claytantor

$ docker-machine create --driver virtualbox --virtualbox-disk-size "32000" default

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$ sudo docker exec -i -t 665b4a1e17b6 /bin/bash

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# Recursively find and replace in files
find . -name "*.txt" -print0 | xargs -0 sed -i '' -e 's/foo/bar/g'

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public class AwsLogQuery {
    private final static Logger logger = LoggerFactory.getLogger(AwsLogQuery.class);
 
    // use the role of the lambda to determine policy.
    protected static final AWSCredentialsProvider CREDENTIALS_PROVIDER =
            new DefaultAWSCredentialsProviderChain();
 
    public static void main(String[] args){
        long startTime = 1476119058044l;
        long endTime = 1476119159726l;

claytantor

clay@freekbox ~/data/nvidia $ python ./test1.py 
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so.8.0 locally
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:951] Found device 0 with properties: 
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.7085

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clay@freekbox ~/data/nvidia/NVIDIA_CUDA-8.0_Samples/bin/x86_64/linux/release $ ./deviceQuery
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 1060 6GB"
  CUDA Driver Version / Runtime Version          8.0 / 8.0
  CUDA Capability Major/Minor version number:    6.1

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%matplotlib inline

import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from pymongo import MongoClient
from bson.objectid import ObjectId
import dateutil.parser

start_date_str = "2015-04-25T07:00:00Z"

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<script>
var node = document.createElement("div");
node.innerHTML = '<a href="https://github.com/claytantor/blueweave"><img style="position: absolute; top: 50px; right: 0; border: 0;" src="https://camo.githubusercontent.com/365986a132ccd6a44c23a9169022c0b5c890c387/68747470733a2f2f73332e616d617a6f6e6177732e636f6d2f6769746875622f726962626f6e732f666f726b6d655f72696768745f7265645f6161303030302e706e67" alt="Fork me on GitHub" data-canonical-src="https://s3.amazonaws.com/github/ribbons/forkme_right_red_aa0000.png"></a>';
document.getElementsByTagName('body')[0].appendChild(node);
</script>

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        // ----- Train the network, evaluating the test set performance at each epoch -----
        int nEpochs = 400;

        for (int i = 0; i < nEpochs; i++) {
            net.fit(trainData);
            trainData.reset();
        }

        //Evaluate on the test set:
        List<String> labels = new ArrayList<>();

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package org.deeplearning4j.examples.util;

import com.fasterxml.jackson.databind.ObjectMapper;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.string.NDArrayStrings;

import java.io.IOException;
import java.util.List;