skye

# ==== Non-communicating processes

# 4x 1 chip per process:
os.environ["TPU_CHIPS_PER_PROCESS_BOUNDS"] = "1,1,1"
os.environ["TPU_PROCESS_BOUNDS"] = "1,1,1"
# Different per process:
os.environ["TPU_VISIBLE_DEVICES"] = "0" # "1", "2", "3"
# Pick a unique port per process
os.environ["TPU_MESH_CONTROLLER_ADDRESS"] = "localhost:8476"
os.environ["TPU_MESH_CONTROLLER_PORT"] = "8476"

mattjj

from functools import partial
from math import factorial

import jax.numpy as np
import matplotlib.pyplot as plt
from jax import jvp, vmap


def f(x):
  return 1./5 * x**3 + 3 * x**2 - x + 1.

siemanko

"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:

xuhdev

# see https://www.topbug.net/blog/2013/04/14/install-and-use-gnu-command-line-tools-in-mac-os-x/

# core
brew install coreutils

# key commands
brew install binutils
brew install diffutils
brew install ed --default-names
brew install findutils --with-default-names

zhongwen

eval = val
iter = imginst
image_list = "imagenet/val.lst"
image_bin = "val.bin"
#mean_r=123.68
#mean_g=116.779
#mean_b=103.939
mean_value = 103.939,116.779,123.68
#no random crop and mirror in test
iter = end