Has anyone seen this error message when accessing Forge:
Console Message:
failed: Error: too_big: The data requested exceeds the maximum size that can be accessed with a single request.
Dislayed in Forge Viewer:
Data view could not be loaded: There is too much data in your Firebase.
This is happening accross all of my dev/uat/prod datasets. These datasets in the exact same form. Were fully and easily accessible across all levels. This is not a big dataset. The whole exported dataset is around 15meg.
Related
I encountered this error when running pipeline.upsert()
S3UploadFailedError: Failed to upload /tmp/tmpexrxqr32/new.tar.gz to jumpstart-cache-prod-ap-southeast-1/source-directory-tarballs/lightgbm/inference/classification/v1.1.2/sourcedir.tar.gz: An error occurred (AccessDenied) when calling the CreateMultipartUpload operation: Access Denied
My pipeline consists of preprocessing, training, evaluating, creating model and transforming step. When i ran these steps seprarately they were working just fine, but when I put them together in a pipeline, the mentioned error occured. Can anyone tell me what is the cause of this error, I did not write any line of code to upload anything to Jumpstart S3.
model = Model(
image_uri=infer_image_uri,
model_data=step_train.properties.ModelArtifacts.S3ModelArtifacts,
sagemaker_session=pipeline_session,
role=role,
source_dir=infer_source_uri,
entry_point="inference.py"
)
When I comment out the entry_point line, pipeline.upsert() returned no error, but the transform job failed. The model I'm using is JumpStart LightGBM.
Nature of the problem
This happens by default because your experiment tries to upload the source code into its default bucket (which is the jumpstart bucket).
You can check the default bucket assigned to pipeline_session by printing pipeline_session.default_bucket().
Evidently you do not have the correct write permissions on that bucket and I encourage you to check them.
When you comment the entry_point, it doesn't give you that error precisely because it doesn't have anything to load. However, the moment it tries to do inference, it does not find the script clearly.
One possible quick and controllable solution
If you want to apply a cheat to verify what I told you, try putting the code_location parameter in the Model.
This way you can control exactly where your pipeline step goes to write. You will clearly need to specify the s3 uri of the desired destination folder.
code_location (str) – Name of the S3 bucket where custom code is
uploaded (default: None). If not specified, the default bucket created
by sagemaker.session.Session is used.
I know a similar question has been asked (link), but the response didn't work for me.
TLDR: I keep running into errors when trying to authenticate Google Cloud Storage in RStudio. I'm not sure what is going wrong and would love advice.
I have downloaded both the GCS_AUTH_FILE (created a service account with service admin privileges'--downloaded the key associated with the service account) and also downloaded GAR_CLIENT_WEB_JSON by creating a OAuth 2.0 Client ID and downloading that associated JSON file.
I've tried authenticating my Google Cloud Storage in several ways and hit different errors.
Way 1-automatic setup:
gcs_setup()
Then I select any one of the options, and get the error: Error in if (file.exists(local_file)) { : argument is of length zero And that error happens no matter which of the three options I select.
Way 2 - basic, following manual setup instructions from the package:
Sys.setenv("GCS_DEFAULT_BUCKET" = "my-default-bucket",
"GCS_AUTH_FILE" = "/fullpath/to/service-auth.json")
gcs_auth()
In this case, GCS_AUTH_FILE is the file that I mentioned at the beginning of this post, and the GCS_DEFAULT_BUCKET is the name of the bucket. When I run the first line, it seems to be working (nothing goes awry and it runs just fine), but when I run gcs_auth() I get taken to a web browser page that states:
"Authorization Error
Error 400: invalid_request
Missing required parameter: client_id"
Way 3: Following the method from the post that I linked above
This way involves manually setting the .Renviron file w/ the GCS_AUTH_FILE and GAR__CLIENT_WEB_JSON locations, and then running gar_auth(). And yet again, I get the exact same error as in Way 2.
Any ideas about what could be going wrong? Thanks for your help. I wasn't sure how to put in totally reproducible code in this case, so if there is a way I should do that, please let me know.
I would like to load data from Google Analytics into Power BI.
After transforming the data in the Query Editor, I apply the changes.
At first, I see the message 'Waiting for www.googleapis.com' and the number of rows increases.
After a while, I get the following error message:
Failed to save modifications to the server. Error returned: 'OLE DB or ODBC error: [DataSource.Error] There was an internal error..'
Rows with errors have been removed in one of the steps and I have a stable Internet connection.
Does anyone have suggestions on how to solve this?
I was also facing this kind of refreshing issue, First go to edit query and verify the data types and change the data types if needed, after that if you still facing this error, you need to keep open the app.powerbi.com while refresh your PBI dashboard, I was followed the above steps and my issue got resolved now.
I am working on placing a rpivotTable inside of a Shiny app. When I try on test data (a data frame with 1000 rows) I am able to run my app from the command line, and others may access the app given my ip:port as enjoyed. However, when I up the size of the data frame being fed into rpivotTable, the app will 'grey' out and I'm not able to serve the app to others.
I have also, successfully tested this same app, spinning up an EC2 instance, and upping the instance type, but the same thing would happen. I was getting a similar error as shown in this post ERROR: [on_request_read] connection reset by peer in R shiny and on this github issue https://github.com/rstudio/shiny/issues/1469. "ERROR: [on_request_read] connection reset by peer"
My syntax is pretty straightforward in terms of calling and rendering the rpivotTable, but as the size of the data frame increases, my app doesn't work. My suspicion is that this is a timeout parameter in the javascript widget?
I had the same problem, and had to upgrade from t3a.medium to t3a.large. That's more than I wanted, but it works now.
I have the shiny application deployed on the Rshiny pro server. The main aim of the application is to process the input excel files and produce the report in the form of word document which has couple of tables and around 15 graphs rendered using the ggplot.
This application works perfect for the input excel files having less than approx. 3500-4500 rows for around 10 metrics.
Now, I am trying to process the excel file with around 4000-4500 rows for around 20 metrics. While processing this file, during report generation(Rmarkdown file processing) it's showing the network error on the UI only. Despite this error on the UI, in the back-end the report file is getting generated, but the generated report doesn't get downloaded. After this error, the report generation action is getting triggered automatically resulting in the generation of two reports which is again doesn't get downloaded.
So, from this observations, I came to the conclusion that on getting the network error, the download report(report generation and downloading) action is getting triggered again by the server.R.
Has anyone been through such strange situation? I am looking for guidance regarding the two problems here-
What can be the reason of getting the network error sometime only?
What is there, which is triggering the download report action twice?
Is there any option to specify the max. session timeout period?
I have found answers to above questions and I have already answered it here.
Though I would like to quickly answer questions in above explained context.
Reason for getting network error: User will be presented with the network error only if the computations(in this case report generation) doesn't get completed within the 45 seconds. This is because the http_keepalive_timeout parameter is not defined in the server configuration and the default value for http_keepalive_timeout parameter is 45 seconds.
Why download report action was getting triggered twice? : It is because the user session with the server was getting terminated during the computations which were happening after clicking the Download action button
. There is parameter called reconnect in the shiny server configuration which is enabled by default. When a user's connection to the server is interrupted, Shiny Server will offer them a dialog that allows them to reconnect to their existing Shiny session for 15 seconds. This implies that the server will keep the Shiny session active on the server for an extra 15 seconds after a user disconnects in case they reconnect. After the 15 seconds, the user's session will be reaped and they will be notified and offered an opportunity to refresh the page. If this setting is true, the server will immediately reap the session of any user who is disconnected.
You can read about it in the shiny server documentation.
Option to specify the max. session timeout period: Yes. There is a parameter called http_keepalive_timeout. It will allow you to specify the maximum session timeout period. You will need to add http_keepalive_timeout parameter to the shiny-server.conf at the top level with the timeout period you want in seconds as shown below.
http_keepalive_timeout 120;
Read more about http_keepalive_timeout here.