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Philippe Tillet
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_sources/getting-started/tutorials/01-vector-add.rst.txt
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_sources/getting-started/tutorials/03-matrix-multiplication.rst.txt
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_sources/getting-started/tutorials/sg_execution_times.rst.txt
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Computation times Computation times
================= =================
**11:48.062** total execution time for **getting-started_tutorials** files: **11:47.213** total execution time for **getting-started_tutorials** files:
+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
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+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
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+---------------------------------------------------------------------------------------------------------+-----------+--------+ +---------------------------------------------------------------------------------------------------------+-----------+--------+
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getting-started/tutorials/02-fused-softmax.html
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Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li> Note however that the PyTorch <cite>softmax</cite> operation is more general and will works on tensors of any shape.</p></li>
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getting-started/tutorials/03-matrix-multiplication.html
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getting-started/tutorials/04-low-memory-dropout.html
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@@ -370,7 +370,7 @@ to explore the <cite>triton/language/random</cite> folder!</p>
<dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p> <dd><p>Nitish Srivastava and Geoffrey Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting”, JMLR 2014</p>
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<dl class="py function"> <dl class="py function">
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<span class="sig-prename descclassname"><span class="pre">triton.</span></span><span class="sig-name descname"><span class="pre">jit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.jit" title="Permalink to this definition"></a></dt> <span class="sig-prename descclassname"><span class="pre">triton.</span></span><span class="sig-name descname"><span class="pre">jit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#triton.jit" title="Permalink to this definition"></a></dt>
<dd><p>Decorator for JIT-compiling a function using the Triton compiler.</p> <dd><p>Decorator for JIT-compiling a function using the Triton compiler.</p>
<dl class="field-list simple"> <dl class="field-list simple">
<dt class="field-odd">Note</dt> <dt class="field-odd">Note</dt>

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