kandi X-RAY | cloud-pipeline Summary
kandi X-RAY | cloud-pipeline Summary
Cloud Pipeline solution wraps AWS, GCP and Azure compute and storage resources into a single service. Providing an easy and scalable approach to accomplish a wide range of scientific tasks. Cloud Pipeline provides a Web-based GUI and also supports CLI, which exposes most of the GUI features. Cloud Pipeline supports Amazon Web Services , Google Cloud Platform and Microsoft Azure Cloud providers to run computing and store data.
Top functions reviewed by kandi - BETA
- Parses a single pipeline run .
- Lists the versions of a bucket .
- Associate a filter field .
- merges parameters from runVO to default configuration
- Commit a container
- Determines if a given permission is granted to the given permissions .
- Reads a list of ACLs from a list of object IDs .
- Matches the common system parameters for the given run .
- Builds an email message .
- Check the status of a tool .
cloud-pipeline Key Features
cloud-pipeline Examples and Code Snippets
Trending Discussions on cloud-pipeline
I am using GCP Vertex AI pipeline (KFP) and using
In a component I am getting a gcp_resources documentation :
ANSWERAnswered 2022-Feb-14 at 21:24
In this case is the best way to extract the information. But, I recommend using the yarl library for complex uri to parse.
You can see this example:
I am currenlty trying to deploy a Vertex pipeline to achieve the following:
Train a custom model (from a custom training python package) and dump model artifacts (trained model and data preprocessor that will be sed at prediction time). This is step is working fine as I can see new resources being created in the storage bucket.
Create a model resource via
ModelUploadOp. This step fails for some reason when specifying
serving_container_portswith the error message in the errors section below. This is somewhat surprising as they are both needed by the prediction container and environment variables are passed as a dict as specified in the documentation.
This step works just fine using
ANSWERAnswered 2022-Feb-04 at 09:10
After some time researching the problem I've stumbled upon this Github issue. The problem was originated by a mismatch between
kubernetes_api docs. In this case,
serving_container_environment_variables is typed as an
Optional[dict[str, str]] whereas it should have been typed as a
Optional[list[dict[str, str]]]. A similar mismatch can be found for
serving_container_ports argument as well. Passing arguments following kubernetes documentation did the trick:
I'm struggling to correctly set Vertex AI pipeline which does the following:
- read data from API and store to GCS and as as input for batch prediction.
- get an existing model (Video classification on Vertex AI)
- create Batch prediction job with input from point 1.
As it will be seen, I don't have much experience with Vertex Pipelines/Kubeflow thus I'm asking for help/advice, hope it's just some beginner mistake. this is the gist of the code I'm using as pipeline
ANSWERAnswered 2021-Dec-21 at 14:35
I'm glad you solved most of your main issues and found a workaround for model declaration.
input.output observation on
gcs_source_uris, the reason behind it is because the way the function/class returns the value. If you dig inside the class/methods of
google_cloud_pipeline_components you will find that it implements a structure that will allow you to use
.outputs from the returned value of the function called.
If you go to the implementation of one of the components of the pipeline you will find that it returns an output array from
convert_method_to_component function. So, in order to have that implemented in your custom class/function your function should return a value which can be called as an attribute. Below is a basic implementation of it.
I am getting Partial credentials found in env error while running below command.
aws sts assume-role-with-web-identity --role-arn $AWS_ROLE_ARN --role-session-name build-session --web-identity-token $BITBUCKET_STEP_OIDC_TOKEN --duration-seconds 1000
I am using below AWS CLI and Python version-...
ANSWERAnswered 2021-Dec-15 at 13:44
Ugh... I was struggling for two days and right after posting it on stackoverflow in the end, I thought of clearing ENV variable and it worked. Somehow AWS Keys were being stored in env, not sure how?. I just cleared them by below cmd and it worked :D
I am using Kubeflow pipelines (KFP) with GCP Vertex AI pipelines. I am using
kfp==1.8.5 (kfp SDK) and
google-cloud-pipeline-components==0.1.7. Not sure if I can find which version of Kubeflow is used on GCP.
I am bulding a component (yaml) using python inspired form this Github issue. I am defining an output like:...
ANSWERAnswered 2021-Nov-18 at 19:26
I didn't realized in the first place that
ConcatPlaceholder accept both Artifact and string. This is exactly what I wanted to achieve:
I am creating a pod from an image which resides on the master node. When I create a pod on the master node to be scheduled on the worker node, I get the status of pod ErrImageNeverPull...
ANSWERAnswered 2020-Nov-08 at 17:01
When kubernetes creates containers, it first looks to local images, and then will try registry(docker registry by default)
You are getting this error because:
your image cant be found localy on your node.
imagePullPolicy: Never, so you will never try to download image from registry
You have few ways of resolving this, but all of them generally instruct you to get image locally and tag it properly.
To get image on your node you can:
build image from existing Dockerfile
Once you get image, tag it and specify in the deployment
No vulnerabilities reported
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