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| <h1 id="use-data-from-s3-for-training">Use data from S3 for training</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>s3://bucket-name/training-data</code>) can be provided as a parameter for any data iterator that takes a file path as input. For example,</p> |
| <div class="highlight"><pre><code class="language-" data-lang="">data_iter = mx.io.ImageRecordIter( |
| path_imgrec="s3://bucket-name/training-data/caltech_train.rec", |
| data_shape=(3, 227, 227), |
| batch_size=4, |
| resize=256) |
| </code></pre></div> |
| <p>Following are detailed instructions on how to use data from S3 for training.</p> |
| |
| <h2 id="step-1-build-mxnet-with-s3-integration-enabled">Step 1: Build MXNet with S3 integration enabled</h2> |
| |
| <p>Follow instructions <a href="/versions/1.8.0/get_started">here</a> to install MXNet from source with the following additional steps to enable S3 integration.</p> |
| |
| <ol> |
| <li>Install <code>libcurl4-openssl-dev</code> and <code>libssl-dev</code> before building MXNet. These packages are required to read/write from AWS S3.</li> |
| <li>Append <code>USE_S3=1</code> to <code>config.mk</code> before building MXNet. |
| <code> |
| echo "USE_S3=1" >> config.mk |
| </code></li> |
| </ol> |
| |
| <h2 id="step-2-configure-s3-authentication-tokens">Step 2: Configure S3 authentication tokens</h2> |
| |
| <p>MXNet requires the S3 environment variables <code>AWS_ACCESS_KEY_ID</code> and <code>AWS_SECRET_ACCESS_KEY</code> to be set. <a 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"><pre><code class="language-" data-lang="">export AWS_ACCESS_KEY_ID=<your-access-key-id> |
| AWS_SECRET_ACCESS_KEY=<your-secret-access-key> |
| </code></pre></div> |
| <h2 id="step-3-upload-data-to-s3">Step 3: Upload data to S3</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>sync</code> command will recursively copy contents from a local directory to a directory in S3.</p> |
| <div class="highlight"><pre><code class="language-" data-lang="">aws s3 sync ./training-data s3://bucket-name/training-data |
| </code></pre></div> |
| <h2 id="step-4-train-with-data-from-s3">Step 4: Train with data from S3</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 href="https://github.com/dmlc/mxnet/blob/master/tests/python/train/test_conv.py"><code>mxnet/tests/python/train/test_conv.py</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"><pre><code class="language-" data-lang=""><span class="err">~/mxnet$</span> sed -i -- 's/data\//s3:\/\/bucket-name\/training-data\//g' ./tests/python/train/test_conv.py |
| |
| ~/mxnet$ git diff ./tests/python/train/test_conv.py |
| <span class="gh">diff --git a/tests/python/train/test_conv.py b/tests/python/train/test_conv.py |
| index 039790e..66a60ce 100644 |
| </span><span class="gd">--- a/tests/python/train/test_conv.py |
| </span><span class="gi">+++ b/tests/python/train/test_conv.py |
| </span><span class="p">@@ -39,14 +39,14 @@</span> def get_iters(): |
| |
| batch_size = 100 |
| train_dataiter = mx.io.MNISTIter( |
| <span class="gd">- image="data/train-images-idx3-ubyte", |
| - label="data/train-labels-idx1-ubyte", |
| </span><span class="gi">+ image="s3://bucket-name/training-data/train-images-idx3-ubyte", |
| + label="s3://bucket-name/training-data/train-labels-idx1-ubyte", |
| </span> 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( |
| <span class="gd">- image="data/t10k-images-idx3-ubyte", |
| - label="data/t10k-labels-idx1-ubyte", |
| </span><span class="gi">+ image="s3://bucket-name/training-data/t10k-images-idx3-ubyte", |
| + label="s3://bucket-name/training-data/t10k-labels-idx1-ubyte", |
| </span> data_shape=(1, 28, 28), |
| label_name='sm_label', |
| batch_size=batch_size, shuffle=True, flat=False, silent=False) |
| </code></pre></div> |
| <p>After the above change <code>test_conv.py</code> will fetch data from S3 instead of the local disk.</p> |
| <div class="highlight"><pre><code class="language-" data-lang="">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 |
| </code></pre></div> |
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