This is just some stencils I created for myself to draw FMC (http://www.fmc-modeling.org/) diagrams with draw.io.
Here is how they look:
This is just some stencils I created for myself to draw FMC (http://www.fmc-modeling.org/) diagrams with draw.io.
Here is how they look:
Year | Budget | |
---|---|---|
1930 | 4.1 | |
1931 | 3.1 | |
1932 | 1.9 | |
1933 | 2.0 | |
1934 | 3.0 | |
1935 | 3.6 | |
1936 | 3.9 | |
1937 | 5.4 | |
1938 | 6.8 |
This is an example of how to automatically log all the messages from docker containers. Logspout listens to containers and automatically routes the logs to OKLog.
To run the example simply issue:
docker-compose up -d
command. The test service here is simply to genereate a "Hello World" message. You would replace this with other services that are in your compose files.
You can browse UI here (there seem to be some issues with UI here, I'm not getting very consistent behavior):
using Printf | |
using NeuralPDE, Flux, ModelingToolkit, GalacticOptim, Optim, DiffEqFlux | |
using Quadrature, Cubature, Cuba | |
@parameters t,x | |
@variables c(..) | |
@derivatives Dt'~t | |
@derivatives Dxx''~x | |
@derivatives Dx'~x |
import random | |
c = random.choice | |
areas = ["rl", "transfer learning", "attention", "distillation", "unsupervised learning", 'multi-task learning'] | |
architectures = ["transformers", "RNNs", "CNNs", "ResNETs", "NeuralODEs", "Autoencoders", "GANs"] | |
applications = ["Games", "NLP", "Driving", "Art", "Robotics", "Health"] | |
datatypes = ["images", "video", "audio", "text", "DNA"] | |
action = ["Advancing", "Discovering", "Examining", "Learning", "Automating"] | |
area, architecture, applicaiton, datatype, action = c(areas), c(architectures), c(applications), c(datatypes), c(action) |
using Flux | |
using Flux: reset! | |
# https://discourse.julialang.org/t/how-to-do-batching-in-fluxs-recurrent-sequence-model-to-take-advantage-of-gpu-during-training/28678 | |
# example: | |
N = 7 | |
D = 2 | |
m = RNN(D,3) | |
m.(rand(D, T)) |
using DifferentialEquations, Flux, Optim, DiffEqFlux, DiffEqSensitivity, Plots | |
using Plots | |
function reaction1!(du, u, p, t) | |
# Reaction: A + 2B → C | |
a, b, c = u | |
k, = p | |
rate = k * a*b^2 | |
du[1] = da = -rate | |
du[2] = db = -2*rate |
{ | |
"action": "reopened", | |
"number": 2, | |
"pull_request": { | |
"url": "https://api.github.com/repos/chovencorp/test123/pulls/2", | |
"id": 75922888, | |
"html_url": "https://github.com/chovencorp/test123/pull/2", | |
"diff_url": "https://github.com/chovencorp/test123/pull/2.diff", | |
"patch_url": "https://github.com/chovencorp/test123/pull/2.patch", | |
"issue_url": "https://api.github.com/repos/chovencorp/test123/issues/2", |
x = ['print "x=", x', 'print "#Hello World"', 'for i in x: print i'] | |
#Hello World | |
print "x =", x | |
print "#Hello World" | |
for i in x: print i |