Jason Lee Ertle jlertle

Based on Summary Trace API,

device_name = tf.test.gpu_device_name()
if not tf.test.is_gpu_available():
    raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
os.makedirs(os.path.join(args.exp_dir, 'plugins/profile'), exist_ok=True)
tf.summary.trace_on(graph=True, profiler=True)
tracing_params = params.copy()

How to use different ssh deploy keys for multiple private github repositories with Go Modules

Let's assume you are using Go Modules and have a go.mod file that contains multiple private repos each with a different ssh key. How can you get go mod download to do the right thing -- i.e. use ssh key A with private repo A and ssh key B with private repo B?

Ok, here we go!

Let's assume you have some github.com user with multiple private repos:

https://github.com/someuser/private-repo-1

This is how to use the Black Python code formatter in VS Code.

Make a Python 3.6 virtualenv for running Black

Black itself requires Python 3.6 to run, but few of our projects are on that version. The VS Code plugin conveniently lets you run black from its own virtualenv.

I had to give a specific version of black this morning. I didn't yesterday. Don't specify the version unless it makes you (and if you do, give the current version, not the one from this doc).

$ cd ~/Envs

	/* Sources:
	See: https://codepen.io/Dafuseder/pen/WEqOVw
	https://gist.github.com/crashingbooth/e9f0b7dbec8aecabf13db3663d28480f
	https://medium.com/code-music-noise/euclidean-rhythms-391d879494df
	*/
	//:Implementation 1 (plain)
	(
	~br = { \| o = 1, p = 4 \|
	(o / p * (0..p - 1)).floor.differentiate.asInteger.put(0, 1);
	};

	import numpy as np

	from keras.layers import Input, Dense
	from keras.models import Model, Sequential


	def dnn(n_inputs, n_outputs, n_hidden_layers=3, hlayer_size=128, probabilistic=True):
	"""Defines simple DNN model
	"""
	x_input = Input(shape=[n_inputs])

	pip install streamlit
	pip install spacy
	python -m spacy download en_core_web_sm
	python -m spacy download en_core_web_md
	python -m spacy download de_core_news_sm

	import keras.backend as K
	import numpy as np


	def gaussian_nll(ytrue, ypreds):
	"""Keras implmementation of multivariate Gaussian negative loglikelihood loss function.
	This implementation implies diagonal covariance matrix.

	Parameters
	----------

	import os
	import json

	import numpy
	import tensorflow

	from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn
	print("Tensorflow version: {0}".format(tensorflow.VERSION))

	os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'