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    <title>article What is the best way to train a DeepLearning model to ensure we do not encounter out of memory (OOM) errors? in Support FAQs</title>
    <link>https://community.databricks.com/t5/support-faqs/what-is-the-best-way-to-train-a-deeplearning-model-to-ensure-we/ta-p/56899</link>
    <description>&lt;DIV class="lia-message-template-content-zone"&gt;
&lt;P&gt;&lt;SPAN&gt;You should use distributed training.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;By distributing the training workload among GPUs or worker nodes, you can optimize resource utilization and reduce the likelihood of &lt;FONT face="courier new,courier"&gt;ConnectionException&lt;/FONT&gt; errors and out of memory (OOM) issues.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;A good option for distributed training is Horovod, a distributed deep learning framework.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;The following resources can provide guidance on how to set up and use Horovod with &lt;/SPAN&gt;&lt;SPAN&gt;Databricks:&lt;/SPAN&gt;&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;&lt;SPAN&gt;&lt;STRONG&gt;HorovodRunner: distributed deep learning with Horovod&lt;/STRONG&gt; (&lt;A title="HorovodRunner: distributed deep learning with Horovod (AWS)" href="https://docs.databricks.com/en/machine-learning/train-model/distributed-training/horovod-runner.html" target="_blank" rel="noopener"&gt;AWS&lt;/A&gt; | &lt;A title="HorovodRunner: distributed deep learning with Horovod (Azure)" href="https://learn.microsoft.com/en-us/azure/databricks/machine-learning/train-model/distributed-training/horovod-runner" target="_blank" rel="noopener"&gt;Azure&lt;/A&gt; | &lt;A title="HorovodRunner: distributed deep learning with Horovod (GCP)" href="https://docs.gcp.databricks.com/en/machine-learning/train-model/distributed-training/horovod-runner.html" target="_blank" rel="noopener"&gt;GCP&lt;/A&gt;)&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI&gt;&lt;SPAN&gt;&lt;STRONG&gt;Distributed training&lt;/STRONG&gt; (&lt;A title="Distributed training (AWS)" href="https://docs.databricks.com/en/machine-learning/train-model/distributed-training/index.html" target="_blank" rel="noopener"&gt;AWS&lt;/A&gt; | &lt;A title="Distributed training (Azure)" href="https://learn.microsoft.com/azure/databricks/machine-learning/train-model/distributed-training/" target="_blank" rel="noopener"&gt;Azure&lt;/A&gt; | &lt;A title="Distributed training (GCP)" href="https://docs.gcp.databricks.com/en/machine-learning/train-model/distributed-training/index.html" target="_blank" rel="noopener"&gt;GCP&lt;/A&gt;)&lt;/SPAN&gt;&lt;/LI&gt;
&lt;/UL&gt;
&lt;/DIV&gt;</description>
    <pubDate>Thu, 11 Jan 2024 01:00:01 GMT</pubDate>
    <dc:creator>Adam_Pavlacka</dc:creator>
    <dc:date>2024-01-11T01:00:01Z</dc:date>
    <item>
      <title>What is the best way to train a DeepLearning model to ensure we do not encounter out of memory (OOM) errors?</title>
      <link>https://community.databricks.com/t5/support-faqs/what-is-the-best-way-to-train-a-deeplearning-model-to-ensure-we/ta-p/56899</link>
      <description>&lt;DIV class="lia-message-template-content-zone"&gt;
&lt;P&gt;&lt;SPAN&gt;You should use distributed training.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;By distributing the training workload among GPUs or worker nodes, you can optimize resource utilization and reduce the likelihood of &lt;FONT face="courier new,courier"&gt;ConnectionException&lt;/FONT&gt; errors and out of memory (OOM) issues.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;A good option for distributed training is Horovod, a distributed deep learning framework.&lt;/SPAN&gt;&lt;/P&gt;
&lt;P&gt;&lt;SPAN&gt;The following resources can provide guidance on how to set up and use Horovod with &lt;/SPAN&gt;&lt;SPAN&gt;Databricks:&lt;/SPAN&gt;&lt;/P&gt;
&lt;UL&gt;
&lt;LI&gt;&lt;SPAN&gt;&lt;STRONG&gt;HorovodRunner: distributed deep learning with Horovod&lt;/STRONG&gt; (&lt;A title="HorovodRunner: distributed deep learning with Horovod (AWS)" href="https://docs.databricks.com/en/machine-learning/train-model/distributed-training/horovod-runner.html" target="_blank" rel="noopener"&gt;AWS&lt;/A&gt; | &lt;A title="HorovodRunner: distributed deep learning with Horovod (Azure)" href="https://learn.microsoft.com/en-us/azure/databricks/machine-learning/train-model/distributed-training/horovod-runner" target="_blank" rel="noopener"&gt;Azure&lt;/A&gt; | &lt;A title="HorovodRunner: distributed deep learning with Horovod (GCP)" href="https://docs.gcp.databricks.com/en/machine-learning/train-model/distributed-training/horovod-runner.html" target="_blank" rel="noopener"&gt;GCP&lt;/A&gt;)&lt;/SPAN&gt;&lt;/LI&gt;
&lt;LI&gt;&lt;SPAN&gt;&lt;STRONG&gt;Distributed training&lt;/STRONG&gt; (&lt;A title="Distributed training (AWS)" href="https://docs.databricks.com/en/machine-learning/train-model/distributed-training/index.html" target="_blank" rel="noopener"&gt;AWS&lt;/A&gt; | &lt;A title="Distributed training (Azure)" href="https://learn.microsoft.com/azure/databricks/machine-learning/train-model/distributed-training/" target="_blank" rel="noopener"&gt;Azure&lt;/A&gt; | &lt;A title="Distributed training (GCP)" href="https://docs.gcp.databricks.com/en/machine-learning/train-model/distributed-training/index.html" target="_blank" rel="noopener"&gt;GCP&lt;/A&gt;)&lt;/SPAN&gt;&lt;/LI&gt;
&lt;/UL&gt;
&lt;/DIV&gt;</description>
      <pubDate>Thu, 11 Jan 2024 01:00:01 GMT</pubDate>
      <guid>https://community.databricks.com/t5/support-faqs/what-is-the-best-way-to-train-a-deeplearning-model-to-ensure-we/ta-p/56899</guid>
      <dc:creator>Adam_Pavlacka</dc:creator>
      <dc:date>2024-01-11T01:00:01Z</dc:date>
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