diff --git a/elephantscale/notebooks/feedforward-credit-card-fraud-pipeline.ipynb b/elephantscale/notebooks/feedforward-credit-card-fraud-pipeline.ipynb
index ccf223e..dba172b 100644
--- a/elephantscale/notebooks/feedforward-credit-card-fraud-pipeline.ipynb
+++ b/elephantscale/notebooks/feedforward-credit-card-fraud-pipeline.ipynb
@@ -4,27 +4,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Feedforward Network with Credit Card Fraud\n",
+    "# Feedforward Network with Credit Card Fraud (Pipeline API version)\n",
     "\n",
-    "Credit Card Transactions Fraud Detection Example:\n",
-    "\n",
-    "The notebook demonstrates how to develop a fraud detection application with the BigDL deep learning library on Apache Spark. We'll try to introduce some techniques that can be used for training a fraud detection model, but some advanced skills is not applicable since the dataset is highly simplified.\n",
-    "\n",
-    "Dataset: Credit Card Fraud Detection https://www.kaggle.com/dalpozz/creditcardfraud\n",
-    "\n",
-    "This dataset presents transactions that occurred in two days, where we got 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions.\n",
-    "\n",
-    "It contains only numerical input variables which are the result of a PCA transformation. \n",
-    "\n",
-    "Unfortunately, due to confidentiality issues, we cannot find the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.\n",
-    "\n",
-    "Contact: yuhao.yang@intel.com\n"
+    "Please view the credit-card-fraud example, which will show how to do this in with an RDD-based approach. Here, we will follow the same approach with a dataframe-based pipeline API based on Spark MLLib.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -124,7 +114,9 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cc_training = spark.read.csv(\"../data/creditcardfraud/creditcard.csv\", header=True, inferSchema=\"true\", mode=\"DROPMALFORMED\")"
@@ -161,7 +153,9 @@
   {
    "cell_type": "code",
    "execution_count": 40,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cc_training = cc_training.select([col(c).cast(\"double\") for c in cc_training.columns])\n",
@@ -206,7 +200,9 @@
   {
    "cell_type": "code",
    "execution_count": 42,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# get the time to split the data.\n",
@@ -273,7 +269,9 @@
   {
    "cell_type": "code",
    "execution_count": 43,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "assembler =  VectorAssembler(inputCols=cols, outputCol=\"assembled\")\n",
@@ -289,23 +287,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### step 2. split the dataset into training and validation dataset.\n",
-    "\n",
-    "Unlike some other training dataset, where the data does not have a time of occurance. For this case, we can know the sequence of the transactions from the Time column. Thus randomly splitting the data into training and validation does not make much sense, since in real world applications, we can only use the history transactions for training and use the latest transactions for validation. Thus we'll split the dataset according the time of occurance."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### step 3. Build the model with BigDL\n",
+    "## step 2. Build the model with BigDL\n",
     "From the research community and industry feedback, a simple neural network turns out be the perfect candidate for the fraud detection training. We will quickly build a multiple layer Perceptron with linear layers.\n",
     "```\n",
     "    val bigDLModel = Sequential()\n",
@@ -380,12 +362,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "# Evaluate our Model\n",
     "\n",
-    "Now we have finished the training of our first model (which is certainly not the best, keep reading!).\n",
-    "\n",
-    "We'll need to think about how do evaluate the trained model:\n",
-    "\n",
-    "Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Since even if the model predicts all the records as normal transactions, it will still get an accuracy above 99%."
+    "Now we are goin to do the Precision, Recall, and AUC. To evaluate our model."
    ]
   },
   {
@@ -433,7 +412,9 @@
   {
    "cell_type": "code",
    "execution_count": 47,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "y_pred = np.array(predictionDF.select('prediction').collect())\n",
@@ -443,7 +424,9 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -474,74 +457,33 @@
     "sn.heatmap(df_cm, annot=True,fmt='d');"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To this point, we have finished the training and evaluation with a simple BigDL model. We can see that even though the recall and precision are high, the area under precision-recall curve is not optimistic. That's because we haven't really apply any technique to handle the imbalanced training data.\n",
-    "\n",
-    "Next we'll try to optimize the training process.\n",
-    "\n",
-    "### step 4. handle the data imbalance\n",
-    "There are several ways to approach this classification problem taking into consideration this unbalance.\n",
-    "\n",
-    "Collect more data? Nice strategy but not applicable in this case.\n",
-    "\n",
-    "Resampling the dataset Essentially this is a method that will process the data to have an approximate 50-50 ratio. One way to achieve this is by OVER-sampling, which is adding copies of the under-represented class (better when there're little data) Another is UNDER-sampling, which deletes instances from the over-represented class (better when there are lots of data)\n",
-    "Apart from under and over sampling, there is a very popular approach called SMOTE (Synthetic Minority Over-Sampling Technique), which is a combination of oversampling and undersampling, but the oversampling approach is not by replicating minority class but constructing new minority class data instance via an algorithm.\n",
-    "\n",
-    "We'll start with Resampling.\n",
-    "\n",
-    "Since there're 492 frauds out of 284,807 transactions, to build a reasonable training dataset, we'll use UNDER-sampling for normal transactions and use OVER-sampling for fraud transactions. By using the sampling rate as fraud -> 10, normal -> 0.05, we can get a training dataset of (5K fraud + 14K normal) transactions. We can use the training data to fit a model.\n",
-    "\n",
-    "Yet we'll soon find that since there're only 5% of all the normal transactions are included in the training data, the model can only cover 5% of all the normal transactions, which is obviousely not optimistic. So how can we get a better converage for the normal transactions without breaking the ideal ratio in the training dataset?\n",
-    "\n",
-    "An immediate improvement would be to train multiple models. For each model, we will run the resampling from the original dataset and get a new training data set. After training, we can select best voting strategy for all the models to make the prediction.\n",
-    "\n",
-    "We'll use Ensembling of neural networks. That's where a Bagging classifier becomes handy. Bagging is an Estimator we developed for ensembling of multiple other Estimator.\n",
-    "\n",
-    "```\n",
-    "package org.apache.spark.ml.ensemble\n",
-    "\n",
-    "class Bagging[M <: Model[M]](override val uid: String)\n",
-    "  extends Estimator[BaggingModel[M]]\n",
-    "  with BaggingParams[M] {\n",
-    "For usage, user need to set the specific Estimator to use and the number of models to be trained:\n",
-    "    val estimator = new Bagging()\n",
-    "      .setPredictor(dlClassifier)\n",
-    "      .setLabelCol(\"Class\")\n",
-    "      .setIsClassifier(true)\n",
-    "      .setNumModels(10)\n",
-    "```\n",
-    "\n",
-    "Internally, Bagging will train $(numModels) models. Each model is trained with the resampled data from the original dataset."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/elephantscale/notebooks/feedforward-credit-card-fraud.ipynb b/elephantscale/notebooks/feedforward-credit-card-fraud.ipynb
index 712d290..43a100b 100644
--- a/elephantscale/notebooks/feedforward-credit-card-fraud.ipynb
+++ b/elephantscale/notebooks/feedforward-credit-card-fraud.ipynb
@@ -8,6 +8,8 @@
     "\n",
     "Let us look at a BigDL example with Credit Card Fraud.  We will train a simple, feedforward neural network with the credit card dataset.\n",
     "\n",
+    "This is using RDDs. The feedforward-credit-card-fraud-pipeline uses the Spark.ML pipeline interface using dataframes.\n",
+    "\n",
     "Let's look briefly at the credit card dataset\n",
     "\n",
     "| Time\" | \"V1\"              | \"V2\"                | \"V3\"             | \"V4\"              | \"V5\"               | \"V6\"                | \"V7\"                | \"V8\"               | \"V9\"               | \"V10\"              | \"V11\"              | \"V12\"              | \"V13\"              | \"V14\"              | \"V15\"             | \"V16\"              | \"V17\"              | \"V18\"              | \"V19\"              | \"V20\"               | \"V21\"              | \"V22\"              | \"V23\"              | \"V24\"              | \"V25\"              | \"V26\"              | \"V27\"                | \"V28\"               | \"Amount\" | \"Class\" | \n",
@@ -25,7 +27,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -58,7 +62,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "## Parameteters\n",
@@ -69,10 +75,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
-    "learning_rate = 0.1\n",
+    "learning_rate = 0.01\n",
     "training_epochs = 100\n",
     "batch_size = 10000\n",
     "display_step = 1\n",
@@ -137,7 +145,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cc_training = spark.read.csv(\"../data/creditcardfraud/creditcard.csv\", header=True, inferSchema=\"true\", mode=\"DROPMALFORMED\")"
@@ -174,7 +184,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "cc_training = cc_training.select([col(c).cast(\"double\") for c in cc_training.columns])\n",
@@ -183,46 +195,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+----+------------------+-------------------+------------------+-------------------+-------------------+-------------------+--------------------+-------------------+------------------+-------------------+------------------+------------------+-------------------+-------------------+-------------------+-------------------+--------------------+-------------------+-------------------+-------------------+--------------------+-------------------+-------------------+-------------------+-------------------+-------------------+--------------------+-------------------+------+-----+\n",
-      "|Time|                V1|                 V2|                V3|                 V4|                 V5|                 V6|                  V7|                 V8|                V9|                V10|               V11|               V12|                V13|                V14|                V15|                V16|                 V17|                V18|                V19|                V20|                 V21|                V22|                V23|                V24|                V25|                V26|                 V27|                V28|Amount|Class|\n",
-      "+----+------------------+-------------------+------------------+-------------------+-------------------+-------------------+--------------------+-------------------+------------------+-------------------+------------------+------------------+-------------------+-------------------+-------------------+-------------------+--------------------+-------------------+-------------------+-------------------+--------------------+-------------------+-------------------+-------------------+-------------------+-------------------+--------------------+-------------------+------+-----+\n",
-      "| 0.0|  -1.3598071336738|-0.0727811733098497|  2.53634673796914|   1.37815522427443| -0.338320769942518|  0.462387777762292|   0.239598554061257| 0.0986979012610507| 0.363786969611213| 0.0907941719789316|-0.551599533260813|-0.617800855762348| -0.991389847235408| -0.311169353699879|   1.46817697209427| -0.470400525259478|   0.207971241929242| 0.0257905801985591|  0.403992960255733|  0.251412098239705|  -0.018306777944153|  0.277837575558899| -0.110473910188767| 0.0669280749146731|  0.128539358273528| -0.189114843888824|   0.133558376740387|-0.0210530534538215|149.62|  1.0|\n",
-      "| 0.0|  1.19185711131486|   0.26615071205963|  0.16648011335321|  0.448154078460911| 0.0600176492822243|-0.0823608088155687| -0.0788029833323113| 0.0851016549148104|-0.255425128109186| -0.166974414004614|  1.61272666105479|  1.06523531137287|   0.48909501589608| -0.143772296441519|  0.635558093258208|  0.463917041022171|  -0.114804663102346| -0.183361270123994| -0.145783041325259|-0.0690831352230203|  -0.225775248033138| -0.638671952771851|  0.101288021253234| -0.339846475529127|  0.167170404418143|  0.125894532368176|-0.00898309914322813| 0.0147241691924927|  2.69|  1.0|\n",
-      "| 1.0| -1.35835406159823|  -1.34016307473609|  1.77320934263119|  0.379779593034328| -0.503198133318193|   1.80049938079263|   0.791460956450422|  0.247675786588991| -1.51465432260583|  0.207642865216696| 0.624501459424895| 0.066083685268831|  0.717292731410831| -0.165945922763554|   2.34586494901581|  -2.89008319444231|    1.10996937869599| -0.121359313195888|  -2.26185709530414|  0.524979725224404|   0.247998153469754|  0.771679401917229|  0.909412262347719| -0.689280956490685| -0.327641833735251| -0.139096571514147| -0.0553527940384261|-0.0597518405929204|378.66|  1.0|\n",
-      "| 1.0|-0.966271711572087| -0.185226008082898|  1.79299333957872| -0.863291275036453|-0.0103088796030823|   1.24720316752486|    0.23760893977178|  0.377435874652262| -1.38702406270197|-0.0549519224713749|-0.226487263835401| 0.178228225877303|  0.507756869957169|  -0.28792374549456| -0.631418117709045|   -1.0596472454325|  -0.684092786345479|   1.96577500349538|   -1.2326219700892| -0.208037781160366|  -0.108300452035545|0.00527359678253453| -0.190320518742841|  -1.17557533186321|  0.647376034602038| -0.221928844458407|  0.0627228487293033| 0.0614576285006353| 123.5|  1.0|\n",
-      "| 2.0| -1.15823309349523|  0.877736754848451|    1.548717846511|  0.403033933955121| -0.407193377311653| 0.0959214624684256|   0.592940745385545| -0.270532677192282| 0.817739308235294|  0.753074431976354|-0.822842877946363|  0.53819555014995|    1.3458515932154|  -1.11966983471731|  0.175121130008994| -0.451449182813529|  -0.237033239362776|-0.0381947870352842|  0.803486924960175|  0.408542360392758|-0.00943069713232919|   0.79827849458971| -0.137458079619063|  0.141266983824769| -0.206009587619756|  0.502292224181569|   0.219422229513348|  0.215153147499206| 69.99|  1.0|\n",
-      "| 2.0|-0.425965884412454|  0.960523044882985|  1.14110934232219| -0.168252079760302|   0.42098688077219|-0.0297275516639742|   0.476200948720027|  0.260314333074874| -0.56867137571251| -0.371407196834471|  1.34126198001957| 0.359893837038039| -0.358090652573631| -0.137133700217612|  0.517616806555742|  0.401725895589603| -0.0581328233640131| 0.0686531494425432|-0.0331937877876282| 0.0849676720682049|  -0.208253514656728| -0.559824796253248|-0.0263976679795373| -0.371426583174346| -0.232793816737034|  0.105914779097957|   0.253844224739337| 0.0810802569229443|  3.67|  1.0|\n",
-      "| 4.0|  1.22965763450793|  0.141003507049326|0.0453707735899449|   1.20261273673594|  0.191880988597645|  0.272708122899098|-0.00515900288250983| 0.0812129398830894| 0.464959994783886|-0.0992543211289237| -1.41690724314928|-0.153825826253651|  -0.75106271556262|   0.16737196252175| 0.0501435942254188| -0.443586797916727| 0.00282051247234708|  -0.61198733994012|-0.0455750446637976|  -0.21963255278686|  -0.167716265815783| -0.270709726172363| -0.154103786809305| -0.780055415004671|   0.75013693580659| -0.257236845917139|  0.0345074297438413|0.00516776890624916|  4.99|  1.0|\n",
-      "| 7.0|-0.644269442348146|   1.41796354547385|   1.0743803763556| -0.492199018495015|  0.948934094764157|  0.428118462833089|    1.12063135838353|  -3.80786423873589| 0.615374730667027|   1.24937617815176|-0.619467796121913| 0.291474353088705|   1.75796421396042|  -1.32386521970526|  0.686132504394383|-0.0761269994382006|    -1.2221273453247| -0.358221569869078|  0.324504731321494| -0.156741852488285|    1.94346533978412|  -1.01545470979971|  0.057503529867291| -0.649709005559993| -0.415266566234811|-0.0516342969262494|   -1.20692108094258|  -1.08533918832377|  40.8|  1.0|\n",
-      "| 7.0| -0.89428608220282|  0.286157196276544|-0.113192212729871| -0.271526130088604|    2.6695986595986|   3.72181806112751|   0.370145127676916|  0.851084443200905|-0.392047586798604| -0.410430432848439|-0.705116586646536|-0.110452261733098| -0.286253632470583| 0.0743553603016731| -0.328783050303565| -0.210077268148783|  -0.499767968800267|  0.118764861004217|   0.57032816746536| 0.0527356691149697| -0.0734251001059225| -0.268091632235551| -0.204232669947878|    1.0115918018785|  0.373204680146282| -0.384157307702294|  0.0117473564581996|   0.14240432992147|  93.2|  1.0|\n",
-      "| 9.0| -0.33826175242575|   1.11959337641566|  1.04436655157316| -0.222187276738296|   0.49936080649727|  -0.24676110061991|   0.651583206489972| 0.0695385865186387|-0.736727316364109| -0.366845639206541|  1.01761446783262| 0.836389570307029|   1.00684351373408| -0.443522816876142|  0.150219101422635|  0.739452777052119|  -0.540979921943059|   0.47667726004282|  0.451772964394125|  0.203711454727929|  -0.246913936910008| -0.633752642406113|  -0.12079408408185| -0.385049925313426|-0.0697330460416923| 0.0941988339514961|   0.246219304619926| 0.0830756493473326|  3.68|  1.0|\n",
-      "|10.0|  1.44904378114715|  -1.17633882535966| 0.913859832832795|  -1.37566665499943|  -1.97138316545323|  -0.62915213889734|    -1.4232356010359| 0.0484558879088564| -1.72040839292037|   1.62665905834133|   1.1996439495421|-0.671439778462005| -0.513947152539479|-0.0950450453999549|  0.230930409124119| 0.0319674667862076|   0.253414715863197|  0.854343814324194| -0.221365413645481| -0.387226474431156|-0.00930189652490052|  0.313894410791098| 0.0277401580170247|  0.500512287104917|   0.25136735874921| -0.129477953726618|  0.0428498709381461| 0.0162532619375515|   7.8|  1.0|\n",
-      "|10.0|  0.38497821518095|  0.616109459176472|-0.874299702595052|-0.0940186259679115|   2.92458437838817|   3.31702716826156|   0.470454671805879|   0.53824722837695|-0.558894612428441|   0.30975539423728|-0.259115563735702|-0.326143233995877|-0.0900467227020648|  0.362832368569793|  0.928903660629178| -0.129486811402759|  -0.809978925963589|  0.359985390219981|   0.70766382644648|   0.12599157561542|   0.049923685888971|  0.238421512225103|0.00912986861262866|  0.996710209581086| -0.767314827174801| -0.492208295340017|   0.042472441919027|-0.0543373883732122|  9.99|  1.0|\n",
-      "|10.0|    1.249998742053|  -1.22163680921816| 0.383930151282291|  -1.23489868766892|  -1.48541947377961| -0.753230164566149|  -0.689404975426345| -0.227487227519552| -2.09401057344842|   1.32372927445937| 0.227666231237246|-0.242681998944186|   1.20541680770748| -0.317630527025074|  0.725674990179153| -0.815612186027305|   0.873936447614439| -0.847788598847099| -0.683192626267037| -0.102755941505071|  -0.231809239223849| -0.483285330117712| 0.0846676908596583|  0.392830885335013|  0.161134553588505| -0.354990039673962|  0.0264155490776107| 0.0424220887282304| 121.5|  1.0|\n",
-      "|11.0|   1.0693735878819|  0.287722129331455| 0.828612726634281|   2.71252042961718| -0.178398016248009|  0.337543730282968| -0.0967168617395962|  0.115981735546597|-0.221082566236194|  0.460230444301678|-0.773656930526689|  0.32338724546722|-0.0110758870883779| -0.178485175177916|  -0.65556427824926|  -0.19992517131173|     0.1240054151819| -0.980496201537345| -0.982916082135047| -0.153197231044512| -0.0368755317335273| 0.0744124028162195|-0.0714074332998586|  0.104743752596029|  0.548264725394119|  0.104094153162781|  0.0214910583643189|  0.021293311477486|  27.5|  1.0|\n",
-      "|12.0|  -2.7918547659339| -0.327770756658658|  1.64175016056605|   1.76747274389883| -0.136588446465306|   0.80759646826532|  -0.422911389711497|  -1.90710747624096| 0.755712908314791|    1.1510869876677| 0.844555470974377|   0.7929439518176|  0.370448092803246| -0.734975105820311|  0.406795710431001| -0.303057623825763|  -0.155868714793874|  0.778265457041536|   2.22186801373788|  -1.58212204356551|    1.15166304848789|  0.222181966098225|   1.02058620426601| 0.0283166513238872| -0.232746324289105|  -0.23555721754117|   -0.16477751177654|-0.0301536365592253|  58.8|  1.0|\n",
-      "|12.0|-0.752417042956605|  0.345485415344747|  2.05732291276727|  -1.46864329840046|   -1.1583936804082|-0.0778498291166733|  -0.608581418236123|0.00360348436201849|-0.436166983515744|  0.747730827192802|-0.793980602837221|-0.770406728847129|   1.04762699748088|  -1.06660368148653|   1.10695345662141|   1.66011355713381|  -0.279265373246772| -0.419994141181313|  0.432535348618175|  0.263450864446125|   0.499624954671111|   1.35365048557231| -0.256573280448308|-0.0650837078816517|-0.0391243535426488|-0.0870864732146962|  -0.180997500092721|  0.129394059390202| 15.99|  1.0|\n",
-      "|12.0|  1.10321543528383|-0.0402962145973447|   1.2673320885949|   1.28909146962552| -0.735997163604068|  0.288069162976262|  -0.586056786337461|  0.189379713679593| 0.782332891785191| -0.267975066537173|-0.450311279515466| 0.936707714991982|  0.708380406186981| -0.468647287707221|  0.354574063407955| -0.246634655717582|-0.00921237772707382| -0.595912405700819|  -0.57568162226261| -0.113910176982092| -0.0246120063374677|  0.196001952806192| 0.0138016541409422|  0.103758331023198|  0.364297540595235| -0.382260574113217|   0.092809187460487| 0.0370505169810008| 12.99|  1.0|\n",
-      "|13.0|-0.436905071360625|  0.918966212909322|  0.92459077438817| -0.727219053596792|  0.915678718106307| -0.127867352079254|   0.707641607333935| 0.0879623554672504| -0.66527135413364| -0.737979823596458|  0.32409781346169| 0.277192107214981|  0.252624256310781| -0.291896460370468| -0.184520169327133|   1.14317370716197|   -0.92870926272403|  0.680469592634687| 0.0254364616880793|-0.0470212823165035|  -0.194795823794671| -0.672637997017793| -0.156857514491897| -0.888386320943716| -0.342413218776576| -0.049026728633951|  0.0796923991551505|  0.131023789452311|  0.89|  1.0|\n",
-      "|14.0| -5.40125766315825|  -5.45014783420644|  1.18630463143652|   1.73623880012095|   3.04910587764025|  -1.76340557365201|   -1.55973769907953|  0.160841747266769|  1.23308974041888|  0.345172827050629| 0.917229867699146| 0.970116716069048| -0.266567764915222| -0.479129929276704| -0.526608502569153|   0.47200411177674|  -0.725480944982201|  0.075081351540202| -0.406866573198217|  -2.19684802485647|  -0.503600328973703|  0.984459785590244|   2.45858857639219| 0.0421188969891572| -0.481630823956716| -0.621272013713977|   0.392053289557744|  0.949594245504846|  46.8|  1.0|\n",
-      "|15.0|   1.4929359769862|  -1.02934573189487|  0.45479473374366|  -1.43802587991702|  -1.55543410136344| -0.720961147043557|   -1.08066413038614|-0.0531271179483221|  -1.9786815953872|   1.63807603690446|  1.07754241162743| -0.63204651464934|  -0.41695716661602| 0.0520105153724404|-0.0429789228232019| -0.166432496451972|   0.304241418614353|  0.554432499062278| 0.0542295152184719| -0.387910172646258|  -0.177649846438814| -0.175073809074822| 0.0400022190621329|  0.295813862676508|   0.33293059939425| -0.220384850672322|  0.0222984359135846|0.00760225559997897|   5.0|  1.0|\n",
-      "+----+------------------+-------------------+------------------+-------------------+-------------------+-------------------+--------------------+-------------------+------------------+-------------------+------------------+------------------+-------------------+-------------------+-------------------+-------------------+--------------------+-------------------+-------------------+-------------------+--------------------+-------------------+-------------------+-------------------+-------------------+-------------------+--------------------+-------------------+------+-----+\n",
-      "only showing top 20 rows\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "cc_training.show()"
+    "cc_training.show(1)"
    ]
   },
   {
@@ -274,7 +254,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# get the time to split the data.\n",
@@ -478,22 +460,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Y+tzqt9Xuz9neGOmzqb1jLXcY7WfxWFnL1to/N9HT09Py41YtoD8MHN30+Cig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\nqEAXERERESkg/xeCBzSYfQK7xQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7efc4f992250>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "loss = np.array(train_summary.read_scalar(\"Loss\"))\n",
@@ -544,7 +516,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "predictions = trained_model.predict(cc_rdd_test).collect()\n",
@@ -596,21 +570,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/elephantscale/notebooks/feedforward-iris-pipeline.ipynb b/elephantscale/notebooks/feedforward-iris-pipeline.ipynb
index b481de8..878eff7 100644
--- a/elephantscale/notebooks/feedforward-iris-pipeline.ipynb
+++ b/elephantscale/notebooks/feedforward-iris-pipeline.ipynb
@@ -4,10 +4,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Feedforward Neural Network on the Iris Dataset\n",
-    "# Using Pipeline API\n",
+    "# Feedforward Neural Network on the Iris Dataset Using Pipeline API\n",
     "\n",
-    "Fisher's Iris dataset  \n",
+    "This notebook shows how to do the iris classification on the Spark mllib dataframe-based pipeline API (as opposed to the RDD API).  For introduction, first see the feedforward-iris.ipynb.\n",
+    "\n",
+    "### Fisher's Iris dataset  \n",
     "\n",
     "We are going to try the same Iris dataset as on feedforward-iris.ipynb, but this time using\n",
     "Spark MLLib's Pipeline-based API.\n",
@@ -41,7 +42,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -103,10 +106,12 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
-    "learning_rate = 0.1\n",
+    "learning_rate = 0.01\n",
     "training_epochs = 100\n",
     "batch_size = 16\n",
     "display_step = 1\n",
@@ -194,7 +199,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "iris_training = spark.read.csv(\"../data/iris/iris_training.csv\", header=True, inferSchema=\"true\", mode=\"DROPMALFORMED\")\n",
@@ -204,7 +211,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "iris_training = iris_training.select([col(c).cast(\"double\") for c in iris_training.columns])\n",
@@ -227,7 +236,9 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "assembler =  VectorAssembler(inputCols=['c1','c2','c3','c4'], outputCol=\"assembled\")\n",
@@ -450,7 +461,9 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "y_pred = np.array(predictionDF.select('prediction').collect())\n",
@@ -496,28 +509,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/elephantscale/notebooks/feedforward-iris.ipynb b/elephantscale/notebooks/feedforward-iris.ipynb
index 7872678..dc1c6ce 100644
--- a/elephantscale/notebooks/feedforward-iris.ipynb
+++ b/elephantscale/notebooks/feedforward-iris.ipynb
@@ -6,7 +6,9 @@
    "source": [
     "# Feedforward Neural Network on the Iris Dataset\n",
     "\n",
-    "Fisher's Iris dataset  \n",
+    "This notebook shows how to do a simple feedforward neural Network on BigDL using the RDD based API. If you are interested in seeing the same example using the dataframe-based Spark MLLib pipeline, please see feedforward-iris-pipeline.ipynb.\n",
+    "\n",
+    "### Fisher's Iris dataset  \n",
     "\n",
     "This dataset contains 150 samples, with 4 dimensions, as follows:\n",
     "\n",
@@ -36,7 +38,9 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -96,7 +100,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "learning_rate = 0.1\n",
@@ -186,7 +192,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "iris_training = spark.read.csv(\"../data/iris/iris_training.csv\", header=True, inferSchema=\"true\", mode=\"DROPMALFORMED\")\n",
@@ -201,7 +209,9 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "iris_k_train = iris_training.rdd.map(list)\n",
@@ -591,7 +601,9 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "predictions = trained_model.predict(iris_rdd_test).collect()\n",
@@ -645,7 +657,9 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "def map_predict_label(outputs, threshold):\n",
@@ -659,7 +673,9 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "true_positive_rate = list()\n",
@@ -673,28 +689,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/elephantscale/notebooks/lstm-20news.ipynb b/elephantscale/notebooks/lstm-20news.ipynb
index 04cb4be..7df4484 100644
--- a/elephantscale/notebooks/lstm-20news.ipynb
+++ b/elephantscale/notebooks/lstm-20news.ipynb
@@ -6,13 +6,20 @@
    "source": [
     "# LSTM Example: 20 Newsgroup Classification\n",
     "\n",
-    "This shows how to do an LSTM classification on the 20 newsgroup collection"
+    "This shows how to do an LSTM classification on the 20 newsgroup collection\n",
+    "\n",
+    "## Requirements\n",
+    "\n",
+    "Running this notebook requires a minimum of 16GB executor memory.  Starting it up in an ordinary jupyter notebook \n",
+    "won't work as you'll get an out of memory exception.  Quit jupyter and make sure you start this with at least --executor-memory 16gb"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import itertools\n",
@@ -32,7 +39,9 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "\n",
@@ -90,7 +99,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "\n",
@@ -112,7 +123,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "\n",
@@ -132,7 +145,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "\n",
@@ -283,28 +298,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/elephantscale/notebooks/lstm-stocks.ipynb b/elephantscale/notebooks/lstm-stocks.ipynb
index 506fe16..ba97dea 100644
--- a/elephantscale/notebooks/lstm-stocks.ipynb
+++ b/elephantscale/notebooks/lstm-stocks.ipynb
@@ -6,13 +6,17 @@
    "source": [
     "# LSTM Stock Market Example\n",
     "\n",
-    "Wouldn't you like to predict the stock market?  (Don't try this at home).  Here's an example of how we can use LSTMs to predict patterns in the S&P 500."
+    "Wouldn't you like to predict the stock market?  (Don't try this at home).  Here's an example of how we can use LSTMs to predict patterns in the S&P 500.\n",
+    "\n",
+    "What is an LSTM?  An LSTM (Long Short Term Memory) is a recurrent neural network that will help us to predict patterns in time-series data."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "\n",
@@ -33,7 +37,9 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "learning_rate = 0.1\n",
@@ -60,7 +66,9 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "#Step 1 Load Data\n",
@@ -70,7 +78,9 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "X_train_rdd = sc.parallelize(X_train).map(lambda x: x.flatten())\n",
@@ -82,7 +92,9 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "x_train_sample = X_train_rdd.zip(y_train_rdd).map(lambda x:Sample.from_ndarray(x[0], x[1]))\n",
@@ -139,7 +151,7 @@
    "source": [
     "# Training The Model\n",
     "\n",
-    "Notice that the model is a regression, not classificaiton model"
+    "Notice that the model is a regression, not a classification model. This is why we are using the MSECriterion. What we are trying to do is predict the next value of the S&P500 given the input sequence of S&P 500 values."
    ]
   },
   {
@@ -178,13 +190,16 @@
    "source": [
     "# Making Predictions\n",
     "\n",
-    "Here we are going to make predictions based on the model.  We will use sliding windows of length 50."
+    "Here we are going to make predictions based on the model.  We will use sliding windows of length 50.\n",
+    "\n",
+    "Here's how this works: We're going to Take sequences of length 50, and then use those 50 steps as inputs to the model. Then we will get the output of the model for the next step, and continue on making predictions."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 43,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
    "outputs": [],
@@ -212,10 +227,20 @@
     "predictions = prediction_seqs"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Plots\n",
+    "\n",
+    "Let us go ahead and plot some of our predictions."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
+    "collapsed": true,
     "scrolled": false
    },
    "outputs": [],
@@ -228,16 +253,30 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.4"
   }
  },
  "nbformat": 4,
diff --git a/elephantscale/notebooks/sp500.csv b/elephantscale/notebooks/sp500.csv
new file mode 100644
index 0000000..f770a07
--- /dev/null
+++ b/elephantscale/notebooks/sp500.csv
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diff --git a/elephantscale/scripts/README.md b/elephantscale/scripts/README.md
new file mode 100644
index 0000000..4af92eb
--- /dev/null
+++ b/elephantscale/scripts/README.md
@@ -0,0 +1,4 @@
+# Scripts Directory
+
+This contains scripts. For video 3, run the run-transfer.sh script.  You will need to start with plenty
+of memory (16gb minimum).
diff --git a/run-bigdl-docker.sh b/run-bigdl-docker.sh
new file mode 100755
index 0000000..6d01f68
--- /dev/null
+++ b/run-bigdl-docker.sh
@@ -0,0 +1,36 @@
+#!/bin/bash
+
+## Usage
+#  ./run-bigdl-docker.sh   <image name>  [optional command]
+#
+#  ./run-bigdl-docker.sh  xxx/yyy
+#
+#  or during developing, give a local docker image id
+#  ./run-bigdl-docker.sh  abcd1234
+#
+#  provide an optional command, here is an example of simple bash
+#  ./run-bigdl-docker.sh  abcd1234  bash
+
+if [ -z "$1" ] ; then
+    echo "Usage:  $0    <image name>    [optional command]"
+    echo "Missing Docker image id.  exiting"
+    exit -1
+fi
+
+image_id="$1"
+cmd="$2"
+name="bigdl"
+
+## remove any previously running containers
+docker rm -f "$name"
+
+# mount the current directory at /work
+this="${BASH_SOURCE-$0}"
+mydir=$(cd -P -- "$(dirname -- "$this")" && pwd -P)
+
+docker run -it  --name "$name" \
+    -p 8888:8888 \
+    -p 6006:6006 \
+    -v"$mydir:/work" \
+    "$image_id" \
+    ${cmd}
diff --git a/run-bigdl-native.sh b/run-bigdl-native.sh
new file mode 100755
index 0000000..11abd84
--- /dev/null
+++ b/run-bigdl-native.sh
@@ -0,0 +1,66 @@
+#!/bin/bash
+
+## Runs BigDL natively
+
+## Usage
+#   BIGDL_HOME=~/apps/BigDL   SPARK_HOME=~/apps/spark    ./run-bigdl.sh
+
+
+# Check environment variables
+if [ -z "${BIGDL_HOME}" ]; then
+    echo "Please set BIGDL_HOME environment variable"
+    exit 1
+fi
+
+if [ -z "${SPARK_HOME}" ]; then
+    echo "Please set SPARK_HOME environment variable"
+    exit 1
+fi
+
+# activate py35 environment
+#source activate py35
+# activate py27 environment
+source activate py27
+conda info -e
+
+
+#setup paths
+export PYSPARK_PYTHON=$(which python)
+export PYSPARK_DRIVER_PYTHON=$(which jupyter)
+echo "### PYSPARK_PYTHON=$PYSPARK_PYTHON"
+echo "### PYSPARK_DRIVER_PYTHON=$PYSPARK_DRIVER_PYTHON"
+# this starts the notebook without a security token
+#export PYSPARK_DRIVER_PYTHON_OPTS="notebook --notebook-dir=./ --ip=* --no-browser --NotebookApp.token=''"
+export PYSPARK_DRIVER_PYTHON_OPTS="notebook --notebook-dir=./ --ip=* --no-browser"
+export BIGDL_JAR_NAME=`ls ${BIGDL_HOME}/lib/ | grep jar-with-dependencies.jar`
+export BIGDL_JAR="${BIGDL_HOME}/lib/$BIGDL_JAR_NAME"
+export BIGDL_PY_ZIP_NAME=`ls ${BIGDL_HOME}/lib/ | grep python-api.zip`
+export BIGDL_PY_ZIP="${BIGDL_HOME}/lib/$BIGDL_PY_ZIP_NAME"
+export BIGDL_CONF=${BIGDL_HOME}/conf/spark-bigdl.conf
+
+# Check files
+if [ ! -f ${BIGDL_CONF} ]; then
+    echo "Cannot find ${BIGDL_CONF}"
+    exit 1
+fi
+
+if [ ! -f ${BIGDL_PY_ZIP} ]; then
+    echo ${BIGDL_PY_ZIP}
+    echo "Cannot find ${BIGDL_PY_ZIP}"
+    exit 1
+fi
+
+if [ ! -f $BIGDL_JAR ]; then
+    echo "Cannot find $BIGDL_JAR"
+    exit 1
+fi
+
+${SPARK_HOME}/bin/pyspark \
+  --master local[4] \
+  --driver-memory 4g \
+  --properties-file ${BIGDL_CONF} \
+  --py-files ${BIGDL_PY_ZIP} \
+  --jars ${BIGDL_JAR} \
+  --conf spark.driver.extraClassPath=${BIGDL_JAR} \
+  --conf spark.executor.extraClassPath=${BIGDL_JAR} \
+  --conf spark.sql.catalogImplementation='in-memory'