gaberogan

// sortKey - simplified
// Cache sort values per node during each sort cycle using a WeakMap.
// Values are computed once per node per sort, then discarded when the next sort starts.
// Avoids: _sortKey on data objects, rebuildAllSortKeys, buildSortKey in gridRefresh,
// SORT_KEY_COMPARATOR symbol, rebuildScheduled timeout fallback.

import { getMasterGrid } from 'lib/agGrid'

// PROPS_SORT_COLS still needed - defines sort order per sport
// (same as current implementation, omitted here for brevity)

gaberogan

// gridRefresh.ts - simplified
// Targeted cell refresh: when socket entities change, scan grid rows for matching keys
// and only refresh those rows. No reverse index maps needed.

import { getMasterGrid } from 'lib/agGrid'
import { throttle } from 'lodash'

const pendingEventIds = new Set<string>()
const pendingPlayerIds = new Set<string>()
const pendingStatTypeIds = new Set<string>()

gaberogan

/**
 * A JavaScript map that lazily expires entries on read
 * @param ttl TTL in seconds
 * @param autoGc Optional garbage collection interval in seconds
 * @example new LazyExpireMap({ ttl : 60, autoGc: 60 })
 */
export class LazyExpireMap extends Map {
  constructor({ ttl, autoGc } = {}) {
    super()

gaberogan

Installation

Carefully follow these READMEs:

• To install pyenv + python: https://github.com/pyenv/pyenv
• To install pyenv-virtualenv: https://github.com/pyenv/pyenv-virtualenv

These are the commands I used on MacOS with ZSH:

brew update

gaberogan

  // Available variables:
  // - Machine
  // - interpret
  // - assign
  // - send
  // - sendParent
  // - spawn
  // - raise
  // - actions

gaberogan

Install pyenv + Python

Install pyenv dependencies:

Ubuntu:

sudo apt-get install build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev libffi-dev

MacOS:

gaberogan

How to install Tensorflow2 with GPU support in Ubuntu 20.04

Installation of Tensorflow2 with GPU support is easy and the only complication can be arisen from the CUDA compability which in turns depends on the Nvidia driver version. Before going farther, please check if your Nvidia Video Card is compatible with the required versions that are defined in this gist, use this link.

Tensorflow offers in its website a table of the compatibility between libraries for the target OS. You can visit that website in the following COMPATIBILITY TABLE that points to the Tensorflow installation from source for linux. For the time writing this gist, Tensorflow2 v2.5.0 requires CUDA v11.2 and CUDNN v8.2. It is really important to match the exact version, otherwise tensorflow will have problems loading the shared libraries as not finding the correct version.

CUDA version also requires for a minimum Nvidia driver version