I just started working on apache superset. I wanted to try the example dataset that superset was offering. i ran the command
superset load_examples
in the virtual environment. The execution->
No PIL installation found
2022-02-03 16:41:54,811:INFO:superset.utils.screenshots:No PIL installation found
Loading examples metadata and related data into examples
Creating default CSS templates
Loading [World Bank's Health Nutrition and Population Stats]
Creating table [World Bank Health Data] reference
Creating a World's Health Bank dashboard
Loading [Birth names]
Creating some slices
Creating a dashboard
Loading [Random long/lat data]
Creating table reference
Creating a slice
Loading [Country Map data]
Creating table reference
Creating a slice
Loading [San Francisco population polygons]
Creating table San Francisco Population Polygons reference
Loading [Flights data]
Done loading table!
Loading [BART lines]
Creating table San Franciso BART Lines reference
Loading [Multi Line]
Creating table [World Bank Health Data] reference
Creating a World's Health Bank dashboard
Creating some slices
Creating a dashboard
Loading [Misc Charts] dashboard
Creating the dashboard
Loading DECK.gl demo
Loading deck.gl dashboard
Creating Scatterplot slice
Creating Screen Grid slice
Creating Hex slice
Creating Grid slice
Creating Polygon slice
Creating Arc slice
Creating Path slice
Creating a dashboard
Seems like it installed without any problems. Now when i go to my superset dashboard, i find World Bank's Data i click on it, and all the charts say
No results were returned for this query. If you expected results to be returned, ensure any filters are configured properly and the datasource contains data for the selected time range.
Then i check all the example dashboards, all of them have the same written on them.
I dont know what could be the issue. Can someone help?
Run superset fab create-admin and create a user with Username(admin) to be able to load the examples.
Then superset init after load_examples
Related
I generated a model on Google Vision (object detection) and I wanted to know if I could add new datasets over time, without having to reprocess the already modeled datasets
I take the example of google :
I have a dataset with roses, tulips ...,
I have already created a moldel with the flowers
And I wanted to add a new dataset with just sunflowers,
without deleting the models of the previous flowers
how I do to add the sunflowers ?
To add new data to your dataset (see Importing images into a non-empty dataset):
Select the dataset from the Datasets page to go to its details page.
On the Dataset details page, select the Import tab.
Selecting the Import tab will take you to the Create dataset page
You can then specify the Google Cloud Storage location of your .csv file and select Import to begin the image import process.
But in your case, you will need to train a new model. If you resume training of your existing model, it will fail. Because your dataset's labels will be changed by adding the sunflower label.
A model with a different number of labels has a different underlying structure (E.g.: the output layer would have more nodes because it has as many nodes as labels) so you can’t resume a model’s training with a dataset that has a different number of labels.
Note that you can add more data to your existing dataset and resume training but only if you add data for the already existing labels.
I am working on a PowerBi project and I need some advice/questions on the best way to approach this project. I am tasked to create a dashboard for employee metrics pulled from an onsite SQL Server database. The managers here are going to have access to the PowerBi cloud, so I will end up uploading this to the cloud. There are 10 or so metrics that need to be shown on the dashboard. We have 5000+ employees. My first thought was to create a table and dump all the metrics into a table and set the PowerBi report to import the data, but that seems excessive and a waste of space to upload all that data to the CLOUD because all of the managers don't need access to every employee. They may want to see 1 or 2 employees' metrics on the dashboard.
My second thought is to (and if this is possible) create a stored procedure that will take the employee id and output a dataset for PowerBi to create a visual for. On the dashboard, have a list of employees and when a manager selects one, PowerBi will call the stored procedure with the employee id and the dataset will be returned for PowerBi to decipher into a visual based on my measurements. I guess I would set the PowerBi report connection type as DIRECT QUERY?
Here are my questions:
Is this possible? Is it possible to what I am thinking for my second plan? Is this how DIRECT QUERY works?
If so, how does DIRECT QUERY work with the PowerBi cloud?
What is setup like? Do I just install the PowerBi Data Gateway/configure it like IMPORT DATA and PowerBi does the rest?
A couple of queries:
What is the frequency of data update ?
In case if it is a batch job, it is ideally preferable to import that data from source into powerbi model and do reporting on the imported data as
a) The performance would be quicker
b) There would be no to and for of data across on prem database and cloud
c) the source would not be impacted constantly
So is the ask to have RLS wherein the managers should see only the employees under them?
Then it is pretty easy to implement RLS in imported version rather than in case of direct query.
Also you won't be able to pass parameters to stored procedures, and you can't execute them in direct query mode. You can however, create table valued functions which give you the ability to use table variables and perform other functions that are more complex in nature in Direct Query mode
you can refer this for additional details :
https://community.powerbi.com/t5/Desktop/Can-i-call-Stored-Procedure-with-Direct-Query/m-p/267141#:~:text=%40Pallavi%20you%20won't%20be,nature%20in%20Direct%20Query%20mode.
I am trying to build an object detection model using Google Cloud Vision. The model should draw bounding boxes around rice
What I have done so far:
I have imported an image set of 15 images
I have used the Google Cloud tool to draw ~550 bounding boxes in 10 images
Where I am stuck:
I have built models before, and the data set was automatically split into train, validation and test set. This time however, Google Cloud is not splitting the data set.
What I have tried:
Downloading the .csv with the labeled data and reimporting it into Google Cloud
Adding more labels beyond the one label I have right now
Deleting and recreating the data set
How can I get Google Cloud to split the data set?
Your problem is that Google Cloud Platform determined your train, test and validation sets when you uploaded your images. Your test and validation images are likely your last 5 images which are not available for training if you have not labeled them yet. If you label all of your images or remove those from the dataset you should be able to train. See this SO answer for more info.
You can verify this by clicking the Export Data option and downloading a CSV of your dataset: you can see that data set categories are already defined, even for images that have not yet been labeled yet.
I have uploaded a report to PBI Cloud with several bookmarks and different data models like tables and different charts. Now I made some changes to this report and published it by replacing the existing one. Some charts were deleted and some new were newly added. When navigating directly to the report in PBI cloud I can see the changes. But the changes are not applied for the App which is connected with that report.
Is there any further step needed to perform so that the models in the PBI Cloud "App" get also updated?
In the workspace there is an Update app button, that you need to click,
This will take you through the process of republishing the app
After setting up my ETL, now I'm preparing the reporting phase. I want to know the diffrence between Import and Connect live a SQL Server analysis Services database methods knowing that I'll be working with measures.
In my example : I need to create a measure which count the emails with failed status by department.
I have (after importing the cube) :
Fact Mailing Count
Mail Status Dimension
Message Template Dimension (having the application name which is the
same name of the department)
Live connect: This will connect power bi to analysis services data model directly.so you will be building your model completely on analysis services project and deploying it frequently. So the power bi will have a live connection to the model and make updates when necessary. when new data processed or new measures or tables created. Here the limitation is you cannot combine multiple sources of data and you have to rely on the SASS Model you have already connected with.
Import: This will import tables to the Power bi and you will be allowed to create and manipulate your facts and dimensions as per your wish inside the power bi(in live connect mode you have to do it in analysis services model itself).
The main obstacle of import mode is When you import large tables will a large number of data, power bi has limitations(1GB) on that.
Creating measures and calculated tables are allowed in both modes.the difference is on which side you create them.
A detailed comparison of both Live connect and Import
https://community.powerbi.com/t5/Community-Blog/Power-BI-Live-connection-vs-Import-comparison-and-limitations/ba-p/84377