stoke
horovod
| stoke | horovod | |
|---|---|---|
| 2 | 8 | |
| 707 | 13,952 | |
| 0.1% | 0.4% | |
| 0.0 | 5.2 | |
| 9 months ago | about 1 month ago | |
| C++ | Python | |
| Apache License 2.0 | GNU General Public License v3.0 or later |
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stoke
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Denuvo plans to offer independent benchmarks in an attempt to prove its DRM doesn't cause performance problems
It might be possible for a compressed/optimized executable to be faster if parts fit into processor cache-lines, but Denuvo specifically makes executables much larger by adding vast numbers of obfuscation instructions.
- Stoke is a stochastic optimizer and program synthesizer
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?
petastorm - Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
DeepDanbooru - AI based multi-label girl image classification system, implemented by using TensorFlow.
mpi4jax - Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
NudeNet - Neural Nets for Nudity Detection and Censoring
onepanel - The open source, end-to-end computer vision platform. Label, build, train, tune, deploy and automate in a unified platform that runs on any cloud and on-premises.
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
seq2seq - A general-purpose encoder-decoder framework for Tensorflow
pytorch-summary - Model summary in PyTorch similar to `model.summary()` in Keras
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
nsfw_model - Keras model of NSFW detector
AdamP - AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights (ICLR 2021)
accelerate - 🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support