nicklatin

# high-low spread estimator (hlse)
def hlse(ohlc_df, frequency='daily'):
    """
    Computes the high-low spread estimator, an estimate of bid-offer spreads, a measure of liquidity risk.
    See Corwin & Schultz (2011) for details: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1106193

    Parameters
    ----------
    ohlc_df: DataFrame
        DataFrame with DatetimeIndex and Open, High, Low and Close (OHLC) prices from which to compute the high-low spread estimates.

moble

As described in detail here, julia can take really excessive amounts of time to execute the first @everywhere statement on many processes — around 1 hour for thousands of processes — even if the actual code being executed everywhere is trivial. Basically, the Distributed functions need to be precompiled to make this happen quickly.

This gist provides a simple way to do so — at least on Slurm clusters (though the same principles should apply elsewhere). Just submit precompile.jl as a batch job (adjusting the SBATCH directives as needed), and it should create a sysimage that you can use to run future batch jobs. Check end of the log of the Slurm job to see exactly how to use the sysimage.

Note that both the original julia process and all processes created with addprocs should use the --sysimage=/path/to/sys_everywhere.so argument. Doing so reduces the time taken to execute the first @everywhere sta

torfjelde

# Use packages to ensure that we trigger Requires.jl.
using Zygote: Zygote
using ReverseDiff: ReverseDiff
using ForwardDiff: ForwardDiff
using Tracker: Tracker
using Memoization: Memoization # used for ReverseDiff.jl cache.

using Turing.Core: ForwardDiffAD, ReverseDiffAD, TrackerAD, ZygoteAD, CHUNKSIZE

const DEFAULT_ADBACKENDS = [

torfjelde

julia> using Turing, Random

julia> @model function gdemo(xs)
           # Assumptions
           σ² ~ InverseGamma(2, 3)
           μ ~ Normal(0, √σ²)
           # Observations
           for i = 1:length(xs)
               xs[i] ~ Normal(μ, √σ²)
           end

twiecki

junpenglao

andrewjong

import torch
from torchvision import datasets

class ImageFolderWithPaths(datasets.ImageFolder):
    """Custom dataset that includes image file paths. Extends
    torchvision.datasets.ImageFolder
    """

    # override the __getitem__ method. this is the method that dataloader calls
    def __getitem__(self, index):

alexlee-gk

This was tested on a ThinkPad P70 laptop with an Intel integrated graphics and an NVIDIA GPU:

lspci | egrep 'VGA|3D'
00:02.0 VGA compatible controller: Intel Corporation Device 191b (rev 06)
01:00.0 VGA compatible controller: NVIDIA Corporation GM204GLM [Quadro M3000M] (rev a1)

A reason to use the integrated graphics for display is if installing the NVIDIA drivers causes the display to stop working properly. In my case, Ubuntu would get stuck in a login loop after installing the NVIDIA drivers. This happened regardless if I installed the drivers from the "Additional Drivers" tab in "System Settings" or the ppa:graphics-drivers/ppa in the command-line.

patik

Squashing Git Commits

The easy and flexible way

This method avoids merge conflicts if you have periodically pulled master into your branch. It also gives you the opportunity to squash into more than 1 commit, or to re-arrange your code into completely different commits (e.g. if you ended up working on three different features but the commits were not consecutive).

Note: You cannot use this method if you intend to open a pull request to merge your feature branch. This method requires committing directly to master.

Switch to the master branch and make sure you are up to date: