protoget

public class Solution {
    public String minWindow(String S, String T) {
        if (S.length() == 0) return "";
        
        Map<Character, Integer> expected = new HashMap<Character, Integer>();
        Map<Character, Integer> actual = new HashMap<Character, Integer>();
        populateMap(expected, T);

        int start = 0, next = 0;
        int bestStart = 0, bestEnd = Integer.MAX_VALUE;

protoget

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

protoget

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

protoget

ps server

import tensorflow as tf

cluster = tf.train.ClusterSpec({"ps": ['localhost:2222'], "worker": ['localhost:2224','localhost:2225']})
server = tf.train.Server(cluster.as_cluster_def(), job_name='ps', task_index=0)

server.join()

protoget

$ python script.py ps
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:200] Initialize GrpcChannelCache for job ps -> {0 -> localhost:9000}
I tensorflow/core/distributed_runtime/rpc/grpc_channel.cc:200] Initialize GrpcChannelCache

protoget

import tensorflow as tf

tf.reset_default_graph()

g1 = tf.Graph()
with g1.as_default():
    print g1
    with tf.name_scope('myscope'):
        keys = [1]
        values = ["foo"]

protoget

# Location of the CUDA Toolkit
CUDA_PATH ?= /usr/local/cuda

# architecture
HOST_ARCH   := $(shell uname -m)
TARGET_ARCH ?= $(HOST_ARCH)

# Adjust this for ARMv7 with a 32-bit filesystem
ifeq ($(TARGET_ARCH), aarch64)
    ifeq ($(shell file /sbin/init | grep 32-bit), 1)

protoget

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

import tensorflow as tf


shape = [2, 2, 2]