tf-keras-deep-head-pose
horovod
tf-keras-deep-head-pose | horovod | |
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1 | 8 | |
70 | 14,014 | |
- | 0.7% | |
0.0 | 4.5 | |
almost 2 years ago | 3 days ago | |
Python | Python | |
- | GNU General Public License v3.0 or later |
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tf-keras-deep-head-pose
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Attempting to train a muli loss model in Keras. Failing miserably!
All I am doing is downloading the code which is available on github (https://github.com/Oreobird/tf-keras-deep-head-pose) so that I can train it myself. However, I am ashamed to say that even just attempting this is giving me issues with regards to the models losses not converging. Where the losses reported in the HopeNet paper gets down to less than 5 per angle, my losses are in the hundreds.
horovod
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Discussion Thread
Broke: using Horovod
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[D] What is the recommended approach to training NN on big data set?
And in case scaling is really important to you. May I suggest you look into Horovod?
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Anyone know of any papers or models for segmenting satellite images of a city into things like roads, buildings, parks, etc?
Training is not the same as inference (doing the segmentation), so that scale is probably off by a lot. One or two orders of magnitude just depending on the specifics of what hardware you're running on, and your training and eval dataset would be several orders of magnitude smaller. FAANGs would parallelize that training as well (don't remember if UNet is inherently parallelizable for training) via their internal equivalent of Horovod, so they'll do a GPU-month worth of training in less than a day.
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Embedding Python
[[email protected]] match_arg (utils/args/args.c:163): unrecognized argument quiet [[email protected]] HYDU_parse_array (utils/args/args.c:178): argument matching returned error [[email protected]] parse_args (ui/mpich/utils.c:1639): error parsing input array [[email protected]] HYD_uii_mpx_get_parameters (ui/mpich/utils.c:1691): unable to parse user arguments [[email protected]] main (ui/mpich/mpiexec.c:127): error parsing parameters I believe this is due to mpich being installed: https://github.com/horovod/horovod/issues/1637
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[D] PyTorch Distributed Training Libraries: What are the current options?
Check out Horovod - https://github.com/horovod/horovod
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[D] GPU buying recommendation
If you just want to run tensorflow or pytorch for a Jupyter notebook, setting the environment shouldn't be difficult. I know that AWS has a marketplace of preconfigured images. However, you can go as advanced as setting up a cluster of gpu-equipped nodes to setup Horovod (https://github.com/horovod/horovod) to do distributed machine learning. Yes, there's a learning curve, but you cannot acquire this skillet any other way.
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SKLean, TensorFlow, etc vs Spark ML?
I'm the maintainer for an open source project called Horovod that allows you to distribute deep learning training (e.g., TensorFlow) on platforms like Spark.
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Cluster machine learning
You'll want to use horovod to run keras in a distributed system. Then use Slurm to manage the cluster and run the job.
What are some alternatives?
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