tspannhw

tspannhw

<?xml version="1.0" ?>
<template encoding-version="1.1">
  <description></description>
  <groupId>1e8778a8-015c-1000-c747-8273276bab45</groupId>
  <name>JSON Array to String List</name>
  <snippet>
    <processGroups>
      <id>51eb9288-0a70-311b-0000-000000000000</id>
      <parentGroupId>5454b790-94fe-3e28-0000-000000000000</parentGroupId>
      <position>

tspannhw

| # |Where                                                                                                         |Type|Date      |Event                         |Talk Title                                                   |Link                                                                                                                    |
|---|--------------------------------------------------------------------------------------------------------------|----|----------|------------------------------|--------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------|
|1  |Virtual                                                                                                       |C   |1/13/2023 |Data Science Online Camp      |Pulsar for ML                                                 |                                                                                                       

mneedham

git clone [email protected]:huggingface/text-embeddings-inference.git
cd text-embeddings-inference

cargo install --path router -F candle -F accelerate

model=BAAI/bge-large-en-v1.5
revision=refs/pr/5
text-embeddings-router --model-id $model --revision $revision --port 8080

davidad

import pandas as pd

# Load the data
df = pd.read_excel('pnas.2118631119.sd01.xlsx')

import matplotlib.pyplot as plt

# Filter the data for ages 22-35
df_filtered = df[(df['AGE'] >= 22) & (df['AGE'] <= 35) & (df['YEAR'] >= 1955) & (df['YEAR'] <= 2040)]

tspannhw

<?xml version="1.0" encoding="UTF-8" standalone="yes"?>
<template encoding-version="1.3">
    <description></description>
    <groupId>bbe01d7d-0188-1000-547f-9f14b009425b</groupId>
    <name>ftp</name>
    <snippet>
        <connections>
            <id>07e20afe-0750-325d-0000-000000000000</id>
            <parentGroupId>a568e7b3-e3ef-305c-0000-000000000000</parentGroupId>
            <backPressureDataSizeThreshold>1 GB</backPressureDataSizeThreshold>

stuartlangridge

from atproto.firehose import FirehoseSubscribeReposClient, parse_subscribe_repos_message
import sys
from atproto import CAR, models
from atproto.cbor import decode_dag, decode_dag_multi
from atproto.xrpc_client.models.utils import get_or_create
import json

class JSONExtra(json.JSONEncoder):
    """raw objects sometimes contain CID() objects, which
    seem to be references to something elsewhere in bluesky.

tspannhw

# Requirements: kafka-python gssapi krbticket
import os
import time
from kafka import KafkaConsumer, KafkaProducer
from krbticket import KrbConfig, KrbCommand

try:
    os.environ['KRB5CCNAME'] = '/tmp/krb5cc_<myusername>'
    kconfig = KrbConfig(principal='araujo', keytab='/path/to/<myusername>.keytab')
    KrbCommand.kinit(kconfig)

ferencerdei

{
    "flowContents": {
        "identifier": "42063a31-a98b-319f-8617-2086e4b7f30b",
        "instanceIdentifier": "a7be953d-0186-1000-5378-dd26f8d2184c",
        "name": "Github_PR_Verification_Bot",
        "comments": "",
        "position": {
            "x": 589.4942055903919,
            "y": 191.17769189766898
        },

tspannhw

import os
from kafka import KafkaProducer, KafkaConsumer

BOOTSTRAP_SERVERS=os.gentenv("KAFKA_BOOTSTRAP_SERVERS").split(",")
TOPIC_NAME="the-topic"
SASL_USERNAME=os.gentenv("KAFKA_SASL_USERNAME")
SASL_PASSWORD=os.gentenv("KAFKA_SASL_PASSWORD")

def consume():
  consumer = KafkaConsumer(TOPIC_NAME, security_protocol="SASL_SSL",  sasl_mechanism="SCRAM-SHA-512", sasl_plain_username=SASL_USERNAME, sasl_plain_password=SASL_PASSWORD, bootstrap_servers=BOOTSTRAP_SERVERS)