mtreviso

"""Adapted from https://github.com/bastings/interpretable_predictions"""
import math

import distrax
import jax
import jax.numpy as jnp


EPS = 1e-6

mtreviso

import numpy as np


def compute_kendall_taus(h, m):
    """
    Compute multiple variants of Kendall's Tau correlations between two rank arrays.

    This function calculates several variants of Kendall's Tau correlations (tau_a, tau_b, tau_c, 
    tau_10, tau_13, tau_14, tau_23) between two input rank arrays. It employs vectorized operations 
    for computation efficiency and can handle tied ranks. This function is an implementation based on 

mtreviso

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""quotacheck.py
This script displays user quotas for a given mount point.
"""

import os
import platform
import subprocess
import sys

mtreviso

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""psinfo.py
This script displays sinfo in a pretty table.
"""
import platform
import subprocess
import sys

try:

mtreviso

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""psqueue.py
This script displays squeue in a pretty table.
"""
import os
import subprocess
import sys

try:

mtreviso

"""
>>> python remove_unused_bibs.py -h

usage: remove_unused_bibs.py [-h] [-b BIB_FILES] [-p PAPER_FILES] [-o OUT_FILE]

Remove unused bibtex entries

options:
  -h, --help            show this help message and exit
  -b BIB_FILES, --bib_files BIB_FILES

mtreviso

mtreviso

Slurm User Quickstart: Launching & Managing Jobs

Table of Contents

1. Introduction
   • Methods to Launch Jobs
   • Checking Job Status
2. Launching Jobs
   • Important Parameters
   • Quality of Service (QoS) Options
3. Job Submission Methods

mtreviso

Slurm Admin Notes: Cluster Management & QoS Setup

Prereq: Slurm is installed and slurmdbd is configured/running. Controller and nodes share the same slurm.conf (including AccountingStorageType=accounting_storage/slurmdbd) and Munge works across nodes.

If Slurm isn’t installed yet, follow: https://mtreviso.github.io/blog/slurm.html

---

Check or Create the Cluster (sardine-cluster)

Show clusters: