Comments (3)
No. However, you can change the sparsity dynamically in your training script by calling:
for i in range(epochs):
grc.compressor.compress_ratio = 0.01*pow(2,i)
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Thank you,
I have also another question.
I think DGC algorithm deploy momentum correction algorithm.
Is it right your code has this algorithm in DGC code ?
The figures for improving the imagenet model speed of DGC original papers are higher than your paper indicators.
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Yes. The momentum correction is implemented in grace_dl/torch/memory/dgc.py .
I think the hyper-parameters used in our experiment are a bit different from the original author's, e.g. batch size, number of nodes, learning rate, whether apply compression only on high rank gradients, etc. Please refer to the original author's repo for an exact reproduction.
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Related Issues (20)
- EFSignSGDCompressor does not seem to use memory HOT 1
- bug: tensorflow natural compression returns 'Python int too large to convert to C long' HOT 2
- A question about one-bit quantization implementation in tensorflow backend. HOT 1
- Discussion about TernGrad HOT 13
- Threshold compression hangs. HOT 1
- Seeking suggestions for embedding into ddp HOT 8
- Problematic error: suddenly interrupt when trainning HOT 3
- Some questions about the grace HOT 2
- [DDP] weight in classifier HOT 1
- Any plans to port to latest Horovod (0.24) ? HOT 3
- DGC GPU usage in GRACE HOT 4
- The question about QSGD algorithm in the paper HOT 1
- Environment build failed on AWS EC2 g4dn instance with OS image (Deep Learning AMI, ami-0184e674549ab8432) HOT 4
- horovodrun command reports an error and cannot run the examples
- Question about the time complexity. HOT 3
- GRACE PyTorch Distributed HOT 6
- TypeError:compensate() missing 1 required positional argument: 'name' HOT 10
- About the usage of GRACE HOT 2
- Global or Local compression HOT 2
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