tensorflow - TensorBoard summary on multiple GPUs -


just wondering if can me on plotting scalars in multiple gpus setting.

what have @ moment is:

 device_index in xrange(args.num_gpus):         tf.device('/gpu:%d' % device_index), tf.name_scope('tower_%d' % device_index) scope:             loss, grads = get_loss_grads()             all_losses.append(loss)             allr_grads.append(grads)             summaries = tf.get_collection(tf.graphkeys.summaries, scope) r_loss = tf.reduce_mean(all_losses)  ...later... summaries = tf.merge_summary(summaries)  sess = tf.session(config=tf.configproto(allow_soft_placement=true))  while training:     summary, loss_value, _ = sess.run(feed, fatch)     writer.add_summary(summary, step)

however, "code" can save last tower. basically, i'd have losses each tower , r_loss displayed in tensorboard.

thanks,

edit:

i can plot each tower now:

 all_summaries = [] device_index in xrange(args.num_gpus):         tf.device('/gpu:%d' % device_index), tf.name_scope('tower_%d' % device_index) scope:             loss, grads = get_loss_grads()             all_losses.append(loss)             allr_grads.append(grads)             summaries = tf.get_collection(tf.graphkeys.summaries, scope)             all_summaries.append(summaries) r_loss = tf.reduce_mean(all_losses)  ...later... summaries = tf.merge_summary(all_summaries)  sess = tf.session(config=tf.configproto(allow_soft_placement=true))  while training:     summary, loss_value, _ = sess.run(feed, fatch)     writer.add_summary(summary, step)

the question how can save/plot r_loss?

edit:

i think have now:

 device_index in xrange(args.num_gpus):         tf.device('/gpu:%d' % device_index), tf.name_scope('tower_%d' % device_index) scope:             loss, grads = get_loss_grads()             all_losses.append(loss)             allr_grads.append(grads)             summaries = tf.get_collection(tf.graphkeys.summaries, scope) r_loss = tf.reduce_mean(all_losses) tf.summary.scalar("reduce_mean_losses", r_oss)  ...later... summaries = tf.merge_summary()  sess = tf.session(config=tf.configproto(allow_soft_placement=true))  while training:     summary, loss_value, _ = sess.run(feed, fatch)     writer.add_summary(summary, step)

i think tf.merge_summary "magically" collecting summaries.


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