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<h1 id="tinygrad-documentation">tinygrad documentation<a class="headerlink" href="#tinygrad-documentation" title="Permanent link">¤</a></h1>
<p>Welcome to the docs for tinygrad. This page is for users of the tinygrad library. tinygrad is not 1.0 yet, but it will be soon. The API has been pretty stable for a while.</p>
<p>While you can <code class="language-python highlight"><span class="n">pip</span> <span class="n">install</span> <span class="n">tinygrad</span></code>, we encourage you to install from source:</p>
<div class="language-bash highlight"><pre><span></span><code>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/tinygrad/tinygrad.git
<span class="nb">cd</span><span class="w"> </span>tinygrad
python3<span class="w"> </span>-m<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
</code></pre></div>
<p>After you have installed tinygrad, try the <a href="mnist/">MNIST tutorial</a>.</p>
<p>If you are new to tensor libraries, learn how to use them by solving puzzles from <a href="https://github.com/obadakhalili/tinygrad-tensor-puzzles">tinygrad-tensor-puzzles</a>.</p>
<p>We also have <a href="developer/developer/">developer docs</a>, and Di Zhu has created a <a href="https://mesozoic-egg.github.io/tinygrad-notes/">bunch of tutorials</a> to help understand how tinygrad works.</p>
<h2 id="tinygrad-usage">tinygrad Usage<a class="headerlink" href="#tinygrad-usage" title="Permanent link">¤</a></h2>
<p>The main class you will interact with is <a href="tensor/">Tensor</a>. It functions very similarly to PyTorch, but has a bit more of a functional style. tinygrad supports <a href="dtypes/">many datatypes</a>. All operations in tinygrad are lazy, meaning they won't do anything until you realize.</p>
<ul>
<li>tinygrad has a built in <a href="nn/">neural network library</a> with some classes, optimizers, and load/save state management.</li>
<li>tinygrad has a JIT to make things fast. Decorate your pure function with <code class="language-python highlight"><span class="n">TinyJit</span></code></li>
<li>tinygrad has amazing support for multiple GPUs, allowing you to shard your Tensors with <code class="language-python highlight"><span class="n">Tensor</span><span class="o">.</span><span class="n">shard</span></code></li>
</ul>
<p>To understand what training looks like in tinygrad, you should read <code class="language-python highlight"><span class="n">beautiful_mnist</span><span class="o">.</span><span class="n">py</span></code></p>
<p>We have a <a href="quickstart/">quickstart guide</a> and a <a href="showcase/">showcase</a></p>
<h2 id="tinygrad-stack">tinygrad Stack<a class="headerlink" href="#tinygrad-stack" title="Permanent link">¤</a></h2>
<p><img src="./tinygrad_vs_others.png" alt="Tinygrad vs others" style="max-width: 1000px; height: auto;" /></p>
<h2 id="differences-from-pytorch">Differences from PyTorch<a class="headerlink" href="#differences-from-pytorch" title="Permanent link">¤</a></h2>
<p>If you are migrating from PyTorch, welcome. Most of the API is the same. We hope you will find tinygrad both familiar and somehow more "correct feeling"</p>
<h3 id="tinygrad-doesnt-have-nnmodule">tinygrad doesn't have nn.Module<a class="headerlink" href="#tinygrad-doesnt-have-nnmodule" title="Permanent link">¤</a></h3>
<p>There's nothing special about a "Module" class in tinygrad, it's just a normal class. <a href="nn/#tinygrad.nn.state.get_parameters"><code class="language-python highlight"><span class="n">nn</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">get_parameters</span></code></a> can be used to recursively search normal classes for valid tensors. Instead of the <code class="language-python highlight"><span class="n">forward</span></code> method in PyTorch, tinygrad just uses <code class="language-python highlight"><span class="fm">__call__</span></code></p>
<h3 id="tinygrad-is-functional">tinygrad is functional<a class="headerlink" href="#tinygrad-is-functional" title="Permanent link">¤</a></h3>
<p>In tinygrad, you can do <a href="tensor/ops/#tinygrad.Tensor.conv2d"><code class="language-python highlight"><span class="n">x</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">w</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span></code></a> or <a href="tensor/ops/#tinygrad.Tensor.sparse_categorical_crossentropy"><code class="language-python highlight"><span class="n">x</span><span class="o">.</span><span class="n">sparse_categorical_crossentropy</span><span class="p">(</span><span class="n">y</span><span class="p">)</span></code></a>. We do also have a <a href="nn/#tinygrad.nn.Conv2d"><code class="language-python highlight"><span class="n">Conv2D</span></code></a> class like PyTorch if you want a place to keep the state, but all stateless operations don't have classes.</p>
<h3 id="tinygrad-is-lazy">tinygrad is lazy<a class="headerlink" href="#tinygrad-is-lazy" title="Permanent link">¤</a></h3>
<p>When you do <code class="language-python highlight"><span class="n">a</span><span class="o">+</span><span class="n">b</span></code> in tinygrad, nothing happens. It's not until you <a href="tensor/properties/#tinygrad.Tensor.realize"><code class="language-python highlight"><span class="n">realize</span></code></a> the Tensor that the computation actually runs.</p>
<h3 id="tinygrad-requires-tinyjit-to-be-fast">tinygrad requires @TinyJit to be fast<a class="headerlink" href="#tinygrad-requires-tinyjit-to-be-fast" title="Permanent link">¤</a></h3>
<p>PyTorch spends a lot of development effort to make dispatch very fast. tinygrad doesn't. We have a simple decorator that will replay the kernels used in the decorated function.</p>
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