', help='number of total epochs to run') trainer = pl.Trainer(max_epochs=hparams.epochs) Additional context I am really enjoying PytorchLightning framework. Thanks 😄

Automate choosing sampler

🚀 Feature Let's automate choosing the sampler. Case 1 (DDP, training): Default to distributedSampler sampler = DistributedSampler(dataset) Case 2 (training): sampler = RandomSampler(dataset) Case 3 (val, test): sampler = SequentialSampler(dataset) Case 4 (tpu, train, val, test): xm.DistributedSampler(dataset) Motivation Samplers are hard and should be automated. @srush

Add prepare_data call for downloading datasets

🚀 Feature Data download and loading data should be separate because it causes issues on distributed environments. Add def prepare_data(self): # download and prep the data def train_dataloader(self): # create dataloader

Remove the prefix 'version_' in tensorboard logging

🚀 Feature Motivation In tensorboard there's an explicit '/', and I think there's no need for this 'version_'. it makes the whole stuff too long. Pitch I can bring a PR for this. Alternatives Additional context

logger is NoneType hence doesn't have any experiment or other functionality in a lightning module

🐛 Bug When trying to use the logging abilities of lightning, I hit a wall, the default and tensorboard loggers both seem to stay uninitialized when calling trainer.fit(model), resulting in crashes everytime I try to log something. To Reproduce Create a lightning module as such class SimpleRegressor(pl.LightningModule): ... Use the logger anywhere to get this kind of stacktrace: d:\Documents\projects\MetaWatch\MetaWatch\notebooks\audio-video-interest\simple_regressor.py in configure_optimizers(self) 105 #see https://pytorch-lightning.readthedocs.io/en/latest/pytorch_lightning.core.lightning.html#pytorch_lightning.core.lightning.LightningModule.configure_optimizers 106 # REQUIRED --> 107 self.logger.experiment.add_hparams({'hidden_layer_size':self.hidden_layer_size, 108 'linear_layer_size':self.linear_layer_size, 109 'lstm_layers':self.lstm_layers}) AttributeError: 'NoneType' object has no attribute 'experiment' Code sample import pytorch_lightning as pl class SimpleRegressor(pl.LightningModule): def __init__(self, cuda=False): super(SimpleRegressor, self).__init__() self.logger.experiment.add_hparams({'hidden_layer_size':1}) Expected behavior To log as described in the documentation. Environment PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.1 OS: Microsoft Windows 10 Pro GCC version: Could not collect CMake version: Could not collect Python version: 3.7 Is CUDA available: Yes CUDA runtime version: Could not collect GPU models and configuration: GPU 0: GeForce GTX 970 Nvidia driver version: 441.12 cuDNN version: Could not collect Versions of relevant libraries: [pip3] numpy==1.18.1 [pip3] pytorch-lightning==0.6.0 [pip3] tinynumpy==1.2.1 [pip3] torch==1.4.0 [pip3] torchvision==0.4.1 [conda] Could not collect Additional context

Support both Namespace and dict for hyperparameter saving/loading.

🚀 Feature Let the user pass a dict to LightningModule so that after model saving it can be restored using load_from_checkpoint or load_from_metrics. Motivation Currently, there is nothing that prevents the user from passing in hyperparameters to LightningModule via dictionary (or even somthing else). However, the model loading/saving assumes it is always a argparse.Namespace. This could potentially be an issue when load_from_checkpoint restores the module with a Namespace passed in instead of dict. Pitch The model saving currently converts Namespace to dict and the model loading converts it back to Namespace. Pitch 1: Also save the type inside the checkpoint, e.g., checkpoint['hparams'] = dict(hparams) checkpoint['hparams_type'] = type(hparams) and when restoring we instantiate with the appropriate type. Pitch 2: Dump the whole hparams object (Namespace, dict, ...) into checkpoint without converting it to dict first and let pickle take care of the rest. Most flexible option but could give problems when loading from checkpoint. Alternatives Somehow restrict the user to only use argparse. Additional context The idea was suggested by @williamFalcon in PR #919.

Improve callback system

Following the new callback system here are a few things we might want to do proposed by people who participated in #889 and #896. Consolidate model hooks and callbacks system. Make the calls at the same moment: #889 (comment) Fix on_train_begin() being called in the early stopping callbacks: #889 (comment) Assert pl_module and trainer are excepted object instances in the callback system train tests: #889 (comment) Incorporate existing callbacks into the new callback system. Edit the changelog ping @Borda @williamFalcon @jeremyjordan @awaelchli

Support IterableDatasets for validation and test, not just train set [blocked by #953]

🚀 Feature Currently Lightning supports IterableDatasets only in the training set (see code). This makes them second-class citizens compared to the map-style datasets, and supporting them seems a low hanging fruit. Motivation This enables having larger test sets that may not fit into a machine's memory (they could be very large in production settings, or of modest size running in a student's cheap laptop). Moreover, datasets are usually generated together (eg train, val, test can come from the same process). It is very likely that the same process has the same signature, so you may end up having IterableDatasets even when their size may not deem it strictly necessary. Pitch Changing a few lines of code by bringing in the checks we are doing for training should be enough unless I'm missing something. Additional context Are there any gotchas that make this harder than it looks?

Automatically pick available GPU

Thanks for this great library! 🚀 Feature I would like to change the behavior of this code: trainer = pl.Trainer( ... snip ..., gpus=1, ) Currently, when setting gpus to an integer n, the first n GPUs are automatically used. I would like to change the behavior such that when multiple GPUs are available, the trainer picks the first available GPU. Motivation When running multiple jobs in parallel on a server with multiple available GPUs, I get the error: RuntimeError: CUDA error: all CUDA-capable devices are busy or unavailable This is because all 4 running jobs are scheduled to GPU 0, even though I have 4 GPUs available. Note: the GPUs are configured to be in "exclusive mode", which means that only one process at a time can use them. Pitch I would like to change the behavior such that when multiple GPUs are available, the trainer picks the first available GPU. Alternatives One could also fix this in client code, like this: def retry_jittered_backoff(f, num_retries=5): # Based on: # https://aws.amazon.com/blogs/architecture/exponential-backoff-and-jitter/ import time import random cap = 1.0 # max sleep time is 1s base = 0.01 # initial sleep time is 10ms sleep = base # initial sleep time is 10ms for i in range(num_retries): try: return f() except RuntimeError as e: if i == num_retries - 1: raise e else: continue time.sleep(sleep) sleep = min(cap, random.uniform(base, sleep * 3)) def pick_gpu(): for i in range(torch.cuda.device_count()): torch.cuda.set_device(i) try: torch.ones(1).cuda() except RuntimeError: continue return i raise RuntimeError("No GPUs available.") def main(.. snip ..): model = Model(.. snip ..) trainer = pl.Trainer( .. snip .., gpus=[retry_jittered_backoff(pick_gpu)], ) trainer.fit(model) This is exactly the kind of boilerplate that I would hope pytorch-lighting can make redundant. PR-able? Would you be open to consider accepting a PR? If so, could you give me some pointers where to add the above code?

refactor len(datasets) call.

🚀 Feature Let's minimize len(dataset) calls and do it as late in the training as we can (ie: ideally right before any training loop). This way, we can open up the path to support iterable datasets more cleanly. Motivation Getting the length prematurely calls datasets at the wrong time often causing double loads. This is a blocker to #948

How to implement pre-training?

❓ Questions and Help What is your question? What would be the best way to implement pre-training? With pre-training, I mean that I want to freeze some layers for a couple of epochs before training all of them. At the moment, I do it in two runs. In the first one, I freeze the layers, train the model, and save its weights. In the second one, I load these weights and train the model without freezing. Is it possible to do it in one run? If so, how?

Process runs on more GPUs than specified

I have a single 8-GPU machine with a faulty GPU0. I'm running imagenet_example.py on 7 GPUs on this machine by specifying gpus=[1,2,3,4,5,6,7] in the Trainer i.e. I do not want to use GPU0 However, when i run nvidia-smi, I see the Trainer's pid shows on all 8 GPUs, just with lower memory on GPU0 (see output below). I also find it to be slower than non-PL code by about 4x. I don't see this behavior if I manually set CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 followed by gpus=7 in Trainer. Similarly, it works fine when using a single GPU with, say, gpus=[1]. I'm not sure if it's relevant but I also see gpu=0 in the tqdm progress bar nvidia-smi with Trainer(gpus=[1,2,3,4,5,6,7]) and CUDA_VISIBLE_DEVICES unset +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 40155 C python 719MiB | | 1 40155 C python 6003MiB | | 2 40155 C python 6019MiB | | 3 40155 C python 6019MiB | | 4 40155 C python 6019MiB | | 5 40155 C python 6019MiB | | 6 40155 C python 6019MiB | | 7 40155 C python 6019MiB | +-----------------------------------------------------------------------------+ nvidia-smi with Trainer(gpus=7) and CUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 1 34452 C python 6003MiB | | 2 34452 C python 6019MiB | | 3 34452 C python 6019MiB | | 4 34452 C python 6019MiB | | 5 34452 C python 6019MiB | | 6 34452 C python 6019MiB | | 7 34452 C python 6019MiB | +-----------------------------------------------------------------------------+ Expected behavior The process should run on the specified GPUs without manually setting CUDA_VISIBLE_DEVICES Environment PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.1 OS: Ubuntu 18.04.3 LTS GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0 CMake version: version 3.10.2 Python version: 3.8 Is CUDA available: Yes CUDA runtime version: Could not collect GPU models and configuration: GPU 0: GeForce RTX 2080 Ti GPU 1: GeForce RTX 2080 Ti GPU 2: GeForce RTX 2080 Ti GPU 3: GeForce RTX 2080 Ti GPU 4: GeForce RTX 2080 Ti GPU 5: GeForce RTX 2080 Ti GPU 6: GeForce RTX 2080 Ti GPU 7: GeForce RTX 2080 Ti Nvidia driver version: 418.87.00 cuDNN version: Could not collect Versions of relevant libraries: [pip] numpy==1.18.1 [pip] pytorch-lightning==0.6.0 [pip] torch==1.4.0 [pip] torch-lr-finder==0.1.2 [pip] torchvision==0.5.0 [conda] blas 1.0 mkl [conda] mkl 2020.0 166 [conda] mkl-service 2.3.0 py38he904b0f_0 [conda] mkl_fft 1.0.15 py38ha843d7b_0 [conda] mkl_random 1.1.0 py38h962f231_0 [conda] pytorch 1.4.0 py3.8_cuda10.1.243_cudnn7.6.3_0 pytorch [conda] pytorch-lightning 0.6.0 pypi_0 pypi [conda] torch-lr-finder 0.1.2 pypi_0 pypi [conda] torchvision 0.5.0 py38_cu101 pytorch

Logging the current learning rate

I'd like to write the current learning rate to a Logger. I imagine this would require a call to scheduler.get_lr() but I'm not sure how I can access the scheduler object and where to place the get_lr call TIA!

Example of gradient accumulation documentation is not correct

📚 Documentation Example of gradient accumulation documentation is not correct : from pytorch_lightning import Trainer from pytorch_lightning.callbacks import GradientAccumulationScheduler # at epoch 5 start accumulating every 2 batches accumulator = GradientAccumulationScheduler(scheduling: {5: 2}) Trainer(accumulate_grad_batches=accumulator) If running the code like this, I receive following error : TypeError: Gradient accumulation supports only int and dict types After reading the source code : pytorch-lightning/pytorch_lightning/trainer/training_tricks.py Lines 32 to 41 in d856989 def configure_accumulated_gradients(self, accumulate_grad_batches): self.accumulate_grad_batches = None if isinstance(accumulate_grad_batches, dict): self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches) elif isinstance(accumulate_grad_batches, int): schedule = {1: accumulate_grad_batches} self.accumulation_scheduler = GradientAccumulationScheduler(schedule) else: raise TypeError("Gradient accumulation supports only int and dict types") It seems the option accumulate_grad_batches can be only int or dict.

Graceful keyboard doesn't work as expected

@jeremyjordan Not sure the fix is what you were looking for? Make sure to also log the message only from proc_0.

Passing dataloader to trainer.fit() doesn't work with tpu (and maybe ddp)

🐛 Bug Receive a AttributeError: Can't pickle local object 'Trainer.__set_fit_dataloaders.<locals>.patch_train_dataloader' error when passing the dataloader directly to trainer.fit(model, train_loader) To Reproduce Steps to reproduce the behavior: Try to call trainer.fit(model, train_loader) in TPU mode. (I suspect that anything that calls mp.spawn will cause this problem, so ddp probably will face this issue too.) Code sample import os import pytorch_lightning as pl import torch from torch.nn import functional as F from torch.utils.data import DataLoader from torchvision import transforms from torchvision.datasets import MNIST import torch_xla.core.xla_model as xm class CoolSystem(pl.LightningModule): def __init__(self, use_tpu=False): super(CoolSystem, self).__init__() # not the best model... self.use_tpu = use_tpu self.l1 = torch.nn.Linear(28 * 28, 10) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) def training_step(self, batch, batch_idx): # REQUIRED x, y = batch y_hat = self.forward(x) loss = F.cross_entropy(y_hat, y) tensorboard_logs = {'train_loss': loss} return {'loss': loss, 'log': tensorboard_logs} def validation_step(self, batch, batch_idx): # OPTIONAL x, y = batch y_hat = self.forward(x) return {'val_loss': F.cross_entropy(y_hat, y)} def validation_end(self, outputs): # OPTIONAL avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean() tensorboard_logs = {'val_loss': avg_loss} return {'avg_val_loss': avg_loss, 'log': tensorboard_logs} def test_step(self, batch, batch_idx): # OPTIONAL x, y = batch y_hat = self.forward(x) return {'test_loss': F.cross_entropy(y_hat, y)} def test_end(self, outputs): # OPTIONAL avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean() tensorboard_logs = {'test_loss': avg_loss} return {'avg_test_loss': avg_loss, 'log': tensorboard_logs} def configure_optimizers(self): # REQUIRED # can return multiple optimizers and learning_rate schedulers # (LBFGS it is automatically supported, no need for closure function) return torch.optim.Adam(self.parameters(), lr=0.0004) if __name__ == '__main__': from pytorch_lightning import Trainer model = CoolSystem(use_tpu=True) dataset = MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()) sampler = torch.utils.data.distributed.DistributedSampler( dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal(), shuffle=True) loader = DataLoader(dataset, sampler=sampler, batch_size=32) # most basic trainer, uses good defaults trainer = Trainer(num_tpu_cores=8) trainer.fit(model, loader) Expected behavior Ideally, specifying the dataloaders as part of the LightningModule should work just the same as passing the dataloaders into trainer.fit() Environment Please copy and paste the output from our environment collection script (or fill out the checklist below manually). You can get the script and run it with: wget https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py # For security purposes, please check the contents of collect_env.py before running it. python collect_env.py Docker image: gcr.io/tpu-pytorch/xla:nightly build steps: pip install git+git://github.com/williamFalcon/pytorch-lightning.git@master --upgrade (I don't have access to the machine right now, so please forgive me on the specific version info temporarily) Proposed solution The issue is here, trying to assign a local function to the model def __set_fit_dataloaders(self, model, train_dataloader, val_dataloaders, test_dataloaders): # when dataloader is passed via fit, patch the train_dataloader # functions to overwrite with these implementations if train_dataloader is not None: if not self.is_overriden('training_step', model): m = 'You called .fit() with a train_dataloader but did not define training_step()' raise MisconfigurationException(m) def patch_train_dataloader(): return train_dataloader model.train_dataloader = patch_train_dataloader Instead of using a closure or a local function, you could use a callable defined at the top-level. This will be pickleable. class DataLoaderPatcher def __init__(self, loader): self.loader = loader def __call__(): return self.loader def __set_fit_dataloaders(self, model, train_dataloader, val_dataloaders, test_dataloaders): # when dataloader is passed via fit, patch the train_dataloader # functions to overwrite with these implementations if train_dataloader is not None: if not self.is_overriden('training_step', model): m = 'You called .fit() with a train_dataloader but did not define training_step()' raise MisconfigurationException(m) model.train_dataloader = DataLoaderPatcher(train_dataloader)

Checkpooint Callback not called when training GAN from documentation

I tried to add a model checkpoint callback to the GAN example from the documentation (https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pl_examples/domain_templates/gan.py), but it is failing silently; no checkpoints are being written. I can't find anything in the checkpoint docs that indicate why it is not working. What's your environment? OS: Linux Packaging: Conda Version : 0.6, pytorch 1.4

Evaluation on subsampled training set during training

❓ Questions and Help What is your question? We can evaluate on the validation set to get validation accuracy etc. Is it possible to evaluate on a subsampled version of the training set as well, for example, the training accuracy? Is there an easy wa to do it with PL? Sorry for the simple question, but I couldn't find the answer in the documentation.

Support storing hparams as a dict

🚀 Feature Right now, we assume model.hparams is an argparse.Namespace. We've had a number of requests to support hparams as a simple dict. Let's do it.

Failing test: test_running_test_pretrained_model_ddp

I think this is another problem stemming from the fact that we don't have a way to pass data back from torch.multiprocessing.spawn. Needs more investigation. def test_running_test_pretrained_model_ddp(tmpdir): """Verify `test()` on pretrained model.""" ... # run test set new_trainer = Trainer(**trainer_options) > new_trainer.test(pretrained_model) tests/test_restore_models.py:60: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ pytorch_lightning/trainer/trainer.py:1189: in test self.run_evaluation(test_mode=True) pytorch_lightning/trainer/evaluation_loop.py:299: in run_evaluation if test_mode and not self.is_overriden('test_step'): _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <pytorch_lightning.trainer.trainer.Trainer object at 0x7f845ec23f90>, f_name = 'test_step', model = None def is_overriden(self, f_name, model=None): if model is None: model = self.get_model() super_object = LightningModule # when code pointers are different, it was overriden > is_overriden = getattr(model, f_name).__code__ is not getattr(super_object, f_name).__code__ E AttributeError: 'NoneType' object has no attribute 'test_step' pytorch_lightning/trainer/model_hooks.py:20: AttributeError

Warnings and import errors when generating docs

🐛 Bug There are many warnings when generating the docs, many are import failures. Because of them, the Trainer docs disappeared, see here: https://pytorch-lightning.readthedocs.io/en/latest/trainer.html To Reproduce Steps to reproduce the behavior: cd docs make clean make html WARNING: autodoc: failed to import module 'pl_examples'; the following exception was raised: Traceback (most recent call last): File "d:\anaconda3\envs\lightning\lib\site-packages\sphinx\ext\autodoc\importer.py", line 32, in import_module return importlib.import_module(modname) File "d:\anaconda3\envs\lightning\lib\importlib\__init__.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "<frozen importlib._bootstrap>", line 1006, in _gcd_import File "<frozen importlib._bootstrap>", line 983, in _find_and_load File "<frozen importlib._bootstrap>", line 967, in _find_and_load_unlocked File "<frozen importlib._bootstrap>", line 677, in _load_unlocked File "<frozen importlib._bootstrap_external>", line 728, in exec_module File "<frozen importlib._bootstrap>", line 219, in _call_with_frames_removed File "C:\Users\aeduw\Documents\Repositories\pytorch-lightning\pl_examples\__init__.py", line 143, in <module> from .basic_examples.lightning_module_template import LightningTemplateModel File "C:\Users\aeduw\Documents\Repositories\pytorch-lightning\pl_examples\basic_examples\lightning_module_template.py", line 21, in <module> class LightningTemplateModel(pl.LightningModule): AttributeError: module 'pytorch_lightning' has no attribute 'LightningModule' Expected behavior no import errors. Environment PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.1 OS: Microsoft Windows 10 Pro GCC version: Could not collect CMake version: Could not collect Python version: 3.7 Is CUDA available: Yes CUDA runtime version: Could not collect GPU models and configuration: GPU 0: GeForce GTX 1070 Nvidia driver version: 442.19 cuDNN version: Could not collect Versions of relevant libraries: [pip] numpy==1.18.1 [pip] pytorch-lightning==0.6.1.dev0 [pip] torch==1.4.0 [conda] blas 1.0 mkl [conda] mkl 2020.0 166 [conda] mkl-service 2.3.0 py37hb782905_0 [conda] mkl_fft 1.0.15 py37h14836fe_0 [conda] mkl_random 1.1.0 py37h675688f_0 [conda] pytorch 1.4.0 py3.7_cuda101_cudnn7_0 pytorch [conda] pytorch-lightning 0.6.1.dev0 dev_0 Additional context

WandbLogger cannot be used with 'ddp'

🐛 Bug wandb modifies init such that a child process calling init returns None if the master process has called init. This seems to cause a bug with ddp, and results in rank zero having experiment = None, which crashes the program. To Reproduce Can be reproduced with the basic MNIST gpu template, simply add a WandbLogger and pass 'ddp' as the distributed backend. -- Process 0 terminated with the following error: Traceback (most recent call last): File "/home/rmrao/anaconda3/lib/python3.6/site-packages/torch/multiprocessing/spawn.py", line 19, in _wrap fn(i, *args) File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_data_parallel.py", line 331, in ddp_train self.run_pretrain_routine(model) File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py", line 757, in run_pretrain_routine self.logger.log_hyperparams(ref_model.hparams) File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/logging/base.py", line 14, in wrapped_fn fn(self, *args, **kwargs) File "/home/rmrao/anaconda3/lib/python3.6/site-packages/pytorch_lightning/logging/wandb.py", line 79, in log_hyperparams self.experiment.config.update(params) AttributeError: 'NoneType' object has no attribute 'config' This occurs with the latest wandb version and with pytorch-lightning 0.6.

Logger emits exception when there's `None` in hparams

To Reproduce My hparams: { 'n': [8000], 'k': [30], 'batch_size': 512, 'data_dir': '/Users/kyoungrok/Resilio Sync/Dataset/2019 TREC/passage_ranking/dataset', 'max_nb_epochs': 500, 'learning_rate': 0.0001, 'nodes': 1, 'distributed_backend': None, 'eval_test_set': False, 'check_val_every_n_epoch': 1, 'accumulate_grad_batches': 1, 'max_epochs': 200, 'min_epochs': 2, 'train_percent_check': 1.0, 'val_percent_check': 1.0, 'test_percent_check': 1.0, 'val_check_interval': 0.95, 'log_save_interval': 100, 'row_log_interval': 100, 'enable_early_stop': True, 'early_stop_metric': 'val_acc', 'early_stop_mode': 'min', 'early_stop_patience': 3, 'gradient_clip_val': -1, 'track_grad_norm': -1, 'model_save_path': '/Users/kyoungrok/Desktop/trec-2019-deep-learning/trec2019/sparse/sparsenet/model_weights', 'model_save_monitor_value': 'val_acc', 'model_save_monitor_mode': 'max', 'model_load_weights_path': None, 'tt_name': 'pt_test', 'tt_description': 'pytorch lightning test', 'tt_save_path': '/Users/kyoungrok/Desktop/trec-2019-deep-learning/trec2019/sparse/sparsenet/test_tube_logs', 'single_run': False, 'nb_hopt_trials': 1, 'log_stdout': False, 'gpus': None, 'single_run_gpu': False, 'default_tensor_type': 'torch.cuda.FloatTensor', 'use_amp': False, 'check_grad_nans': False, 'amp_level': 'O2', 'on_cluster': False, 'fast_dev_run': True, 'enable_tqdm': False, 'overfit': -1, 'interactive': False, 'debug': False, 'local': False, 'lr_scheduler_milestones': None, 'k_inference_factor': 1.5, 'weight_sparsity': [0.3], 'boost_strength': 1.5, 'boost_strength_factor': 0.85, 'dropout': 0.0, 'use_batch_norm': True, 'normalize_weights': False, 'hpc_exp_number': None } I see the following errors because I have None value in my hparams dict Traceback (most recent call last): File "sparsenet_trainer.py", line 73, in <module> main(hparam_trial) File "sparsenet_trainer.py", line 47, in main trainer.fit(model) File "/Users/kyoungrok/anaconda3/envs/trec/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 707, in fit self.run_pretrain_routine(model) File "/Users/kyoungrok/anaconda3/envs/trec/lib/python3.7/site-packages/pytorch_lightning/trainer/trainer.py", line 757, in run_pretrain_routine self.logger.log_hyperparams(ref_model.hparams) File "/Users/kyoungrok/anaconda3/envs/trec/lib/python3.7/site-packages/pytorch_lightning/logging/base.py", line 14, in wrapped_fn fn(self, *args, **kwargs) File "/Users/kyoungrok/anaconda3/envs/trec/lib/python3.7/site-packages/pytorch_lightning/logging/tensorboard.py", line 88, in log_hyperparams self.experiment.add_hparams(hparam_dict=params, metric_dict={}) File "/Users/kyoungrok/anaconda3/envs/trec/lib/python3.7/site-packages/torch/utils/tensorboard/writer.py", line 300, in add_hparams exp, ssi, sei = hparams(hparam_dict, metric_dict) File "/Users/kyoungrok/anaconda3/envs/trec/lib/python3.7/site-packages/torch/utils/tensorboard/summary.py", line 156, in hparams raise ValueError('value should be one of int, float, str, bool, or torch.Tensor') ValueError: value should be one of int, float, str, bool, or torch.Tensor Environment PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: None OS: Mac OSX 10.15.3 GCC version: Could not collect CMake version: version 3.16.1 Python version: 3.7 Is CUDA available: No CUDA runtime version: No CUDA GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA Versions of relevant libraries: [pip] numpy==1.18.1 [pip] pytorch-lightning==0.6.0 [pip] torch==1.4.0 [pip] torchvision==0.5.0 [conda] blas 1.0 mkl [conda] mkl 2019.4 233 [conda] mkl-service 2.3.0 py37hfbe908c_0 [conda] mkl_fft 1.0.15 py37h5e564d8_0 [conda] mkl_random 1.1.0 py37ha771720_0 [conda] pytorch 1.4.0 py3.7_0 pytorch [conda] pytorch-lightning 0.6.0 pypi_0 pypi [conda] torchvision 0.5.0 py37_cpu pytorch

TypeError: __init__() got an unexpected keyword argument 'precision'

🐛 Bug I followed the guide to "use 16bit precision" from the documentation But when I do: trainer = Trainer(amp_level='O1', precision=16, gpus=1) trainer.fit(model) I get the error message: TypeError: __init__() got an unexpected keyword argument 'precision' I am using lightning version: 0.6.0

Simplification: Merge load_from_metrics and load_from_checkpoint

🚀 Feature The two ways of loading a LightningModule from checkpoint only differ in one argument, the tags_csv. Motivation The code is almost identical for both and the purpose is the same. If we merge these two into one function, it would simplify the API. Pitch Combine load_from_metrics(cls, weights_path, tags_csv, map_location=None) and load_from_checkpoint(cls, checkpoint_path, map_location=None) into a single signature: load_from_checkpoint(cls, checkpoint_path, tags_csv=None, map_location=None) and make load_from_metrics deprecated. Alternatives keep as is, not a big deal :)

GPT2-large on Colab TPU seems to time out

🐛 Bug When training gpt2-large on a colab tpu, gpt2-large doesn't work To Reproduce See the colab notebook: https://colab.research.google.com/drive/1An6D3wh_H4dbmlEUHYOXZYxkH6S7VKu9 This is the relevant part of the stack trace: INFO:root:training on 8 TPU cores 2020-03-02 00:43:14.794597: I tensorflow/compiler/xla/xla_client/mesh_service.cc:208] Waiting to connect to client mesh master (300 seconds) localhost:57271 2020-03-02 00:43:14.857680: I tensorflow/compiler/xla/xla_client/mesh_service.cc:208] Waiting to connect to client mesh master (300 seconds) localhost:57271 2020-03-02 00:43:14.918609: I tensorflow/compiler/xla/xla_client/mesh_service.cc:208] Waiting to connect to client mesh master (300 seconds) localhost:57271 2020-03-02 00:43:14.974498: I tensorflow/compiler/xla/xla_client/mesh_service.cc:208] Waiting to connect to client mesh master (300 seconds) localhost:57271 2020-03-02 00:43:15.031540: I tensorflow/compiler/xla/xla_client/mesh_service.cc:208] Waiting to connect to client mesh master (300 seconds) localhost:57271 2020-03-02 00:43:15.087601: I tensorflow/compiler/xla/xla_client/mesh_service.cc:208] Waiting to connect to client mesh master (300 seconds) localhost:57271 2020-03-02 00:43:15.142553: I tensorflow/compiler/xla/xla_client/mesh_service.cc:208] Waiting to connect to client mesh master (300 seconds) localhost:57271 E0302 00:43:22.445484458 1536 server_chttp2.cc:40] {"created":"@1583109802.445465277","description":"Only 1 addresses added out of total 2 resolved","file":"external/com_github_grpc_grpc/src/core/ext/transport/chttp2/server/chttp2_server.cc","file_line":404,"referenced_errors":[{"created":"@1583109802.445463004","description":"Address family not supported by protocol","errno":97,"file":"external/com_github_grpc_grpc/src/core/lib/iomgr/socket_utils_common_posix.cc","file_line":420,"os_error":"Address family not supported by protocol","syscall":"socket","target_address":"[::1]:57271"}]} 2020-03-02 00:43:24.109498: I tensorflow/compiler/xla/xla_client/computation_client.cc:197] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:57271 2020-03-02 00:43:24.429623: I tensorflow/compiler/xla/xla_client/computation_client.cc:197] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:57271 2020-03-02 00:43:24.712988: I tensorflow/compiler/xla/xla_client/computation_client.cc:197] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:57271 2020-03-02 00:43:24.731491: I tensorflow/compiler/xla/xla_client/computation_client.cc:197] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:57271 2020-03-02 00:43:24.867584: I tensorflow/compiler/xla/xla_client/computation_client.cc:197] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:57271 2020-03-02 00:43:24.883436: I tensorflow/compiler/xla/xla_client/computation_client.cc:197] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:57271 2020-03-02 00:43:25.112841: I tensorflow/compiler/xla/xla_client/computation_client.cc:197] Fetching mesh configuration for worker tpu_worker:0 from mesh service at localhost:57271 INFO:root:INIT TPU local core: 0, global rank: 0 2020-03-02 00:44:11.382078: I tensorflow/compiler/xla/xla_client/mesh_service.cc:208] Waiting to connect to client mesh master (300 seconds) localhost:57271 INFO:root:INIT TPU local core: 2, global rank: 2 INFO:root:INIT TPU local core: 5, global rank: 5 2020-03-02 00:44:15.925331: E tensorflow/compiler/xla/xla_client/tf_logging.cc:11] Failed to meet rendezvous 'pl.Trainer.run_pretrain_routine': Socket closed (14) Traceback (most recent call last): File "finetune.py", line 129, in <module> trainer.fit(model) File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py", line 976, in fit xmp.spawn(self.tpu_train, args=(model,), nprocs=self.num_tpu_cores, start_method=start_method) File "/usr/local/lib/python3.6/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 182, in spawn start_method=start_method) File "/usr/local/lib/python3.6/dist-packages/torch/multiprocessing/spawn.py", line 158, in start_processes while not context.join(): File "/usr/local/lib/python3.6/dist-packages/torch/multiprocessing/spawn.py", line 108, in join (error_index, name) Exception: process 1 terminated with signal SIGKILL Expected behavior The code works when training on gpt2 (124M) but doesn't when training on gpt2-large (774M) Environment Collecting environment information... PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.1 OS: Ubuntu 18.04.3 LTS GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0 CMake version: version 3.12.0 Python version: 3.6 Is CUDA available: No CUDA runtime version: 10.1.243 GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5 Versions of relevant libraries: [pip3] numpy==1.17.5 [pip3] torch==1.4.0 [pip3] torchsummary==1.5.1 [pip3] torchtext==0.3.1 [pip3] torchvision==0.5.0 [conda] Could not collect Additional context

Precision=16 with TPUs bug

🐛 Bug Setting precision=16 when training with a TPU throws an error To Reproduce see colab: https://colab.research.google.com/drive/1s-ZDIqzgKQ1Byf-Lw58RZ8LGgmdB6qjB Relavent stack trace: Exception in device=TPU:0: str expected, not int Traceback (most recent call last): File "/usr/local/lib/python3.6/dist-packages/torch_xla/distributed/xla_multiprocessing.py", line 119, in _start_fn fn(gindex, *args) File "/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/distrib_parts.py", line 492, in tpu_train os.environ['XLA_USE_BF16'] = 1 File "/usr/lib/python3.6/os.py", line 674, in __setitem__ value = self.encodevalue(value) File "/usr/lib/python3.6/os.py", line 744, in encode raise TypeError("str expected, not %s" % type(value).__name__) TypeError: str expected, not int To fix this all that should be needed should be casting 1 to a string os.environ['XLA_USE_BF16'] = str(1) Environment Collecting environment information... PyTorch version: 1.4.0 Is debug build: No CUDA used to build PyTorch: 10.1 OS: Ubuntu 18.04.3 LTS GCC version: (Ubuntu 7.4.0-1ubuntu1~18.04.1) 7.4.0 CMake version: version 3.12.0 Python version: 3.6 Is CUDA available: No CUDA runtime version: 10.1.243 GPU models and configuration: Could not collect Nvidia driver version: Could not collect cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5 Versions of relevant libraries: [pip3] numpy==1.17.5 [pip3] torch==1.4.0 [pip3] torchsummary==1.5.1 [pip3] torchtext==0.3.1 [pip3] torchvision==0.5.0 [conda] Could not collect