From a0afb79898bdcf96c0a8216788f38e49f7dca516 Mon Sep 17 00:00:00 2001 From: Soumith Chintala Date: Mon, 16 Jan 2017 18:32:40 -0500 Subject: [PATCH] add pic to readme --- README.md | 4 ++++ docs/source/_static/img/dynamic_graph.gif | Bin 0 -> 264025 bytes .../_static/img/tensor_illustration.png | Bin 18886 -> 18944 bytes 3 files changed, 4 insertions(+) create mode 100644 docs/source/_static/img/dynamic_graph.gif diff --git a/README.md b/README.md index 0fa7628197aac..66ca5ffa1d080 100644 --- a/README.md +++ b/README.md @@ -50,6 +50,8 @@ Elaborating further: If you use numpy, then you have used Tensors (a.k.a ndarray). +

+ PyTorch provides Tensors that can live either on the CPU or the GPU, and accelerate compute by a huge amount. @@ -75,6 +77,8 @@ from several research papers on this topic, as well as current and past work suc While this technique is not unique to PyTorch, it's one of the fastest implementations of it to date. You get the best of speed and flexibility for your crazy research. +

+ ### Python first PyTorch is not a Python binding into a monolothic C++ framework. diff --git a/docs/source/_static/img/dynamic_graph.gif b/docs/source/_static/img/dynamic_graph.gif new file mode 100644 index 0000000000000000000000000000000000000000..b4f17374e034911dfaf7d639a695f036f9166244 GIT binary patch literal 264025 zcmY(p2T+qw^r-!&(n&y?NC`baK$>(Rq4y?TL=aG_hN1)oNuh+^5u`{bGz~?hsdNw! zX(A{pO$0>+K?Q{TKJWj2bLZZjXUpuIot@co_Uzd;H8;`J^4^4~fd2ph2mtYLuxd#O zo<|EAVnm&_&_QOZWLLf7_{fJ14NVPAO^pv9Ha%)>Ztd^w9~~VV8lN1QoPIeuJux-+ za(4dJ>&4lHck^%GFD!k0`(b5idF{i>`tsVw%KGQ^jjd0cU*CV;oBe$9?(50Qx08+S z{V!j?Z-3kQ{^RHF?%vP6UqAK^w)Ysjzfbmm9sK_N=g*(RgM-6^!@s`|PY#bxj{YA1 zJ^Ficbae9fgz=ZbIR3vlVI2PtCk)2_f${%`|6TDv(kI7^e;of0|3~isEu8$?Ki>a& zyz~9<=KA6C+k?fKKQrUMMtgrfZQFa&u+v&I(^NE6SMY+C`-+yaRGYL>mHwkN|3^;7 zx8(S*q^K`hgsn2dcGayPRpGmpVY`*#d$g#%%Fw-v(4Q6f|IKa%ez)>}u~Tv5NBNB} zMIoQEgFa+jeVuk?oa)(6be+F_VI{#3Q5`bNjXL2+~T;r;`sd1gnOll1!dI2GV1;Exs=s>}y9S>?4k 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