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new 575ea41 Build at Mon Mar 30 11:16:13 PDT 2020
575ea41 is described below
commit 575ea41fcd8552e47a90def3092165316b119e1c
Author: tqchen <[email protected]>
AuthorDate: Mon Mar 30 11:16:14 2020 -0700
Build at Mon Mar 30 11:16:13 PDT 2020
---
2018/07/12/vta-release-announcement.html | 2 +-
2018/08/10/DLPack-Bridge.html | 2 +-
2018/10/03/auto-opt-all.html | 6 +++---
2019/01/19/Golang.html | 4 ++--
2019/03/18/tvm-apache-announcement.html | 2 +-
2019/04/29/opt-cuda-quantized.html | 8 ++++----
atom.xml | 26 +++++++++++++-------------
community.html | 2 +-
rss.xml | 28 ++++++++++++++--------------
9 files changed, 40 insertions(+), 40 deletions(-)
diff --git a/2018/07/12/vta-release-announcement.html
b/2018/07/12/vta-release-announcement.html
index eb259ab..9304549 100644
--- a/2018/07/12/vta-release-announcement.html
+++ b/2018/07/12/vta-release-announcement.html
@@ -289,7 +289,7 @@ This kind of high-level visibility is essential to system
designers who want to
<h2 id="get-started">Get Started!</h2>
<ul>
<li>TVM and VTA Github page can be found here: <a
href="https://github.com/dmlc/tvm";>https://github.com/dmlc/tvm</a>.</li>
- <li>You can get started with easy to follow <a
href="https://docs.tvm.ai/vta/tutorials/index.html";>tutorials on programming
VTA with TVM</a>.</li>
+ <li>You can get started with easy to follow <a
href="https://tvm.apache.org/docs//vta/tutorials/index.html";>tutorials on
programming VTA with TVM</a>.</li>
<li>For more technical details on VTA, read our <a
href="https://arxiv.org/abs/1807.04188";>VTA technical report</a> on ArXiv.</li>
</ul>
diff --git a/2018/08/10/DLPack-Bridge.html b/2018/08/10/DLPack-Bridge.html
index 9383267..9849d29 100644
--- a/2018/08/10/DLPack-Bridge.html
+++ b/2018/08/10/DLPack-Bridge.html
@@ -245,7 +245,7 @@ schedule:</p>
<p>For brevity, we do not cover TVM’s large collection of scheduling primitives
that we can use to optimize matrix multiplication. If you wish to make a custom
GEMM operator run <em>fast</em> on your hardware device, a detailed tutorial
can be
-found <a
href="https://docs.tvm.ai/tutorials/optimize/opt_gemm.html";>here</a>.</p>
+found <a
href="https://tvm.apache.org/docs//tutorials/optimize/opt_gemm.html";>here</a>.</p>
<p>We then convert the TVM function into one that supports PyTorch tensors:</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre
class="highlight"><code> <span class="kn">from</span> <span
class="nn">tvm.contrib.dlpack</span> <span class="kn">import</span> <span
class="n">to_pytorch_func</span>
diff --git a/2018/10/03/auto-opt-all.html b/2018/10/03/auto-opt-all.html
index 6ee0229..87f8122 100644
--- a/2018/10/03/auto-opt-all.html
+++ b/2018/10/03/auto-opt-all.html
@@ -542,9 +542,9 @@ for inference deployment. TVM just provides such a
solution.</p>
<h2 id="links">Links</h2>
<p>[1] benchmark: <a
href="https://github.com/dmlc/tvm/tree/master/apps/benchmark";>https://github.com/dmlc/tvm/tree/master/apps/benchmark</a><br
/>
-[2] Tutorial on tuning for ARM CPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_arm.html";>https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_arm.html</a><br
/>
-[3] Tutorial on tuning for Mobile GPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_mobile_gpu.html";>https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_mobile_gpu.html</a><br
/>
-[4] Tutorial on tuning for NVIDIA/AMD GPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_cuda.html";>https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_cuda.html</a><br
/>
+[2] Tutorial on tuning for ARM CPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_arm.html";>https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_arm.html</a><br
/>
+[3] Tutorial on tuning for Mobile GPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_mobile_gpu.html";>https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_mobile_gpu.html</a><br
/>
+[4] Tutorial on tuning for NVIDIA/AMD GPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_cuda.html";>https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_cuda.html</a><br
/>
[5] Paper about AutoTVM: <a href="https://arxiv.org/abs/1805.08166";>Learning
to Optimize Tensor Program</a><br />
[6] Paper about Intel CPU (by AWS contributors) : <a
href="https://arxiv.org/abs/1809.02697";>Optimizing CNN Model Inference on
CPUs</a></p>
diff --git a/2019/01/19/Golang.html b/2019/01/19/Golang.html
index e22416a..cd312b9 100644
--- a/2019/01/19/Golang.html
+++ b/2019/01/19/Golang.html
@@ -176,7 +176,7 @@ deploy deep learning models from a variety of frameworks to
a choice of hardware
<p>The TVM import and compilation process generates a graph JSON, a module and
a params. Any application that
integrates the TVM runtime can load these compiled modules and perform
inference. A detailed tutorial of module
-import and compilation using TVM can be found at <a
href="https://docs.tvm.ai/tutorials/";>tutorials</a>.</p>
+import and compilation using TVM can be found at <a
href="https://tvm.apache.org/docs//tutorials/";>tutorials</a>.</p>
<p>TVM now supports deploying compiled modules through Golang. Golang
applications can make use of this
to deploy the deep learning models through TVM. The scope of this blog is the
introduction of <code class="highlighter-rouge">gotvm</code> package,
@@ -206,7 +206,7 @@ Developers can make use of TVM to import and compile deep
learning models and ge
<center> Import, Compile, Integrate and Deploy</center>
<p></p>
-<p>TVM <a
href="https://docs.tvm.ai/tutorials/#compile-deep-learning-models";>Compile Deep
Learning Models</a> tutorials
+<p>TVM <a
href="https://tvm.apache.org/docs//tutorials/#compile-deep-learning-models";>Compile
Deep Learning Models</a> tutorials
are available to compile models from all frameworks supported by the TVM
frontend. This compilation process
generates the artifacts required to integrate and deploy the model on a
target.</p>
diff --git a/2019/03/18/tvm-apache-announcement.html
b/2019/03/18/tvm-apache-announcement.html
index cc911ba..98e350d 100644
--- a/2019/03/18/tvm-apache-announcement.html
+++ b/2019/03/18/tvm-apache-announcement.html
@@ -176,7 +176,7 @@
<p>We would like to take this chance to thank the Allen School for supporting
the SAMPL team that gave birth to the TVM project. We would also like to thank
the Halide project which provided the basis for TVM’s loop-level IR and initial
code generation. We would like to thank our Apache incubator mentors for
introducing the project to Apache and providing useful guidance. Finally, we
would like to thank the TVM community and all of the organizations, as listed
above, that supported the d [...]
-<p>See also the <a
href="https://news.cs.washington.edu/2019/03/18/allen-schools-tvm-deep-learning-compiler-framework-transitions-to-apache/";>Allen
School news about the transition here</a>, <a
href="https://sampl.cs.washington.edu/tvmconf/#about-tvmconf";>TVM conference
program slides and recordings</a>, and <a
href="https://docs.tvm.ai/contribute/community.html";>our community guideline
here</a>. Follow us on Twitter: <a
href="https://twitter.com/ApacheTVM";>@ApacheTVM</a>.</p>
+<p>See also the <a
href="https://news.cs.washington.edu/2019/03/18/allen-schools-tvm-deep-learning-compiler-framework-transitions-to-apache/";>Allen
School news about the transition here</a>, <a
href="https://sampl.cs.washington.edu/tvmconf/#about-tvmconf";>TVM conference
program slides and recordings</a>, and <a
href="https://tvm.apache.org/docs//contribute/community.html";>our community
guideline here</a>. Follow us on Twitter: <a
href="https://twitter.com/ApacheTVM";>@ApacheTVM</a>.</p>
</div>
</div>
diff --git a/2019/04/29/opt-cuda-quantized.html
b/2019/04/29/opt-cuda-quantized.html
index 5301531..40c7157 100644
--- a/2019/04/29/opt-cuda-quantized.html
+++ b/2019/04/29/opt-cuda-quantized.html
@@ -201,7 +201,7 @@ With an efficient dot product operator, we can implement
high-level operators su
This is a typical use case of <code class="highlighter-rouge">dp4a</code>.
TVM uses tensorization to support calling external intrinsics.
We do not need to modify the original computation declaration; we use the
schedule primitive <code class="highlighter-rouge">tensorize</code> to replace
the accumulation with <code class="highlighter-rouge">dp4a</code> tensor
intrinsic.
-More details of tensorization can be found in the <a
href="https://docs.tvm.ai/tutorials/language/tensorize.html";>tutorial</a>.</p>
+More details of tensorization can be found in the <a
href="https://tvm.apache.org/docs//tutorials/language/tensorize.html";>tutorial</a>.</p>
<h2 id="data-layout-rearrangement">Data Layout Rearrangement</h2>
<p>One of the challenges in tensorization is that we may need to design
special computation logic to adapt to the requirement of tensor intrinsics.
@@ -243,7 +243,7 @@ We also do some manual tiling such as splitting axes by 4
or 16 to facilitate ve
<p>In quantized 2d convolution, we design a search space that includes a set
of tunable options, such as the tile size, the axes to fuse, configurations of
loop unrolling and double buffering.
The templates of quantized <code class="highlighter-rouge">conv2d</code> and
<code class="highlighter-rouge">dense</code> on CUDA are registered under
template key <code class="highlighter-rouge">int8</code>.
During automatic tuning, we can create tuning tasks for these quantized
operators by setting the <code class="highlighter-rouge">template_key</code>
argument.
-Details of how to launch automatic optimization can be found in the <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_relay_cuda.html";>AutoTVM
tutorial</a>.</p>
+Details of how to launch automatic optimization can be found in the <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_relay_cuda.html";>AutoTVM
tutorial</a>.</p>
<h1 id="general-workflow">General Workflow</h1>
@@ -262,14 +262,14 @@ Details of how to launch automatic optimization can be
found in the <a href="htt
<div class="language-python highlighter-rouge"><div class="highlight"><pre
class="highlight"><code><span class="n">net</span> <span class="o">=</span>
<span class="n">relay</span><span class="o">.</span><span
class="n">quantize</span><span class="o">.</span><span
class="n">quantize</span><span class="p">(</span><span
class="n">net</span><span class="p">,</span> <span class="n">params</span><span
class="o">=</span><span class="n">params</span><span class="p">)</span>
</code></pre></div></div>
-<p>Then, we use AutoTVM to extract tuning tasks for the operators in the model
and perform automatic optimization. The <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_relay_cuda.html";>AutoTVM
tutorial</a> provides an example for this.</p>
+<p>Then, we use AutoTVM to extract tuning tasks for the operators in the model
and perform automatic optimization. The <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_relay_cuda.html";>AutoTVM
tutorial</a> provides an example for this.</p>
<p>Finally, we build the model and run inference in the quantized mode.</p>
<div class="language-python highlighter-rouge"><div class="highlight"><pre
class="highlight"><code><span class="k">with</span> <span
class="n">relay</span><span class="o">.</span><span
class="n">build_config</span><span class="p">(</span><span
class="n">opt_level</span><span class="o">=</span><span
class="mi">3</span><span class="p">):</span>
<span class="n">graph</span><span class="p">,</span> <span
class="n">lib</span><span class="p">,</span> <span class="n">params</span>
<span class="o">=</span> <span class="n">relay</span><span
class="o">.</span><span class="n">build</span><span class="p">(</span><span
class="n">net</span><span class="p">,</span> <span class="n">target</span><span
class="p">)</span>
</code></pre></div></div>
<p>The result of <code class="highlighter-rouge">relay.build</code> is a
deployable library.
-We can either run inference <a
href="https://docs.tvm.ai/tutorials/frontend/from_mxnet.html#execute-the-portable-graph-on-tvm";>on
the GPU</a> directly or deploy <a
href="https://docs.tvm.ai/tutorials/frontend/deploy_model_on_rasp.html#deploy-the-model-remotely-by-rpc";>on
the remote devices</a> via RPC.</p>
+We can either run inference <a
href="https://tvm.apache.org/docs//tutorials/frontend/from_mxnet.html#execute-the-portable-graph-on-tvm";>on
the GPU</a> directly or deploy <a
href="https://tvm.apache.org/docs//tutorials/frontend/deploy_model_on_rasp.html#deploy-the-model-remotely-by-rpc";>on
the remote devices</a> via RPC.</p>
<h1 id="benchmark">Benchmark</h1>
<p>To verify the performance of the quantized operators in TVM, we benchmark
the performance of several popular network models including VGG-19, ResNet-50
and Inception V3.
diff --git a/atom.xml b/atom.xml
index 146647c..4a77194 100644
--- a/atom.xml
+++ b/atom.xml
@@ -4,7 +4,7 @@
<title>TVM</title>
<link href="https://tvm.apache.org"; rel="self"/>
<link href="https://tvm.apache.org"/>
- <updated>2020-03-30T10:20:31-07:00</updated>
+ <updated>2020-03-30T11:16:12-07:00</updated>
<id>https://tvm.apache.org</id>
<author>
<name></name>
@@ -158,7 +158,7 @@ With an efficient dot product operator, we can implement
high-level operators su
This is a typical use case of <code
class="highlighter-rouge">dp4a</code>.
TVM uses tensorization to support calling external intrinsics.
We do not need to modify the original computation declaration; we use the
schedule primitive <code
class="highlighter-rouge">tensorize</code> to replace the
accumulation with <code
class="highlighter-rouge">dp4a</code> tensor intrinsic.
-More details of tensorization can be found in the <a
href="https://docs.tvm.ai/tutorials/language/tensorize.html">tutorial</a>.</p>
+More details of tensorization can be found in the <a
href="https://tvm.apache.org/docs//tutorials/language/tensorize.html">tutorial</a>.</p>
<h2 id="data-layout-rearrangement">Data Layout
Rearrangement</h2>
<p>One of the challenges in tensorization is that we may need to design
special computation logic to adapt to the requirement of tensor intrinsics.
@@ -200,7 +200,7 @@ We also do some manual tiling such as splitting axes by 4
or 16 to facilitate ve
<p>In quantized 2d convolution, we design a search space that includes a
set of tunable options, such as the tile size, the axes to fuse, configurations
of loop unrolling and double buffering.
The templates of quantized <code
class="highlighter-rouge">conv2d</code> and <code
class="highlighter-rouge">dense</code> on CUDA are
registered under template key <code
class="highlighter-rouge">int8</code>.
During automatic tuning, we can create tuning tasks for these quantized
operators by setting the <code
class="highlighter-rouge">template_key</code> argument.
-Details of how to launch automatic optimization can be found in the <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_relay_cuda.html">AutoTVM
tutorial</a>.</p>
+Details of how to launch automatic optimization can be found in the <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_relay_cuda.html">AutoTVM
tutorial</a>.</p>
<h1 id="general-workflow">General Workflow</h1>
@@ -219,14 +219,14 @@ Details of how to launch automatic optimization can be
found in the <a href=&
<div class="language-python highlighter-rouge"><div
class="highlight"><pre
class="highlight"><code><span
class="n">net</span> <span
class="o">=</span> <span
class="n">relay</span><span
class="o">.</span><span
class="n">quantize</span><span
class="o">.</span><span
class="n">quantize</spa [...]
</code></pre></div></div>
-<p>Then, we use AutoTVM to extract tuning tasks for the operators in the
model and perform automatic optimization. The <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_relay_cuda.html">AutoTVM
tutorial</a> provides an example for this.</p>
+<p>Then, we use AutoTVM to extract tuning tasks for the operators in the
model and perform automatic optimization. The <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_relay_cuda.html">AutoTVM
tutorial</a> provides an example for this.</p>
<p>Finally, we build the model and run inference in the quantized
mode.</p>
<div class="language-python highlighter-rouge"><div
class="highlight"><pre
class="highlight"><code><span
class="k">with</span> <span
class="n">relay</span><span
class="o">.</span><span
class="n">build_config</span><span
class="p">(</span><span
class="n">opt_level</span><span
class="o">=< [...]
<span class="n">graph</span><span
class="p">,</span> <span
class="n">lib</span><span
class="p">,</span> <span
class="n">params</span> <span
class="o">=</span> <span
class="n">relay</span><span
class="o">.</span><span
class="n">build</span><span
class="p">(</span><s [...]
</code></pre></div></div>
<p>The result of <code
class="highlighter-rouge">relay.build</code> is a deployable
library.
-We can either run inference <a
href="https://docs.tvm.ai/tutorials/frontend/from_mxnet.html#execute-the-portable-graph-on-tvm">on
the GPU</a> directly or deploy <a
href="https://docs.tvm.ai/tutorials/frontend/deploy_model_on_rasp.html#deploy-the-model-remotely-by-rpc">on
the remote devices</a> via RPC.</p>
+We can either run inference <a
href="https://tvm.apache.org/docs//tutorials/frontend/from_mxnet.html#execute-the-portable-graph-on-tvm">on
the GPU</a> directly or deploy <a
href="https://tvm.apache.org/docs//tutorials/frontend/deploy_model_on_rasp.html#deploy-the-model-remotely-by-rpc">on
the remote devices</a> via RPC.</p>
<h1 id="benchmark">Benchmark</h1>
<p>To verify the performance of the quantized operators in TVM, we
benchmark the performance of several popular network models including VGG-19,
ResNet-50 and Inception V3.
@@ -277,7 +277,7 @@ We show that automatic optimization in TVM makes it easy
and flexible to support
<p>We would like to take this chance to thank the Allen School for
supporting the SAMPL team that gave birth to the TVM project. We would also
like to thank the Halide project which provided the basis for TVM’s loop-level
IR and initial code generation. We would like to thank our Apache incubator
mentors for introducing the project to Apache and providing useful guidance.
Finally, we would like to thank the TVM community and all of the organizations,
as listed above, that supported [...]
-<p>See also the <a
href="https://news.cs.washington.edu/2019/03/18/allen-schools-tvm-deep-learning-compiler-framework-transitions-to-apache/">Allen
School news about the transition here</a>, <a
href="https://sampl.cs.washington.edu/tvmconf/#about-tvmconf">TVM
conference program slides and recordings</a>, and <a
href="https://docs.tvm.ai/contribute/community.html">our community
guideline here</a>. Follow us on Twitter [...]
+<p>See also the <a
href="https://news.cs.washington.edu/2019/03/18/allen-schools-tvm-deep-learning-compiler-framework-transitions-to-apache/">Allen
School news about the transition here</a>, <a
href="https://sampl.cs.washington.edu/tvmconf/#about-tvmconf">TVM
conference program slides and recordings</a>, and <a
href="https://tvm.apache.org/docs//contribute/community.html">our
community guideline here</a>. Follow us o [...]
</content>
</entry>
@@ -300,7 +300,7 @@ deploy deep learning models from a variety of frameworks to
a choice of hardware
<p>The TVM import and compilation process generates a graph JSON, a
module and a params. Any application that
integrates the TVM runtime can load these compiled modules and perform
inference. A detailed tutorial of module
-import and compilation using TVM can be found at <a
href="https://docs.tvm.ai/tutorials/">tutorials</a>.</p>
+import and compilation using TVM can be found at <a
href="https://tvm.apache.org/docs//tutorials/">tutorials</a>.</p>
<p>TVM now supports deploying compiled modules through Golang. Golang
applications can make use of this
to deploy the deep learning models through TVM. The scope of this blog is the
introduction of <code
class="highlighter-rouge">gotvm</code> package,
@@ -330,7 +330,7 @@ Developers can make use of TVM to import and compile deep
learning models and ge
<center> Import, Compile, Integrate and Deploy</center>
<p></p>
-<p>TVM <a
href="https://docs.tvm.ai/tutorials/#compile-deep-learning-models">Compile
Deep Learning Models</a> tutorials
+<p>TVM <a
href="https://tvm.apache.org/docs//tutorials/#compile-deep-learning-models">Compile
Deep Learning Models</a> tutorials
are available to compile models from all frameworks supported by the TVM
frontend. This compilation process
generates the artifacts required to integrate and deploy the model on a
target.</p>
@@ -1113,9 +1113,9 @@ for inference deployment. TVM just provides such a
solution.</p>
<h2 id="links">Links</h2>
<p>[1] benchmark: <a
href="https://github.com/dmlc/tvm/tree/master/apps/benchmark">https://github.com/dmlc/tvm/tree/master/apps/benchmark</a><br
/>
-[2] Tutorial on tuning for ARM CPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_arm.html">https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_arm.html</a><br
/>
-[3] Tutorial on tuning for Mobile GPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_mobile_gpu.html">https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_mobile_gpu.html</a><br
/>
-[4] Tutorial on tuning for NVIDIA/AMD GPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_cuda.html">https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_cuda.html</a><br
/>
+[2] Tutorial on tuning for ARM CPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_arm.html">https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_arm.html</a><br
/>
+[3] Tutorial on tuning for Mobile GPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_mobile_gpu.html">https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_mobile_gpu.html</a><br
/>
+[4] Tutorial on tuning for NVIDIA/AMD GPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_cuda.html">https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_cuda.html</a><br
/>
[5] Paper about AutoTVM: <a
href="https://arxiv.org/abs/1805.08166">Learning to Optimize
Tensor Program</a><br />
[6] Paper about Intel CPU (by AWS contributors) : <a
href="https://arxiv.org/abs/1809.02697">Optimizing CNN Model
Inference on CPUs</a></p>
@@ -1210,7 +1210,7 @@ schedule:</p>
<p>For brevity, we do not cover TVM’s large collection of scheduling
primitives
that we can use to optimize matrix multiplication. If you wish to make a custom
GEMM operator run <em>fast</em> on your hardware device, a
detailed tutorial can be
-found <a
href="https://docs.tvm.ai/tutorials/optimize/opt_gemm.html">here</a>.</p>
+found <a
href="https://tvm.apache.org/docs//tutorials/optimize/opt_gemm.html">here</a>.</p>
<p>We then convert the TVM function into one that supports PyTorch
tensors:</p>
<div class="language-python highlighter-rouge"><div
class="highlight"><pre
class="highlight"><code> <span
class="kn">from</span> <span
class="nn">tvm.contrib.dlpack</span> <span
class="kn">import</span> <span
class="n">to_pytorch_func</span>
@@ -1397,7 +1397,7 @@ This kind of high-level visibility is essential to system
designers who want to
<h2 id="get-started">Get Started!</h2>
<ul>
<li>TVM and VTA Github page can be found here: <a
href="https://github.com/dmlc/tvm">https://github.com/dmlc/tvm</a>.</li>
- <li>You can get started with easy to follow <a
href="https://docs.tvm.ai/vta/tutorials/index.html">tutorials on
programming VTA with TVM</a>.</li>
+ <li>You can get started with easy to follow <a
href="https://tvm.apache.org/docs//vta/tutorials/index.html">tutorials
on programming VTA with TVM</a>.</li>
<li>For more technical details on VTA, read our <a
href="https://arxiv.org/abs/1807.04188">VTA technical
report</a> on ArXiv.</li>
</ul>
</content>
diff --git a/community.html b/community.html
index b88e61f..3cc31b9 100644
--- a/community.html
+++ b/community.html
@@ -200,7 +200,7 @@ Please reach out are interested working in aspects that are
not on the roadmap.<
<p>As a community project, we welcome contributions!
The package is developed and used by the community.</p>
-<p><a href="https://docs.tvm.ai/contribute"; class="link-btn">TVM Contributor
Guideline</a></p>
+<p><a href="https://tvm.apache.org/docs//contribute"; class="link-btn">TVM
Contributor Guideline</a></p>
<p><br /></p>
diff --git a/rss.xml b/rss.xml
index 00804bd..967dd59 100644
--- a/rss.xml
+++ b/rss.xml
@@ -5,8 +5,8 @@
<description>TVM - </description>
<link>https://tvm.apache.org</link>
<atom:link href="https://tvm.apache.org"; rel="self"
type="application/rss+xml" />
- <lastBuildDate>Mon, 30 Mar 2020 10:20:31 -0700</lastBuildDate>
- <pubDate>Mon, 30 Mar 2020 10:20:31 -0700</pubDate>
+ <lastBuildDate>Mon, 30 Mar 2020 11:16:12 -0700</lastBuildDate>
+ <pubDate>Mon, 30 Mar 2020 11:16:12 -0700</pubDate>
<ttl>60</ttl>
@@ -153,7 +153,7 @@ With an efficient dot product operator, we can implement
high-level operators su
This is a typical use case of <code
class="highlighter-rouge">dp4a</code>.
TVM uses tensorization to support calling external intrinsics.
We do not need to modify the original computation declaration; we use the
schedule primitive <code
class="highlighter-rouge">tensorize</code> to replace the
accumulation with <code
class="highlighter-rouge">dp4a</code> tensor intrinsic.
-More details of tensorization can be found in the <a
href="https://docs.tvm.ai/tutorials/language/tensorize.html">tutorial</a>.</p>
+More details of tensorization can be found in the <a
href="https://tvm.apache.org/docs//tutorials/language/tensorize.html">tutorial</a>.</p>
<h2 id="data-layout-rearrangement">Data Layout
Rearrangement</h2>
<p>One of the challenges in tensorization is that we may need to design
special computation logic to adapt to the requirement of tensor intrinsics.
@@ -195,7 +195,7 @@ We also do some manual tiling such as splitting axes by 4
or 16 to facilitate ve
<p>In quantized 2d convolution, we design a search space that includes a
set of tunable options, such as the tile size, the axes to fuse, configurations
of loop unrolling and double buffering.
The templates of quantized <code
class="highlighter-rouge">conv2d</code> and <code
class="highlighter-rouge">dense</code> on CUDA are
registered under template key <code
class="highlighter-rouge">int8</code>.
During automatic tuning, we can create tuning tasks for these quantized
operators by setting the <code
class="highlighter-rouge">template_key</code> argument.
-Details of how to launch automatic optimization can be found in the <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_relay_cuda.html">AutoTVM
tutorial</a>.</p>
+Details of how to launch automatic optimization can be found in the <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_relay_cuda.html">AutoTVM
tutorial</a>.</p>
<h1 id="general-workflow">General Workflow</h1>
@@ -214,14 +214,14 @@ Details of how to launch automatic optimization can be
found in the <a href=&
<div class="language-python highlighter-rouge"><div
class="highlight"><pre
class="highlight"><code><span
class="n">net</span> <span
class="o">=</span> <span
class="n">relay</span><span
class="o">.</span><span
class="n">quantize</span><span
class="o">.</span><span
class="n">quantize</spa [...]
</code></pre></div></div>
-<p>Then, we use AutoTVM to extract tuning tasks for the operators in the
model and perform automatic optimization. The <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_relay_cuda.html">AutoTVM
tutorial</a> provides an example for this.</p>
+<p>Then, we use AutoTVM to extract tuning tasks for the operators in the
model and perform automatic optimization. The <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_relay_cuda.html">AutoTVM
tutorial</a> provides an example for this.</p>
<p>Finally, we build the model and run inference in the quantized
mode.</p>
<div class="language-python highlighter-rouge"><div
class="highlight"><pre
class="highlight"><code><span
class="k">with</span> <span
class="n">relay</span><span
class="o">.</span><span
class="n">build_config</span><span
class="p">(</span><span
class="n">opt_level</span><span
class="o">=< [...]
<span class="n">graph</span><span
class="p">,</span> <span
class="n">lib</span><span
class="p">,</span> <span
class="n">params</span> <span
class="o">=</span> <span
class="n">relay</span><span
class="o">.</span><span
class="n">build</span><span
class="p">(</span><s [...]
</code></pre></div></div>
<p>The result of <code
class="highlighter-rouge">relay.build</code> is a deployable
library.
-We can either run inference <a
href="https://docs.tvm.ai/tutorials/frontend/from_mxnet.html#execute-the-portable-graph-on-tvm">on
the GPU</a> directly or deploy <a
href="https://docs.tvm.ai/tutorials/frontend/deploy_model_on_rasp.html#deploy-the-model-remotely-by-rpc">on
the remote devices</a> via RPC.</p>
+We can either run inference <a
href="https://tvm.apache.org/docs//tutorials/frontend/from_mxnet.html#execute-the-portable-graph-on-tvm">on
the GPU</a> directly or deploy <a
href="https://tvm.apache.org/docs//tutorials/frontend/deploy_model_on_rasp.html#deploy-the-model-remotely-by-rpc">on
the remote devices</a> via RPC.</p>
<h1 id="benchmark">Benchmark</h1>
<p>To verify the performance of the quantized operators in TVM, we
benchmark the performance of several popular network models including VGG-19,
ResNet-50 and Inception V3.
@@ -272,7 +272,7 @@ We show that automatic optimization in TVM makes it easy
and flexible to support
<p>We would like to take this chance to thank the Allen School for
supporting the SAMPL team that gave birth to the TVM project. We would also
like to thank the Halide project which provided the basis for TVM’s loop-level
IR and initial code generation. We would like to thank our Apache incubator
mentors for introducing the project to Apache and providing useful guidance.
Finally, we would like to thank the TVM community and all of the organizations,
as listed above, that supported [...]
-<p>See also the <a
href="https://news.cs.washington.edu/2019/03/18/allen-schools-tvm-deep-learning-compiler-framework-transitions-to-apache/">Allen
School news about the transition here</a>, <a
href="https://sampl.cs.washington.edu/tvmconf/#about-tvmconf">TVM
conference program slides and recordings</a>, and <a
href="https://docs.tvm.ai/contribute/community.html">our community
guideline here</a>. Follow us on Twitter [...]
+<p>See also the <a
href="https://news.cs.washington.edu/2019/03/18/allen-schools-tvm-deep-learning-compiler-framework-transitions-to-apache/">Allen
School news about the transition here</a>, <a
href="https://sampl.cs.washington.edu/tvmconf/#about-tvmconf">TVM
conference program slides and recordings</a>, and <a
href="https://tvm.apache.org/docs//contribute/community.html">our
community guideline here</a>. Follow us o [...]
</description>
<link>https://tvm.apache.org/2019/03/18/tvm-apache-announcement</link>
<guid>https://tvm.apache.org/2019/03/18/tvm-apache-announcement</guid>
@@ -295,7 +295,7 @@ deploy deep learning models from a variety of frameworks to
a choice of hardware
<p>The TVM import and compilation process generates a graph JSON, a
module and a params. Any application that
integrates the TVM runtime can load these compiled modules and perform
inference. A detailed tutorial of module
-import and compilation using TVM can be found at <a
href="https://docs.tvm.ai/tutorials/">tutorials</a>.</p>
+import and compilation using TVM can be found at <a
href="https://tvm.apache.org/docs//tutorials/">tutorials</a>.</p>
<p>TVM now supports deploying compiled modules through Golang. Golang
applications can make use of this
to deploy the deep learning models through TVM. The scope of this blog is the
introduction of <code
class="highlighter-rouge">gotvm</code> package,
@@ -325,7 +325,7 @@ Developers can make use of TVM to import and compile deep
learning models and ge
<center> Import, Compile, Integrate and Deploy</center>
<p></p>
-<p>TVM <a
href="https://docs.tvm.ai/tutorials/#compile-deep-learning-models">Compile
Deep Learning Models</a> tutorials
+<p>TVM <a
href="https://tvm.apache.org/docs//tutorials/#compile-deep-learning-models">Compile
Deep Learning Models</a> tutorials
are available to compile models from all frameworks supported by the TVM
frontend. This compilation process
generates the artifacts required to integrate and deploy the model on a
target.</p>
@@ -1108,9 +1108,9 @@ for inference deployment. TVM just provides such a
solution.</p>
<h2 id="links">Links</h2>
<p>[1] benchmark: <a
href="https://github.com/dmlc/tvm/tree/master/apps/benchmark">https://github.com/dmlc/tvm/tree/master/apps/benchmark</a><br
/>
-[2] Tutorial on tuning for ARM CPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_arm.html">https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_arm.html</a><br
/>
-[3] Tutorial on tuning for Mobile GPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_mobile_gpu.html">https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_mobile_gpu.html</a><br
/>
-[4] Tutorial on tuning for NVIDIA/AMD GPU: <a
href="https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_cuda.html">https://docs.tvm.ai/tutorials/autotvm/tune_nnvm_cuda.html</a><br
/>
+[2] Tutorial on tuning for ARM CPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_arm.html">https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_arm.html</a><br
/>
+[3] Tutorial on tuning for Mobile GPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_mobile_gpu.html">https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_mobile_gpu.html</a><br
/>
+[4] Tutorial on tuning for NVIDIA/AMD GPU: <a
href="https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_cuda.html">https://tvm.apache.org/docs//tutorials/autotvm/tune_nnvm_cuda.html</a><br
/>
[5] Paper about AutoTVM: <a
href="https://arxiv.org/abs/1805.08166">Learning to Optimize
Tensor Program</a><br />
[6] Paper about Intel CPU (by AWS contributors) : <a
href="https://arxiv.org/abs/1809.02697">Optimizing CNN Model
Inference on CPUs</a></p>
@@ -1205,7 +1205,7 @@ schedule:</p>
<p>For brevity, we do not cover TVM’s large collection of scheduling
primitives
that we can use to optimize matrix multiplication. If you wish to make a custom
GEMM operator run <em>fast</em> on your hardware device, a
detailed tutorial can be
-found <a
href="https://docs.tvm.ai/tutorials/optimize/opt_gemm.html">here</a>.</p>
+found <a
href="https://tvm.apache.org/docs//tutorials/optimize/opt_gemm.html">here</a>.</p>
<p>We then convert the TVM function into one that supports PyTorch
tensors:</p>
<div class="language-python highlighter-rouge"><div
class="highlight"><pre
class="highlight"><code> <span
class="kn">from</span> <span
class="nn">tvm.contrib.dlpack</span> <span
class="kn">import</span> <span
class="n">to_pytorch_func</span>
@@ -1392,7 +1392,7 @@ This kind of high-level visibility is essential to system
designers who want to
<h2 id="get-started">Get Started!</h2>
<ul>
<li>TVM and VTA Github page can be found here: <a
href="https://github.com/dmlc/tvm">https://github.com/dmlc/tvm</a>.</li>
- <li>You can get started with easy to follow <a
href="https://docs.tvm.ai/vta/tutorials/index.html">tutorials on
programming VTA with TVM</a>.</li>
+ <li>You can get started with easy to follow <a
href="https://tvm.apache.org/docs//vta/tutorials/index.html">tutorials
on programming VTA with TVM</a>.</li>
<li>For more technical details on VTA, read our <a
href="https://arxiv.org/abs/1807.04188">VTA technical
report</a> on ArXiv.</li>
</ul>
</description>
