horovod
d2l-en
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| horovod | d2l-en | |
|---|---|---|
| 8 | 6 | |
| 13,952 | 21,628 | |
| 0.9% | 3.1% | |
| 5.2 | 8.7 | |
| about 1 month ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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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.
d2l-en
- which book to chose for deep learning :lan Goodfellow or francois chollet
- d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
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How to pre-train BERT on different objective tasks using HuggingFace
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
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The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
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I created a way to learn machine learning through Jupyter
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.
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.
Pytorch-UNet - PyTorch implementation of the U-Net for image semantic segmentation with high quality images
DeepDanbooru - AI based multi-label girl image classification system, implemented by using TensorFlow.
DeepADoTS - Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".
mpi4jax - Zero-copy MPI communication of JAX arrays, for turbo-charged HPC applications in Python :zap:
TF-Watcher - Monitor your ML jobs on mobile devices📱, especially for Google Colab / Kaggle
NudeNet - Neural Nets for Nudity Detection and Censoring
99-ML-Learning-Projects - A list of 99 machine learning projects for anyone interested to learn from coding and building projects
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.
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
thinc - 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries