title: How much RAM do I need to train ssd_mobilenet_v2 model on GPU?
body: <p>I want to train object detector using Tensorflow API's model SSD MobileNet v2 on a relatively big dataset (~3000 images for training and ~500 for testing). I've successfully managed all the necessary preprocessing steps, created <em>train.record</em> and <em>test.record</em> files and tried to run the training of the model with <em>train.py</em>, but the training process was killed by the kernel.</p>
  
  <pre><code>&gt;INFO:tensorflow:Restoring parameters from /home/yurii/.../second_attempt/model.ckpt
  &gt;INFO:tensorflow:Restoring parameters from /home/yurii/.../second_attempt/model.ckpt
  &gt;INFO:tensorflow:Running local_init_op.
  &gt;INFO:tensorflow:Running local_init_op.
  &gt;INFO:tensorflow:Done running local_init_op.
  &gt;INFO:tensorflow:Done running local_init_op.
  &gt;INFO:tensorflow:Starting Session.
  &gt;INFO:tensorflow:Starting Session.
  &gt;INFO:tensorflow:Saving checkpoint to path /home/yurii/.../second_attempt/model.ckpt
  &gt;INFO:tensorflow:Saving checkpoint to path /home/yurii/.../second_attempt/model.ckpt
  &gt;INFO:tensorflow:Starting Queues.
  &gt;INFO:tensorflow:Starting Queues.
  &gt;Killed
  </code></pre>
  
  <p>I've found some info, stating that the issue could be because of the lack of RAM on my machine. Previously I trained the model on the smaller dataset (280 images for training and 40 for testing) and everything worked properly.
  So,approximately, how much RAM do I need to train MobileNet on my dataset?
  I am using Asus X555L with 4Gb RAM available, GPU is Nvidia GeForce 920M (2Gb, 3.5 compute capacity), CUDA version is 9.0.176, cudnn version is 7.5, tensorflow version is 1.7.0, Nvidia driver version is 384.130</p>