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new 8fec862 Publish triggered by CI
8fec862 is described below
commit 8fec86223d4d728058f86acfd4b3d483e8e159ef
Author: mxnet-ci <mxnet-ci>
AuthorDate: Fri Aug 7 18:43:58 2020 +0000
Publish triggered by CI
---
api/faq/cloud.html | 32 ++++------------------
.../tutorials/deploy/run-on-aws/use_sagemaker.rst | 19 ++++---------
api/python/docs/searchindex.js | 2 +-
.../tutorials/deploy/run-on-aws/use_sagemaker.html | 12 ++------
date.txt | 1 -
feed.xml | 2 +-
6 files changed, 14 insertions(+), 54 deletions(-)
diff --git a/api/faq/cloud.html b/api/faq/cloud.html
index cfd9134..5ab2623 100644
--- a/api/faq/cloud.html
+++ b/api/faq/cloud.html
@@ -482,39 +482,17 @@ and maintain the resources for precisely the amount of
time needed.</p>
how to set up an AWS cluster with <em>MXNet</em>. We show how to:</p>
<ul>
- <li><a href="#use-amazon-s3-to-host-data">Use Amazon S3 to host data</a></li>
- <li><a href="#set-up-an-ec2-gpu-instance">Set up an EC2 GPU instance with
all dependencies installed</a></li>
+ <li><a href="#use-pre-installed-ec2-gpu-instance">Use Pre-installed EC2 GPU
Instance</a></li>
<li><a href="#build-and-run-mxnet-on-a-gpu-instance">Build and run MXNet on
a single computer</a></li>
<li><a href="#set-up-an-ec2-gpu-cluster-for-distributed-training">Set up an
EC2 GPU cluster for distributed training</a></li>
</ul>
-<h3 id="use-amazon-s3-to-host-data">Use Amazon S3 to Host Data</h3>
-
-<p>Amazon S3 provides distributed data storage which proves especially
convenient for hosting large datasets.
-To use S3, you need <a
href="https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSGettingStartedGuide/AWSCredentials.html";>AWS
credentials</a>,
-including an <code class="highlighter-rouge">ACCESS_KEY_ID</code> and a <code
class="highlighter-rouge">SECRET_ACCESS_KEY</code>.</p>
-
-<p>To use <em>MXNet</em> with S3, set the environment variables <code
class="highlighter-rouge">AWS_ACCESS_KEY_ID</code> and
-<code class="highlighter-rouge">AWS_SECRET_ACCESS_KEY</code> by adding the
following two lines in
-<code class="highlighter-rouge">~/.bashrc</code> (replacing the strings with
the correct ones):</p>
-
-<div class="language-bash highlighter-rouge"><div class="highlight"><pre
class="highlight"><code><span class="nb">export </span><span
class="nv">AWS_ACCESS_KEY_ID</span><span class="o">=</span>AKIAIOSFODNN7EXAMPLE
-<span class="nb">export </span><span
class="nv">AWS_SECRET_ACCESS_KEY</span><span
class="o">=</span>wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
-</code></pre></div></div>
-
-<p>There are several ways to upload data to S3. One simple way is to use
-<a href="https://s3tools.org/s3cmd";>s3cmd</a>. For example:</p>
-
-<div class="language-bash highlighter-rouge"><div class="highlight"><pre
class="highlight"><code>wget http://data.mxnet.io/mxnet/data/mnist.zip
-unzip mnist.zip <span class="o">&&</span> s3cmd put t<span
class="k">*</span><span class="nt">-ubyte</span> s3://dmlc/mnist/
-</code></pre></div></div>
-
<h3 id="use-pre-installed-ec2-gpu-instance">Use Pre-installed EC2 GPU
Instance</h3>
<p>The <a
href="https://aws.amazon.com/marketplace/search/results?x=0&y=0&searchTerms=Deep+Learning+AMI";>Deep
Learning AMIs</a>
are a series of images supported and maintained by Amazon Web Services for use
on Amazon Elastic Compute Cloud (Amazon EC2) and contain the latest MXNet
release.</p>
-<p>Now you can launch <em>MXNet</em> directly on an EC2 GPU instance.<br />
+<p>Now you can launch <em>MXNet</em> directly on an EC2 GPU instance.
You can also use <a href="https://jupyter.org";>Jupyter</a> notebook on EC2
machine.
Here is a <a href="https://github.com/dmlc/mxnet-notebooks";>good tutorial</a>
on how to connect to a Jupyter notebook running on an EC2 instance.</p>
@@ -524,8 +502,8 @@ on how to connect to a Jupyter notebook running on an EC2
instance.</p>
<p><a
href="https://aws.amazon.com/marketplace/search/results?x=0&y=0&searchTerms=Deep+Learning+Base+AMI";>Deep
Learning Base AMIs</a>
provide a foundational image with NVIDIA CUDA, cuDNN, GPU drivers, Intel
MKL-DNN, Docker and Nvidia-Docker, etc. for deploying your own custom deep
-learning environment. You may follow the [MXNet Build From Source
-instructions](<https://mxnet.apache.org/get_started/build_from_source
easily on
+learning environment. You may follow the <a
href="https://mxnet.apache.org/get_started/build_from_source";>MXNet Build From
Source
+instructions</a> easily on
the Deep Learning Base AMIs.</p>
<h3 id="set-up-an-ec2-gpu-cluster-for-distributed-training">Set Up an EC2 GPU
Cluster for Distributed Training</h3>
@@ -596,7 +574,7 @@ Put all of the record files into a folder, and point the
data path to the folder
<h4 id="use-yarn-and-sge">Use YARN and SGE</h4>
<p>Although using SSH can be simple when you don’t have a cluster scheduling
framework,
-<em>MXNet</em> is designed to be portable to various platforms.<br />
+<em>MXNet</em> is designed to be portable to various platforms.
We provide scripts available in <a
href="https://github.com/dmlc/dmlc-core/tree/master/tracker";>tracker</a>
to allow running on other cluster frameworks, including Hadoop (YARN) and SGE.
We welcome contributions from the community of examples of running
<em>MXNet</em> on your favorite distributed platform.</p>
diff --git
a/api/python/docs/_sources/tutorials/deploy/run-on-aws/use_sagemaker.rst
b/api/python/docs/_sources/tutorials/deploy/run-on-aws/use_sagemaker.rst
index d627bef..dc8052b 100644
--- a/api/python/docs/_sources/tutorials/deploy/run-on-aws/use_sagemaker.rst
+++ b/api/python/docs/_sources/tutorials/deploy/run-on-aws/use_sagemaker.rst
@@ -18,7 +18,7 @@
Run on Amazon SageMaker
-----------------------
-This chapter will give a high level overview about Amazon SageMaker,
+This chapter will give a high level overview about running MXNet on Amazon
SageMaker,
in-depth tutorials can be found on the `Sagemaker
website <https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html>`__.
@@ -29,16 +29,7 @@ charged by time. Within this notebook you can `fetch,
explore and
prepare training
data
<https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-notebooks-instances.html>`__.
-::
-
- import mxnet as mx
- import sagemaker
- mx.test_utils.get_cifar10() # Downloads Cifar-10 dataset to ./data
- sagemaker_session = sagemaker.Session()
- inputs = sagemaker_session.upload_data(path='data/cifar',
- key_prefix='data/cifar10')
-
-Once the data is ready, you can easily launch training via the SageMaker
+With your own data on the notebook instance, you can easily launch training
via the SageMaker
SDK. So there is no need to manually configure and log into EC2
instances. You can either bring your own model or use SageMaker's
`built-in
@@ -51,11 +42,11 @@ instance:
::
from sagemaker.mxnet import MXNet as MXNetEstimator
- estimator = MXNetEstimator(entry_point='train.py',
+ estimator = MXNetEstimator(entry_point='train.py',
role=sagemaker.get_execution_role(),
- train_instance_count=1,
+ train_instance_count=1,
train_instance_type='local',
- hyperparameters={'batch_size': 1024,
+ hyperparameters={'batch_size': 1024,
'epochs': 30})
estimator.fit(inputs)
diff --git a/api/python/docs/searchindex.js b/api/python/docs/searchindex.js
index 3901527..fef226f 100644
--- a/api/python/docs/searchindex.js
+++ b/api/python/docs/searchindex.js
@@ -1 +1 @@
-Search.setIndex({docnames:["api/autograd/index","api/contrib/autograd/index","api/contrib/index","api/contrib/io/index","api/contrib/ndarray/index","api/contrib/onnx/index","api/contrib/quantization/index","api/contrib/symbol/index","api/contrib/tensorboard/index","api/contrib/tensorrt/index","api/contrib/text/index","api/gluon/block","api/gluon/constant","api/gluon/contrib/index","api/gluon/data/index","api/gluon/data/vision/datasets/index","api/gluon/data/vision/index","api/gluon/data/
[...]
\ No newline at end of file
+Search.setIndex({docnames:["api/autograd/index","api/contrib/autograd/index","api/contrib/index","api/contrib/io/index","api/contrib/ndarray/index","api/contrib/onnx/index","api/contrib/quantization/index","api/contrib/symbol/index","api/contrib/tensorboard/index","api/contrib/tensorrt/index","api/contrib/text/index","api/gluon/block","api/gluon/constant","api/gluon/contrib/index","api/gluon/data/index","api/gluon/data/vision/datasets/index","api/gluon/data/vision/index","api/gluon/data/
[...]
\ No newline at end of file
diff --git a/api/python/docs/tutorials/deploy/run-on-aws/use_sagemaker.html
b/api/python/docs/tutorials/deploy/run-on-aws/use_sagemaker.html
index 51bd2d1..aabbd30 100644
--- a/api/python/docs/tutorials/deploy/run-on-aws/use_sagemaker.html
+++ b/api/python/docs/tutorials/deploy/run-on-aws/use_sagemaker.html
@@ -775,7 +775,7 @@ Show Source
<div class="section" id="run-on-amazon-sagemaker">
<h1>Run on Amazon SageMaker<a class="headerlink"
href="#run-on-amazon-sagemaker" title="Permalink to this headline">¶</a></h1>
-<p>This chapter will give a high level overview about Amazon SageMaker,
+<p>This chapter will give a high level overview about running MXNet on Amazon
SageMaker,
in-depth tutorials can be found on the <a class="reference external"
href="https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html";>Sagemaker
website</a>.</p>
<p>SageMaker offers Jupyter notebooks and supports MXNet out-of-the box.
@@ -784,15 +784,7 @@ free tier. However, more powerful CPU instances or GPU
instances are
charged by time. Within this notebook you can <a class="reference external"
href="https://docs.aws.amazon.com/sagemaker/latest/dg/how-it-works-notebooks-instances.html";>fetch,
explore and
prepare training
data</a>.</p>
-<div class="highlight-default notranslate"><div
class="highlight"><pre><span></span><span class="kn">import</span> <span
class="nn">mxnet</span> <span class="k">as</span> <span class="nn">mx</span>
-<span class="kn">import</span> <span class="nn">sagemaker</span>
-<span class="n">mx</span><span class="o">.</span><span
class="n">test_utils</span><span class="o">.</span><span
class="n">get_cifar10</span><span class="p">()</span> <span class="c1">#
Downloads Cifar-10 dataset to ./data</span>
-<span class="n">sagemaker_session</span> <span class="o">=</span> <span
class="n">sagemaker</span><span class="o">.</span><span
class="n">Session</span><span class="p">()</span>
-<span class="n">inputs</span> <span class="o">=</span> <span
class="n">sagemaker_session</span><span class="o">.</span><span
class="n">upload_data</span><span class="p">(</span><span
class="n">path</span><span class="o">=</span><span
class="s1">'data/cifar'</span><span class="p">,</span>
- <span class="n">key_prefix</span><span
class="o">=</span><span class="s1">'data/cifar10'</span><span
class="p">)</span>
-</pre></div>
-</div>
-<p>Once the data is ready, you can easily launch training via the SageMaker
+<p>With your own data on the notebook instance, you can easily launch training
via the SageMaker
SDK. So there is no need to manually configure and log into EC2
instances. You can either bring your own model or use SageMaker’s
<a class="reference external"
href="https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html";>built-in
diff --git a/date.txt b/date.txt
deleted file mode 100644
index 50c2cbd..0000000
--- a/date.txt
+++ /dev/null
@@ -1 +0,0 @@
-Fri Aug 7 12:43:21 UTC 2020
diff --git a/feed.xml b/feed.xml
index 921c852..e036f9d 100644
--- a/feed.xml
+++ b/feed.xml
@@ -1 +1 @@
-<?xml version="1.0" encoding="utf-8"?><feed
xmlns="http://www.w3.org/2005/Atom"; ><generator uri="https://jekyllrb.com/";
version="4.0.0">Jekyll</generator><link
href="https://mxnet.apache.org/feed.xml"; rel="self" type="application/atom+xml"
/><link href="https://mxnet.apache.org/"; rel="alternate" type="text/html"
/><updated>2020-08-07T12:33:36+00:00</updated><id>https://mxnet.apache.org/feed.xml</id><title
type="html">Apache MXNet</title><subtitle>A flexible and efficient library for
deep [...]
\ No newline at end of file
+<?xml version="1.0" encoding="utf-8"?><feed
xmlns="http://www.w3.org/2005/Atom"; ><generator uri="https://jekyllrb.com/";
version="4.0.0">Jekyll</generator><link
href="https://mxnet.apache.org/feed.xml"; rel="self" type="application/atom+xml"
/><link href="https://mxnet.apache.org/"; rel="alternate" type="text/html"
/><updated>2020-08-07T18:34:13+00:00</updated><id>https://mxnet.apache.org/feed.xml</id><title
type="html">Apache MXNet</title><subtitle>A flexible and efficient library for
deep [...]
\ No newline at end of file
