<story><title>Train TensorFlow models faster and at lower cost on Cloud TPU Pods</title><url>https://cloud.google.com/blog/products/ai-machine-learning/now-you-can-train-ml-models-faster-and-lower-cost-cloud-tpu-pods</url></story><parent_chain></parent_chain><comment><author>etaioinshrdlu</author><text>Does this require distributed training?&lt;p&gt;My understanding and experience is that it is not always trivial to get linear training speedups with additional machines.&lt;p&gt;It can be sometimes hard to get any speedup at all.&lt;p&gt;The difference with gradient descent can be described simply as:&lt;p&gt;* Single-machine training: take more steps&lt;p&gt;* Distributed training: take fewer but more confident and accurate steps. This could allow you to take bigger steps (learning rate) as well, but there is a limit to this as well.&lt;p&gt;They are not equivalent processes and it is an area of active research how to get an equivalent result with distributed training.</text></comment>
<story><title>Train TensorFlow models faster and at lower cost on Cloud TPU Pods</title><url>https://cloud.google.com/blog/products/ai-machine-learning/now-you-can-train-ml-models-faster-and-lower-cost-cloud-tpu-pods</url></story><parent_chain></parent_chain><comment><author>ethikal</author><text>Full ML Perf results can be found here: &lt;a href=&quot;https:&amp;#x2F;&amp;#x2F;mlperf.org&amp;#x2F;results&amp;#x2F;&quot; rel=&quot;nofollow&quot;&gt;https:&amp;#x2F;&amp;#x2F;mlperf.org&amp;#x2F;results&amp;#x2F;&lt;/a&gt;</text></comment>