Adam Paszke, Sam Gross, Soumith Chintala, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia
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1
2 Adam Paszke, Sam Gross, Soumith Chintala, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Alban Desmaison, Andreas Kopf, Edward Yang, Zach Devito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy & Team
3 What is PyTorch?
4 What is PyTorch efficient ndarray library with GPU support automatic differentiation engine machine learning primitives gradient based optimization package Machine Learning Ecosystem CUDA Probabilistic Modeling Data Loading NumPy like interface Deep Learning Visualization Reinforcement Learning Utility packages for image and text data
5 Tensor (ndarray) library
6 Tensor (ndarray) library NumPy-like API ( np.ndarray <-> torch.tensor ) Very fast acceleration on NVIDIA GPUs
7 Tensor (ndarray) library NumPy-like API ( np.ndarray <-> torch.tensor ) - Easy creation of Tensors with various dtypes and on different devices - Scientific computing methods (linear algebra, reduction, etc.) - Fast conversion from and to np.ndarray
8 Tensor (ndarray) library NumPy-like API ( np.ndarray <-> torch.tensor )
9 ndarray library Numpy PyTorch
10 Tensor (ndarray) library Very fast acceleration on NVIDIA GPUs - Efficient scientific computing on GPU - Clean device management - Seamless conversion to and from CPU
11 Tensor (ndarray) library Very fast acceleration on NVIDIA GPUs
12 Automatic Differentiation Engine
13 Automatic Differentiation Engine Computation as a graph built on-the-fly
14 Automatic Differentiation Engine Computation as a graph built on-the-fly x = torch.randn(1, 10) prev_h = torch.randn(1, 20) W_h = torch.randn(20, 20) W_x = torch.randn(20, 10)
15 Automatic Differentiation Engine Computation as a graph built on-the-fly x = torch.randn(1, 10) prev_h = torch.randn(1, 20) W_h = torch.randn(20, 20) W_x = torch.randn(20, 10) MM MM i2h = torch.mm(w_x, x.t()) h2h i2h
16 Automatic Differentiation Engine Computation as a graph built on-the-fly x = torch.randn(1, 10) prev_h = torch.randn(1, 20) W_h = torch.randn(20, 20) W_x = torch.randn(20, 10) MM MM i2h = torch.mm(w_x, x.t()) h2h = torch.mm(w_h, prev_h.t()) h2h i2h
17 Automatic Differentiation Engine Computation as a graph built on-the-fly x = torch.randn(1, 10) prev_h = torch.randn(1, 20) W_h = torch.randn(20, 20) W_x = torch.randn(20, 10) MM MM i2h = torch.mm(w_x, x.t()) h2h = torch.mm(w_h, prev_h.t()) next_h = i2h + h2h next_h = next_h.tanh() h2h Add Tanh i2h
18 Automatic Differentiation Engine Computation as a graph built on-the-fly x = torch.randn(1, 10) prev_h = torch.randn(1, 20) W_h = torch.randn(20, 20) W_x = torch.randn(20, 10) MM MM i2h = torch.mm(w_x, x.t()) h2h = torch.mm(w_h, prev_h.t()) next_h = i2h + h2h next_h = next_h.tanh() h2h Add Tanh i2h loss = next_h.sum() loss
19 Automatic Differentiation Engine Computation as a graph built on-the-fly x = torch.randn(1, 10) prev_h = torch.randn(1, 20) W_h = torch.randn(20, 20) W_x = torch.randn(20, 10) MM MM i2h = torch.mm(w_x, x.t()) h2h = torch.mm(w_h, prev_h.t()) next_h = i2h + h2h next_h = next_h.tanh() h2h Add Tanh i2h loss = next_h.sum() loss.backward() loss
20 Automatic Differentiation Engine Computation as a graph built on-the-fly x = torch.randn(1, 10) prev_h = torch.randn(1, 20) W_h = torch.randn(20, 20) W_x = torch.randn(20, 10) MM MM i2h = torch.mm(w_x, x.t()) h2h = torch.mm(w_h, prev_h.t()) next_h = i2h + h2h next_h = next_h.tanh() h2h Add Tanh i2h loss = next_h.sum() loss.backward() # compute gradients! Gradient w.r.t. the input Tensors is computed step-by-step from loss to top in reverse order loss
21 Automatic Differentiation Engine Computation as a graph built on-the-fly - Can use Python primitives to build the graph (e.g. for-loop) Functions with efficient backward implementations - torch.matmul, torch.fft, torch.svd, torch.trtrs, etc. Gradients by automatic backpropagation through the graph - Higher-order gradients (backward traversal is also a graph) - Multi-device graphs
22 Efficient Machine Learning Primitives
23 Machine Learning primitives Deep Learning - torch.nn Reinforcement Learning, Probabilistic Modeling - torch.distributions and more... - scientific computing methods + autograd
24 Neural Networks Figure by Md. Rezaul Karim
25 Neural Networks Figure by Md. Rezaul Karim
26 Convolutional Neural Networks (CNN) Figure by Yann LeCun et al.
27 Convolutional Neural Networks (CNN) Figure by Yann LeCun et al.
28 Recurrent Neural Networks (RNN) Figure by Pranoy Radhakrishnan
29 Recurrent Neural Networks (RNN) Figure by Pranoy Radhakrishnan
30 Gradient based optimization package
31 Gradient based optimization package State-of-the-art optimization algorithms - torch.optim.* - SGD, Adam, RMSProp, L-BFGS, etc. Learning Rate scheduler - torch.optim.lr_scheduler.* Extensible API
32 Machine Learning Ecosystem
33 Work items in practice Writing Building models Implementing Checkpointing Dataset loaders Training loop models Interfacing with Dealing with Building Building optimizers environments GPUs Baselines
34 Work items in practice Writing Building models Implementing Checkpointing Dataset loaders Training loop models Python + PyTorch - an environment to do all of this Interfacing with Dealing with Building Building optimizers environments GPUs Baselines
35 Writing Data Loaders every dataset is slightly differently formatted
36 Writing Data Loaders every dataset is slightly differently formatted have to be preprocessed and normalized differently
37 Writing Data Loaders every dataset is slightly differently formatted have to be preprocessed and normalized differently need a multithreaded Data loader to feed GPUs fast enough
38 Writing Data Loaders PyTorch solution: share data loaders across the community!
39 Writing Data Loaders PyTorch solution: share data loaders across the community!
40 Writing Data Loaders PyTorch solution: use regular Python to write Datasets: leverage existing Python code
41 Writing Data Loaders PyTorch solution: use regular Python to write Datasets: leverage existing Python code Example: ParlAI
42 Writing Data Loaders PyTorch solution: Code in practice
43 Writing Data Loaders PyTorch solution: Code in practice Research Workflows Pain Points Core Philisophy Upcoming Features
44 Writing Data Loaders PyTorch solution: Code in practice
45 Interfacing with environments Cars Simulations Video games Internet
46 Interfacing with environments Cars Simulations Video games Internet Pretty much every environment provides a Python API
47 Interfacing with environments Cars Simulations Video games Internet Natively interact with the environment directly
48 Visualization Tensorboard-PyTorch Visdom github.com/lanpa/tensorboard-pytorch github.com/facebookresearch/visdom
49 Debugging PyTorch is a Python extension
50 Debugging PyTorch is a Python extension Use your favorite Python debugger
51 Debugging PyTorch is a Python extension Use your favorite Python debugger
52 Debugging PyTorch is a Python extension Use your favorite Python debugger Use the most popular debugger:
53 Debugging PyTorch is a Python extension Use your favorite Python debugger Use the most popular debugger: print(foo)
54 Identifying bottlenecks PyTorch is a Python extension Use your favorite Python profiler
55 Identifying bottlenecks PyTorch is a Python extension Use your favorite Python profiler: Line_Profiler
56 Compilation Time PyTorch is written for the impatient
57 Compilation Time PyTorch is written for the impatient Absolutely no compilation time when writing your scripts whatsoever
58 Compilation Time PyTorch is written for the impatient Absolutely no compilation time when writing your scripts whatsoever All core kernels are pre-compiled
59 Ecosystem Use the entire Python ecosystem at your will
60 Ecosystem Use the entire Python ecosystem at your will Including SciPy, Scikit-Learn, etc.
61 Ecosystem Use the entire Python ecosystem at your will Including SciPy, Scikit-Learn, etc.
62 Machine Learning Ecosystem Shared model zoo (pretrained models) torchvision
63 Machine Learning Ecosystem Shared model zoo (pretrained models) github.com/aaron-xichen/pytorch-playground
64 Machine Learning Ecosystem Probabilistic programming github.com/probtorch/probtorch
65 Machine Learning Ecosystem Gaussian Processes
66 Machine Learning Ecosystem Image-to-Image Translation pix2pix & CycleGAN github.com/junyanz/pytorch-cyclegan-and-pix2pix pix2pixhd github.com/nvidia/pix2pixhd
67 Machine Learning Ecosystem Machine Translation OpenNMT-py: Open-Source Neural Machine Translation github.com/opennmt/opennmt-py FAIR Sequence-to-Sequence Toolkit github.com/facebookresearch/fairseq-py
68 Machine Learning Ecosystem General Linguistics Tasks
69 Machine Learning Ecosystem Sentiment Discovery
70 Machine Learning Ecosystem Optical Flow Estimation FlowNet 2.0 github.com/nvidia/flownet2-pytorch
71 and many more... Distributed training Profiling Extending autograd C++ interface (ATen) and C++ extensions ONNX JIT...
72 With from
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