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fchollet's profile
François Chollet
François Chollet
François Chollet
Verified account
@fchollet

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François CholletVerified account

@fchollet

Deep learning @google. Creator of Keras. Author of 'Deep Learning with Python'. Opinions are my own.

United States
fchollet.com
Joined August 2009

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    1. François Chollet‏Verified account @fchollet 30 Oct 2017
      Replying to @fchollet @xpasky @bzamecnik

      When training from an ImageDataGenerator, performance was worse (65% or so efficiency). Potentially because of slow disk I/O on EC2

      1 reply 0 retweets 1 like
    2. François Chollet‏Verified account @fchollet 30 Oct 2017
      Replying to @fchollet @xpasky @bzamecnik

      Adrian got 97% efficiency on a GoogleNet-like with 4 GPUs (74 min to 19 min): https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning/ … -with 4 GPUs, I had low 90s efficiency

      1 reply 3 retweets 5 likes
    3. François Chollet‏Verified account @fchollet 30 Oct 2017
      Replying to @fchollet @xpasky @bzamecnik

      Overall I think our parallelism model is sound (per-device forward and backwards pass on sub-batches + CPU concat, keep params on CPU)

      1 reply 0 retweets 1 like
    4. François Chollet‏Verified account @fchollet 30 Oct 2017
      Replying to @fchollet @xpasky @bzamecnik

      But it would need auditing, in particular to check whether we correctly place the grads computation on each device (now we leave it to TF)

      1 reply 0 retweets 1 like
    5. Petr Baudis‏ @xpasky 30 Oct 2017
      Replying to @fchollet @bzamecnik

      His NVIDIA DevBox is PCIe 16x, Azure is PCIe 8x, custom builds often even worse. Scaling also impacted by small batch sizes (64 -> 32).

      1 reply 0 retweets 0 likes
    6. Petr Baudis‏ @xpasky 30 Oct 2017
      Replying to @xpasky @fchollet @bzamecnik

      Overally we focus on small batch sizes, as I see huge sample sizes in many real world applications, including internally at @RossumAi.

      2 replies 0 retweets 0 likes
    7. François Chollet‏Verified account @fchollet 30 Oct 2017
      Replying to @xpasky @bzamecnik @RossumAi

      We have significant overhead (shuffling the params from CPU to GPU at every step; split+concat). Need time(process(sub_batch)) >> overhead

      1 reply 0 retweets 1 like
    8. François Chollet‏Verified account @fchollet 30 Oct 2017
      Replying to @fchollet @xpasky and

      Not doable to reduce the overhead, so to have a speedup you need to keep your per-sub-batch processing time high (large models or batches)

      1 reply 0 retweets 1 like
    9. Petr Baudis‏ @xpasky 30 Oct 2017
      Replying to @fchollet @bzamecnik @RossumAi

      We have ideas to reduce the overhead - https://github.com/rossumai/keras-multi-gpu/blob/master/blog/docs/conclusion.md … Most of it is the samples, first step: StagingArea https://gist.github.com/bzamecnik/f76e480edf98e95ab263fd1a123af7a5 …pic.twitter.com/QdnICU4VAm

      2 replies 0 retweets 0 likes
    10. Bohumír Zámečník‏ @bzamecnik 30 Oct 2017
      Replying to @xpasky @fchollet @RossumAi

      This is just async prefetch to 1 GPU. Then it needs StagingArea placed at each GPU. Also we need async loading to TF (Dataset API/queue).

      1 reply 0 retweets 0 likes
      François Chollet‏Verified account @fchollet 30 Oct 2017
      Replying to @bzamecnik @xpasky @RossumAi

      Haven't look at it in too much detail for the time being, but is there a way to package such optimizations as a PR for `multi_gpu_model`?

      1:04 PM - 30 Oct 2017
      • 1 Like
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      3 replies 0 retweets 1 like
        1. New conversation
        1. Bohumír Zámečník‏ @bzamecnik 30 Oct 2017
          Replying to @fchollet @xpasky @RossumAi

          If we manage to get it right and see performance improvement, yeah, it would be good to make it convenient.

          1 reply 0 retweets 2 likes
        2. François Chollet‏Verified account @fchollet 30 Oct 2017
          Replying to @bzamecnik @xpasky @RossumAi

          Cool, looking forward to it :)

          0 replies 0 retweets 1 like
          3. End of conversation
        1. Petr Baudis‏ @xpasky 30 Oct 2017
          Replying to @fchollet @bzamecnik @RossumAi

          It is our aim long-term, though not sure if we can follow up with that right away. PR#8286 could bootstrap the ecosystem for avolkov et al.

          0 replies 0 retweets 0 likes
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        1. Petr Baudis‏ @xpasky 30 Oct 2017
          Replying to @fchollet @bzamecnik @RossumAi

          The changes to use TF queue / Dataset API might be scary big though. Maybe there are other ways around that.

          0 replies 0 retweets 0 likes
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