You must continue executing the code after the wp_safe_remote_get() failed to execute offline.
By default, the wp_safe_remote_get() stops code execution offline.
We get the error not when we try to download the content, but after applying the variable. Before using the downloaded content, you need to check the variable for errors with the is_wp_error() function
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'm trying to open the Artifactory Users Management page, following the Admin->Security->Users tab.
Then I'm getting the following error:
Any idea of what might be causing it? Also, which log I can check this? Couldn't find anything yet.
The server error generally indicates there is problem fetching the user details from Artifactory. This can happen due to any of the following reasons:
when you have a high volume of users and the request is timing out.
There is a chance that you might have created a username with a
special character which is not allowed (using the REST method or some
other method)
There is an issue with the backend database
And the best place to troubleshoot is to first check the request log a good valid entry looks like below:
20200715164402|104|REQUEST|165.225.104.49|admin|GET|/ui/users|HTTP/1.1|200|0
Next check the artifactory.log file for java stack or check catalina.out under tomcat/logs directory.
I have a DialogFlow setup using a firebase function for fulfillments.
I attempted to add two regions to .region() in my index.js file. This led to me deleting my existing firebase function (which had been running on "us-central-1") and adding two new ones for the newly added regions.
After doing so, my dialogflow setup completely fails to do fulfillments. Instead, i get "Webhook call failed. Error: UNKNOWN" with no other details. I tried removing .region() in my index.js, thereby creating a new firebase function similar to the original, but without luck.
I have also tried to add my fulfillment code directly in the inline editor, but this does not work either.
I am at a loss for what to do here. Has anyone experienced similar issues or perhaps know a fix? Please note that the setup worked completely fine prior to adding .region() and deleting the existing firebase function.
NOTE: I am getting a weird error when deploying through the inline editor: "Permission 'cloudfunctions.functions.SetIamPolicy' denied on resource '(my resource)' (or resource may not exist)."
Regarding the following error:
Permission 'cloudfunctions.functions.SetIamPolicy' denied on resource '(my resource)' (or resource may not exist).
I also encountered this when I deleted the function and tried to redeploy it.
I discovered that this occurs when the user (i.e. you) deploying the function does not have sufficient permissions to set IAM policies. In my case, the project was owned by another user whilst I had limited access. After being given owner access, although you likely only need permissions to manage IAM, the function deploys without any errors.
Although you moved the location of the function, you don't mention that you changed the URL for the webhook in Dialogflow to reflect this new location. The URL for Firebase Cloud Functions include the region where the function runs, so if you change the region, you also need to change the fulfillment URL.
When I start my jobs using fast export they sometimes end with an error:
TDWALLETERROR(543): Teradata Wallet error. The helper process is already being traced
When I restart them, they work.
I'm using saved-key protection scheme.
Can someone explain to me why is that error occuring and how to fix it?
Looks like you have a trace activated in one of the scripts, run in the system.
Teradata has a shiffer code that attempts to validate if tracing is running during the wallet invocation - which triggers this error.
This is really frustrating. I have a 104 MB JSON file that I want to upload to my Firebase database through the web front end, but after a random period of time (I've timed it, it's not constant, anywhere from 2 to 20 seconds) I get the error:
There was a problem contacting the server. Try uploading your file again.
So I do try again, and it just keeps failing. I've uploaded files nearly this big before, and the limit for stored data in the realtime DB is 1 GB,
I'm not even close to that. Why does it keep failing to upload?
This is the error I get in chrome dev tools:
Failed to load resource: net::ERR_CONNECTION_ABORTED
https://project.firebaseio.com/.upload?auth=eyJhbGciOiJIUzI1NiIsInR5cCI6…Q3NiwiYWRtaW4iOnRydWUsInYiOjB9.CihvjvLSlx43nOBynAJeyibkBRtygeRlG4Yo1t3jKVA
Failed to load resource: net::ERR_CONNECTION_ABORTED
If I click on the link that shows up in the error, it's a page with the words POST request required.
Turns out the answer is to ignore the web importer entirely and use firebase-import. It worked perfectly first time, and only took a minute to upload the whole json. And it also has merging capabilities.
Using firebase-import as the accepted answer suggested, I get error:
Error: WRITE_TOO_BIG: Data to write exceeds the maximum size that can be modified with a single request.
However, with the firebase-cli I was successful in deleting my entire database:
firebase database:remove /
It seems like it automatically traverses down your database tree to find requests that are under the limit size, then it does multiple delete requests automatically. It takes some time, but definitely works.
You can also import via a json file:
firebase database:set / data.json
I'm unsure if firebase database:set supports merging.