labeling – BarChart LabelingFunction-> Label right overlaps with the label of the graphic when BarOrigin -> Left

data = <|"Location NH" -> 1, "Location MO" -> 1, "Location WA" -> 2,
"Location OH" -> 3, "Location CO" -> 4, "Location CA" -> 6,
"Location TX" -> 7, "Location IL" -> 150 |>

Bar graphic[data, LabelingFunction -> Right, BarOrigin -> Left, 
 ChartLabels -> Automatic, ImageSize -> 600]

The label is superimposed with the axis and graphic label.

enter the description of the image here

Is there any way to make the label appear on the left side of the bar instead of on the left side of the bar? Similar to Over, which shows the label above the top of the bar?

Bar graphic[data, LabelingFunction -> Above, ChartLabels -> Automatic, 
 ImageSize -> 600]

enter the description of the image here

tools -> export -> template

Items are not saved, is it intended?

modify project -> edit in the notebook

Ser opens as ADMIN

javascript: What is the meaning and difference of these in JS: () =>, _ =>, function () =>

Learning JavaScript I noticed that the identical examples contain these differences:

1) object.event (& # 39; event-name & # 39 ;, () => {})

2) object.event (& # 39; event-name & # 39 ;, _ => {})

3) object.event (& # 39; event-name & # 39 ;, function () => {})

All are used for the same purpose, there is no difference. They can also put or omit semicolons after each line, put or omit brackets when the call works as "application.quit", "application.quit ();"

All this seems confusing after working with C #. Can anyone completely clarify all the uses and differences of these styles?

python – Ideal architecture for ML training -> Workflow of the API service, with multiple models / services?

I am planning to build a workflow / environment to train and serve the NLP classifiers, which follows something like:

  1. The model training system takes in annotated documents from a subset of a variety of preconfigured sources, along with a set of user-defined parameters on how to execute the model (for example, what n-gram functions will be generated, either for apply negation / stemming). etc)
  2. The model training system issues a model file to an S3 cube
  3. A flask-based API service loads the model from S3 at startup and uses it to provide real-time predictions

However, there are some caveats:

  • The training workflow will be integrated into multiple independent services, not just one
  • Each service can have several models attached (therefore, an incoming POST of a document would receive a response with multiple classifications, based on multiple predictions of different models)
  • Calls per minute per service would be relatively low (maybe a call to a service every few minutes)

I've researched existing offers like SageMaker, but that's limited to one API service per model. Apparently, it is also designed for API services that would receive thousands of calls per second, which is not at all cost-effective for my needs.


As such, here is my plan:

Pre- / Post-processing package. Have a repo code that contains all the preprocessing or postprocessing methods that can be called by the classification channel (both in training and in prediction). All these methods include a large amount of logical variation, dictated by input parameters. This code, by itself, is not implemented anywhere.

Training service. A high-resource EC2 instance that imports the package before or after processing, has input connectors to all possible data sources and sends it to an S3 repository. Data scientists will enter a set of parameters and data sources and execute the training in this instance.

Storage model. The output models are stored in several groups of S3, according to the organizational structure related to the data sources and the type of classifier.

API Services. A series of low-resource flask-based API services that use configuration files to dictate which models to load from the S3 cube. Is also you would need to import the preprocessing / postprocessing package, so that you can apply those methods during the prediction of incoming documents.


So, my questions:

Does this general architecture make sense? Or are there sections that I should rethink?

Should I look for systems that are profitable and that can handle this better than building the entire ecosystem?

mqtt – EMQ X Broker Cluster -> HAProxy -> mosquitto_sub -> network protocol error when communicating with the broker

configuration of the test environment in Hyper-V 2012R2, openSUSE Leap 15.1, using the cluster of 2 nodes EMQ X Broker version 3.1, HAProxy v1.6.

Everything works, except when I tried to execute a mosquitto_sub.exe command in the IP address of HA_Proxy. I receive an error: "A network protocol error occurred when communicating with the broker". I can run a mosquitto_sub.exe command to the IP of any of the intermediaries and it works fine: you can send / receive messages. Attached is the haproxy.cfg file.

Thank you!

haproxy.cfg

drivers: AMD Radeon R5M430 GPU error when trying to use DRI_PRIME = 1 -> Failed to allocate the virtual address for the buffer

Good morning to everybody,
I recently installed the latest version of Ubuntu (19.04).

When I saw that Ubuntu was not using my dedicated AMD Gpu, I started searching online and many people talked about "DRI_PRIME = 1" as a command prefix to put in the terminal to launch my applications.

But when I tried to use it, I found this strange error message:

radeon: Failed to assign the virtual address for the buffer:
radeon: size: 65536 bytes
radeon: alignment: 4096 bytes
radeon: domains: 4
radeon: va: 0x0000000100000000
radeon: Failed to unassign the virtual address for the buffer:
radeon: size: 65536 bytes
radeon: va: 0x100000000
radeon: Failed to assign the virtual address for the buffer:
radeon: size: 65536 bytes
radeon: alignment: 4096 bytes
radeon: domains: 4
radeon: va: 0x0000000100000000
radeon: Failed to unassign the virtual address for the buffer:
radeon: size: 65536 bytes
radeon: va: 0x100000000
radeonsi: Error creating a context.
radeon: Failed to assign the virtual address for the buffer:
radeon: size: 65536 bytes
radeon: alignment: 4096 bytes
radeon: domains: 4
radeon: va: 0x0000000100000000
radeon: Failed to unassign the virtual address for the buffer:
radeon: size: 65536 bytes
radeon: va: 0x100000000
radeon: Failed to assign the virtual address for the buffer:
radeon: size: 65536 bytes
radeon: alignment: 4096 bytes
radeon: domains: 4
radeon: va: 0x0000000100000000
radeon: Failed to unassign the virtual address for the buffer:
radeon: size: 65536 bytes
radeon: va: 0x100000000
radeonsi: Error creating a context.

X Failed request error: GLXBadContext
Failed request major opcode: 152 (GLX)
Opcode minor failed request: 6 (X_GLXIsDirect)
Serial number of the failed application: 35
Current serial number in the output stream: 34

I also tried to use two xrandr commands:

xrandr --listproviders

xrandr --setprovideroffloadsink 0x3f 0x65

Waiting for no one to respond

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