release v0.12

.gitignore

@@ -8,6 +8,7 @@ datasets/
 model_weights/
 app/models/
 pip-wheel-metadata/
+test_tube_exp/
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

@@ -36,9 +36,26 @@ To use lightning do 2 things:
 2. [Define a LightningModel](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py).     
 
 ## What does lightning control for me?
-Everything! Except the following three things:
+Everything!    
+Except for these 6 core functions which you define:    
 
-**What happens in the training loop**    
+```{.python}
+# what to do in the training loop
+def training_step(self, data_batch, batch_nb):
+
+# what to do in the validation loop
+def validation_step(self, data_batch, batch_nb):
+
+# how to aggregate validation_step outputs
+def validation_end(self, outputs):
+
+# and your dataloaders
+def tng_dataloader():
+def val_dataloader():
+def test_dataloader():
+```
+
+**Could be as complex as seq-2-seq + attention**    
 
 ```python
 # define what happens for training here
@@ -46,25 +63,39 @@ def training_step(self, data_batch, batch_nb):
     x, y = data_batch
     
     # define your own forward and loss calculation
-    out = self.forward(x)
-    loss = my_loss(out, y)
+    hidden_states = self.encoder(x)
+     
+    # even as complex as a seq-2seq + attn model
+    # (this is just a toy, non-working example to illustrate)
+    start_token = '<SOS>'
+    last_hidden = torch.zeros(...)
+    loss = 0
+    for step in range(max_seq_len):
+        attn_context = self.attention_nn(hidden_states, start_token)
+        pred = self.decoder(start_token, attn_context, last_hidden) 
+        last_hidden = pred
+        pred = self.predict_nn(pred)
+        loss += self.loss(last_hidden, y[step])
+        
+    #toy example as well
+    loss = loss / max_seq_len
     return {'loss': loss} 
 ```
 
-**What happens in the validation loop**      
+**Or as basic as CNN image classification**      
 
 ```python
 # define what happens for validation here
 def validation_step(self, data_batch, batch_nb):    
     x, y = data_batch
     
-    # define your own forward and loss calculation
+    # or as basic as a CNN classification
     out = self.forward(x)
     loss = my_loss(out, y)
     return {'loss': loss} 
 ```
 
-**And what to do with the output of all validation batches**    
+**And you also decide how to collate the output of all validation steps**    
 
 ```python
 def validation_end(self, outputs):
@@ -84,22 +115,52 @@ def validation_end(self, outputs):
     tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
     return tqdm_dic
 ```
+   
+## TensorboardX    
+Lightning is fully integrated with tensorboardX.   
 
-## Lightning gives you options to control the following:
+<p align="center">
+  <a href="https://williamfalcon.github.io/pytorch-lightning/">
+    <img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/tf_loss.png" width="900px">
+  </a>
+</p>
 
-**Checkpointing**    
+Lightning also adds a text column with all the hyperparameters for this experiment.      
 
-- Model saving
-- Model loading 
+<p align="center">
+  <a href="https://williamfalcon.github.io/pytorch-lightning/">
+        <img alt="" src="https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/_static/tf_tags.png" width="900px">
+  </a>
+</p>
 
-**Computing cluster (SLURM)**    
+Simply note the path you set for the Experiment    
+``` {.python}   
+from test_tube import Experiment
+from pytorch-lightning import  Trainer
 
-- Automatic checkpointing   
-- Automatic saving, loading  
-- Running grid search on a cluster 
-- Walltime auto-resubmit   
+exp = Experiment(save_dir='/some/path')
+trainer = Trainer(experiment=exp)
+...
+```   
 
-**Debugging**  
+And run tensorboard from that dir   
+```bash
+tensorboard --logdir /some/path     
+```    
+
+## Lightning automatically automates all of the following ([each is also configurable](https://williamfalcon.github.io/pytorch-lightning/Trainer/)):
+
+###### Checkpointing    
+
+- [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
+- [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics) 
+
+###### Computing cluster (SLURM)    
+
+- [Running grid search on a cluster](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster) 
+- [Walltime auto-resubmit](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit)   
+
+###### Debugging  
 
 - [Fast dev run](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run)
 - [Inspect gradient norms](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms)
@@ -109,7 +170,7 @@ def validation_end(self, outputs):
 - [Pring which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
 
 
-**Distributed training**    
+###### Distributed training    
 
 - [16-bit mixed precision](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision)
 - [Multi-GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU)
@@ -118,26 +179,27 @@ def validation_end(self, outputs):
 - [Self-balancing architecture](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture)
 
 
-**Experiment Logging**   
+###### Experiment Logging   
 
 - [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
 - Log arbitrary metrics
 - [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
-- [Process position](Logging/#process-position)
+- [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
 - [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters) 
 - [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run) 
 - [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
 
-**Training loop**    
+###### Training loop    
 
 - [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
 - [Anneal Learning rate](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#anneal-learning-rate)
 - [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
 - [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
+- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
 - [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
 
-**Validation loop**    
+###### Validation loop    
 
 - [Check validation every n epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs)
 - [Set how much of the validation set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check)

docs/LightningModule/RequiredTrainerInterface.md

@@ -1,4 +1,4 @@
-# Lightning module  
+# Lightning Module interface
 [[Github Code](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/root_module.py)]
 
 A lightning module is a strict superclass of nn.Module, it provides a standard interface for the trainer to interact with the model.
@@ -9,22 +9,22 @@ Otherwise, to Define a Lightning Module, implement the following methods:
 
 **Required**:  
 
-- [training_step](LightningModule.md#training_step)   
-- [validation_step](LightningModule.md#validation_step)
-- [validation_end](LightningModule.md#validation_end)
+- [training_step](RequiredTrainerInterface.md#training_step)   
+- [validation_step](RequiredTrainerInterface.md#validation_step)
+- [validation_end](RequiredTrainerInterface.md#validation_end)
     
-- [configure_optimizers](LightningModule.md#configure_optimizers)
-- [get_save_dict](LightningModule.md#get_save_dict)
-- [load_model_specific](LightningModule.md#load_model_specific)
+- [configure_optimizers](RequiredTrainerInterface.md#configure_optimizers)
+- [get_save_dict](RequiredTrainerInterface.md#get_save_dict)
+- [load_model_specific](RequiredTrainerInterface.md#load_model_specific)
 
-- [tng_dataloader](LightningModule.md#tng_dataloader)
-- [tng_dataloader](LightningModule.md#tng_dataloader)
-- [test_dataloader](LightningModule.md#test_dataloader)
+- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
+- [tng_dataloader](RequiredTrainerInterface.md#tng_dataloader)
+- [test_dataloader](RequiredTrainerInterface.md#test_dataloader)
 
 **Optional**:   
 
-- [update_tng_log_metrics](LightningModule.md#update_tng_log_metrics)
-- [add_model_specific_args](LightningModule.md#add_model_specific_args)
+- [update_tng_log_metrics](RequiredTrainerInterface.md#update_tng_log_metrics)
+- [add_model_specific_args](RequiredTrainerInterface.md#add_model_specific_args)
 
 ---
 
@@ -361,7 +361,7 @@ def add_model_specific_args(parent_parser, root_dir)
 ```
 Lightning has a list of default argparse commands.
 This method is your chance to add or modify commands specific to your model.
-The argument parser is available anywhere in your model by calling self.hparams
+The [hyperparameter argument parser](https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser/) is available anywhere in your model by calling self.hparams.
 
 ##### Return
 An argument parser
@@ -391,4 +391,4 @@ def add_model_specific_args(parent_parser, root_dir):
     parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
     parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
     return parser
-```
+```

docs/LightningModule/methods.md

@@ -0,0 +1,49 @@
+Lightning modules are strict superclasses of torch.nn.Module. A LightningModule offers the following in addition to that API.
+
+---    
+### freeze 
+Freeze all params for inference
+```{.python}
+model = MyLightningModule(...)
+model.freeze()
+```
+
+---    
+### load_from_metrics
+This is the easiest/fastest way which uses the meta_tags.csv file from test-tube to rebuild the model.
+The meta_tags.csv file can be found in the test-tube experiment save_dir.       
+
+```{.python}
+pretrained_model = MyLightningModule.load_from_metrics(
+    weights_path='/path/to/pytorch_checkpoint.ckpt',
+    tags_csv='/path/to/test_tube/experiment/version/meta_tags.csv',
+    on_gpu=True,
+    map_location=None
+)
+    
+# predict 
+pretrained_model.freeze()
+y_hat = pretrained_model(x)
+```
+
+**Params**    
+
+| Param  | description  |
+|---|---|
+|  weights_path | Path to a pytorch checkpoint  |
+|  tags_csv | Path to meta_tags.csv file generated by the test-tube Experiment  |
+|  on_gpu | if True, puts model on GPU. Make sure to use transforms option if model devices have changed  |
+|  map_location | A dictionary mapping saved weight GPU devices to new GPU devices |
+
+**Returns**    
+
+LightningModule - The pretrained LightningModule
+
+---    
+### unfreeze 
+Unfreeze all params for inference
+```{.python}
+model = MyLightningModule(...)
+model.unfreeze()
+```
+

docs/LightningModule/properties.md

@@ -0,0 +1,40 @@
+A LightningModule has the following properties which you can access at any time
+
+--- 
+#### current_epoch
+The current epoch   
+
+---
+#### dtype    
+Current dtype    
+
+---
+#### experiment    
+An instance of test-tube Experiment which you can use to log anything for tensorboarX.   
+```{.python}
+self.experiment.add_embedding(...)   
+self.experiment.log({'val_loss': 0.9})   
+self.experiment.add_scalars(...)   
+```
+
+--- 
+#### global_step   
+Total training batches seen across all epochs   
+
+--- 
+#### gradient_clip   
+The current gradient clip value    
+
+---
+#### on_gpu    
+True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior.    
+
+---
+#### trainer    
+Last resort access to any state the trainer has. Changing certain properties here could affect your training run.
+```{.python}   
+self.trainer.optimizers
+self.trainer.current_epoch
+...   
+```   
+

docs/Trainer/Checkpointing.md

@@ -0,0 +1,22 @@
+Lightning can automate saving and loading checkpoints.
+
+---
+### Model saving
+To enable checkpointing, define the checkpoint callback and give it to the trainer.
+
+``` {.python}
+from pytorch_lightning.utils.pt_callbacks import ModelCheckpoint
+
+checkpoint_callback = ModelCheckpoint(
+    filepath='/path/to/store/weights.ckpt',
+    save_best_only=True,
+    verbose=True,
+    monitor='val_loss',
+    mode='min'
+)
+
+trainer = Trainer(checkpoint_callback=checkpoint_callback)
+```
+
+
+

docs/Trainer/SLURM Managed Cluster.md

@@ -24,7 +24,7 @@ hparams = parser.parse_args()
 ```    
     
      
-(2). Define the cluster options (over 5 nodes and 8 gpus)    
+(2). Define the cluster options in the [SlurmCluster object](https://williamfalcon.github.io/test-tube/hpc/SlurmCluster/) (over 5 nodes and 8 gpus)    
 
 ```{.python}
 from test_tube.hpc import SlurmCluster

docs/Trainer/Training Loop.md

@@ -38,6 +38,16 @@ Use this to turn off early stopping and run training to the [max_epoch](#force-t
 trainer = Trainer(enable_early_stop=True)
 ```
 
+---
+#### Gradient Clipping 
+Use this to turn off early stopping and run training to the [max_epoch](#force-training-for-min-or-max-epochs)
+``` {.python}
+# DEFAULT (ie: don't clip)
+trainer = Trainer(gradient_clip=0)
+```
+
+
+
 ---
 #### Inspect gradient norms
 Looking at grad norms can help you figure out where training might be going wrong.

docs/Trainer/index.md

@@ -24,10 +24,8 @@ But of course the fun is in all the advanced things it can do:
 
 **Computing cluster (SLURM)**    
 
-- Automatic checkpointing   
-- Automatic saving, loading  
-- Running grid search on a cluster
-- Walltime auto-resubmit   
+- [Running grid search on a cluster](SLURM%20Managed%20Cluster/#running-grid-search-on-a-cluster)
+- [Walltime auto-resubmit](SLURM%20Managed%20Cluster/#walltime-auto-resubmit)   
 
 **Debugging**  
 

docs/examples/Examples.md

@@ -0,0 +1,171 @@
+### Template model definition
+In 99% of cases you want to just copy [this template](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py) to start a new lightningModule and change the core of what your model is actually trying to do.
+
+```bash
+# get a copy of the module template
+wget https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py
+```
+
+---    
+### Trainer Example 
+
+** \_\_main__ function**    
+
+Normally, we want to let the \_\_main__ function start the training.
+Inside the main we parse training arguments with whatever hyperparameters we want. Your LightningModule will have a 
+chance to add hyperparameters.   
+
+```{.python}
+from test_tube import HyperOptArgumentParser
+
+if __name__ == '__main__':
+
+    # use default args given by lightning
+    root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
+    parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
+    add_default_args(parent_parser, root_dir)
+
+    # allow model to overwrite or extend args
+    parser = ExampleModel.add_model_specific_args(parent_parser)
+    hyperparams = parser.parse_args()
+
+    # train model
+    main(hyperparams)
+```
+**Main Function**      
+
+The main function is your entry into the program. This is where you init your model, checkpoint directory, and launch the training.
+The main function should have 3 arguments:   
+- hparams: a configuration of hyperparameters.    
+- slurm_manager: Slurm cluster manager object (can be None)
+- dict: for you to return any values you want (useful in meta-learning, otherwise set to _)    
+
+```{}
+def main(hparams, cluster, results_dict):
+    """
+    Main training routine specific for this project
+    :param hparams:
+    :return:
+    """
+    # init experiment
+    log_dir = os.path.dirname(os.path.realpath(__file__))
+    exp = Experiment(
+        name='test_tube_exp',
+        debug=True,
+        save_dir=log_dir,
+        version=0,
+        autosave=False,
+        description='test demo'
+    )
+    
+    # set the hparams for the experiment
+    exp.argparse(hparams)
+    exp.save()
+
+    # build model
+    model = MyLightningModule(hparams)
+
+    # callbacks
+    early_stop = EarlyStopping(
+        monitor=hparams.early_stop_metric,
+        patience=hparams.early_stop_patience,
+        verbose=True,
+        mode=hparams.early_stop_mode
+    )
+
+    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
+    checkpoint = ModelCheckpoint(
+        filepath=model_save_path,
+        save_function=None,
+        save_best_only=True,
+        verbose=True,
+        monitor=hparams.model_save_monitor_value,
+        mode=hparams.model_save_monitor_mode
+    )
+
+    # configure trainer
+    trainer = Trainer(
+        experiment=exp,
+        cluster=cluster,
+        checkpoint_callback=checkpoint,
+        early_stop_callback=early_stop,
+    )
+
+    # train model
+    trainer.fit(model)
+```
+
+
+
+
+The __main__ function will start training on your **main** function. If you use the HyperParameterOptimizer
+in hyper parameter optimization mode, this main function will get one set of hyperparameters. If you use it as a simple
+argument parser you get the default arguments in the argument parser.
+
+So, calling main(hyperparams) runs the model with the default argparse arguments.       
+```{.python}
+main(hyperparams)
+```
+
+---
+#### CPU hyperparameter search      
+
+```{.python}
+# run a grid search over 20 hyperparameter combinations.
+hyperparams.optimize_parallel_cpu(
+    main_local,
+    nb_trials=20,
+    nb_workers=1
+)
+```
+
+---
+#### Hyperparameter search on a single or multiple GPUs       
+```{.python}
+# run a grid search over 20 hyperparameter combinations.
+hyperparams.optimize_parallel_gpu(
+    main_local,
+    nb_trials=20,
+    nb_workers=1,
+    gpus=[0,1,2,3]
+)
+```
+
+---
+#### Hyperparameter search on a SLURM HPC cluster   
+```{.python}    
+def optimize_on_cluster(hyperparams):
+    # enable cluster training
+    cluster = SlurmCluster(
+        hyperparam_optimizer=hyperparams,
+        log_path=hyperparams.tt_save_path,
+        test_tube_exp_name=hyperparams.tt_name
+    )
+
+    # email for cluster coms
+    cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
+
+    # configure cluster
+    cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
+    cluster.job_time = '48:00:00'
+    cluster.gpu_type = '1080ti'
+    cluster.memory_mb_per_node = 48000
+
+    # any modules for code to run in env
+    cluster.add_command('source activate pytorch_lightning')
+
+    # name of exp
+    job_display_name = hyperparams.tt_name.split('_')[0]
+    job_display_name = job_display_name[0:3]
+
+    # run hopt
+    print('submitting jobs...')
+    cluster.optimize_parallel_cluster_gpu(
+        main,
+        nb_trials=hyperparams.nb_hopt_trials,
+        job_name=job_display_name
+    )
+
+# run cluster hyperparameter search    
+optimize_on_cluster(hyperparams)
+```

docs/index.md

@@ -1,35 +1,33 @@
-# PYTORCH-LIGHTNING DOCUMENTATION
+###### New project Quick Start    
+To start a new project define these two files.   
 
-###### Main Docs
-- [LightningModule](Pytorch-Lightning/LightningModule)  
-- [Trainer](Trainer/)  
-
-###### New project Quick Start
-1. [Define a LightningModule](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/lightning_module_template.py)  
+1. [Define a LightningModule](/LightningModule/RequiredTrainerInterface/#template-model-definition)  
 2. Pick a trainer      
     - [Basic CPU Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/trainer_cpu_template.py) 
     - [GPU cluster Trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/trainer_gpu_cluster_template.py)
 
+###### Docs shortcuts
+- [LightningModule](LightningModule/RequiredTrainerInterface/)  
+- [Trainer](Trainer/)  
+
 ###### Quick start examples 
-- CPU example   
-- Single GPU example   
-- Multi-gpu example 
-- SLURM cluster grid search example      
+- [CPU example](examples/Examples/#cpu-hyperparameter-search)   
+- [Hyperparameter search on single GPU](examples/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus)    
+- [Hyperparameter search on multiple GPUs on same node](examples/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus)  
+- [Hyperparameter search on a SLURM HPC cluster](examples/Examples/#Hyperparameter search on a SLURM HPC cluster)      
 
 
 ###### Checkpointing    
 
-- Model saving
-- Model loading 
+- [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
+- [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics) 
 
-######Computing cluster (SLURM)    
+###### Computing cluster (SLURM)    
 
-- Automatic checkpointing   
-- Automatic saving, loading  
-- Running grid search on a cluster 
-- Walltime auto-resubmit   
+- [Running grid search on a cluster](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster) 
+- [Walltime auto-resubmit](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit)   
 
-######Debugging  
+###### Debugging  
 
 - [Fast dev run](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run)
 - [Inspect gradient norms](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms)
@@ -39,7 +37,7 @@
 - [Pring which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
 
 
-######Distributed training    
+###### Distributed training    
 
 - [16-bit mixed precision](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision)
 - [Multi-GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU)
@@ -48,22 +46,23 @@
 - [Self-balancing architecture](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture)
 
 
-######Experiment Logging   
+###### Experiment Logging   
 
 - [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
 - Log arbitrary metrics
 - [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
-- [Process position](Logging/#process-position)
+- [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
 - [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters) 
 - [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run) 
 - [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
 
-######Training loop    
+###### Training loop    
 
 - [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
 - [Anneal Learning rate](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#anneal-learning-rate)
 - [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
 - [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
+- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
 - [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/Pytorch-Lightning/LightningModule/#configure_optimizers)
 - [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
 

docs/source/examples/__init__.py

@@ -1 +0,0 @@
-from .example_model import ExampleModel

docs/source/examples/basic_trainer.py

@@ -1,74 +0,0 @@
-import os
-import sys
-
-from test_tube import HyperOptArgumentParser, Experiment
-from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.utils.arg_parse import add_default_args
-from pytorch_lightning.callbacks.pt_callbacks import EarlyStopping, ModelCheckpoint
-from docs.source.examples.example_model import ExampleModel
-
-
-def main(hparams):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    # init experiment
-    exp = Experiment(
-        name=hparams.tt_name,
-        debug=hparams.debug,
-        save_dir=hparams.tt_save_path,
-        version=hparams.hpc_exp_number,
-        autosave=False,
-        description=hparams.tt_description
-    )
-
-    exp.argparse(hparams)
-    exp.save()
-
-    # build model
-    model = ExampleModel(hparams)
-
-    # callbacks
-    early_stop = EarlyStopping(
-        monitor='val_acc',
-        patience=3,
-        mode='min',
-        verbose=True,
-    )
-
-    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
-    checkpoint = ModelCheckpoint(
-        filepath=model_save_path,
-        save_function=None,
-        save_best_only=True,
-        verbose=True,
-        monitor='val_acc',
-        mode='min'
-    )
-
-    # configure trainer
-    trainer = Trainer(
-        experiment=exp,
-        checkpoint_callback=checkpoint,
-        early_stop_callback=early_stop,
-    )
-
-    # train model
-    trainer.fit(model)
-
-
-if __name__ == '__main__':
-
-    # use default args given by lightning
-    root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
-    parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
-    add_default_args(parent_parser, root_dir)
-
-    # allow model to overwrite or extend args
-    parser = ExampleModel.add_model_specific_args(parent_parser)
-    hyperparams = parser.parse_args()
-
-    # train model
-    main(hyperparams)

docs/source/examples/example_model.py

@@ -1,211 +0,0 @@
-import torch.nn as nn
-import numpy as np
-from pytorch_lightning.root_module.root_module import LightningModule
-from test_tube import HyperOptArgumentParser
-from torchvision.datasets import MNIST
-import torchvision.transforms as transforms
-import torch
-import torch.nn.functional as F
-import os, pdb
-from collections import OrderedDict
-
-
-class ExampleModel(LightningModule):
-    """
-    Sample model to show how to define a template
-    """
-
-    def __init__(self, hparams):
-        # init superclass
-        super(ExampleModel, self).__init__(hparams)
-
-        self.batch_size = hparams.batch_size
-
-        # build model
-        self.__build_model()
-
-    # ---------------------
-    # MODEL SETUP
-    # ---------------------
-    def __build_model(self):
-        """
-        Layout model
-        :return:
-        """
-        self.c_d1 = nn.Linear(in_features=self.hparams.in_features, out_features=self.hparams.hidden_dim)
-        self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)
-        self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)
-
-        self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim, out_features=self.hparams.out_features)
-
-    # ---------------------
-    # TRAINING
-    # ---------------------
-    def forward(self, x):
-
-        x = self.c_d1(x)
-        x = torch.tanh(x)
-        x = self.c_d1_bn(x)
-        x = self.c_d1_drop(x)
-
-        x = self.c_d2(x)
-        logits = F.log_softmax(x, dim=1)
-
-        return logits
-
-    def loss(self, labels, logits):
-        nll = F.nll_loss(logits, labels)
-        return nll
-
-    def training_step(self, data_batch, batch_i):
-        """
-        Called inside the training loop
-        :param data_batch:
-        :return:
-        """
-        # forward pass
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
-
-        # calculate loss
-        loss_val = self.loss(y, y_hat)
-
-        output = OrderedDict({
-            'loss': loss_val,
-            'tqdm_metrics': {}
-        })
-        return output
-
-    def validation_step(self, data_batch, batch_i):
-        """
-        Called inside the validation loop
-        :param data_batch:
-        :return:
-        """
-        x, y = data_batch
-        x = x.view(x.size(0), -1)
-        y_hat = self.forward(x)
-
-        loss_val = self.loss(y, y_hat)
-
-        # acc
-        labels_hat = torch.argmax(y_hat, dim=1)
-        val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
-
-        output = OrderedDict({
-            'val_loss': loss_val,
-            'val_acc': torch.tensor(val_acc),
-        })
-        return output
-
-
-    def validation_end(self, outputs):
-        """
-        Called at the end of validation to aggregate outputs
-        :param outputs: list of individual outputs of each validation step
-        :return:
-        """
-        val_loss_mean = 0
-        val_acc_mean = 0
-        for output in outputs:
-            val_loss_mean += output['val_loss']
-            val_acc_mean += output['val_acc']
-
-        val_loss_mean /= len(outputs)
-        val_acc_mean /= len(outputs)
-        tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
-        return tqdm_dic
-
-    def update_tng_log_metrics(self, logs):
-        return logs
-
-    # ---------------------
-    # MODEL SAVING
-    # ---------------------
-    def get_save_dict(self):
-        checkpoint = {'state_dict': self.state_dict()}
-        return checkpoint
-
-    def load_model_specific(self, checkpoint):
-        self.load_state_dict(checkpoint['state_dict'])
-        pass
-
-    # ---------------------
-    # TRAINING SETUP
-    # ---------------------
-    def configure_optimizers(self):
-        """
-        return whatever optimizers we want here
-        :return: list of optimizers
-        """
-        optimizer = self.choose_optimizer(self.hparams.optimizer_name, self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
-        self.optimizers = [optimizer]
-        return self.optimizers
-
-    def __dataloader(self, train):
-        # init data generators
-        transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
-
-        dataset = MNIST(root=self.hparams.data_root, train=train, transform=transform, download=True)
-
-        loader = torch.utils.data.DataLoader(
-            dataset=dataset,
-            batch_size=self.hparams.batch_size,
-            shuffle=True
-        )
-
-        return loader
-
-    @property
-    def tng_dataloader(self):
-        if self._tng_dataloader is None:
-            try:
-                self._tng_dataloader = self.__dataloader(train=True)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._tng_dataloader
-
-    @property
-    def val_dataloader(self):
-        if self._val_dataloader is None:
-            try:
-                self._val_dataloader = self.__dataloader(train=False)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._val_dataloader
-
-    @property
-    def test_dataloader(self):
-        if self._test_dataloader is None:
-            try:
-                self._test_dataloader = self.__dataloader(train=False)
-            except Exception as e:
-                print(e)
-                raise e
-        return self._test_dataloader
-
-    @staticmethod
-    def add_model_specific_args(parent_parser, root_dir):
-        parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
-
-        # param overwrites
-        # parser.set_defaults(gradient_clip=5.0)
-
-        # network params
-        parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
-        parser.add_argument('--in_features', default=28*28)
-        parser.add_argument('--out_features', default=10)
-        parser.add_argument('--hidden_dim', default=50000) # use 500 for CPU, 50000 for GPU to see speed difference
-
-        # data
-        parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
-
-        # training params (opt)
-        parser.opt_list('--learning_rate', default=0.001, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
-                        tunable=False)
-        parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
-        parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
-        return parser

docs/source/examples/fully_featured_trainer.py

@@ -1,210 +0,0 @@
-import os
-import sys
-import numpy as np
-from time import sleep
-import torch
-
-from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
-from pytorch_lightning.models.trainer import Trainer
-from pytorch_lightning.utils.arg_parse import add_default_args
-
-from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
-
-SEED = 2334
-torch.manual_seed(SEED)
-np.random.seed(SEED)
-
-# ---------------------
-# DEFINE MODEL HERE
-# ---------------------
-from docs.source.examples.example_model import ExampleModel
-# ---------------------
-
-AVAILABLE_MODELS = {
-    'model_template': ExampleModel
-}
-
-
-"""
-Allows training by using command line arguments
-Run by:
-# TYPE YOUR RUN COMMAND HERE
-"""
-
-
-def main_local(hparams):
-    main(hparams, None, None)
-
-
-def main(hparams, cluster, results_dict):
-    """
-    Main training routine specific for this project
-    :param hparams:
-    :return:
-    """
-    on_gpu = hparams.gpus is not None and torch.cuda.is_available()
-
-    device = 'cuda' if on_gpu else 'cpu'
-    hparams.__setattr__('device', device)
-    hparams.__setattr__('on_gpu', on_gpu)
-    hparams.__setattr__('nb_gpus', torch.cuda.device_count())
-    hparams.__setattr__('inference_mode', hparams.model_load_weights_path is not None)
-
-    # delay each training start to not overwrite logs
-    process_position, current_gpu = TRAINING_MODEL.get_process_position(hparams.gpus)
-    sleep(process_position + 1)
-
-    # init experiment
-    log_dir = os.path.dirname(os.path.realpath(__file__))
-    exp = Experiment(
-        name='test_tube_exp',
-        debug=True,
-        save_dir=log_dir,
-        version=0,
-        autosave=False,
-        description='test demo'
-    )
-
-    exp.argparse(hparams)
-    exp.save()
-
-    # build model
-    print('loading model...')
-    model = TRAINING_MODEL(hparams)
-    print('model built')
-
-    # callbacks
-    early_stop = EarlyStopping(
-        monitor=hparams.early_stop_metric,
-        patience=hparams.early_stop_patience,
-        verbose=True,
-        mode=hparams.early_stop_mode
-    )
-
-    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
-    checkpoint = ModelCheckpoint(
-        filepath=model_save_path,
-        save_function=None,
-        save_best_only=True,
-        verbose=True,
-        monitor=hparams.model_save_monitor_value,
-        mode=hparams.model_save_monitor_mode
-    )
-
-    # gpus are ; separated for inside a node and , within nodes
-    gpu_list = None
-    if hparams.gpus is not None:
-        gpu_list = [int(x) for x in hparams.gpus.split(';')]
-
-    # configure trainer
-    trainer = Trainer(
-        experiment=exp,
-        cluster=cluster,
-        checkpoint_callback=checkpoint,
-        early_stop_callback=early_stop,
-        gpus=gpu_list
-    )
-
-    # train model
-    trainer.fit(model)
-
-
-def get_default_parser(strategy, root_dir):
-
-    possible_model_names = list(AVAILABLE_MODELS.keys())
-    parser = HyperOptArgumentParser(strategy=strategy, add_help=False)
-    add_default_args(parser, root_dir, possible_model_names=possible_model_names, rand_seed=SEED)
-    return parser
-
-
-def get_model_name(args):
-    for i, arg in enumerate(args):
-        if 'model_name' in arg:
-            return args[i+1]
-
-
-def optimize_on_cluster(hyperparams):
-    # enable cluster training
-    cluster = SlurmCluster(
-        hyperparam_optimizer=hyperparams,
-        log_path=hyperparams.tt_save_path,
-        test_tube_exp_name=hyperparams.tt_name
-    )
-
-    # email for cluster coms
-    cluster.notify_job_status(email='add_email_here', on_done=True, on_fail=True)
-
-    # configure cluster
-    cluster.per_experiment_nb_gpus = hyperparams.per_experiment_nb_gpus
-    cluster.job_time = '48:00:00'
-    cluster.gpu_type = '1080ti'
-    cluster.memory_mb_per_node = 48000
-
-    # any modules for code to run in env
-    cluster.add_command('source activate pytorch_lightning')
-
-    # name of exp
-    job_display_name = hyperparams.tt_name.split('_')[0]
-    job_display_name = job_display_name[0:3]
-
-    # run hopt
-    print('submitting jobs...')
-    cluster.optimize_parallel_cluster_gpu(
-        main,
-        nb_trials=hyperparams.nb_hopt_trials,
-        job_name=job_display_name
-    )
-
-
-if __name__ == '__main__':
-
-    model_name = get_model_name(sys.argv)
-    if model_name is None:
-        model_name = 'model_template'
-
-    # use default args
-    root_dir = os.path.dirname(os.path.realpath(__file__))
-    parent_parser = get_default_parser(strategy='random_search', root_dir=root_dir)
-
-    # allow model to overwrite or extend args
-    TRAINING_MODEL = AVAILABLE_MODELS[model_name]
-    parser = TRAINING_MODEL.add_model_specific_args(parent_parser, root_dir)
-    hyperparams = parser.parse_args()
-
-    # format GPU layout
-    os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-
-    # ---------------------
-    # RUN TRAINING
-    # ---------------------
-
-    # cluster and CPU
-    if hyperparams.on_cluster:
-        # run on HPC cluster
-        print('RUNNING ON SLURM CLUSTER')
-        gpu_ids = hyperparams.gpus.split(';')
-        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpu_ids)
-        optimize_on_cluster(hyperparams)
-
-    elif hyperparams.gpus is None:
-        # run on cpu
-        print('RUNNING ON CPU')
-        main(hyperparams, None, None)
-
-    # single or multiple GPUs on same machine
-    gpu_ids = hyperparams.gpus.split(';')
-    if hyperparams.interactive:
-        # run on 1 gpu
-        print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {gpu_ids}')
-        os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(gpu_ids)
-        main(hyperparams, None, None)
-
-    else:
-        # multiple GPUs on same machine
-        print(f'RUNNING MULTI GPU. GPU ids: {gpu_ids}')
-        hyperparams.optimize_parallel_gpu(
-            main_local,
-            gpu_ids=gpu_ids,
-            nb_trials=hyperparams.nb_hopt_trials,
-            nb_workers=len(gpu_ids)
-        )

examples/new_project_templates/__init__.py

@@ -0,0 +1 @@
+from .lightning_module_template import LightningTemplateModel

examples/new_project_templates/trainer_cpu_template.py

@@ -41,7 +41,6 @@ def main(hparams):
     model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
     checkpoint = ModelCheckpoint(
         filepath=model_save_path,
-        save_function=None,
         save_best_only=True,
         verbose=True,
         monitor='val_acc',

examples/new_project_templates/trainer_gpu_cluster_template.py

@@ -17,7 +17,7 @@ np.random.seed(SEED)
 # ---------------------
 # DEFINE MODEL HERE
 # ---------------------
-from examples.new_project_templates.lightning_module_template import LightningTemplateModel
+from lightning_module_template import LightningTemplateModel
 # ---------------------
 
 AVAILABLE_MODELS = {
@@ -56,11 +56,10 @@ def main(hparams, cluster, results_dict):
 
     # init experiment
     log_dir = os.path.dirname(os.path.realpath(__file__))
+    log_dir = os.path.join(log_dir, 'test_tube_demo_logs')
     exp = Experiment(
         name='test_tube_exp',
-        debug=True,
         save_dir=log_dir,
-        version=0,
         autosave=False,
         description='test demo'
     )
@@ -84,7 +83,6 @@ def main(hparams, cluster, results_dict):
     model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
     checkpoint = ModelCheckpoint(
         filepath=model_save_path,
-        save_function=None,
         save_best_only=True,
         verbose=True,
         monitor=hparams.model_save_monitor_value,
@@ -102,7 +100,7 @@ def main(hparams, cluster, results_dict):
         cluster=cluster,
         checkpoint_callback=checkpoint,
         early_stop_callback=early_stop,
-        gpus=gpu_list
+        gpus=gpu_list,
     )
 
     # train model

mkdocs.yml

@@ -1,9 +1,10 @@
 site_name: Pytorch lightning Documentation
-theme: readthedocs
+theme:
+  name: 'material'
 docs_dir: docs
 repo_url: https://github.com/williamFalcon/pytorch-lightning
 site_dir: 'site'
-site_description: 'Documentation for Pytorch Pytorch-Lightning, the researcher version of keras.'
+site_description: 'Documentation for Pytorch LightningModule, the researcher version of keras.'
 
 dev_addr: '0.0.0.0:8000'
 #google_analytics: ['UA-aasd', 'sitename']

pytorch_lightning/callbacks/pt_callbacks.py

@@ -170,12 +170,11 @@ class ModelCheckpoint(Callback):
         period: Interval (number of epochs) between checkpoints.
     """
 
-    def __init__(self, filepath, save_function, monitor='val_loss', verbose=0,
+    def __init__(self, filepath, monitor='val_loss', verbose=0,
                  save_best_only=False, save_weights_only=False,
                  mode='auto', period=1, prefix=''):
         super(ModelCheckpoint, self).__init__()
         self.monitor = monitor
-        self.save_function = save_function
         self.verbose = verbose
         self.filepath = filepath
         self.save_best_only = save_best_only

pytorch_lightning/models/trainer.py

@@ -33,6 +33,7 @@ class Trainer(TrainerIO):
     def __init__(self,
                  experiment,
                  checkpoint_callback, early_stop_callback,
+                 gradient_clip=0,
                  cluster=None,
                  process_position=0,
                  current_gpu_name=0,
@@ -53,6 +54,7 @@ class Trainer(TrainerIO):
                  nb_sanity_val_steps=5):
 
         # Transfer params
+        self.gradient_clip = gradient_clip
         self.check_val_every_n_epoch = check_val_every_n_epoch
         self.enable_early_stop = enable_early_stop
         self.track_grad_norm = track_grad_norm
@@ -126,13 +128,15 @@ class Trainer(TrainerIO):
     def __tng_tqdm_dic(self):
         tqdm_dic = {
             'tng_loss': '{0:.3f}'.format(self.avg_loss),
-            'gpu': '{}'.format(self.current_gpu_name),
             'v_nb': '{}'.format(self.experiment.version),
             'epoch': '{}'.format(self.current_epoch),
             'batch_nb':'{}'.format(self.batch_nb),
         }
         tqdm_dic.update(self.tqdm_metrics)
 
+        if self.on_gpu:
+            tqdm_dic['gpu'] = '{}'.format(self.current_gpu_name)
+
         return tqdm_dic
 
     def __layout_bookeeping(self, model):
@@ -242,7 +246,9 @@ class Trainer(TrainerIO):
     # -----------------------------
     def fit(self, model):
 
+        # give model convenience properties
         model.trainer = self
+        model.experiment = self.experiment
 
         # transfer data loaders from model
         self.__get_dataloaders(model)
@@ -367,7 +373,8 @@ class Trainer(TrainerIO):
                         metrics.update(grad_norm_dic)
 
                     # log metrics
-                    self.experiment.log(metrics)
+                    scalar_metrics = self.__metrics_to_scalars(metrics, blacklist=self.__log_vals_blacklist())
+                    self.experiment.log(scalar_metrics, global_step=self.global_step)
                     self.experiment.save()
 
                 # hook
@@ -394,6 +401,24 @@ class Trainer(TrainerIO):
                 if stop:
                     return
 
+    def __metrics_to_scalars(self, metrics, blacklist=[]):
+        new_metrics = {}
+        for k, v in metrics.items():
+            if type(v) is torch.Tensor:
+                v = v.item()
+
+            if type(v) is dict:
+                v = self.__metrics_to_scalars(v)
+
+            if k not in blacklist:
+                new_metrics[k] = float(v)
+
+        return new_metrics
+
+    def __log_vals_blacklist(self):
+        """avoid logging some vals lightning uses to maintain state"""
+        blacklist = {'batch_nb', 'v_nb', 'epoch', 'gpu'}
+        return blacklist
 
     def __run_tng_batch(self, data_batch, batch_nb):
         if data_batch is None:
@@ -441,6 +466,11 @@ class Trainer(TrainerIO):
         # gradient update with accumulated gradients
         if (self.batch_nb + 1) % self.accumulate_grad_batches == 0:
 
+            # clip gradients
+            if self.gradient_clip > 0:
+                model = self.model.module if self.data_parallel else self.model
+                torch.nn.utils.clip_grad_norm(model.parameters(), self.gradient_clip)
+
             # update gradients across all optimizers
             for optimizer in self.optimizers:
                 optimizer.step()

pytorch_lightning/root_module/model_saving.py

@@ -107,6 +107,9 @@ class TrainerIO(object):
         # save exp to make sure we get all the metrics
         experiment.save()
 
+        # close experiment to avoid issues
+        experiment.close()
+
         ckpt_number = self.max_ckpt_in_folder(folderpath) + 1
 
         if not os.path.exists(folderpath):

pytorch_lightning/root_module/root_module.py

@@ -24,9 +24,9 @@ class LightningModule(GradInformation, ModelIO, OptimizerConfig, ModelHooks):
         self.fast_dev_run = hparams.fast_dev_run
         self.overfit = hparams.overfit
         self.gradient_clip = hparams.gradient_clip
-        self.num = 2
         self.trainer = None
         self.from_lightning = True
+        self.experiment = None
 
         # track if gpu was requested for checkpointing
         self.on_gpu = False

requirements.txt

@@ -1,8 +1,8 @@
+
 atomicwrites==1.2.1
 attrs==18.2.0
 certifi==2018.11.29
 cffi==1.11.5
-h5py==2.9.0
 imageio==2.4.1
 mkl-fft==1.0.6
 mkl-random==1.0.2
@@ -21,7 +21,15 @@ scikit-learn==0.20.2
 scipy==1.2.0
 six==1.12.0
 sklearn==0.0
-test-tube==0.6282
+tensorboard==1.14.0
+tensorboardX==1.7
+tensorflow==1.14.0
+test-tube==0.643
 torch==1.0.0
 torchvision==0.2.1
-tqdm==4.28.1
+tqdm==4.32.1
+twine==1.13.0
+urllib3==1.25.3
+webencodings==0.5.1
+Werkzeug==0.15.4
+wrapt==1.11.2

setup.py

@@ -7,7 +7,7 @@ from setuptools import setup, find_packages
 # http://blog.ionelmc.ro/2014/05/25/python-packaging/
 setup(
     name="pytorch-lightning",
-    version='0.111',
+    version='0.12',
     description="The Keras for ML researchers using PyTorch",
     author="William Falcon",
     author_email="waf2107@columbia.edu",
@@ -17,9 +17,10 @@ setup(
     keywords=["deep learning", "pytorch", "AI"],
     python_requires=">=3.5",
     install_requires=[
-        "torch>=1.0.0",
+        "torch>=1.1.0",
         "tqdm",
-        "test-tube",
+        "test-tube>=0.65",
+        "tensorflow>=1.14.0"
     ],
     packages=find_packages(),
     long_description=open("README.md", encoding="utf-8").read(),