netharn 0.6.2
Train and deploy pytorch models
License: Apache 2
Requires Python: >=3.7


NetHarn - a PyTorch Network Harness
-----------------------------------

|GitlabCIPipeline| |GitlabCICoverage| |Pypi| |Downloads| 


UPDATE 2021-10-05: Netharn as a Training loop will no longer be maintained. Use
pytorch-lightning instead. This library may slowly morph into an extension of
lightning.


The main webpage for this project is: https://gitlab.kitware.com/computer-vision/netharn

If you want a framework for your pytorch training loop that
(1) chooses directory names based on hashes of hyperparameters,
(2) can write a single-file deployment of your model by statically auto-extracting the in-code definition of the model topology and zipping it with the weights, 
(3) has brief terminal output and a rich logging output, 
(4) has rule-based monitoring of validation loss and can reduce the learning rate or early stop, 
(5) has tensorboard and/or matplotlib visualizations of training statistics, and 
(6) is designed to be extended, then you might be interested in NetHarn. 

NAME:
    NetHarn (pronounced "net-harn")
FRAMEWORK:
    PyTorch
FEATURES: 
    * hyperparameter tracking
    * training directory management
    * callback-based public API 
    * XPU - code abstraction for [cpu, gpu, multi-gpu].
    * single-file deployments (NEW in version ``0.1.0``).
    * reasonable test coverage using pytest and xdoctest
    * CI testing on appveyor and travis (note a few tests are failing due to minor issues)
    * A rich utility set
    * Extensions of PyTorch objects (e.g. critions, initializers, layers,
      optimizers, schedulers)
BUILTINS:
   - training loop boilerplate
   - snapshots / checkpoints
   - progress bars (backend_choices: [progiter, tqdm])
   - data provenance of training history in ``train_info.json``
   - tensorboard metric visualization (optional)
DESIGN PHILOSOPHY: 
   Avoid boilerplate, built-it yourself when you need to, and don't repeat yourself.
   Experiments should be strongly tied to the choice of hyperparameters, and
   the framework should be able to construct a directory heirarchy based on
   these hyperparameters.
SLOGAN: 
    Rein and train.
USAGE PATTERNS:
    (1) Write code for a torch object  (i.e. Dataset, Model, Criterion, Initializer, and Scheduler) just as you normally would.
    (2) Inherit from the ``netharn.FitHarn`` object, define ``run_batch``, ``on_batch``, ``on_epoch``, etc...
    (3) Create an instance of ``netharn.HyperParams`` to specify your dataset, model, criterion, etc...
    (4) Create an instance of your ``FitHarn`` object with those hyperparameters.
    (5) Then execute its ``run`` method.
    (6) ???
    (7) profit
EXAMPLES:
    * ToyData2d classification with netharn.models.ToyNet2d (see doctest in netharn/fit_harn.py:__DOC__:0)
    * MNIST digit classification with MnistNet (netharn/examples/mnist.py)
    * Cifar10 category classification with ResNet50 / dpn91 (netharn/examples/cifar.py)
    * Voc2007+2012 object detection with YOLOv2 (netharn/examples/yolo_voc.py)
    * IBEIS metric learning with SiameseLP (netharn/examples/siam_ibeis.py)
STABILITY:
   Mostly harmless. Most tests pass, the current failures are probably not
   critical. I'm able to use it on my machine (tm). In this early stage of
   development, there are still a few pain points. Issues and PRs welcome.
KNOWN BUGS:
   * The metrics for computing detection mAP / AP might not be correct.
   * The YOLO example gets to about 70% mAP (using Girshik's mAP code) whereas we should be hitting 74-76%
AUTHORS COMMENTS:
   * The MNIST, CIFAR, and VOC examples will download the data as needed.
   * The CIFAR example for ResNet50 achieves 95.72% accuracy, outperforming the
     best DPN92 result (95.16%) that I'm aware of.
     This result seems real, I do not believe I've made an error in measurement
     (but this has need been peer-reviewed so, caveat emptor).  I've reproduced
     this results a few times. You can use the code in examples/cifar.py to see
     if you can too (please tell me if you cannot). 
   * The YOLO example is based of of EAVise's excellent lightnet (https://gitlab.com/EAVISE/lightnet/) package.
   * I reimplemented the CocoAPI (see netharn.data.coco_api), because I had some
     (probably minor) issue with the original implementation. I've extended it
     quite a bit, and I'd recommend using it.
   * The metric-learning example requires code requires the ibeis software:
     `https://github.com/Erotemic/ibeis`.
DEPENDENCIES:
    * torch
    * numpy
    * Cython
    * ubelt
    * xdoctest
    * ... (see requirements.txt)


Features (continued)
====================

* Hyperparameter tracking: The hash of your hyperparameters determines the
  directory data will be written to. We also allow for a "nicer" means to
  manage directory structures. Given a ``HyperParams`` object, we create the
  symlink ``{workdir}/fit/name/{name}`` which points to
  ``{workdir}/fit/runs/{name}/{hashid}``.

* Automatic restarts: 
  Calling ``FitHarn.run`` twice restarts training from where you left off by
  default (as long as the hyperparams haven't changed).

* "Smart" Snapshot cleanup:  
  Maintaining model weights files can be a memory hog. Depending the settings
  of ``harn.preferences``, ``netharn.FitHarn`` will periodically remove
  less-recent or low-scoring snapshots.

* Deployment files: 
  Model weights and architecture are together written as one
  reasonably-portable zip-file. We also package training metadata to maintain
  data provinence and make reproducing experiments easier. 

* Restart from any pretrained state: 
  use ``netharn.initializers.PretainedInitializer``. 

* Utilities for building networks in torch:
  Layers like ``netharn.layers.ConvNormNd`` make it easy to build networks for
  n=1, 2, or 3 dimensional data. 

* Analytic output shape and receptive field:
  Netharn defines a ``netharn.layers.AnalyticModule``, which can automatically
  define ``forward``, ``output_shape_for`` and ``receptive_field_for`` if users
  define a special ``_output_for`` method, written with the
  ``netharn.analytic_for.Output``, ``netharn.analytic_for.Hidden``, and
  ``netharn.analytic_for.OutputFor`` special callables.

* Example tasks:
  Baseline code for standard tasks like: object segmentation, classification,
  and detection are defined in ``netharn.examples``. The examples also provide
  example use cases for ``ndsampler``, ``kwimage``, ``kwannot``, and
  ``kwplot``. 


Installation
============

In the future these instructions may actually be different than the developer
setup instructions, but for now they are the same.

.. code-block:: bash

    mkdir -p ~/code
    git clone git@github.com:Erotemic/netharn.git ~/code/netharn
    cd ~/code/netharn
    ./run_developer_setup.sh


While all netharn dependencies should be available on pypi (with manylinux2010
wheels for binary packages), there are other packages developed concurrently
with netharn. To install the development version of these dependencies then run
``python super_setup.py ensure`` to check out the repos and ensure they are on
the correct branch, ``python super_setup.py develop`` to build everything in
development mode, and ``python super_setup.py pull`` to update to the latest on
the branch.

Description
===========

Parameterized fit harnesses for PyTorch.

Trains models and keeps track of your hyperparameters.

This is a clean port of the good parts developed in my research repo: ``clab``. 

See the netharn/examples folder for example usage. The doctests are also a good
resource. It would be nice if we had better docs.

NetHarn is a research framework for training and deploying arbitrary PyTorch
models.  It was designed for the purpose of minimizing training-loop
boilerplate and tracking hyperparameters to encourage reproducible research.
NetHarn separates the problem of training a model into the following core
hyperparameter components: the datasets, model, criterion, initializer,
optimizer, and learning rate scheduler.  Runs with different hyperparameters
are automatically logged to separate directories which makes it simple to
compare the results of two experiments.  NetHarn also has the ability to create
a single-file deployment of a trained model that is independent of the system
used to train it.  This makes it fast and simple for research results to be
externally verified and moved into production.


Core Callback Structure
=======================

Netharn is designed around inheriting from the ``netharn.FitHarn`` class,
overloading several methods, and then creating an instance of your custom
FitHarn with specific hyperparameters.

FitHarn allows you to customize the execution of the training loop via its
callback system. You write a callback simply overloading one of these
methods. There are callbacks with and without default behavior.

The ones with default behavior directly influence the learning process.
While these don't have to be overwritten, they usually should be as
different tasks require slightly different ways of moving data through the
training pipeline.

The ones without default behavior allow the developer to execute custom
code at special places in the training loop. These are usually used for
logging custom metrics and outputing visualizations.

The following note lists the callbacks in roughly the order in which they are
called by the ``FitHarn.run`` method. The tree structure denotes loop nesting.

.. code-block:: 

    ├─ after_initialize (no default) - runs after FitHarn is initialized
    │  │
    │  ├─ before_epochs (no default) - runs once before all train/vali/test
    │  │  │    epochs on each iteration
    │  │  │
    │  │  ├─ prepare_epoch (no default) - runs before each train, vali,
    │  │  │  │    and test epoch
    │  │  │  │
    │  │  │  ├─ prepare_batch (has default behavior) - transfer data from
    │  │  │  │    CPU to the XPU
    │  │  │  │
    │  │  │  ├─ run_batch (has default behavior) - execute the forward pass
    │  │  │  │    and compute the loss
    │  │  │  │
    │  │  │  ├─ backpropogate (has default behavior) - accumulate gradients
    │  │  │  │    and take an optimization step
    │  │  │  │
    │  │  │  └─ on_batch (no default) - runs after `run_batch` and
    │  │  │       `backpropogate` on every batch
    │  │  │
    │  │  └─ on_epoch (no default) - runs after each train, vali, and test
    │  │         epoch finishes.  Any custom scalar metrics returned in a
    │  │         dictionary will be recorded by the FitHarn loggers.
    │  │
    │  └─ after_epochs (no default) - runs after the all data splits are
    │         finished with  the current epoch.
    │
    └─ on_complete (no default) - runs after the main loop is complete


Given a custom FitHarn class see the "Toy Example" section for details on how
to construct hyperparamters and execute the training loop (i.e.
``FitHarn.run``).


Developer Setup:
================


In the future these instructions might be different from the install
instructions, but for now they are the same.

.. code-block:: bash

    sudo apt-get install python3 python-dev python3-dev \
     build-essential libssl-dev libffi-dev \
     libxml2-dev libxslt1-dev zlib1g-dev \
     python-pip

    mkdir -p ~/code
    git clone git@github.com:Erotemic/netharn.git ~/code/netharn
    cd ~/code/netharn

    ./run_developer_setup.sh


Documentation
=============

Netharn's documentation is currently sparse. I typically do most of my
documenting in the code itself using docstrings. In the future much of this
will likely be consolidated in a read-the-docs style documentation page, but
for now you'll need to look at the code to read the docs.

The main concept provided by netharn is the "FitHarn", which has a decent
module level docstring, and a lot of good class / method level docstrings: 
https://gitlab.kitware.com/computer-vision/netharn/-/blob/master/netharn/fit_harn.py

The examples folder has better docstrings with task-level documentation: 

The simplest is the mnist example:
https://gitlab.kitware.com/computer-vision/netharn/-/blob/master/netharn/examples/mnist.py

The CIFAR example builds on the mnist example:
https://gitlab.kitware.com/computer-vision/netharn/-/blob/master/netharn/examples/cifar.py

I'd recommend going through those two examples, as they have the best documentation. 

The segmentation example:
https://gitlab.kitware.com/computer-vision/netharn/-/blob/master/netharn/examples/segmentation.py

and object detection example: 
https://gitlab.kitware.com/computer-vision/netharn/-/blob/master/netharn/examples/object_detection.py

have less documentation, but provide more real-world style examples of how netharn is used. 

There is an applied segmentation example that is specific to the CAMVID dataset:
https://gitlab.kitware.com/computer-vision/netharn/-/blob/master/netharn/examples/sseg_camvid.py

And there is an applied VOC detection example:
https://gitlab.kitware.com/computer-vision/netharn/-/blob/master/netharn/examples/yolo_voc.py

This README also contains a toy example.

Toy Example:
============

This following example is the doctest in ``netharn/fit_harn.py``. It
demonstrates how to use NetHarn to train a model to solve a toy problem.  

In this toy problem, we do not extend the netharn.FitHarn object, so we are using
the default behavior of ``run_batch``. The default ``on_batch``, and
``on_epoch`` do nothing, so only loss will be the only measurement of
performance.

For further examples please see the examples directory. These example show how
to extend netharn.FitHarn to measure performance wrt a particular problem.  The
MNIST and CIFAR examples are the most simple. The YOLO example is more complex.
The IBEIS example depends on non-public data / software, but can still be
useful to look at.  Its complexity is more than CIFAR but less than YOLO.


.. code-block:: python

    >>> import netharn 
    >>> hyper = netharn.HyperParams(**{
    >>>     # ================
    >>>     # Environment Components
    >>>     'name'        : 'demo',
    >>>     'workdir'     : ub.ensure_app_cache_dir('netharn/demo'),
    >>>     'xpu'         : netharn.XPU.coerce('auto'),
    >>>     # workdir is a directory where intermediate results can be saved
    >>>     # "name" symlinks <workdir>/fit/name/<name> -> ../runs/<hashid>
    >>>     # XPU auto select a gpu if idle and VRAM>6GB else a cpu
    >>>     # ================
    >>>     # Data Components
    >>>     'datasets'    : {  # dict of plain ol torch.data.Dataset instances
    >>>         'train': netharn.data.ToyData2d(size=3, border=1, n=256, rng=0),
    >>>         'vali': netharn.data.ToyData2d(size=3, border=1, n=64, rng=1),
    >>>         'test': netharn.data.ToyData2d(size=3, border=1, n=64, rng=2),
    >>>     },
    >>>     'loaders'     : {'batch_size': 4}, # DataLoader instances or kw
    >>>     # ================
    >>>     # Algorithm Components
    >>>     # Note the (cls, kw) tuple formatting
    >>>     'model'       : (netharn.models.ToyNet2d, {}),
    >>>     'optimizer'   : (netharn.optimizers.SGD, {
    >>>         'lr': 0.01
    >>>     }),
    >>>     # focal loss is usually better than netharn.criterions.CrossEntropyLoss
    >>>     'criterion'   : (netharn.criterions.FocalLoss, {}),
    >>>     'initializer' : (netharn.initializers.KaimingNormal, {
    >>>         'param': 0,
    >>>     }),
    >>>     # The scheduler adjusts learning rate over the training run
    >>>     'scheduler'   : (netharn.schedulers.ListedScheduler, {
    >>>         'points': {'lr': {0: 0.1, 2: 10.0, 4: .15, 6: .05, 9: .01}},
    >>>         'interpolation': 'linear',
    >>>     }),
    >>>     'monitor'     : (netharn.Monitor, {
    >>>         'max_epoch': 10,
    >>>         'patience': 7,
    >>>     }),
    >>>     # dynamics are a config option that modify the behavior of the main
    >>>     # training loop. These parameters effect the learned model.
    >>>     'dynamics'   : {'batch_step': 2},
    >>> })
    >>> harn = netharn.FitHarn(hyper)
    >>> # non-algorithmic behavior preferences (do not change learned models)
    >>> harn.preferences['num_keep'] = 10
    >>> harn.preferences['auto_prepare_batch'] = True
    >>> # start training.
    >>> harn.initialize(reset='delete')  # delete removes an existing run
    >>> harn.run()  # note: run calls initialize it hasn't already been called.
    >>> # xdoc: +IGNORE_WANT

Running this code produes the following output:

.. code-block:: 

   RESET HARNESS BY DELETING EVERYTHING IN TRAINING DIR
   Symlink: /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum -> /home/joncrall/.cache/netharn/demo/_mru
   ... already exists
   Symlink: /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum -> /home/joncrall/.cache/netharn/demo/fit/name/demo
   ... already exists
   ... and points to the right place
   INFO: Initializing tensorboard (dont forget to start the tensorboard server)
   INFO: Model has 824 parameters
   INFO: Mounting ToyNet2d model on GPU(0)
   INFO: Exported model topology to /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/ToyNet2d_2a3f49.py
   INFO: Initializing model weights with: <netharn.initializers.nninit_core.KaimingNormal object at 0x7fc67eff0278>
   INFO:  * harn.train_dpath = '/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum'
   INFO:  * harn.name_dpath  = '/home/joncrall/.cache/netharn/demo/fit/name/demo'
   INFO: Snapshots will save to harn.snapshot_dpath = '/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/torch_snapshots'
   INFO: ARGV:
       /home/joncrall/.local/conda/envs/py36/bin/python /home/joncrall/.local/conda/envs/py36/bin/ipython
   INFO: dont forget to start:
       tensorboard --logdir ~/.cache/netharn/demo/fit/name
   INFO: === begin training 0 / 10 : demo ===
   epoch lr:0.0001 │ vloss is unevaluated  0/10... rate=0 Hz, eta=?, total=0:00:00, wall=19:36 EST
   train loss:0.173 │ 100.00% of 64x8... rate=11762.01 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   vali loss:0.170 │ 100.00% of 64x4... rate=9991.94 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   test loss:0.170 │ 100.00% of 64x4... rate=24809.37 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   INFO: === finish epoch 0 / 10 : demo ===
   epoch lr:0.00505 │ vloss: 0.1696 (n_bad=00, best=0.1696)  1/10... rate=1.24 Hz, eta=0:00:07, total=0:00:00, wall=19:36 EST
   train loss:0.175 │ 100.00% of 64x8... rate=13522.14 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   vali loss:0.167 │ 100.00% of 64x4... rate=23598.31 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   test loss:0.167 │ 100.00% of 64x4... rate=20354.22 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   INFO: === finish epoch 1 / 10 : demo ===
   epoch lr:0.01 │ vloss: 0.1685 (n_bad=00, best=0.1685)  2/10... rate=1.28 Hz, eta=0:00:06, total=0:00:01, wall=19:36 EST
   train loss:0.177 │ 100.00% of 64x8... rate=15723.99 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   vali loss:0.163 │ 100.00% of 64x4... rate=29375.56 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   test loss:0.163 │ 100.00% of 64x4... rate=29664.69 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   INFO: === finish epoch 2 / 10 : demo ===

   <JUST MORE OF THE SAME; REMOVED FOR BREVITY>

   epoch lr:0.001 │ vloss: 0.1552 (n_bad=00, best=0.1552)  9/10... rate=1.11 Hz, eta=0:00:00, total=0:00:08, wall=19:36 EST
   train loss:0.164 │ 100.00% of 64x8... rate=13795.93 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   vali loss:0.154 │ 100.00% of 64x4... rate=19796.72 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   test loss:0.154 │ 100.00% of 64x4... rate=21396.73 Hz, eta=0:00:00, total=0:00:00, wall=19:36 EST
   INFO: === finish epoch 9 / 10 : demo ===
   epoch lr:0.001 │ vloss: 0.1547 (n_bad=00, best=0.1547) 10/10... rate=1.13 Hz, eta=0:00:00, total=0:00:08, wall=19:36 EST




   INFO: Maximum harn.epoch reached, terminating ...
   INFO: 



   INFO: training completed
   INFO: harn.train_dpath = '/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum'
   INFO: harn.name_dpath  = '/home/joncrall/.cache/netharn/demo/fit/name/demo'
   INFO: view tensorboard results for this run via:
       tensorboard --logdir ~/.cache/netharn/demo/fit/name
   [DEPLOYER] Deployed zipfpath=/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/deploy_ToyNet2d_lnejaaum_009_GAEYQT.zip
   INFO: wrote single-file deployment to: '/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/deploy_ToyNet2d_lnejaaum_009_GAEYQT.zip'
   INFO: exiting fit harness.

Furthermore, if you were to run that code when ``'--verbose' in sys.argv``,
then it would produce this more detailed description of what it was doing:

.. code-block:: 

   RESET HARNESS BY DELETING EVERYTHING IN TRAINING DIR
   Symlink: /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum -> /home/joncrall/.cache/netharn/demo/_mru
   ... already exists
   Symlink: /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum -> /home/joncrall/.cache/netharn/demo/fit/name/demo
   ... already exists
   ... and points to the right place
   DEBUG: Initialized logging
   INFO: Initializing tensorboard (dont forget to start the tensorboard server)
   DEBUG: harn.train_info[hyper] = {
       'model': (
           'netharn.models.toynet.ToyNet2d',
           {
               'input_channels': 1,
               'num_classes': 2,
           },
       ),
       'initializer': (
           'netharn.initializers.nninit_core.KaimingNormal',
           {
               'mode': 'fan_in',
               'param': 0,
           },
       ),
       'optimizer': (
           'torch.optim.sgd.SGD',
           {
               'dampening': 0,
               'lr': 0.0001,
               'momentum': 0,
               'nesterov': False,
               'weight_decay': 0,
           },
       ),
       'scheduler': (
           'netharn.schedulers.scheduler_redesign.ListedScheduler',
           {
               'interpolation': 'linear',
               'optimizer': None,
               'points': {'lr': {0: 0.0001, 2: 0.01, 5: 0.015, 6: 0.005, 9: 0.001}},
           },
       ),
       'criterion': (
           'netharn.criterions.focal.FocalLoss',
           {
               'focus': 2,
               'ignore_index': -100,
               'reduce': None,
               'reduction': 'mean',
               'size_average': None,
               'weight': None,
           },
       ),
       'loader': (
           'torch.utils.data.dataloader.DataLoader',
           {
               'batch_size': 64,
           },
       ),
       'dynamics': (
           'Dynamics',
           {
               'batch_step': 4,
               'grad_norm_max': None,
           },
       ),
   }
   DEBUG: harn.hyper = <netharn.hyperparams.HyperParams object at 0x7fb19b4b8748>
   DEBUG: make XPU
   DEBUG: harn.xpu = <XPU(GPU(0)) at 0x7fb12af24668>
   DEBUG: Criterion: FocalLoss
   DEBUG: Optimizer: SGD
   DEBUG: Scheduler: ListedScheduler
   DEBUG: Making loaders
   DEBUG: Making model
   DEBUG: ToyNet2d(
     (layers): Sequential(
       (0): Conv2d(1, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
       (1): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
       (2): ReLU(inplace)
       (3): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
       (4): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
       (5): ReLU(inplace)
       (6): Conv2d(8, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     )
     (softmax): Softmax()
   )
   INFO: Model has 824 parameters
   INFO: Mounting ToyNet2d model on GPU(0)
   DEBUG: Making initializer
   DEBUG: Move FocalLoss() model to GPU(0)
   DEBUG: Make optimizer
   DEBUG: Make scheduler
   DEBUG: Make monitor
   DEBUG: Make dynamics
   INFO: Exported model topology to /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/ToyNet2d_2a3f49.py
   INFO: Initializing model weights with: <netharn.initializers.nninit_core.KaimingNormal object at 0x7fb129e732b0>
   DEBUG: calling harn.initializer=<netharn.initializers.nninit_core.KaimingNormal object at 0x7fb129e732b0>
   INFO:  * harn.train_dpath = '/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum'
   INFO:  * harn.name_dpath  = '/home/joncrall/.cache/netharn/demo/fit/name/demo'
   INFO: Snapshots will save to harn.snapshot_dpath = '/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/torch_snapshots'
   INFO: ARGV:
       /home/joncrall/.local/conda/envs/py36/bin/python /home/joncrall/.local/conda/envs/py36/bin/ipython --verbose
   INFO: dont forget to start:
       tensorboard --logdir ~/.cache/netharn/demo/fit/name
   INFO: === begin training 0 / 10 : demo ===
   DEBUG: epoch lr:0.0001 │ vloss is unevaluated
   epoch lr:0.0001 │ vloss is unevaluated  0/10... rate=0 Hz, eta=?, total=0:00:00, wall=19:56 EST
   DEBUG: === start epoch 0 ===
   DEBUG: log_value(epoch lr, 0.0001, 0
   DEBUG: log_value(epoch momentum, 0, 0
   DEBUG: _run_epoch 0, tag=train, learn=True
   DEBUG:  * len(loader) = 8
   DEBUG:  * loader.batch_size = 64
   train loss:-1.000 │ 0.00% of 64x8... rate=0 Hz, eta=?, total=0:00:00, wall=19:56 ESTDEBUG: Making batch iterator
   DEBUG: Starting batch iteration for tag=train, epoch=0
   train loss:0.224 │ 100.00% of 64x8... rate=12052.25 Hz, eta=0:00:00, total=0:00:00, wall=19:56 EST
   DEBUG: log_value(train epoch loss, 0.22378234565258026, 0
   DEBUG: Finished batch iteration for tag=train, epoch=0
   DEBUG: _run_epoch 0, tag=vali, learn=False
   DEBUG:  * len(loader) = 4
   DEBUG:  * loader.batch_size = 64
   vali loss:-1.000 │ 0.00% of 64x4... rate=0 Hz, eta=?, total=0:00:00, wall=19:56 ESTDEBUG: Making batch iterator
   DEBUG: Starting batch iteration for tag=vali, epoch=0
   vali loss:0.175 │ 100.00% of 64x4... rate=23830.75 Hz, eta=0:00:00, total=0:00:00, wall=19:56 EST
   DEBUG: log_value(vali epoch loss, 0.1749105490744114, 0
   DEBUG: Finished batch iteration for tag=vali, epoch=0
   DEBUG: epoch lr:0.0001 │ vloss: 0.1749 (n_bad=00, best=0.1749)
   DEBUG: _run_epoch 0, tag=test, learn=False
   DEBUG:  * len(loader) = 4
   DEBUG:  * loader.batch_size = 64
   test loss:-1.000 │ 0.00% of 64x4... rate=0 Hz, eta=?, total=0:00:00, wall=19:56 ESTDEBUG: Making batch iterator
   DEBUG: Starting batch iteration for tag=test, epoch=0
   test loss:0.176 │ 100.00% of 64x4... rate=28606.65 Hz, eta=0:00:00, total=0:00:00, wall=19:56 EST
   DEBUG: log_value(test epoch loss, 0.17605290189385414, 0
   DEBUG: Finished batch iteration for tag=test, epoch=0
   DEBUG: Saving snapshot to /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/torch_snapshots/_epoch_00000000.pt
   DEBUG: Snapshot saved to /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/torch_snapshots/_epoch_00000000.pt
   DEBUG: new best_snapshot /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/torch_snapshots/_epoch_00000000.pt
   DEBUG: Plotting tensorboard data
   Populating the interactive namespace from numpy and matplotlib
   INFO: === finish epoch 0 / 10 : demo ===

   <JUST MORE OF THE SAME; REMOVED FOR BREVITY>

   INFO: === finish epoch 8 / 10 : demo ===
   DEBUG: epoch lr:0.001 │ vloss: 0.2146 (n_bad=08, best=0.1749)
   epoch lr:0.001 │ vloss: 0.2146 (n_bad=08, best=0.1749)  9/10... rate=1.20 Hz, eta=0:00:00, total=0:00:07, wall=19:56 EST
   DEBUG: === start epoch 9 ===
   DEBUG: log_value(epoch lr, 0.001, 9
   DEBUG: log_value(epoch momentum, 0, 9
   DEBUG: _run_epoch 9, tag=train, learn=True
   DEBUG:  * len(loader) = 8
   DEBUG:  * loader.batch_size = 64
   train loss:-1.000 │ 0.00% of 64x8... rate=0 Hz, eta=?, total=0:00:00, wall=19:56 ESTDEBUG: Making batch iterator
   DEBUG: Starting batch iteration for tag=train, epoch=9
   train loss:0.207 │ 100.00% of 64x8... rate=13580.13 Hz, eta=0:00:00, total=0:00:00, wall=19:56 EST
   DEBUG: log_value(train epoch loss, 0.2070118673145771, 9
   DEBUG: Finished batch iteration for tag=train, epoch=9
   DEBUG: _run_epoch 9, tag=vali, learn=False
   DEBUG:  * len(loader) = 4
   DEBUG:  * loader.batch_size = 64
   vali loss:-1.000 │ 0.00% of 64x4... rate=0 Hz, eta=?, total=0:00:00, wall=19:56 ESTDEBUG: Making batch iterator
   DEBUG: Starting batch iteration for tag=vali, epoch=9
   vali loss:0.215 │ 100.00% of 64x4... rate=29412.91 Hz, eta=0:00:00, total=0:00:00, wall=19:56 EST
   DEBUG: log_value(vali epoch loss, 0.21514184772968292, 9
   DEBUG: Finished batch iteration for tag=vali, epoch=9
   DEBUG: epoch lr:0.001 │ vloss: 0.2148 (n_bad=09, best=0.1749)
   DEBUG: _run_epoch 9, tag=test, learn=False
   DEBUG:  * len(loader) = 4
   DEBUG:  * loader.batch_size = 64
   test loss:-1.000 │ 0.00% of 64x4... rate=0 Hz, eta=?, total=0:00:00, wall=19:56 ESTDEBUG: Making batch iterator
   DEBUG: Starting batch iteration for tag=test, epoch=9
   test loss:0.216 │ 100.00% of 64x4... rate=25906.58 Hz, eta=0:00:00, total=0:00:00, wall=19:56 EST
   DEBUG: log_value(test epoch loss, 0.21618007868528366, 9
   DEBUG: Finished batch iteration for tag=test, epoch=9
   DEBUG: Saving snapshot to /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/torch_snapshots/_epoch_00000009.pt
   DEBUG: Snapshot saved to /home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/torch_snapshots/_epoch_00000009.pt
   DEBUG: Plotting tensorboard data
   INFO: === finish epoch 9 / 10 : demo ===
   DEBUG: epoch lr:0.001 │ vloss: 0.2148 (n_bad=09, best=0.1749)
   epoch lr:0.001 │ vloss: 0.2148 (n_bad=09, best=0.1749) 10/10... rate=1.21 Hz, eta=0:00:00, total=0:00:08, wall=19:56 EST




   INFO: Maximum harn.epoch reached, terminating ...
   INFO: 



   INFO: training completed
   INFO: harn.train_dpath = '/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum'
   INFO: harn.name_dpath  = '/home/joncrall/.cache/netharn/demo/fit/name/demo'
   INFO: view tensorboard results for this run via:
       tensorboard --logdir ~/.cache/netharn/demo/fit/name
   [DEPLOYER] Deployed zipfpath=/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/deploy_ToyNet2d_lnejaaum_000_JWPNDC.zip
   INFO: wrote single-file deployment to: '/home/joncrall/.cache/netharn/demo/fit/runs/demo/lnejaaum/deploy_ToyNet2d_lnejaaum_000_JWPNDC.zip'
   INFO: exiting fit harness.

Related Packages
================

pytorch-lightning (https://github.com/PyTorchLightning/pytorch-lightning) has
very similar goals to netharn. Currently, there are strengths and weaknesses to
both, but in the future I do see one consuming functionality of the other.
Currently (2020-10-21), pytorch-lightning does distributed training better,
whereas netharn's logging and hyperparameter management outshines
pytorch-lightning.


Consumer Packages
=================

The bioharn package (https://gitlab.kitware.com/jon.crall/bioharn) implements
extensions of the classifier and detector examples in the netharn/examples
folder as well as prediction and evaluation scripts.



.. |Pypi| image:: https://img.shields.io/pypi/v/netharn.svg
   :target: https://pypi.python.org/pypi/netharn

.. |Downloads| image:: https://img.shields.io/pypi/dm/netharn.svg
   :target: https://pypistats.org/packages/netharn

.. |ReadTheDocs| image:: https://readthedocs.org/projects/netharn/badge/?version=latest
    :target: http://netharn.readthedocs.io/en/latest/

.. # See: https://ci.appveyor.com/project/jon.crall/netharn/settings/badges
.. .. |Appveyor| image:: https://ci.appveyor.com/api/projects/status/py3s2d6tyfjc8lm3/branch/master?svg=true
.. :target: https://ci.appveyor.com/project/jon.crall/netharn/branch/master

.. |GitlabCIPipeline| image:: https://gitlab.kitware.com/computer-vision/netharn/badges/master/pipeline.svg
   :target: https://gitlab.kitware.com/computer-vision/netharn/-/jobs

.. |GitlabCICoverage| image:: https://gitlab.kitware.com/computer-vision/netharn/badges/master/coverage.svg?job=coverage
    :target: https://gitlab.kitware.com/computer-vision/netharn/commits/master