zhanwenchen

import os
import tensorflow as tf

trained_checkpoint_prefix = 'checkpoints/dev'
export_dir = os.path.join('models', '0') # IMPORTANT: each model folder must be named '0', '1', ... Otherwise it will fail!

loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
    # Restore from checkpoint
    loader = tf.train.import_meta_graph(trained_checkpoint_prefix + '.meta')

OhBonsai

package main

import (
	"log"
	"github.com/streadway/amqp"
	"time"
	"os"
	"errors"
)

RamonYeung

''' Script for downloading all GLUE data.

Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized, 
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).

mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC

svx

#!/bin/sh
# based on https://gist.github.com/ipedrazas/9c622404fb41f2343a0db85b3821275d

# delete all evicted pods from all namespaces
kubectl get pods --all-namespaces | grep Evicted | awk '{print $2 " --namespace=" $1}' | xargs kubectl delete pod

# delete all containers in ImagePullBackOff state from all namespaces
kubectl get pods --all-namespaces | grep 'ImagePullBackOff' | awk '{print $2 " --namespace=" $1}' | xargs kubectl delete pod

# delete all containers in ImagePullBackOff or ErrImagePull or Evicted state from all namespaces

matthewfeickert

import json
import argparse

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

matplotlib.rcParams['text.usetex'] = True

# Inspiration came from https://stackoverflow.com/q/3609852/8931942

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

W4ngatang

''' Script for downloading all GLUE data.

Note: for legal reasons, we are unable to host MRPC.
You can either use the version hosted by the SentEval team, which is already tokenized, 
or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
You should then rename and place specific files in a folder (see below for an example).

mkdir MRPC
cabextract MSRParaphraseCorpus.msi -d MRPC

jmbjorndalen

#!/usr/bin/env python3
# Combining coroutines running in an asyncio event loop with 
# blocking tasks in thread pool and process pool executors.
# 
# Based on https://pymotw.com/3/asyncio/executors.html, but this version runs both
# threads and processes at the same time and interleaves them with asyncio coroutines.
# 
# All appears to be working. 
#

bojand

Just documenting docs, articles, and discussion related to gRPC and load balancing.

https://github.com/grpc/grpc/blob/master/doc/load-balancing.md

Seems gRPC prefers thin client-side load balancing where a client gets a list of connected clients and a load balancing policy from a "load balancer" and then performs client-side load balancing based on the information. However, this could be useful for traditional load banaling approaches in clound deployments.

https://groups.google.com/forum/#!topic/grpc-io/8s7UHY_Q1po

gRPC "works" in AWS. That is, you can run gRPC services on EC2 nodes and have them connect to other nodes, and everything is fine. If you are using AWS for easy access to hardware then all is fine. What doesn't work is ELB (aka CLB), and ALBs. Neither of these support HTTP/2 (h2c) in a way that gRPC needs.

ipedrazas

kubectl get pods | grep Evicted | awk '{print $1}' | xargs kubectl delete pod