diff --git a/docs/notebooks/mnist.ipynb b/docs/notebooks/mnist.ipynb index 0c01ff4..26e6aa1 100644 --- a/docs/notebooks/mnist.ipynb +++ b/docs/notebooks/mnist.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4798d95a2aa741e49bf5ad316d92320a", + "id": "01154f26a6249ac45bcb99b52dc62edc", "metadata": {}, "source": [ "# MNIST: Digits Classification" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "9b87507ae43f2a53ade1732aef459954", + "id": "80b224e799809080245ff42e3473caad", "metadata": { "tags": [ "add-colab-badge" @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "8a98b0c432c8aa1addbd7c375393c1a5", + "id": "e5107d09a39303dc2d4b81a90d4e654d", "metadata": {}, "source": [ "The MNIST dataset is considered to be the \"hello world\" dataset of machine\n", @@ -42,7 +42,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6218ce653dbc9305d4c6a323308a6de4", + "id": "4c55c692f122036284f4d6abde0f5511", "metadata": { "tags": [ "remove-cell" @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "2a5fe5a56a3579bd6eac791774214d37", + "id": "e0e0a1d3d2c9e8afd2c904d23ea04f02", "metadata": { "tags": [ "remove-cell" @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "2e3848974c88806207b69f4b2db8dab9", + "id": "d020351036eb08909fe3187a6323da11", "metadata": {}, "source": [ "## Setup and importing libraries\n", @@ -81,7 +81,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7107ee82571c64ff67175678f96f7286", + "id": "e0a632a42854103508f91edc9b71526c", "metadata": {}, "outputs": [], "source": [ @@ -103,7 +103,7 @@ }, { "cell_type": "markdown", - "id": "c5c1e2d27147ca3be50d5a50dc6fd692", + "id": "c66517ee95c79135e221eba94fa62708", "metadata": {}, "source": [ "Let's break down the code cell above.\n", @@ -129,7 +129,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5942db20bd08b3dc154be6981b65f80b", + "id": "f9c1ba180838f8f654a491176de92545", "metadata": {}, "outputs": [], "source": [ @@ -142,7 +142,7 @@ }, { "cell_type": "markdown", - "id": "94b609f8782897a85c3a178bfc94bcb4", + "id": "2734de543c70efa2ad650172cc9f4504", "metadata": {}, "source": [ "We do this so we don't have to re-download the dataset it every time we need to\n", @@ -156,7 +156,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6fc2ab05dacbcf86e4a5b510c3c91348", + "id": "4273543faba4b63c1fc75715788a8a51", "metadata": {}, "outputs": [], "source": [ @@ -211,7 +211,7 @@ }, { "cell_type": "markdown", - "id": "a846e3f8c24799b0c13bdcaa43b27771", + "id": "ce2ad477df7ec7a8bd1c816b3a8a2239", "metadata": {}, "source": [ "The `Dataset` and `Model` objects expose function decorators where we define\n", @@ -236,7 +236,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b16f523c696bf4020c865c84311876cc", + "id": "0cee1a9484d2fa222ed791a0cfc82abc", "metadata": {}, "outputs": [], "source": [ @@ -252,7 +252,7 @@ }, { "cell_type": "markdown", - "id": "181110f25a9900a68af89efafe88194f", + "id": "b3decdb95a6e6f62592c457c16d64ecb", "metadata": {}, "source": [ "Note that we pass a dictionary of `hyperparameters` when we invoke\n", @@ -273,7 +273,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3ac4c807a1c06e7c190bf05e557982af", + "id": "44521a73476d5d8da885fa92cda10ccf", "metadata": {}, "outputs": [], "source": [ @@ -291,7 +291,7 @@ }, { "cell_type": "markdown", - "id": "ca1faf97556cdf19364962acfc0a826d", + "id": "fa7cc47ee6d5a8eba53f15a96907e5bd", "metadata": {}, "source": [ "We also need to take care to handle the `None` case when we press the `clear`\n", @@ -301,7 +301,7 @@ { "cell_type": "code", "execution_count": null, - "id": "875ff8a49e9ce31289f264d95df720b1", + "id": "c12eb3c990be632d6adee545373204d3", "metadata": { "tags": [ "remove-output" @@ -322,7 +322,7 @@ }, { "cell_type": "markdown", - "id": "025b1a6a6396ab9ecb09b68a35b4d1de", + "id": "329484c84f9d053cf599334478606c25", "metadata": {}, "source": [ "You might notice that the model may not perform as well as you might expect...\n", diff --git a/docs/notebooks/quickdraw.ipynb b/docs/notebooks/quickdraw.ipynb index 9111409..010bb92 100644 --- a/docs/notebooks/quickdraw.ipynb +++ b/docs/notebooks/quickdraw.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "5cd042ee881b402194c76c647771e6e6", + "id": "37bce278cc5fea03c366e0f990944d3c", "metadata": {}, "source": [ "# QuickDraw: A Pictionary App" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "2ec49dce340cbaff131310d1ec85f859", + "id": "756e8ce989a0efe2b057e1f94cf56085", "metadata": { "tags": [ "add-colab-badge" @@ -22,7 +22,7 @@ }, { "cell_type": "markdown", - "id": "1e6d463688463909572c3990d4536105", + "id": "f3267e93a24923b19934e60a3f6473ee", "metadata": {}, "source": [ "In this example, we'll see how to create pictionary app that uses the\n", @@ -49,7 +49,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c16aac2036c8fb83ecfcd6adab7a8048", + "id": "836650e3d5651889ff00e5e52f143347", "metadata": { "tags": [ "remove-cell" @@ -63,7 +63,7 @@ }, { "cell_type": "markdown", - "id": "7ac7fd935288387a6dcc88e14efceecf", + "id": "c438b4470d607630d9d73fea7d4616f6", "metadata": { "tags": [ "remove-cell" @@ -76,7 +76,7 @@ }, { "cell_type": "markdown", - "id": "847c54ecf06243ba1f5c08c85048458b", + "id": "8c38d6048d90a4a18c6eae6bb9e81e4f", "metadata": {}, "source": [ "First let's import everything we need:" @@ -85,7 +85,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d85c9effe5535742db4a39c89dddcc48", + "id": "99040ad07fce92c17f9d47463b12e6b1", "metadata": {}, "outputs": [], "source": [ @@ -102,7 +102,7 @@ }, { "cell_type": "markdown", - "id": "dd93aa7ff170a26407eeb7d9d01eab74", + "id": "4f99448e249e0dfdad87d4c4cfa06c2c", "metadata": {}, "source": [ "Then let's implement some helper functions for downloading the quickdraw data and loading it\n", @@ -112,7 +112,7 @@ { "cell_type": "code", "execution_count": null, - "id": "70e6feb8ebde5eb5155e6153b7c7935a", + "id": "4d5181957a584641a27b2fac9138a84e", "metadata": {}, "outputs": [], "source": [ @@ -158,7 +158,7 @@ }, { "cell_type": "markdown", - "id": "0ea6a5859452147077ea272427eaf7a1", + "id": "7101b781e3fba66d75fb046f971559dd", "metadata": {}, "source": [ "### QuickDraw Dataset\n", @@ -169,7 +169,7 @@ { "cell_type": "code", "execution_count": null, - "id": "46d3ad2db3a409facc59d294f5a96e0b", + "id": "1d68378625908021a1d8c32d23517da4", "metadata": {}, "outputs": [], "source": [ @@ -204,7 +204,7 @@ }, { "cell_type": "markdown", - "id": "11f38c4ad0c9ee160e8826086aec81f4", + "id": "271b216ea8dfa53cdd0c272a6d3f93e7", "metadata": {}, "source": [ "As you'll see later, this class is important so that the `transformers` library can\n", @@ -218,7 +218,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9f48e5190aceac04f0236d7fe087fbf8", + "id": "3c0fe10fb821f5dd7f3d6079b2b36427", "metadata": {}, "outputs": [], "source": [ @@ -244,7 +244,7 @@ }, { "cell_type": "markdown", - "id": "b94180a57b08a918c4d4c8214524efa2", + "id": "22cf308e2c4b33c02d9b4395b1ca4dd3", "metadata": {}, "source": [ "As you can see it's a fairly straightforward 2D ConvNet architecture that uses a square\n", @@ -256,7 +256,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f2f30cb5e2f66e4ef14262a5fddfcea5", + "id": "ba4d98295f8830b888dede53baa9443c", "metadata": {}, "outputs": [], "source": [ @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "52deb00f0a506abcb574560f830a8323", + "id": "8aa9401abf3128dcf16d4f00cae35a6b", "metadata": {}, "source": [ "Then, let's define helper functions to compute the accuracy metric, which will be how we'll\n", @@ -286,7 +286,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b99a4f1ced8632c62b036333dd7300e5", + "id": "52e90796eb832038c9ebca2cc6d35b47", "metadata": {}, "outputs": [], "source": [ @@ -309,7 +309,7 @@ }, { "cell_type": "markdown", - "id": "efa1500a5ba38e87e36697862c04127d", + "id": "087b792ae4a376ecb71fd5843e8d0674", "metadata": {}, "source": [ "Finally, let's create a `train_quickdraw` function that will serve as the main entrypoint\n", @@ -319,7 +319,7 @@ { "cell_type": "code", "execution_count": null, - "id": "53a4da71235d29050a137348e80873e0", + "id": "762a211acdd96807187720a7319704ee", "metadata": {}, "outputs": [], "source": [ @@ -363,7 +363,7 @@ }, { "cell_type": "markdown", - "id": "a375ac6da6ded8a1fd093b8bb74b6b30", + "id": "b04ae3cfd56bf0b5e6686c7fc5ba0798", "metadata": {}, "source": [ "Why did we go through all of this trouble of implementing the dataset and model classes/functions\n", @@ -385,7 +385,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e6bf495da7713e99f3d8448e9941498b", + "id": "fbf43a7cb1ed6eb02d99b0a47a968050", "metadata": {}, "outputs": [], "source": [ @@ -404,7 +404,7 @@ }, { "cell_type": "markdown", - "id": "f4a61ef0e8f558f24072812bece47536", + "id": "463f5a7c152446162f4b21d2a08c9d3e", "metadata": {}, "source": [ "### Reading the Dataset\n", @@ -415,7 +415,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dee0f756cd402dcb849616911f0e4285", + "id": "7481d73ad040b7df0110c422e993d81e", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "d20e0665ac37f79f7d5979b704e591b8", + "id": "bf314c04d6fe33e4cb32a03daf555284", "metadata": {}, "source": [ "### Training\n", @@ -441,7 +441,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5677bbc83b05988bf44f83dafa70b527", + "id": "6e411570bdb950f84ff32caa26cd0572", "metadata": {}, "outputs": [], "source": [ @@ -471,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "f839843173f727517faaf6d4bae07409", + "id": "a9f0af2f0a555bc0818f900a1b15ef22", "metadata": {}, "source": [ "### Prediction\n", @@ -483,7 +483,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3be5785d6537b47af9489894846da3e4", + "id": "ebab3e24e4ded8ac9ed449afd91c9434", "metadata": {}, "outputs": [], "source": [ @@ -494,7 +494,7 @@ }, { "cell_type": "markdown", - "id": "c61e52226c8aebd118cab51958daced8", + "id": "4965e2e1c73fec616c3ad897a5d17470", "metadata": {}, "source": [ "Then we can define a `predictor` function that consumes the output of `feature_loader`:" @@ -503,7 +503,7 @@ { "cell_type": "code", "execution_count": null, - "id": "23c094a260bab95de2826b82e7274a2a", + "id": "761b44393033b6fc2ad35b2f04fa64da", "metadata": {}, "outputs": [], "source": [ @@ -521,7 +521,7 @@ }, { "cell_type": "markdown", - "id": "9a28633879ad39b0007035fa7bc9b8d3", + "id": "213866f3236a849cc81eec31ce47f959", "metadata": {}, "source": [ "### Training a Model Locally\n", @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "805c9ba91ce293bfee6800edb54d5997", + "id": "afdbadfeee31a4f48178ba89b016e5f9", "metadata": {}, "outputs": [], "source": [ @@ -556,7 +556,7 @@ }, { "cell_type": "markdown", - "id": "05a61b0b3fc3b589a8d169910abbe573", + "id": "17a4f828d3ea7c021f563a9549c89d7a", "metadata": {}, "source": [ "### Serving on a Gradio Widget\n", @@ -571,7 +571,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4620dce4be04b88d96d7ce515e88453c", + "id": "ba1055bf7d4037a2738e18e7f229391f", "metadata": { "tags": [ "remove-output" @@ -592,7 +592,7 @@ }, { "cell_type": "markdown", - "id": "51a9957c34514d47ffd18c814fbf2da1", + "id": "1e30288b84797a6a441140e4031bfe91", "metadata": {}, "source": [ "You might notice that the model may not perform as well as you might expect...\n", diff --git a/docs/source/tutorials/mnist.md b/docs/source/tutorials/mnist.md index fac50db..09bd703 100644 --- a/docs/source/tutorials/mnist.md +++ b/docs/source/tutorials/mnist.md @@ -4,7 +4,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.15.0 + jupytext_version: 1.15.2 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/docs/source/tutorials/quickdraw.md b/docs/source/tutorials/quickdraw.md index 5be36d1..624523f 100644 --- a/docs/source/tutorials/quickdraw.md +++ b/docs/source/tutorials/quickdraw.md @@ -4,7 +4,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.15.0 + jupytext_version: 1.15.2 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/docs/tutorials/mnist.md b/docs/tutorials/mnist.md index 76bfb7e..aa0b3ad 100644 --- a/docs/tutorials/mnist.md +++ b/docs/tutorials/mnist.md @@ -4,7 +4,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.13.8 + jupytext_version: 1.15.2 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/docs/tutorials/quickdraw.md b/docs/tutorials/quickdraw.md index 856596f..97c8d94 100644 --- a/docs/tutorials/quickdraw.md +++ b/docs/tutorials/quickdraw.md @@ -4,7 +4,7 @@ jupytext: extension: .md format_name: myst format_version: 0.13 - jupytext_version: 1.13.8 + jupytext_version: 1.15.2 kernelspec: display_name: Python 3 (ipykernel) language: python diff --git a/requirements-dev.txt b/requirements-dev.txt index cb07c79..8239eeb 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -16,4 +16,3 @@ tensorflow>=2.13.0 torch types-requests uvicorn -jupytext diff --git a/requirements-docs.txt b/requirements-docs.txt index 065a7f4..a221d69 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -3,9 +3,9 @@ furo gradio<=3.0.10 -ipython!=8.7.0 # 8.7.0 leads to docs build issue "Pygments lexer name 'ipython3' is not known" +ipython joblib -jupytext +jupytext==1.15.2 myst-nb sphinx sphinx-autodoc-typehints