| # EDIT 10/04/2022 - This version was provided by @jayelm who fixed some bugs and made the function much more robust | |
| import os | |
| import subprocess | |
| import time | |
| def assign_free_gpus(threshold_vram_usage=1500, max_gpus=2, wait=False, sleep_time=10): | |
| """ | |
| Assigns free gpus to the current process via the CUDA_AVAILABLE_DEVICES env variable | |
| This function should be called after all imports, |
Twitter thread: https://twitter.com/theshawwn/status/1456925974919004165
Hacker News thread: https://news.ycombinator.com/item?id=29128998
November 6, 2021
jnp.device_put(1) is deceptively simple to write in JAX. But on a TPU, what actually happens? How does a tensor containing the value 1 actually get onto a TPU?
Turns out, the answer is "C++", and a lot of it.
| /* libtpujesus.c | |
| Copyright 2021 Shawn Presser | |
| Permission is hereby granted, free of charge, to any person obtaining | |
| a copy of this software and associated documentation files (the | |
| "Software"), to deal in the Software without restriction, including | |
| without limitation the rights to use, copy, modify, merge, publish, | |
| distribute, sublicense, and/or sell copies of the Software, and to | |
| permit persons to whom the Software is furnished to do so, subject to | |
| the following conditions: |
They basically all suggest that apparent improvements to the state of the art in ML and related fields are often not real, or at least the result of factors other than what the authors claim.
The state of sparsity in deep neural networks
What is the state of neural network pruning?
On the State of the Art of Evaluation in Neural Language Models
Do Transformer Modifications Transfer Across Implementations and Applications?
| from imgaug import augmenters as iaa | |
| import imgaug as ia | |
| ia.seed(4) | |
| import tensorflow as tf | |
| tf.random.set_seed(666) | |
| aug = iaa.RandAugment(n=2, m=9) | |
| BATCH_SIZE = 224 |
| def downsample_2d(X, sz): | |
| """ | |
| Downsamples a stack of square images. | |
| Args: | |
| X: a stack of images (batch, channels, ny, ny). | |
| sz: the desired size of images. | |
| Returns: | |
| The downsampled images, a tensor of shape (batch, channel, sz, sz) |
| """Configuration library for experiments.""" | |
| from typing import Dict, Any | |
| import logging | |
| import pprint | |
| import sys | |
| import argparse | |
| logger = logging.getLogger(__name__) | |
| parser = argparse.ArgumentParser(description=__doc__, fromfile_prefix_chars="@") |
UPDATE (March 2020, thanks @ic): I don't know the exact AMI version but yum install docker now works on the latest Amazon Linux 2. The instructions below may still be relevant depending on the vintage AMI you are using.
Amazon changed the install in Linux 2. One no-longer using 'yum' See: https://aws.amazon.com/amazon-linux-2/release-notes/
sudo amazon-linux-extras install docker
sudo service docker start