coral-cnn
Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation (by Raschka-research-group)
horovod
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. (by horovod)
coral-cnn | horovod | |
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4 | 8 | |
330 | 13,969 | |
2.1% | 0.5% | |
0.0 | 5.2 | |
about 3 years ago | about 2 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
coral-cnn
Posts with mentions or reviews of coral-cnn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-08-16.
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[D] Why is Ordinal Regression so overlooked?
The most recent and usable DL attempt I have found is the CORAL/CORN frameworks (keras, pytorch) which have just a few stars, and that's it.
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[D] can regression models be used for ranking?
To your question, there are specific types of models called ordinal regression / ordinal classification models that do not assume a metric distance between values. E.g., if you have "20/hr, $15/hr, $0/hr" these models don't assume that the distance between 0 and 15 is 3x the distance between 20 and 15. It just assumes 20 > 15 > 0. We worked on this a bit in the context of neural networks: https://www.sciencedirect.com/science/article/pii/S016786552030413X , https://raschka-research-group.github.io/coral_pytorch/
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[D] Modeling class errors
If you are interested, I recently worked on a simple ordinal regression approach for neural networks here: https://www.sciencedirect.com/science/article/pii/S016786552030413X
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[R] [D] What machine learning methods can be used for ordinal regression?
Just took a quick look at that paper, it sounds like a good approach. If you are interested, we recently developed an ordinal regression approach with implementation in PyTorch (https://github.com/Raschka-research-group/coral-cnn). Someone also recently ported it to Keras: https://github.com/ck37/coral-ordinal. I haven't read the paper you mentioned in detail, but it seems our method is similar except that we add the probabilities that are >0.5 and that we have theoretical guarantees. rank consistency.
horovod
Posts with mentions or reviews of horovod.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-12-08.
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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.