SAP Data Intelligence: Create your first ML Scenario

[AndreasForster]

SAP Data Intelligence is SAP's platform for data management, data orchestration and Machine Learning (ML) deployment. Whether your data is structured or unstructured, in SAP HANA or outside, SAP Data Intelligence can be very useful.

This blog shows how to create a very basic ML project in SAP Data Intelligence, We will train a linear regression model in Python with just a single predictor column. Based on how many minutes it takes a person run a half-marathon, the model will estimate the person's marathon time. That trained model will then be exposed as REST-API for inference. Your business applications could now leverage these predictions in your day-to-day processes. After this introductory tutorial you will be familiar with the core components of an ML scenario in SAP Data Intelligence, and the implementation of more advanced requirements is likely to follow a similar pattern.

For those who prefer R instead of Python, Ingo Peter has published a blog which implements this  first ML Scenario with R.

Should you have access to a system you can follow hands-on yourself. You can currently request a trial or talk to your Account Executive anytime.

Update on 3 April 2023:

• Please note that SAP Data Intelligence is not intended as one-stop-shop Data Science Platform.


• Recently an issue caused the "Python Producer" and "Python Consumer" template pipelines to disappear. See SAP Note 3316646 to rectify.


• This blog was created with Python 3.6, which isn't supported anymore. See the comment below by tarek.abou-warda on using Python 3.9 instead.


• Examples on using the embedded Machine Learning in SAP HANA, SAP HANA Cloud and SAP Datasphere from SAP Data Intelligence are given in the book "Data Science mit SAP HANA" (that book exists only in German, unfortunately there is no English edition)

Table of contents

• 
  
  
  
  • Training data
  
  
  • Data connection
  
  
  • Data exploration and free-style Data Science
    
    
    
    • Train regression model
    
    
    • Save regression model
    
    
    • Load and apply regression model
    
    
    
    
  
  
  • Deployment
    
    
    
    • Training pipeline
    
    
    • Prediction / Inference with REST-API
    
    
    
    
  
  
  • Summary
  
  
  • Looking for more?
  
  
  
  

 

Training data

Our dataset is a small CSV file, which contains the running times (in minutes) of 117 people, who ran both the Zurich Marathon as well as a local Half-Marathon in the same region, the Greifenseelauf. The first rows show the fastest runners:

ID	HALFMARATHON_MINUTES	MARATHON_MINUTES
1	73	149
2	74	154
3	78	158
4	73	165
5	74	172

 

SAP Data Intelligence can connect to range of sources. However, to follow this tutorial hands-on, please place this this file into SAP Data Intelligence's internal DI_DATA_LAKE.

• Open the "Metadata Explorer"


• Select "Browse Connections"


• Click "DI_DATA_LAKE"


• Select "shared" and upload the file

 

 

 

Data connection

Since we are using the built-in DI_DATA_LAKE you do not need any additional connection. If the file was located outside SAP Data Intelligence, then you would create an additional connection in the Connection Management.

 

If the file was located in an S3 bucket for instance, you would need to create a new connection and and set the "Connection Type" to "S3". Enter the bucket's endpoint, the region and the access key as well as the secret key. Also, set the "Root Path" to the name of your bucket.

 

Data exploration and free-style Data Science

Now start work on the Machine Learning project. On the main page of SAP Data Intelligence click into the "ML Scenario Manager", where you will carry out all Machine Learning related activities.

Click the little "+"-sign on the top right to create a new scenario. Name it "Marathon time predictions" and you enter further details into the "Business Question" section. Click "Create".

 

You see the empty scenario. You will use the Notebooks to explore the data and to script the regression model in Python. Pipelines bring the code into production. Executions of these pipelines will create Machine Learning models, which are then deployed as REST-API for inference.

 

But one step at a time. Next, you load and explore the data in a Notebook. Select the "Notebooks" tab, then click the "+"-sign.  Name the Notebook "10 Data exploration and model training". Click "Create" and the Notebook opens up. You will be prompted for the kernel, keep the default of "Python 3".

 

Now you are free to script in Python, to explore the data and train the regression model. The central connection to your DI_DATA_LAKE can be leveraged through Python.

from hdfs import InsecureClient

import pandas as pd

client = InsecureClient('http://datalake:50070')

 

The CSV file can then be loaded into a pandas DataFrame. Just make sure that the value assigned to the bucket corresponds with your own bucket name.

with client.read('/shared/i056450/RunningTimes.csv') as reader:

    df_data = pd.read_csv(reader, delimiter=';')

 

Use Python to explore the data. Display the first few rows for example.

df_data.head(5)

 

Or plot the runners' half-marathon time against the full marathon time.

x = df_data[["HALFMARATHON_MINUTES"]]

y_true = df_data["MARATHON_MINUTES"]



%matplotlib inline

import matplotlib.pyplot as plot

plot.scatter(x, y_true, color = 'darkblue');

plot.xlabel("Minutes Half-Marathon");

plot.ylabel("Minutes Marathon");

 

There is clearly a linear relationship. No surprise, If you can run a marathon fast, you are also likely to be one of the faster half-marathon runners.

 

Train regression model

Begin by installing the scikit-learn library, which is very popular for Machine Learning in Python on tabular data such as ours.

!pip install sklearn​

 

Then continue by training the linear regression model with the newly installed scikit-learn library. In case you are not familiar with a linear regression, you might enjoy the openSAP course "Introduction to Statistics for Data Science".

from sklearn.linear_model import LinearRegression

lm = LinearRegression()

lm.fit(x, y_true)

 

Plot the linear relationship that was estimated.

plot.scatter(x, y_true, color = 'darkblue');

plot.plot(x, lm.predict(x), color = 'red');

plot.xlabel("Actual Minutes Half-Marathon");

plot.ylabel("Actual Minutes Marathon");

 

Calculate the Root Mean Squared Error (RMSE) as quality indicator of the model's performance on the training data. This indicator will be shown later on in the ML Scenario Manager.

In this introductory example the RMSE is calculated rather manually. This way, I hope, it easier to understand its meaning, in case you have not yet been familiar with it. Most Data Scientists would probably leverage a Python package to shorten this bit.

import numpy as np

y_pred = lm.predict(x)

mse = np.mean((y_pred - y_true)**2)

rmse = np.sqrt(mse)

rmse = round(rmse, 2)

print("RMSE: " , str(rmse))

print("n: ", str(len(x)))

 

A statistician may want to run further tests on the regression, ie on the distribution of the errors. We skip these steps in this example.

 

Save regression model

You could apply the model immediately on a new observation. However, to deploy the model later on, we will be using one graphical pipeline to train and save the model and a second pipeline for inference.

Hence at this point we also spread the Python code across two separate Notebooks for clarity.  Therefore we finish this Notebook by saving the model. The production pipeline will save the model as pickled object. Hence the model is also saved here in the Notebook as pickle object as well.

import pickle

pickle.dump(lm, open("marathon_lm.pickle.dat", "wb"))

 

Don't forget to save the Notebook itself as well.

 

Load and apply regression model

To create the second Notebook, go back to the overview page of your ML Scenario and click the "+"-sign in the Notebooks section.

 

Name the new Notebook "20 Apply model", confirm "Python 3" as kernel and the empty Notebook opens up. Use the following code to load the model that has just been saved.

import pickle

lm_loaded = pickle.load(open("marathon_lm.pickle.dat", "rb"))

 

It's time to apply the model. Predict a runner's marathon time if the person runs a half-marathon in 2 hours.

x_new = 120

predictions = lm_loaded.predict([[x_new]])

round(predictions[0], 2)

 

The model estimates a time of just under 4 hours and 24 minutes.

 

Deployment

Now everything is in place to start deploying the model in two graphical pipelines.

• One pipeline to train the model and save it into the ML Scenario.


• And Another pipeline to surface the model as REST-API for inference

 

Training pipeline

To create the graphical pipeline to retrain the model, go to your ML Scenario's main page, select the "Pipelines" tab and click the "+"-sign.

 

Name the pipeline "10 Train" and select the "Python Producer"-template.



 

You should see the following pipeline, which we just need to adjust. In principle, the pipeline loads data with the "Read File"-operator. That data is passed to a Python-operator, in which the ML model is trained. The same Python-operator stores the model in the ML Scenario through the "Artifact Producer". The Python-operator's second output can pass a quality metric of the model to the same ML Scenario. Once both model and metric are saved, the pipeline's execution is ended with the "Graph Terminator".

 

Now adjust the template to our scenario. Begin with the data connection. Select the "Read File"-operator and click the "Open Configuration" option.

 

In the Configuration panel on the right-hand side open the "Connection"-configuration, set "Configuration type" to "Connection Management" and you can set the "Connection ID" to the "DI_DATA_LAKE.

 

Save this configuration. Now open the "Path" setting and select the RunningTimes.csv file you had uploaded.

 

Next we adjust the Python code that trains the regression model. Select the "Python 3"-operator and click the Script-option.

 

The template code opens up. It shows how to pass the model and metrics into the ML Scenario. Replace the whole code with the following. That code receives the data from the "Read File"-operator, uses the code from the Notebook to train the model and passes the trained model as well as its quality indicator (RMSE) to the ML Scenario.

# Example Python script to perform training on input data & generate Metrics & Model Blob

def on_input(data):

    

    # Obtain data

    import pandas as pd

    import io

    df_data = pd.read_csv(io.StringIO(data), sep=";")

    

    # Get predictor and target

    x = df_data[["HALFMARATHON_MINUTES"]]

    y_true = df_data["MARATHON_MINUTES"]

    

    # Train regression

    from sklearn.linear_model import LinearRegression

    lm = LinearRegression()

    lm.fit(x, y_true)

    

    # Model quality

    import numpy as np

    y_pred = lm.predict(x)

    mse = np.mean((y_pred - y_true)**2)

    rmse = np.sqrt(mse)

    rmse = round(rmse, 2)

    

    # to send metrics to the Submit Metrics operator, create a Python dictionary of key-value pairs

    metrics_dict = {"RMSE": str(rmse), "n": str(len(df_data))}

    

    # send the metrics to the output port - Submit Metrics operator will use this to persist the metrics 

    api.send("metrics", api.Message(metrics_dict))



    # create & send the model blob to the output port - Artifact Producer operator will use this to persist the model and create an artifact ID

    import pickle

    model_blob = pickle.dumps(lm)

    api.send("modelBlob", model_blob)

    

api.set_port_callback("input", on_input)

 

Close the Script-window, then hit "Save" in the menu bar.

 

Before running the pipeline, we just need to create a Docker image for the Python operator. This gives the flexibility to leverage virtually any Python library, you just need to provide the Docker file, which installs the necessary libraries. You find the Dockerfiles by clicking into the "Repository"-tab on the left, then right-click the "Dockerfiles" folder and select "Create Docker File".

 

Name the file python36marathon.



 

Enter this code into the Docker File window. This code leverages a base image that comes with SAP Data Intelligence and installs the necessary libraries on it. It's advised to specify the versions of the libraries to ensure that new versions of these libraries do not impact your environment.

FROM $com.sap.sles.base

RUN pip3.6 install --user numpy==1.16.4

RUN pip3.6 install --user pandas==0.24.0

RUN pip3.6 install --user sklearn

 

SAP Data Intelligence uses tags to indicate the content of the Docker File. These tags are used in a graphical pipeline to specify in which Docker image an Operator should run. Open the Configuration panel for the Docker File with the icon on the top-right hand corner.

 

Add a custom tag to be able to specify that this Docker File is for the Marathon case. Name it  "marathontimes". No further tags need to be added, as the base image com.sap.sles.base already contains all other tags that are required ("sles", "python36", "tornado").

The "Configuration" should look like:

 

Now save the Docker file and click the "Build"-icon to start building the Docker image.



 

Wait a few minutes and you should receive a confirmation that the build completed successfully.

 

Now you just need to configure the Python operator, which trains the model, to use this Docker image. Go back to the graphical pipeline "10 Train". Right-click the "Python 3"-operator and select "Group".

 

Such a group can contain one or more graphical operators. And on this group level you can specify which Docker image should be used. Select the group, which surrounds the "Python 3" Operator. Now in the group's Configuration select the tag "marathontimes". Save the graph.



 

The pipeline is now complete and we can run it. Go back to the ML Scenario. On the top left you notice a little orange icon, which indicates that the scenario was changed after the current version was created.

 

Earlier versions of SAP Data Intelligence were only able to execute pipelines that had no changes made to them after the last version was taken. This requirement has been removed, hence there is no need to create a new version at the moment. However, when implementing a productive solution, I strongly suggest to deploy only versioned content.

You can immediately execute this graph, to train and save the model! Select the pipeline in the ML Scenario and click the "Execute" button on the right.

 

Skip the optional steps until you get to the "Pipeline Parameters". Set "newArtifactName" to lm_model. The trained regression model will be saved under this name.

 

Click "Save". Wait a few seconds until the pipeline executes and completes. Just refresh the page once in a while and you should see the following screen. The metrics section shows the trained model's quality indicator (RMSE = 16.96) as well as the number of records that were used to train the model (n = 116). The model itself was saved successfully under the name "lm_model".

 

If you scroll down on the page, you see how the model's metrics as well as the model itself have become part of the ML scenario.

 

We have our model, now we want to use it for real-time inference.

Prediction / Inference with REST-API

Go back to the main page of your ML Scenario and create a second pipeline. This pipeline will provide the REST-API to obtain predictions in real-time. Hence call the pipeline "20 Apply REST-API". Now select the template "Python Consumer". This template contains a pipeline that provides a REST-API. We just need to configure it for our model.

 

The "OpenAPI Servlow" operator provides the REST-API. The "Artifact Consumer" loads the trained model from our ML scenario and the "Python36 - Inference" operator ties the two operators together. It receives the input from the REST-API call (here the half-marathon time) and uses the loaded model to create the prediction, which is then returned by the "OpenAPI Servlow" to the client, which had called the REST-API.

 

We only need to change the "Python36 - Inference"-operator. Open its "Script" window. At the top of the code, in the on_model() function, replace the single line "model = model_blob" with

import pickle

model = pickle.loads(model_blob)

 

Add a variable to store the prediction. Look for the on_input() function and add the line "prediction = None" as shown:

def on_input(msg):

    error_message = ""

    success = False

    prediction = None # This line needs to be added

 

Below the existing comment "# obtain your results" add the syntax to extract the input data (half-marathon time) and to carry out the prediction:

# obtain your results

hm_minutes = json.loads(user_data)['half_marathon_minutes']

prediction = model.predict([[hm_minutes]])

 

Further at the bottom of the page, change the "if success:" section to:

if success:

    # apply carried out successfully, send a response to the user

    msg.body = json.dumps({'marathon_minutes_prediction': round(prediction[0], 1)})

 

Close the editor window. Finally, you just need assign the Docker image to the "Python36 - Inference" operator. As before, right-click the operator and select "Group". Add the tag "marathontimes".

 

Save the changes and go back to the ML Scenario. Now deploy the new pipeline. Select the pipeline "20 Apply REST-API" and click the "Deploy" icon.

 

Click through the screens until you can select the trained model from a drop-down. Click "Save".

 

Wait a few seconds and the pipeline is running!

 

As long as that pipeline is running, you have the REST-API for inference. So let's use it! There are plenty of applications you could use to test the REST_API. My personal preference is Postman, hence the following steps are using Postman. You are free to use any other tool of course.

Copy the deployment URL from the above screen. Do not worry, should you receive a message along the lines of "No service at path XYZ does not exist". This URL is not yet complete, hence the error might come up should you try to call the URL.

Now open Postman and enter the Deployment URL as request URL. Extend the URL with v1/uploadjson/. Change the request type from "GET" to "POST".



 

Go to the "Authorization"-tab, select "Basic Auth" and enter your user name and password for SAP Data Intelligence. The user name starts with your tenant's name, followed by a backslash and your actual user name.



 

Go to the "Headers"-tab and enter the key "X-Requested-With" with value "XMLHttpRequest".



 

Finally, pass the input data to the REST-API. Select the "Body"-tab, choose "raw" and enter this JSON syntax:

{

    "half_marathon_minutes": 120

}

 

Press "Send" and you should see the prediction that comes from SAP Data Intelligence! Should you run a half-marathon in 2 hours, the model estimates a marathon time of under 2 hours 24 minutes. Try the REST-API with different values to see how the predictions change. Just don't extrapolate, stay within the half-marathon times of the training data.



 

Should you prefer to authenticate against the REST-API with a certificate, instead of a password, then see Certificate Authentication to call an OpenAPI Servlow pipeline on SAP Data Intelligence.

 

Summary

You have used Python in Jupyter Notebooks to train a Machine Learning model and the code has been deployed in a graphical pipeline for productive use. Your business processes and application can now leverage these predictions. The business users who benefit from these predictions, are typically not exposed to the underlying technologies. They should just receive the information they need, where and when they need it.

Let me give an example that is more business related than running times. Here is a screenshot of a Conversational AI chatbot, which uses such a REST-API from SAP Data Intelligence to estimate the price of a vehicle. The end users is having a chat with a bot and just needs to provide the relevant information about a car to receive a price estimate.



Happy predicting!

 

 

Looking for more?

If you liked this exercise and you want to keep going, you could follow the next blog, which uses SAP Data Intelligence to train a Machine Learning model inside SAP HANA, using the Machine Learning that is embedded inside SAP HANA:
SAP Data Intelligence: Deploy your first HANA ML pipelines

In case you want to stick with native Python, you may wonder how to apply the concept from this blog with your own, more realistic, dataset, that contains more than a single predictor. Below are the most import differences for using three predictors instead of just one. In this new example, we predict the price of a used vehicle, using the car's mileage, horse power, and the year in which the car was build.

Training the model in Jupyter:

from sklearn.linear_model import LinearRegression

x = df_data[['YEAR', 'HP', 'KILOMETER']]

y_true = df_data[['PRICE']]

lm = LinearRegression()

lm.fit(x, y_true)

Applying the model in Jupyter:

x_new = [[2005, 150, 50000]]

predictions = lm_loaded.predict(x_new)

round(predictions[0][0], 2)

Training the model in the pipeline. Just as in Jupyter:

    x = df_data[['YEAR', 'HP', 'KILOMETER']]

    y_true = df_data[['PRICE']]

Inference pipeline, retrieving the new predictor values and creating the new prediction:

                input_year = json.loads(user_data)['YEAR']

                input_hp = json.loads(user_data)['HP']

                input_kilometer = json.loads(user_data)['KILOMETER']

                prediction = model.predict([[input_year, input_hp, input_kilometer]])

Inference pipeline, returning the prediction:

        msg.body = json.dumps({'car_price': round(prediction[0][0], 2)})

Passing the values from Postman:

{

    "YEAR": 2005,

    "HP": 200,

    "KILOMETER": 50000

}

[AndreasForster]

Hi andres.levano.01 , hi sergio.pena , hi mohamed.abdelrasoul ,
I am bit puzzled that the Docker code doesn't build for you.
Maybe your systems have something in common, that prevents this.
I wouldnt be surprised for example, if your Docker cannot connect to the web, and therefore cannot download packages.

Can you please test whether you can build the following code from the documentation.

DI Cloud
https://help.sap.com/viewer/1c1341f6911f4da5a35b191b40b426c8/Cloud/en-US/781938a8d99944d099c94ac8139...
FROM $com.sap.sles.base
RUN pip3.6 install --user numpy=="1.16.1"

DI on premise
https://help.sap.com/viewer/aff95eebc2e04c44816e6ff0d21c3c88/3.0.latest/en-US/d49a07c5d66c413ab14731...
FROM $com.sap.sles.base
RUN pip3.6 install --user numpy

Andreas

[former_member327053]

Hi andreas.forster ,

tried again the above, but still facing the below error.

can you please advise if there is a way to reinitialize the DI to have a fresh copy or do I need to reinstall anything again? as I think there might be an issue with the installation.

 

------

error building docker image. Docker daemon error: The command '/bin/sh -c pip3.6 install –user numpy==”1.16.1″' returned a non-zero code: 1

------

[AndreasForster]

Hi mohamed.abdelrasoul , andres.levano.01 , sergio.pena , kdsdi
To test this out I have created my on DI trial instance and can now reproduce the error. The Docker code from the documentation, which builds fine in the full DI version,

FROM $com.sap.sles.base

RUN pip3.6 install --user numpy

is giving this error in the trial:
error building docker image. Docker daemon error: The command ‘/bin/sh -c pip3.6 install –user numpy’ returned a non-zero code: 1

The following Docker build succeeds in the DI Trial. However, I am not sure if that image is intended to be used. So far I have not managed at least to also install pandas and sklearn

FROM $com.sap.sles.ml.python

RUN python3.6 -m pip install numpy

Let me check with the colleagues that look after the DI trial.
Andreas

[former_member686373]

Hi Andreas,

 

I test it and i get other error. I check my conexion but it is fine.

 

[object Object],DEBUG,"[stream] [91mNo matching distribution found for tensorflow

[0m",vflow,container,35605,func1

[object Object],DEBUG,"[stream] Removing intermediate container 35cdef30f81e",vflow,container,35605,func1

[object Object],DEBUG,"[error] The command '/bin/sh -c python3.6 -m pip install tensorflow' returned a non-zero code: 1",vflow,container,35605,func1

[object Object],ERROR,"Error building docker image: The command '/bin/sh -c python3.6 -m pip install tensorflow' returned a non-zero code: 1",vflow,container,35269,buildImageCoreDocker

[former_member435889]

Hi Andreas

First of all, thanks for this blog, very little information is available on DI and this helps a lot

In my DI, I have setup an s3 connection and it gives me an "ok" value during connection check. In fact, I am able to connect to aws s3, get the data and run the ML scenario in Jupyter notebook in ML Scenario manager. However, when I try to train the model, the read file gives me a blank screen while trying to get a file, manually keying in the file name does not help and as a result the training job abends.

 

 

Any pointers would be really helpful

Regards

Vinieth

[AndreasForster]

Hello Vinieth, You can get a more detailed error message in the Monitoring tile on the DI launchpad.
There might be a tidy up process. So in case your dead pipleline does not show anymore, just produce the error once more and it should show up.
Andreas

[AndreasForster]

Hi mohamed.abdelrasoul  , andres.levano.01  sergio.pena , kdsdi ,
Dimitri Vorobiev was able to analyse this behaviour further and identified that the issue is Docker on AWS not being able to connect to the web. Hence Docker cannot download / install the required Python packages. He is also proposing a workaround, please see
https://answers.sap.com/questions/13080892/error-building-docker-image.html?childToView=13084906#com...

Andreas

[ACroll]

Hi Andreas,

very interesting and well-written post!

I was able to create the Python Rest API and tested with Postman successfully.

However, if I try to access the same API from inside a SAPUI5 application (with authorization included), I get access error/failure (403). My research points to CORS restricting access to the remote server (Data Intelligence).

Did you run into issues like this or have any thought on how to overcome it?

SAPUI5 app and DI tenant are not on the same server/origin.

thanks!

Alex

[AndreasForster]

Hi Alex, I love that you want to extend this and put a UI5 front-end on top! That would make for a great hands-on tutorial. I haven't worked much with UI5 though, but because of CORS Postman requires the X-Requested-With header with value XMLHttpRequest.
Maybe this can also be added to the UI5 app?

[ACroll]

Hi Andreas,

I have tried this, still getting 403 (Forbidden) error in console when accessing the API via a button.

onPress: function(oEvent) {

			var oView = this.getView();

			var oResourceBundle = oView.getModel("i18n").getResourceBundle();

			var url = oResourceBundle.getText("url").toString().trim();



			$.ajax({

				type: "POST",

				url: url,

				dataType: "json",

				// beforeSend: function(xhr) {

				// 	xhr.setRequestHeader("Authorization", "Basic Z******=");

				// },

				headers: {

					"X-Requested-With": "XMLHttpRequest",

					"Authorization": "Basic Z******="

				},

				success: function(response) {



				},

				error: function(response) {



				}

			});

		}

This is the ajax call I am making from my SAPUI5 app. As far as I could find, it's how it should be done, but I am open to suggestions of course 🙂

[AndreasForster]

Hi Alexander, Maybe a question on the SAP Community could reach the UI5 experts, that have done something similar before?

[virenp_devi]

Brilliant blog..thank you..very helpful

[former_member649056]

Hi Philipp,

Could you please also help me with how to export a CSV file to the data lake? My code is like below:

result = pd.merge(dataset1, dataset, left_on = 'Income', right_on = [0])



!pip install hdfs

from hdfs import InsecureClient

client = InsecureClient('http://datalake:50070')

with client.write('/shared/DEMO_15761/CustomerSegmentation_Results.csv', encoding = 'utf-8') as writer:

    result.to_csv(writer)

I got an error saying that “Invalid file path or buffer object type: <class ‘hdfs.util.AsyncWriter’>”

Thanks for any hint.

Nina

[miguelmeza]

Hi Andreas.

 

I've been following your tutorial and got to a point where i get an error and can not continue.

 

I was creating the dockerfile with the content and the tag that you specified. However, When I click "save" the save and build icons are disabled and when I hover above them I get a "prohibited" red icon. In the logs I can see a "warning" log but the message only says "1". Are you familiar with this error?

 

I appreciate your help

 

[AndreasForster]

Hi Miguel, It seems the latest DI Cloud release introduced an issue.
Our colleagues are working on a fix. felix.bartler posted a workaround
https://answers.sap.com/questions/13127511/sap-di-cloud-cant-add-tags-to-dockerfile.html
Greetings, Andreas

jens.rannacher

[former_member365114]

Can the SAP DI ML Model APIs be authentication using Token as well?

If Yes, where can we find these token on the SAP DI Application.

User/Password is not the best way for API AUthentication.

I use the below to call the API but throws error that invalid authentication.

In that case I want to understand if the user needs to have any bear min privilege ?

response = requests.request("POST", url, data=payload, headers=headers,auth=HTTPBasicAuth('user', 'password'))

Thank you!

[AndreasForster]

Hello Rahul,
The Python Consumer template is designed for basic auth with user name and password
https://help.sap.com/viewer/97fce0b6d93e490fadec7e7021e9016e/Cloud/en-US/2a351907e4764e3b8224d3cd0cb...
You could ask on the wider forum whether there are alternatives?
https://answers.sap.com/index.html

If you get an invalid authentication, please ensure your user is prefixed by the tenant, ie

default\yourusername

Andreas

[former_member704859]

Hi Elisa,

I am trying to design a similar training pipeline where input data is coming from HANA table. I get an error if I try to use HANA Client Operator with Artifact Producer. If I use Read File Operator, it works fine. Did it work for you? Can you share how did you achieve this scenario?

 

Thanks

Achin Kimtee

[former_member704859]

Hello Andreas,

It's not very clear how to use Constant Operator in combination with Format Converter, HANA Client operator and Artifact Producer. Will you be able to share how the pipeline would look if we use HANA Client Operator to read the HANA table instead of the Read File operator?

Regards

Achin Kimtee

 

[AndreasForster]

Hi Achin, To change the data source from CSV to SAP HANA in the training pipeline, the python code just needs to be adjusted slightly as SAP HANA is providing the data in JSON format (not CSV).

import json

df_data =  pd.read_json(io.StringIO(data))

The full code is then:

import json

import io

import pandas as pd



def on_input(data):

    

    # Obtain data

    df_data =  pd.read_json(io.StringIO(data))

    

    # Get predictor and target

    x = df_data[["HALFMARATHON_MINUTES"]]

    y_true = df_data["MARATHON_MINUTES"]

    

    # Train regression

    from sklearn.linear_model import LinearRegression

    lm = LinearRegression()

    lm.fit(x, y_true)

    

    # Model quality

    import numpy as np

    y_pred = lm.predict(x)

    mse = np.mean((y_pred - y_true)**2)

    rmse = np.sqrt(mse)

    rmse = round(rmse, 2)

    

    # to send metrics to the Submit Metrics operator, create a Python dictionary of key-value pairs

    metrics_dict = {"RMSE": str(rmse), "n": str(len(df_data))}

    

    # send the metrics to the output port - Submit Metrics operator will use this to persist the metrics 

    api.send("metrics", api.Message(metrics_dict))



    # create & send the model blob to the output port - Artifact Producer operator will use this to persist the model and create an artifact ID

    import pickle

    model_blob = pickle.dumps(lm)

    api.send("modelBlob", model_blob)

    

api.set_port_callback("input", on_input)

 

That's the pipeline.

 

The Constant Generator is just sending the SELECT to the HANA Client, ie
SELECT * FROM ML.RUNNINGTIMES

Just to mention, that if the data is in SAP HANA, I suggest to try using the ML algorithms in HANA to avoid the data extraction (Predictive Algorithm Library, Automated Predictive Algorithm Library). Here is an example

https://blogs.sap.com/2020/04/21/sap-data-intelligence-deploy-your-first-hana-ml-pipelines/

Andreas

 

 

[former_member704859]

Hi Andreas,

Thanks for your quick and detailed response. It helped me to move in the right direction. There are 2 other issues which I am facing when I design the pipeline as you suggested.

1. I used constant generator and put select query in the content but in the output of first SAP HANA Client operator, ordering of columns is randomly changing. It’s not the same sequence as present in the HANA table. As ordering changes randomly, python operator can’t accept the same and consequently, pipeline fails.
2. Float values are coming as 52456/1000 instead of 52.456.

Did you come across any such issues in your pipeline? Any inputs are welcome.

Training Pipeline using HANA Table as Source

Regards

Achin Kimtee

[AndreasForster]

Hi Achin, You can control the column sequence by replacing the * with the column names.
SELECT * FROM ML.RUNNINGTIMES

I am surprised though that Python is expecting the columns in a certain order, since the code is retrieving the columns by name, not by index?
y_true = df_data["MARATHON_MINUTES"]

[former_member704859]

Hi andreas.forster ,

Even though I give column names instead of select *, the columns does not come in order.

But you are right. Even though the columns are not in order,the code accepted it and ran successfully. I could successfully generate model using HANA table instead of file.

Regards

Achin Kimtee

[emanuele_fumeo]

Hi Alexander, Hi Andreas,

have you further investigated this topic?

I was trying to create a very simple web UI with some Javascript to consume the REST API of the OpenAPI block directly, but I stumped on the same error.

The only solution that worked for me was to completely bypass the CORS problem with a proxy in between... but this is not a "clean" solution.

I would really appreciate if you could share any thought or even breakthrough 🙂

Thanks and Kind Regards,

Emanuele

[ACroll]

Hi Emanuele,

I have not investigated much further, but have received a response from SAP DI Team that it might have been a bug that prevented the API call up to SAPUI5 version 1.6 or so...

Could not test this because our gateway currently has a SAPUI5 version below this...

Can you share your solution with the proxy? It may not be clean, but maybe worth a try for prototypes/showcases 🙂

Thanks and best,

Alex

[former_member712398]

Hi Andreas,

Thanks for blog. As I am following the steps in the blog but still I am not able to build the docker file.

 

https://answers.sap.com/questions/13177961/couldnt-create-docker-image-on-di-30.html?childToView=131...

Posted this query quite a while ago & proposed solution still have similar error. I am working on Sap DI 3.0

 

Looking forward for the solution.

[AndreasForster]

Hello Rachit,
ffad982802c345bdb6e7377a267f27ce posted some answers to your question on the community.
Could you please try those steps and post the outcome on that forum?
I will subscribe to the question and will try to help if I have any further idea.
Andreas

[former_member712398]

Hi Andreas,

 

Thanks for the response. I have followed the ffad982802c345bdb6e7377a267f27ce suggestion but still I am facing same issue. Here is the error I am facing

Messagefailed to prepare graph description: failed to prepare image: error building docker image. Docker daemon error: The command ‘/bin/sh -c python3.6 -m pip –no-cache-dir install –user pandas’ returned a non-zero code: 1

 

I am using SAP DI trail 3.0.

Will be really helpful, if you can help me to resolve this query.

 

Regards,

Rachit Agarwal

[AndreasForster]

Hello Rachit, Let's try to narrow it down. It was working successfully with this code and just one tag of your choosing

FROM $com.sap.sles.base
RUN pip3.6 install --user numpy==1.16.4
RUN pip3.6 install --user pandas==0.24.0
RUN pip3.6 install --user sklearn

If the build gives the same error, please try building this single line
FROM $com.sap.sles.base

If this succeeeds we know that Docker is working in principle at least
Then build
FROM $com.sap.sles.base
RUN pip3.6 install --user numpy==1.16.4

Should this fail there is a problem adding additional libraries.
This could indicate that DI cannot download libraries from the web. Please see this post from  dimitri
https://answers.sap.com/questions/13080892/error-building-docker-image.html?childToView=13084906#com...

[former_member712398]

Thanks Andreas, for giving a helping end. I think Docker is working fine – as I am able to build by running

FROM $com.sap.sles.base

Adding libraries is causing similar error.

Even I tried to follow Dimitri suggested steps but it’s not helping me out at the moment. After deleting the pipeline I am atleast able to run build the docker but as soon as I tag it while executing the pipeline it fails with same error.

Can you please let me know the way around to it.

Regards,

Rachit Agarwal

[AndreasForster]

Hello Rachit,
The test shows that there is some problem with your Docker environment downloading packages.
I don't know any steps to troubleshoot this beyond Dimitri's suggestion.
Since that trial issue is not specific to this blog, I suggest to continue the discussion with the whole community through the separate question you had posted.
Andreas

[former_member716899]

Hi Prashant,

I also had the problem in DI_DATA_LAKE, so I followed the steps mentioned above. I

am getting error in artifact producer stating “runtimeStorage via $VSYSTEM_RUNTIME_STORE_

MOUNT_PATH not found”.

PS : While debugging I found the error caused due to the code (if condition) in artifact producer  :

var ( //deprecated

	flagCheckRuntimeStorage = true

	flagUsingFileStorage    = true

	gRuntimeStoragePath     = "/tenant/runtime"

)





if flagCheckRuntimeStorage && flagUsingFileStorage {

		runtimeStoragePath, ok := os.LookupEnv("VSYSTEM_RUNTIME_STORE_MOUNT_PATH")

		if !ok {

			ProcessErrorSetup("runtimeStorage", errors.New("runtimeStorage via $VSYSTEM_RUNTIME_STORE_MOUNT_PATH not found"))

			return

		}

		if _, err := os.Stat(runtimeStoragePath); os.IsNotExist(err) {

			ProcessErrorSetup("runtimeStoragePath", err)

			return

		}

		Logf("ArtifactProducer: using the following runtimeStorage: %q", runtimeStoragePath)

	}

Please help me to move forward!

[PrashantJayaram]

Hi Raevanth,

I actually haven't done anything further with Data Intelligence since March, so it's quite likely you're on a newer version.  Maybe my solution no longer works.  Are you using an S3 bucket?

I'm not actually on the DI team, so take my suggestion with a grain of salt, but I suppose one "hacky" fix would be to just change flagCheckRuntimeStorage to false.  Maybe this will break something else though.

If you'd like I can inquire further.  I have a colleague who has done more with DI than I have.  However you may want to try a more "official" DI support channel first.  I'm sure when you started your DI trial there was some support information provided.

By the way, was your DI_DATA_LAKE problem this by any chance?  Just happened to stumble onto it.

 

[former_member716899]

Yes, I used S3 bucket. My DI_DATA_LAKE connection was not stable so I was getting error something like this “DataLakeEndpoint  “502 Bad Gateway while building the artifact producer”.  So I thought of using S3 connection.

Thanks for the clarification, will look some alternative.

[PrashantJayaram]

Good luck!

[former_member704859]

Hi andreas.forster ,

As suggested in this post, I did the API call from Postman and it worked for 2-3 times. But after that it started throwing below error.

Illegal URL request path '/openapi/service/1381f941-8c02-477b-8679-57a07dcd9752/v1/uploadjson/'. No executing host found

I have stopped the pipeline, re-deployed it but still throwing the same error. Can you pls help with this error?

Postman API POST Error

Regards

Achin Kimtee

[AndreasForster]

Hi achinank, Every time the pipleline is started, the url of the REST-API changes.
Do you get the predictions when using the url of the latest deployment?
Andreas

[former_member704859]

Hi andreas.forster ,

After re-deployment of pipeline, I am using the updated URL but the issue is still there. What's weird is, it works sometimes and then it stops suddenly with the error I shared in the previous comment.

 

Regards

Achin Kimtee

 

[AndreasForster]

Hello achinank, If the pipeline is still shown as "Running", I am not sure what could be causing this message. Maybe network connectivity, but I am guessing. Can you please ask to the wider community on https://answers.sap.com/questions/ask.html
In case the pipeline stopped with an error, the "Monitoring" tile could show further details, on why it stopped.

[former_member725287]

Hi andreas.forster

thanks for the comprehensive and interesting tutorial.

I am running the Trial Version 3.0 on GCP Hyperscaler and I have an issue in the training pipeline: i'm able to see the final model score, but the Artifact Producer runs in the following error.

Graph failure: ArtifactProducer: InArtifact: ProduceArtifact: response status from DataLakeEndpoint "502 Bad Gateway": "{\n \"message\": \"Error while executing type extension: Error during exchange with Storage Gateway: 400 - undefined\",\n \"code\": \"10102\",\n \"module\": \"dh-app-connection\",\n \"guid\": \"e9c0e17d-c96a-4da2-8dfe-788a56a6bc89\",\n \"statusCode\": 500,\n \"causes\": [],\n \"hostname\": \"datahub-app-core-f1fc5a8fb5566f4e7e2284-6577c59bf7-txhh5\"\n}"

Could you please indicate me what might be the problem?

Thanks in Advance,

Andrea

[AndreasForster]

Hello  andrea.scalvini , Last month dimitri made a comment that there "is a bug in our automated deployment of DI Trial Edition when deploying on Google Cloud".

He is giving some suggestions in his post, which might be relevant for you as well.

https://answers.sap.com/questions/13204754/runtime-storage-error-in-artifact-producer-sap-di.html

In case that's not resolving it, can you please post this behaviour as question to the wider community

https://answers.sap.com/index.html

[former_member725287]

It worked, thank you very much (and to dimitri for the support.

I add a further information that could be useful for someone else, in the Pipeline 20 Apply REST-API, inside the Python36 - Inference there is this line of code:

model_ready = False

To have response from the API, it needs to be changed to:

model_ready = True

otherwise it would respond with "Model has not yet reached the input port - try again"

[former_member714828]

Hello everyone!

 

First of all great tutorial! I am having problems with the execution of the graph. I am using s3 bucket but i've got this message when execute the graph.

I would apreceate some help here to complete this example.

Thanks in Advance,

Petar

[AndreasForster]

Hello Petar, The s3_files connection is just needed to load the CSV file for training the model.
The Artifact Producer should only use the built-in DI_DATA_LAKE connection.
Maybe you changed some configuration of the Artifact Producer, or (if you are using DI on-prem) the DI_DATA_LAKE might not be configured incorrectly?
Andreas

[former_member714828]

Thank you for the fast forward, if i dont have the DI_DATA_LAKE dont work? Its there anyway to work without the DI_DATA_LAKE, because in our onprem we dont have installed the DI_DATA_LAKE.

[AndreasForster]

Yes, you need the DI_DATA_LAKE connection to make this work.
This can still be setup fully on-prem though. Just passing on a comment from a colleauge that helped another on-prem customer:
You can use an S3 interface such as rook-Ceph. It is essentially an S3 API wrapper around a block storage that is hosted on-premise. You can either deploy rook directly in Kubernetes, which would then leverage the storage provisioner for Kubernetes, or set up a dedicated Ceph filesystem server. From the SAP Data Intelligence perspective it will be the same as connecting to an Amazon S3 storage bucket, except the hostname will be different.

I believe that such a setup with rook-Ceph is supported. But please contact support in case you need an official statement for this.
Andreas

[former_member742168]

First of all, thank you, andreas.forster, for this great blog post, it helps a lot!

I am facing issues with using `get_datahub_connection` method inside JupyterLab. It looks like this method doesn't exist in my notebook. The only one thing I've found inside is a few wrappers (JSON, logging, requests) and two connection contexts, but for HANA connections.

Is it possible to get connected with a predefined S3 connection? What about other connections? Is it possible to use them directly inside JupyterLab?

[AndreasForster]

Hello michlimes, Great to hear you find the blog useful!
get_datahub_connection was deprecated in a recent release. I just haven't updated the blog yet, as I understand that the DI trial is still providing it. Maybe you are in a productive DI cloud system?
If so, the following code should obtain credentials from the central connection.

import requests

connectionId = 's3_files'

connection_content = requests.get("http://vsystem-internal:8796/app/datahub-app-connection/connectionsFull/%s" % connectionId).content

[former_member742168]

andreas.forster In the meantime I've found this in NotebookConnectionContext 🙂 I have only one doubt, correct me if I'm wrong, but does SAP DI responses with credentials in plain text? Is this endpoint available for all DI users?

[AndreasForster]

Hi michlimes , Yes it is clear text at the moment. I understand that this is currently being changed though. christian86