Dev

.gitignore

@@ -9,7 +9,6 @@ workdir
 doctrees/
 .buildinfo
 docs/source/_*
-dist
-/build
+dist/
+build/
 neural_pipeline.egg-info
-

.travis.yml

@@ -5,6 +5,7 @@ matrix:
   - python: 3.5
   - python: 3.6
   - python: 3.7
+  - python: 3.8
     dist: xenial
     sudo: true
 
@@ -14,7 +15,7 @@ install:
 - pip install coveralls
 
 script:
-- coverage run --source=neural_pipeline -m unittest -v tests/test.py
+- coverage run --source=piepline -m unittest -v tests/test.py
 
 after_success:
     coveralls

README.md

@@ -1,11 +1,11 @@
-# Neural Pipeline
+# PiePline
 
 Neural networks training pipeline based on PyTorch. Designed to standardize training process and accelerate experiments.
 
-[![Build Status](https://travis-ci.org/toodef/neural-pipeline.svg?branch=master)](https://travis-ci.org/toodef/neural-pipeline)
-[![Coverage Status](https://coveralls.io/repos/github/toodef/neural-pipeline/badge.svg?branch=master)](https://coveralls.io/github/toodef/neural-pipeline?branch=master)
-[![Maintainability](https://api.codeclimate.com/v1/badges/1feaafcc614adf27c30f/maintainability)](https://codeclimate.com/github/toodef/neural-pipeline/maintainability)
-[![Gitter chat](https://badges.gitter.im/neural-pipeline/gitter.png)](https://gitter.im/neural-pipeline/community)
+[![Build Status](https://travis-ci.org/PiePline/piepline.svg?branch=master)](https://travis-ci.org/PiePline/piepline)
+[![Coverage Status](https://coveralls.io/repos/github/PiePline/piepline/badge.svg?branch=master)](https://coveralls.io/github/PiePline/piepline?branch=master)
+[![Maintainability](https://api.codeclimate.com/v1/badges/7da18cb28e7e7dc13268/maintainability)](https://codeclimate.com/github/PiePline/piepline/maintainability)
+[![Gitter chat](https://badges.gitter.im/piepline/gitter.png)](https://gitter.im/piepline/community)
 
 * Core is about 2K lines, covered by tests, that you don't need to write again
 * Flexible and customizable training process
@@ -16,50 +16,52 @@ Neural networks training pipeline based on PyTorch. Designed to standardize trai
 
 # Getting started:
 ### Documentation
-[![Documentation Status](https://readthedocs.org/projects/neural-pipeline/badge/?version=master)](https://neural-pipeline.readthedocs.io/en/master/?badge=master)
-* [See the full documentation there](https://neural-pipeline.readthedocs.io/en/master/)
-* [Read getting started guide](https://neural-pipeline.readthedocs.io/en/master/getting_started/index.html)
+[![Documentation Status](https://readthedocs.org/projects/piepline/badge/?version=stable)](https://piepline.readthedocs.io/en/stable/?badge=stable)
+* [See the full documentation there](https://piepline.readthedocs.io/en/stable/)
+* [Read getting started guide](https://piepline.readthedocs.io/en/stable/getting_started/index.html)
 
 ### See the examples
-* MNIST classification - [notebook](https://github.com/toodef/neural-pipeline/blob/master/examples/notebooks/img_classification.ipynb), [file](https://github.com/toodef/neural-pipeline/blob/master/examples/files/img_classification.py), [Kaggle kernel](https://www.kaggle.com/toodef/cnn-training-with-less-code)
-* Segmentation - [notebook](https://github.com/toodef/neural-pipeline/blob/master/examples/notebooks/img_segmentation.ipynb), [file](https://github.com/toodef/neural-pipeline/blob/master/examples/files/img_segmentation.py)
-* Resume training process - [file](https://github.com/toodef/neural-pipeline/blob/master/examples/files/resume_train.py)
+* MNIST classification - [notebook](https://github.com/toodef/piepline/blob/master/examples/notebooks/img_classification.ipynb), [file](https://github.com/toodef/piepline/blob/master/examples/files/img_classification.py), [Kaggle kernel](https://www.kaggle.com/toodef/cnn-training-with-less-code)
+* Segmentation - [notebook](https://github.com/toodef/piepline/blob/master/examples/notebooks/img_segmentation.ipynb), [file](https://github.com/toodef/piepline/blob/master/examples/files/img_segmentation.py)
+* Resume training process - [file](https://github.com/toodef/piepline/blob/master/examples/files/resume_train.py)
 
-### Neural Pipeline short overview:
+### PiePline short overview:
 ```python
 import torch
 
 from neural_pipeline.builtin.monitors.tensorboard import TensorboardMonitor
-from neural_pipeline import DataProducer, AbstractDataset, TrainConfig, TrainStage,\
+from neural_pipeline.monitoring import LogMonitor
+from neural_pipeline import DataProducer, TrainConfig, TrainStage,\
     ValidationStage, Trainer, FileStructManager
 
 from somethig import MyNet, MyDataset
 
 fsm = FileStructManager(base_dir='data', is_continue=False)
-model = MyNet()
+model = MyNet().cuda()
 
 train_dataset = DataProducer([MyDataset()], batch_size=4, num_workers=2)
 validation_dataset = DataProducer([MyDataset()], batch_size=4, num_workers=2)
 
-train_config = TrainConfig([TrainStage(train_dataset), ValidationStage(validation_dataset)], torch.nn.NLLLoss(),
+train_config = TrainConfig(model, [TrainStage(train_dataset),
+                                   ValidationStage(validation_dataset)], torch.nn.NLLLoss(),
                            torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.5))
 
-trainer = Trainer(model, train_config, fsm, torch.device('cuda:0')).set_epoch_num(50)
+trainer = Trainer(train_config, fsm, torch.device('cuda:0')).set_epoch_num(50)
 trainer.monitor_hub.add_monitor(TensorboardMonitor(fsm, is_continue=False))\
                    .add_monitor(LogMonitor(fsm))
 trainer.train()
 ```
 This example of training MyNet on MyDataset with vizualisation in Tensorflow and with metrics logging for further experiments comparison.
 
 # Installation:
-[![PyPI version](https://badge.fury.io/py/neural-pipeline.svg)](https://badge.fury.io/py/neural-pipeline)
-[![PyPI Downloads/Month](https://pepy.tech/badge/neural-pipeline/month)](https://pepy.tech/project/neural-pipeline)
-[![PyPI Downloads](https://pepy.tech/badge/neural-pipeline)](https://pepy.tech/project/neural-pipeline)
+[![PyPI version](https://badge.fury.io/py/piepline.svg)](https://badge.fury.io/py/piepline)
+[![PyPI Downloads/Month](https://pepy.tech/badge/piepline/month)](https://pepy.tech/project/piepline)
+[![PyPI Downloads](https://pepy.tech/badge/piepline)](https://pepy.tech/project/piepline)
 
-`pip install neural-pipeline`
+`pip install piepline`
 
 ##### For `builtin` module using install:
 `pip install tensorboardX matplotlib`
 
 ##### Install latest version before it's published on PyPi
-`pip install -U git+https://github.com/toodef/neural-pipeline`
+`pip install -U git+https://github.com/PiePline/piepline`

docs/requirements.txt

@@ -4,4 +4,4 @@ sphinx_rtd_theme
 tqdm
 tensorboardX
 numpy
-https://download.pytorch.org/whl/cpu/torch-1.0.0-cp37-cp37m-linux_x86_64.whl
+torch==1.5.0+cpu

docs/source/api/builtin/index.rst

@@ -5,4 +5,3 @@ In builtin module contains all modules that can't be tested, or have specific fi
 .. toctree::
 
     monitors
-    models

docs/source/api/builtin/monitors.rst

@@ -1,9 +1,9 @@
 Tensorboard
 ==============================
-.. automodule:: neural_pipeline.builtin.monitors.tensorboard
+.. automodule:: piepline.builtin.monitors.tensorboard
     :members:
 
 Matplotlib
 ==============================
-.. automodule:: neural_pipeline.builtin.monitors.mpl
+.. automodule:: piepline.builtin.monitors.mpl
     :members:

docs/source/api/data_processor.rst

@@ -1,9 +1,9 @@
 Data Processor
 ==============================
-.. automodule:: neural_pipeline.data_processor.data_processor
+.. automodule:: piepline.data_processor.data_processor
     :members:
 
 Model
 ==============================
-.. automodule:: neural_pipeline.data_processor.model
+.. automodule:: piepline.data_processor.model
     :members:

docs/source/api/data_producer.rst

@@ -1,4 +1,4 @@
 Data Producer
 ==============================
-.. automodule:: neural_pipeline.data_producer.data_producer
+.. automodule:: piepline.data_producer.data_producer
     :members:

docs/source/api/monitoring.rst

@@ -1,4 +1,4 @@
 Monitoring
 ==============================
-.. automodule:: neural_pipeline.monitoring
+.. automodule:: piepline.monitoring
     :members:

docs/source/api/predict.rst

@@ -1,4 +1,4 @@
 Predictor
 ==============================
-.. automodule:: neural_pipeline.predict
+.. automodule:: piepline.predict
     :members:

docs/source/api/train.rst

@@ -1,4 +1,4 @@
 Trainer
 ==============================
-.. automodule:: neural_pipeline.train
+.. automodule:: piepline.train
     :members:

docs/source/api/train_config.rst

@@ -1,4 +1,4 @@
 Train Config
 ==============================
-.. automodule:: neural_pipeline.train_config.train_config
+.. automodule:: piepline.train_config.train_config
     :members:

docs/source/api/utils.rst

@@ -1,4 +1,4 @@
 File structure management utils
 ===============================
-.. automodule:: neural_pipeline.utils.file_structure_manager
+.. automodule:: piepline.utils.fsm
     :members:

docs/source/conf.py

@@ -19,22 +19,22 @@
 
 # -- Project information -----------------------------------------------------
 
-project = 'Neural Pipeline'
+project = 'PiePline'
 copyright = '2019, Anton Fedotov'
 author = 'Anton Fedotov'
 
 # import importlib.util
 #
-# spec = importlib.util.spec_from_file_location("neural_pipeline",
+# spec = importlib.util.spec_from_file_location("piepline",
 #                                               os.path.join(os.path.dirname(os.path.abspath(__file__)),
-#                                                            '..', '..', 'neural_pipeline', '__init__.py'))
-# neural_pipeline = importlib.util.module_from_spec(spec)
-# spec.loader.exec_module(neural_pipeline)
+#                                                            '..', '..', 'piepline', '__init__.py'))
+# piepline = importlib.util.module_from_spec(spec)
+# spec.loader.exec_module(piepline)
 
-import neural_pipeline
+import piepline
 
 # The short X.Y version
-version = neural_pipeline.__version__
+version = piepline.__version__
 # The full version, including alpha/beta/rc tags
 release = version
 
@@ -127,7 +127,7 @@
 # -- Options for HTMLHelp output ---------------------------------------------
 
 # Output file base name for HTML help builder.
-htmlhelp_basename = 'NeuralPipelinedoc'
+htmlhelp_basename = 'PiePLinedoc'
 
 # -- Options for LaTeX output ------------------------------------------------
 
@@ -153,7 +153,7 @@
 # (source start file, target name, title,
 #  author, documentclass [howto, manual, or own class]).
 latex_documents = [
-    (master_doc, 'NeuralPipeline.tex', 'Neural Pipeline Documentation',
+    (master_doc, 'PiePline.tex', 'PiePline Documentation',
      'Anton Fedotov', 'manual'),
 ]
 
@@ -162,7 +162,7 @@
 # One entry per manual page. List of tuples
 # (source start file, name, description, authors, manual section).
 man_pages = [
-    (master_doc, 'neuralpipeline', 'Neural Pipeline Documentation',
+    (master_doc, 'neuralpipeline', 'PiePline Documentation',
      [author], 1)
 ]
 
@@ -172,8 +172,8 @@
 # (source start file, target name, title, author,
 #  dir menu entry, description, category)
 texinfo_documents = [
-    (master_doc, 'NeuralPipeline', 'Neural Pipeline Documentation',
-     author, 'NeuralPipeline', 'Neural networks training pipeline based on PyTorch. Designed to standardize training process and to increase coding preformance.',
+    (master_doc, 'PiePline', 'PiePline Documentation',
+     author, 'PiePline', 'Neural networks training pipeline based on PyTorch. Designed to standardize training process and to increase coding preformance.',
      'Miscellaneous'),
 ]
 

docs/source/getting_started/continue.rst

@@ -25,6 +25,6 @@ If we need to do some more training epochs but doesn't have previously defined o
 
 
 Parameter ``from_best_checkpoint=False`` tell Trainer, that it need continue from last checkpoint.
-Neural Pipeline can save best checkpoints by specified rule. For more information about it read about `enable_lr_decaying <https://neural-pipeline.readthedocs.io/en/master/api/train.html#neural_pipeline.train.Trainer.enable_best_states_saving>`_ method of `Trainer`.
+PiePline can save best checkpoints by specified rule. For more information about it read about `enable_lr_decaying <https://piepline.readthedocs.io/en/master/api/train.html#piepline.train.Trainer.enable_best_states_saving>`_ method of `Trainer`.
 
 Don't worry about incorrect training history displaying. If history also exists - monitors just add new data to it.

docs/source/getting_started/dataset.rst

@@ -1,7 +1,7 @@
 Implement dataset class
 =======================
 
-In Neural Pipeline dataset is iterable class. This means, that class need contain ``__getitem__`` and ``__len__`` methods.
+In PiePline dataset is iterable class. This means, that class need contain ``__getitem__`` and ``__len__`` methods.
 
 For every i-th output, dataset need produce Python ``dict`` with keys 'data' and 'target'.
 
@@ -32,7 +32,7 @@ For work with this dataset we need wrap it by ``DataProducer``:
 
 .. code:: python
 
-    from neural_pipeline import DataProducer
+    from piepline import DataProducer
 
     # create train and validation datasets objects
     train_dataset = DataProducer([MNISTDataset('data/dataset', True)], batch_size=4, num_workers=2)

docs/source/getting_started/index.rst

@@ -1,13 +1,13 @@
 Getting started guide
 =====================
 
-First of all look at main classes of Neural Pipeline:
+First of all look at main classes of PiePline:
 
-* `Trainer <https://neural-pipeline.readthedocs.io/en/master/api/train.html#neural_pipeline.train.Trainer>`_ - class, that implements training process
-* `TrainConfig <https://neural-pipeline.readthedocs.io/en/master/api/train_config.html#neural_pipeline.train_config.train_config.TrainConfig>`_ - class, that store hyperparameters
-* `AbstractTrainStage <https://neural-pipeline.readthedocs.io/en/master/api/train_config.html#neural_pipeline.train_config.train_config.AbstractStage>`_ - base class for single stage of training process. Don't worry, Neural Pipeline have predefined classes for common use cases: `TrainStage <https://neural-pipeline.readthedocs.io/en/master/api/train_config.html#neural_pipeline.train_config.train_config.TrainStage>`_, `ValidationStage <https://neural-pipeline.readthedocs.io/en/master/api/train_config.html#neural_pipeline.train_config.train_config.ValidationStage>`_ and more common - `StandardStage <https://neural-pipeline.readthedocs.io/en/master/api/train_config.html#neural_pipeline.train_config.train_config.StandardStage>`_
-* `DataProducer <https://neural-pipeline.readthedocs.io/en/master/api/data_producer.html#neural_pipeline.data_producer.data_producer.DataProducer>`_ - class, that unite datasets and unite it's interface
-* `FileStructManager <https://neural-pipeline.readthedocs.io/en/master/api/utils.html#neural_pipeline.utils.file_structure_manager.FileStructManager>`_ - class, that manage file structure
+* `Trainer <https://piepline.readthedocs.io/en/master/api/train.html#piepline.train.Trainer>`_ - class, that implements training process
+* `TrainConfig <https://piepline.readthedocs.io/en/master/api/train_config.html#piepline.train_config.train_config.TrainConfig>`_ - class, that store hyperparameters
+* `AbstractTrainStage <https://piepline.readthedocs.io/en/master/api/train_config.html#piepline.train_config.train_config.AbstractStage>`_ - base class for single stage of training process. Don't worry, PiePline have predefined classes for common use cases: `TrainStage <https://piepline.readthedocs.io/en/master/api/train_config.html#piepline.train_config.train_config.TrainStage>`_, `ValidationStage <https://piepline.readthedocs.io/en/master/api/train_config.html#piepline.train_config.train_config.ValidationStage>`_ and more common - `StandardStage <https://piepline.readthedocs.io/en/master/api/train_config.html#piepline.train_config.train_config.StandardStage>`_
+* `DataProducer <https://piepline.readthedocs.io/en/master/api/data_producer.html#piepline.data_producer.data_producer.DataProducer>`_ - class, that unite datasets and unite it's interface
+* `FileStructManager <https://piepline.readthedocs.io/en/master/api/utils.html#piepline.utils.file_structure_manager.FileStructManager>`_ - class, that manage file structure
 
 Training stages needed for customize training process. With it `Trainer` work by this scheme (dataflow scheme for single epoch):
 
@@ -23,4 +23,4 @@ Training stages needed for customize training process. With it `Trainer` work by
    training
    continue
 
-After this tutorial look to `segmentation example <https://github.com/toodef/neural-pipeline/blob/master/examples/notebooks/img_segmentation.ipynb>`_ for explore how to work with specific metrics.
+After this tutorial look to `segmentation example <https://github.com/toodef/piepline/blob/master/examples/notebooks/img_segmentation.ipynb>`_ for explore how to work with specific metrics.

docs/source/getting_started/train_config.rst

@@ -8,7 +8,7 @@ Respectively ``ValidatioStage`` do same but in ``eval()`` mode.
 
 .. code:: python
 
-    from neural_pipeline import TrainConfig, TrainStage, ValidationStage
+    from piepline import TrainConfig, TrainStage, ValidationStage
 
     # define train stages
     train_stages = [TrainStage(train_dataset), ValidationStage(validation_dataset)]

docs/source/getting_started/trainer.rst

@@ -28,7 +28,7 @@ Now we need build our training process. It's done by implements ``Trainer`` clas
 
 .. code:: python
 
-    from neural_pipeline import FileStructManager, Trainer
+    from piepline import FileStructManager, Trainer
 
     # define file structure for experiment
     fsm = FileStructManager(base_dir='data', is_continue=False)

docs/source/getting_started/training.rst

@@ -14,7 +14,7 @@ That's all. Console output will look like that:
 | Epoch: [3]; train: [0.000182, 0.180328, 5.218509]; validation: [0.000135, 0.155546, 2.512275]
 | train:  31%|███           | 4651/15000 [00:31<01:07, 154.06it/s, loss=[0.154871]]
 
-First 3 lines is standard output of `ConsoleMonitor <https://neural-pipeline.readthedocs.io/en/master/api/monitoring.html#neural_pipeline.monitoring.ConsoleMonitor>`_.
+First 3 lines is standard output of `ConsoleMonitor <https://piepline.readthedocs.io/en/master/api/monitoring.html#piepline.monitoring.ConsoleMonitor>`_.
 This monitor included for ``MonitorHub`` by default.
 Every line show loss values of correspondence stage in format [min, mean, max] values.
 
@@ -29,7 +29,7 @@ For do it we need before training connect builtin `TensorboardMonitor` to `Train
 
 .. code:: python
 
-    from neural_pipeline.builtin.monitors.tensorboard import TensorboardMonitor
+    from piepline.builtin.monitors.tensorboard import TensorboardMonitor
 
     trainer.monitor_hub.add_monitor(TensorboardMonitor(fsm, is_continue=False))