- Getting started
- DSP in pytorch
- Custom functions
- It’s just as well it’s easy to roll your own recurrent nets because the default implementations are bad
- Logging and visualizing training
- Utility libraries, derived software
They claim aim for fancy applications such as reversible learning and what-have-you which are easier in thpytorche’s dynamic graph construction style, which resembles (in outcome if not implementation details) the dynamic styles of jax, most julia autodiffs, and tensorflow in “eager” mode.
PyTorch has a unique [sic] way of building neural networks: using and replaying a tape recorder.
Most frameworks such as TensorFlow, Theano, Caffe and CNTK have a static view of the world. One has to build a neural network, and reuse the same structure again and again. Changing the way the network behaves means that one has to start from scratch. [… Pytorch] allows you to change the way your network behaves arbitrarily with zero lag or overhead.
Of course the overhead is not truly zero; rather they have shifted the overhead baseline down a little. But whatever, it’s comparatively convenient.
Not, however, as convenient for my purposes as frameworks that avoid industrial neural frameworks altogether when what I usually want is fairly basic autodiff.
DSP in pytorch
There is some bad advice in the manual
nnexports two kinds of interfaces – modules and their functional versions. You can extend it in both ways, but we recommend using modules for all kinds of layers, that hold any parameters or buffers, and recommend using a functional form parameter-less operations like activation functions, pooling, etc.
So, important missing information.
If your desired loss is already just a composition of existing functions, you don’t need to define a
The given options are not a binarism but two things you need to do in concert. A better summary would be:
If you need to have a function which is differentiable in a non-trivial way, implement a Function
If you need to bundle a Function with some state or differentiable parameters, additionally wrap it in a
Some people claim you can also create custom layers using plain python functions. However, these don’t work as layers, in an
nn.Sequentialmodel, so I’m not sure how to take this advice.
It’s just as well it’s easy to roll your own recurrent nets because the default implementations are bad
The default RNN layer is heavily optimised using cuDNN, which is sweet, but you only have a choice of 2 activation functions, and neither of them is “linear”.
Ding Ke made a beautiful and simple RNN implementation.
These used to be horribly slow. But recent pytorch includes JITed RNN.
Logging and visualizing training
- Leveraging tensorflow’s handy diagnostic GUI,
tensorboard: tensorboardX or perhaps tensorboard-logger.
Fiddly. The official way is via ONNX.
conda install -c ezyang onnx pydot # or pip install onnx pydot
brew cask install netron # or pip install netron brew install graphviz
Also available, pytorchviz.
pip install git+https://github.com/szagoruyko/pytorchviz
from pytorchviz import make_dot y = model(x) make_dot(y, params = dict(model.named_parameters())
Utility libraries, derived software
Pytorch ships with a lot of included functionality, so you don’t necessarily ned to wrap it in anything else. Nonetheless, you can for specific use. NB this is list not up to date.
Lightning is the latest hot hotness in my circles.
Lightning is a very lightweight wrapper on PyTorch that decouples the science code from the engineering code. It’s more of a style-guide than a framework. By refactoring your code, we can automate most of the non-research code.
To use Lightning, simply refactor your research code into the LightningModule format (the science) and Lightning will automate the rest (the engineering). Lightning guarantees tested, correct, modern best practices for the automated parts.
- If you are a researcher, Lightning is infinitely flexible, you can modify everything down to the way .backward is called or distributed is set up.
- If you are a scientist or production team, lightning is very simple to use with best practice defaults.
Why do I want to use lightning?
Every research project starts the same, a model, a training loop, validation loop, etc. As your research advances, you're likely to need distributed training, 16-bit precision, checkpointing, gradient accumulation, etc.
Lightning sets up all the boilerplate state-of-the-art training for you so you can focus on the research.
Like other deep learning frameworks, there is some basic NLP support in pytorch; see pytorch.text.
flair is a commercially-backed NLP framework.
Pump graphs to a visualisation server. No pytorch-specific, but seems well-integrated. visdom
pytorch + Bayes = pyro, an
We believe the critical ideas to solve AI will come from a joint effort among a worldwide community of people pursuing diverse approaches. By open sourcing Pyro, we hope to encourage the scientific world to collaborate on making AI tools more flexible, open, and easy-to-use. We expect the current (alpha!) version of Pyro will be of most interest to probabilistic modelers who want to leverage large data sets and deep networks, PyTorch users who want easy-to-use Bayesian computation, and data scientists ready to explore the ragged edge of new technology.
pyprob is a PyTorch-based library for probabilistic programming and inference compilation. The main focus of this library is on coupling existing simulation codebases with probabilistic inference with minimal intervention.
The main advantage of pyprob, compared against other probabilistic programming languages like Pyro, is a fully automatic amortized inference procedure based on importance sampling. pyprob only requires a generative model to be specified. Particularly, pyprob allows for efficient inference using inference compilation which trains a recurrent neural network as a proposal network.
In Pyro such an inference network requires the user to explicitly define the control flow of the network, which is due to Pyro running the inference network and generative model sequentially. However, in pyprob the generative model and inference network runs concurrently. Thus, the control flow of the model is directly used to train the inference network. This alleviates the need for manually defining its control flow.
inferno is a grab-bag library for torch.
Current features include:
a basic Trainer class to encapsulate the training boilerplate (iteration/epoch loops, validation and checkpoint creation),
a graph API for building models with complex architectures, powered by networkx.
easy data-parallelism over multiple GPUs,
a submodule for torch.nn.Module-level parameter initialization,
a submodule for data preprocessing / transforms,
[other stuff that is not especially useful to do with a library]
I’m not sold on this one; A whole new library to reduce an already small amount of boilerplate, without adding any new non-trivial capabilities.
TNT is a reimplementation of some lua library that lua torch users used, that the current generation of ML users never witnessed. I think it aims to be semi-official library for pytorch, but it’s not especially active.
TNT (imported as torchnet) is a framework for PyTorch which provides a set of abstractions for PyTorch aiming at encouraging code re-use as well as encouraging modular programming. It provides powerful dataloading, logging, and visualization utilities. …
For example, TNT provides simple methods to record model performance in the torchnet.meter module and to log them to Visdom (or in the future, TensorboardX) with the
Apparently you use normal python garbage collector analysis.
import torch import gc for obj in gc.get_objects(): try: if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)): print(type(obj), obj.size()) except Exception as e: pass
See also usual python debugging.
Baydin, Atılım Güneş, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, et al. 2019. “Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale,” August. http://arxiv.org/abs/1907.03382.
Cheuk, Kin Wai, Kat Agres, and Dorien Herremans. 2019. “nnAUDIO: A Pytorch Audio Processing Tool Using 1d Convolution Neural Networks,” 2.
Le, Tuan Anh, Atılım Güneş Baydin, and Frank Wood. 2017. “Inference Compilation and Universal Probabilistic Programming.” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 54:1338–48. Proceedings of Machine Learning Research. Fort Lauderdale, FL, USA: PMLR. http://arxiv.org/abs/1610.09900.