Re: Recommended Configuration

[Pat Ferrel]

That is enough for a development machine and may work if you data is relatively 
small but for big data clusters of CPU with a fair amount of RAM and Storage 
are required. The telling factor is partly how big your data is but also how is 
combines to form models, which will depend on which recommender you are using. 

We usually build big clusters to analyze the data then downsize them when we 
see how much is needed. if you have small data, < 1m events, you may try a 
single machine. 


On Dec 15, 2017, at 3:59 AM, GMAIL  wrote:

Hi. 
Could you tell me recommended configuration for comfort work PredictionIO 
Recommender Template. 
I read that I need 16Gb RAM, but what about the rest (CPU/Storage/GPU(?))? 

P.S. sorry for my English.

Re: universal-recommender and 0.12.0-incubating

[Thibaut Gensollen]

Yes but you will need the branch 0-7-0-snapshot and carefully follow the
instructions.

Regards

Le 15 déc. 2017 17:48, "VI, Tran Tan Phong"  a
écrit :

> Hi all,
>
>
>
> Did someone successfully build, train and deploy a
> « universal-recommender » with Prediction IO “0.12.0-incubating” &
> Elasticsearch 5.5.2?
>
>
>
> Thanks,
>
> Phong
> This message contains information that may be privileged or confidential
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> are not authorized to read, print, retain, copy, disseminate, distribute,
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RE: Error: "unable to undeploy"

[VI, Tran Tan Phong]

Hi Noelia,

Yes of course, simply by using another port, for example 8001.

Phong

De : Noelia Osés Fernández [mailto:no...@vicomtech.org]
Envoyé : jeudi 14 décembre 2017 16:16
À : user@predictionio.apache.org
Cc : u...@predictionio.incubator.apache.org
Objet : Re: Error: "unable to undeploy"

Just one more question :)
Is it possible to deploy more than one app in a single PIO? If so, how can I do 
it?
Thanks!

On 14 December 2017 at 15:57, Александр Лактионов 
> wrote:
Hi Noelia,
you dont have to redeploy your app after train. It will be hot-swapped and the 
previous procces (ran by pio deploy) will change recommendations automatically
14 дек. 2017 г., в 17:56, Noelia Osés Fernández 
> написал(а):

Hi,
The first time after reboot that I train and deploy my PIO app everything works 
well. However, if I then retrain and deploy again, I get the following error:

[INFO] [MasterActor] Undeploying any existing engine instance at 
http://0.0.0.0:8000
[ERROR] [MasterActor] Another process might be occupying 
0.0.0.0:8000. Unable to undeploy.
[ERROR] [TcpListener] Bind failed for TCP channel on endpoint 
[/0.0.0.0:8000]
[WARN] [HttpListener] Bind to /0.0.0.0:8000 failed
[ERROR] [MasterActor] Bind failed. Retrying... (2 more trial(s))
[WARN] [HttpListener] Bind to /0.0.0.0:8000 failed
[ERROR] [TcpListener] Bind failed for TCP channel on endpoint 
[/0.0.0.0:8000]
[ERROR] [MasterActor] Bind failed. Retrying... (1 more trial(s))
[ERROR] [TcpListener] Bind failed for TCP channel on endpoint 
[/0.0.0.0:8000]
[WARN] [HttpListener] Bind to /0.0.0.0:8000 failed
[ERROR] [MasterActor] Bind failed. Retrying... (0 more trial(s))
[ERROR] [TcpListener] Bind failed for TCP channel on endpoint 
[/0.0.0.0:8000]
[WARN] [HttpListener] Bind to /0.0.0.0:8000 failed
[ERROR] [MasterActor] Bind failed. Shutting down.

I thought it was possible to retrain an app that was running and then deploy 
again.
Is this not possible?

How can I kill the running instance?
I've tried the trick in handmade's integration test but it doesn't work:

deploy_pid=`jps -lm | grep "onsole deploy" | cut -f 1 -d ' '`
echo "Killing the deployed test PredictionServer"
kill "$deploy_pid"

I still get the same error after doing this.

Any help is much appreciated.
Best regards,
Noelia











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