Jerry Vigil jerryvig

Short answer

gpt-oss-120b typically requires a workstation/server GPU with ~60–80 GB of VRAM for single‑GPU local inference (80 GB is the common “safe” target). Multi‑GPU setups (model‑/tensor‑parallelism) can reduce per‑GPU VRAM requirements but add complexity. (ollama.com) Recommended hardware (concise)

GPU VRAM: 80 GB (single‑GPU recommended). LM Studio notes “best with ≥60 GB VRAM” but most vendor docs and deploy guides target an 80 GB card (NVIDIA A100/H100 class) for the full 120B. Multi‑GPU with NVLink/NVSwitch can be used instead of one 80 GB card. (xlxm.cn) System RAM: 64–128 GB (128 GB recommended if you’ll run other services, caching, or multi‑GPU setups). Some production guides recommend 128 GB+ for comfort. (gptoss.net) CPU: Many cores for throughput; a modern server/workstation CPU (16+ cores recommended; 32+ cores for heavy multi‑client/production). High PCIe bandwidth and many PCIe lanes are helpful when attaching big GPUs. (gptoss.net) Storage: NVMe SSD (fast) — plan for ~100 GB to store th

Venmo’s business‑rules library offers a Python‑based domain‑specific language (DSL) that decouples business decision logic from application code. In technical terms, you use the library by following these steps:

Defining Variables:
You create a subclass of a provided base (typically something like BaseVariables) where you expose the data points from your domain model. Each method that returns a value is decorated (for example, with @numeric_rule_variable or @string_rule_variable) so that the engine knows both the type of data and which operators are valid for that variable.
Defining Actions:
Similar to variables, you define an actions class (subclassing BaseActions) where each method represents a business operation to be performed. The methods are annotated with @rule_action, along with a description of the expected parameters. This lets the engine know what side‑effects to trigger once a rule’s conditions are met.
**Building

Need to simplify this to a series of steps that's easy to follow.

Update the CROP Python backend to return the entire strings for "Percent depth of coverage" and "Mean depth of coverage" but do this only for the report type RT_EXOME_DIAGNOSTIC_V4. For exome_v4, the returned dictionary values for percent_depth_of_coverage and mean_depth_of_coverage will be strings like Proportion of tested genes with high confidence: 99.1% and Mean depth of coverage: 50x.

For all other exome report types (RT_EXOME_DIAGNOSTIC, RT_EXOME_DIAGNOSTIC_V2, & RT_EXOME_DIAGNOSTIC_V3), the existing behavior, must be preserved in which the percent_depth_of_coverage and mean_depth_of_coverage dict values are still returned as stringified values of only the numbers such as 99.8% and 50x. This is because the report rendering service code will be updated asynchronous to these changes, and we don't control when those rendering service changes are released.

The relevant code needs updates in at leas

	FROM node:12.18.2-buster-slim

	# Create app directory
	WORKDIR /usr/src/app

	# Install app dependencies
	# A wildcard is used to ensure both package.json AND package-lock.json are copied
	# where available (npm@5+)
	COPY package*.json ./

	{
	"reportType": "carrier_couples",
	"reportSubtype": "standard",
	"phiContentCarrierCouples": {
	"maternalPHI": {
	"name": "Rosalind Franklin",
	"dob": "05/01/1981",
	"gender": "Female",
	"mrn": "12344321",
	"invitaeNo": "RQ123456",

	{
	"reportType": "carrier_couples",
	"reportSubtype": "standard",
	"phiContentCarrierCouples": {
	"maternalPHI": {
	"name": "Rosalind Franklin",
	"dob": "05/01/1981",
	"gender": "Female",
	"mrn": "12344321",
	"invitaeNo": "RQ123456",

	import io
	import logging
	import threading

	import requests


	logging.basicConfig(level=logging.DEBUG)

	def print_response_data(received, data):

	import {applyMiddleware, combineReducers, createStore} from 'redux'
	import { createMiddleware } from 'redux-beacon'
	import GoogleTagManager from '@redux-beacon/google-tag-manager'
	import {reducer as formReducer} from 'redux-form'
	import {createLogger} from 'redux-logger'
	import reduxThunk, {ThunkMiddleware} from 'redux-thunk'

	import {reducer as appState} from '~/store/appState'
	import {reducer as healthHistoryState} from '~/store/healthHistoryState'
	import {reducer as kitState} from '~/store/kitState'

	// this is just a rudimentary http2 client example in TypeScript.
	import * as http2 from 'http2';

	function main(): void {
	let data = '';

	const client = http2.connect('https://www.google.com', {});
	client.on('error', (err) => {
	console.log('Client raised error.');
	console.error(err);

	import * as fs from 'fs';
	import * as https from 'https';

	function afterWrite(err): void {
	if (err) {
	throw new Error('Failed to write to file.');
	}
	}

	function writeSomeData(err, fd): void {