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)
+    hidden_states = self.encoder(x)
-    loss = my_loss(out, y)
+     
+    # 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):
@@ -84,8 +115,40 @@ 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>
+
+Lightning also adds a text column with all the hyperparameters for this experiment.      
+
+<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>
+
+Simply note the path you set for the Experiment    
+``` {.python}   
+from test_tube import Experiment
+from pytorch-lightning import  Trainer
+
+exp = Experiment(save_dir='/some/path')
+trainer = Trainer(experiment=exp)
+...
+```   
+
+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    
 

docs/LightningModule/properties.md

@@ -8,12 +8,21 @@ 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
+#### global_step   
 Total training batches seen across all epochs   
 
 --- 
-#### gradient_clip
+#### gradient_clip   
 The current gradient clip value    
 
 ---
@@ -21,5 +30,11 @@ The current gradient clip value
 True if your model is currently running on GPUs. Useful to set flags around the LightningModule for different CPU vs GPU behavior.    
 
 ---
-#### Trainer
+#### 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
+...   
+```   
+

examples/new_project_templates/__init__.py

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

examples/new_project_templates/trainer_gpu_cluster_template.py

@@ -56,6 +56,7 @@ 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',
         save_dir=log_dir,

requirements.txt

@@ -1,41 +1,32 @@
-absl-py==0.7.1
+
-astor==0.8.0
+atomicwrites==1.2.1
-bleach==3.1.0
+attrs==18.2.0
-certifi==2019.6.16
+certifi==2018.11.29
-cffi==1.12.3
+cffi==1.11.5
-chardet==3.0.4
+imageio==2.4.1
-docutils==0.14
+mkl-fft==1.0.6
-gast==0.2.2
-google-pasta==0.1.7
-grpcio==1.21.1
-h5py==2.9.0
-idna==2.8
-imageio==2.5.0
-Keras-Applications==1.0.8
-Keras-Preprocessing==1.1.0
-Markdown==3.1.1
-mkl-fft==1.0.12
 mkl-random==1.0.2
-numpy==1.16.4
+more-itertools==5.0.0
+numpy==1.15.4
 olefile==0.46
-pandas==0.24.2
+pandas==0.23.4
-Pillow==6.0.0
+Pillow==5.3.0
-pkginfo==1.5.0.1
+pluggy==0.8.0
-protobuf==3.8.0
+py==1.7.0
 pycparser==2.19
-Pygments==2.4.1
+pytest==4.0.2
-python-dateutil==2.8.0
+python-dateutil==2.7.5
-pytz==2019.1
+pytz==2018.7
-readme-renderer==24.0
+scikit-learn==0.20.2
-requests==2.22.0
+scipy==1.2.0
-requests-toolbelt==0.9.1
 six==1.12.0
+sklearn==0.0
 tensorboard==1.14.0
 tensorboardX==1.7
 tensorflow==1.14.0
-tensorflow-estimator==1.14.0
-termcolor==1.1.0
 test-tube==0.643
+torch==1.0.0
+torchvision==0.2.1
 tqdm==4.32.1
 twine==1.13.0
 urllib3==1.25.3

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='0.113',
+    version='0.12',
     description="The Keras for ML researchers using PyTorch",
     author="William Falcon",
     author_email="waf2107@columbia.edu",
@@ -17,9 +17,9 @@ setup(
     keywords=["deep learning", "pytorch", "AI"],
     python_requires=">=3.5",
     install_requires=[
-        "torch>=1.0.0",
+        "torch>=1.1.0",
         "tqdm",
-        "test-tube>=0.643",
+        "test-tube>=0.65",
         "tensorflow>=1.14.0"
     ],
     packages=find_packages(),