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<div class="section" id="use-data-from-s3-for-training">
<span id="use-data-from-s3-for-training"></span><h1>Use data from S3 for training<a class="headerlink" href="#use-data-from-s3-for-training" title="Permalink to this headline"></a></h1>
<p>AWS S3 is a cloud-based object storage service that allows storage and retrieval of large amounts of data at a very low cost. This makes it an attractive option to store large training datasets. MXNet is deeply integrated with S3 for this purpose.</p>
<p>An S3 protocol URL (like <code class="docutils literal"><span class="pre">s3://bucket-name/training-data</span></code>) can be provided as a parameter for any data iterator that takes a file path as input. For example,</p>
<div class="highlight-python"><div class="highlight"><pre><span></span><span class="n">data_iter</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">io</span><span class="o">.</span><span class="n">ImageRecordIter</span><span class="p">(</span>
<span class="n">path_imgrec</span><span class="o">=</span><span class="s2">"s3://bucket-name/training-data/caltech_train.rec"</span><span class="p">,</span>
<span class="n">data_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">227</span><span class="p">,</span> <span class="mi">227</span><span class="p">),</span>
<span class="n">batch_size</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span>
<span class="n">resize</span><span class="o">=</span><span class="mi">256</span><span class="p">)</span>
</pre></div>
</div>
<p>Following are detailed instructions on how to use data from S3 for training.</p>
<div class="section" id="step-1-build-mxnet-with-s3-integration-enabled">
<span id="step-1-build-mxnet-with-s3-integration-enabled"></span><h2>Step 1: Build MXNet with S3 integration enabled<a class="headerlink" href="#step-1-build-mxnet-with-s3-integration-enabled" title="Permalink to this headline"></a></h2>
<p>Follow instructions <a class="reference external" href="http://mxnet.io/get_started/install.html">here</a> to install MXNet from source with the following additional steps to enable S3 integration.</p>
<ol>
<li><p class="first">Install <code class="docutils literal"><span class="pre">libcurl4-openssl-dev</span></code> and <code class="docutils literal"><span class="pre">libssl-dev</span></code> before building MXNet. These packages are required to read/write from AWS S3.</p>
</li>
<li><p class="first">Append <code class="docutils literal"><span class="pre">USE_S3=1</span></code> to <code class="docutils literal"><span class="pre">config.mk</span></code> before building MXNet.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>echo "USE_S3=1" >> config.mk
</pre></div>
</div>
</li>
</ol>
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<div class="section" id="step-2-configure-s3-authentication-tokens">
<span id="step-2-configure-s3-authentication-tokens"></span><h2>Step 2: Configure S3 authentication tokens<a class="headerlink" href="#step-2-configure-s3-authentication-tokens" title="Permalink to this headline"></a></h2>
<p>MXNet requires the S3 environment variables <code class="docutils literal"><span class="pre">AWS_ACCESS_KEY_ID</span></code> and <code class="docutils literal"><span class="pre">AWS_SECRET_ACCESS_KEY</span></code> to be set. <a class="reference external" href="https://aws.amazon.com/blogs/security/wheres-my-secret-access-key/">Here</a> are instructions to get the access keys from AWS console.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>export AWS_ACCESS_KEY_ID=<your-access-key-id>
AWS_SECRET_ACCESS_KEY=<your-secret-access-key>
</pre></div>
</div>
</div>
<div class="section" id="step-3-upload-data-to-s3">
<span id="step-3-upload-data-to-s3"></span><h2>Step 3: Upload data to S3<a class="headerlink" href="#step-3-upload-data-to-s3" title="Permalink to this headline"></a></h2>
<p>There are several ways to upload data to S3. One easy way is to use the AWS command line utility. For example, the following <code class="docutils literal"><span class="pre">sync</span></code> command will recursively copy contents from a local directory to a directory in S3.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>aws s3 sync ./training-data s3://bucket-name/training-data
</pre></div>
</div>
</div>
<div class="section" id="step-4-train-with-data-from-s3">
<span id="step-4-train-with-data-from-s3"></span><h2>Step 4: Train with data from S3<a class="headerlink" href="#step-4-train-with-data-from-s3" title="Permalink to this headline"></a></h2>
<p>Once the data is in S3, it is very straightforward to use it from MXNet. Any data iterator that can read/write data from a local drive can also read/write data from S3.</p>
<p>Let’s modify an existing example code in MXNet repository to read data from S3 instead of local disk. <a class="reference external" href="https://github.com/dmlc/mxnet/blob/master/tests/python/train/test_conv.py"><code class="docutils literal"><span class="pre">mxnet/tests/python/train/test_conv.py</span></code></a> trains a convolutional network using MNIST data from local disk. We’ll do the following change to read the data from S3 instead.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>~/mxnet$ sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py
~/mxnet$ git diff ./tests/python/train/test_conv.py
diff --git a/tests/python/train/test_conv.py b/tests/python/train/test_conv.py
index 039790e..66a60ce 100644
--- a/tests/python/train/test_conv.py
+++ b/tests/python/train/test_conv.py
@@ -39,14 +39,14 @@ def get_iters():
batch_size = 100
train_dataiter = mx.io.MNISTIter(
- image="data/train-images-idx3-ubyte",
- label="data/train-labels-idx1-ubyte",
+ image="s3://bucket-name/training-data/train-images-idx3-ubyte",
+ label="s3://bucket-name/training-data/train-labels-idx1-ubyte",
data_shape=(1, 28, 28),
label_name='sm_label',
batch_size=batch_size, shuffle=True, flat=False, silent=False, seed=10)
val_dataiter = mx.io.MNISTIter(
- image="data/t10k-images-idx3-ubyte",
- label="data/t10k-labels-idx1-ubyte",
+ image="s3://bucket-name/training-data/t10k-images-idx3-ubyte",
+ label="s3://bucket-name/training-data/t10k-labels-idx1-ubyte",
data_shape=(1, 28, 28),
label_name='sm_label',
batch_size=batch_size, shuffle=True, flat=False, silent=False)
</pre></div>
</div>
<p>After the above change <code class="docutils literal"><span class="pre">test_conv.py</span></code> will fetch data from S3 instead of the local disk.</p>
<div class="highlight-python"><div class="highlight"><pre><span></span>python ./tests/python/train/test_conv.py
[21:59:19] src/io/s3_filesys.cc:878: No AWS Region set, using default region us-east-1
[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 60000 images, shuffle=1, shape=(100,1,28,28)
[21:59:21] src/io/iter_mnist.cc:94: MNISTIter: load 10000 images, shuffle=1, shape=(100,1,28,28)
INFO:root:Start training with [cpu(0)]
Start training with [cpu(0)]
INFO:root:Epoch[0] Resetting Data Iterator
Epoch[0] Resetting Data Iterator
INFO:root:Epoch[0] Time cost=11.277
Epoch[0] Time cost=11.277
INFO:root:Epoch[0] Validation-accuracy=0.955100
Epoch[0] Validation-accuracy=0.955100
INFO:root:Finish fit...
Finish fit...
INFO:root:Finish predict...
Finish predict...
INFO:root:final accuracy = 0.955100
final accuracy = 0.955100
</pre></div>
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<h3><a href="../index.html">Table Of Contents</a></h3>
<ul>
<li><a class="reference internal" href="#">Use data from S3 for training</a><ul>
<li><a class="reference internal" href="#step-1-build-mxnet-with-s3-integration-enabled">Step 1: Build MXNet with S3 integration enabled</a></li>
<li><a class="reference internal" href="#step-2-configure-s3-authentication-tokens">Step 2: Configure S3 authentication tokens</a></li>
<li><a class="reference internal" href="#step-3-upload-data-to-s3">Step 3: Upload data to S3</a></li>
<li><a class="reference internal" href="#step-4-train-with-data-from-s3">Step 4: Train with data from S3</a></li>
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