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horovod
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| onepanel | horovod | |
|---|---|---|
| 4 | 8 | |
| 696 | 13,942 | |
| 0.0% | 0.9% | |
| 0.0 | 5.8 | |
| about 1 year ago | 28 days ago | |
| Go | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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onepanel
- Onepanel - open source machine learning IDE that you can deploy in any cloud or on-premises
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- Onepanel – Cloud-native deep learning platform
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[P] Onepanel - latest open source release now includes browser accessible deep learning desktop, hyperparameter tuning and Python DSL for defining parallel data processing or training pipelines.
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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?
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
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DeepDanbooru - AI based multi-label girl image classification system, implemented by using TensorFlow.
mpi-operator - Kubernetes Operator for MPI-based applications (distributed training, HPC, etc.)
mpi4jax - Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
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MetaSpore - A unified end-to-end machine intelligence platform
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
skyhookml - SkyhookML is an easy-to-use web platform for computer vision.
seq2seq - A general-purpose encoder-decoder framework for Tensorflow