pipelines | Machine Learning Pipelines for Kubeflow | Machine Learning library

 by   kubeflow Python Version: 2.0.0-rc.2 License: Apache-2.0

kandi X-RAY | pipelines Summary

kandi X-RAY | pipelines Summary

pipelines is a Python library typically used in Artificial Intelligence, Machine Learning, Tensorflow applications. pipelines has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However pipelines has 44 bugs. You can install using 'pip install pipelines' or download it from GitHub, PyPI.

Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.
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            kandi-support Support

              pipelines has a medium active ecosystem.
              It has 3210 star(s) with 1438 fork(s). There are 103 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 686 open issues and 2673 have been closed. On average issues are closed in 323 days. There are 272 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pipelines is 2.0.0-rc.2

            kandi-Quality Quality

              OutlinedDot
              pipelines has 44 bugs (19 blocker, 0 critical, 17 major, 8 minor) and 2836 code smells.

            kandi-Security Security

              pipelines has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pipelines code analysis shows 0 unresolved vulnerabilities.
              There are 214 security hotspots that need review.

            kandi-License License

              pipelines is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              pipelines releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are available. Examples and code snippets are not available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed pipelines and discovered the below as its top functions. This is intended to give you an instant insight into pipelines implemented functionality, and help decide if they suit your requirements.
            • Create a pre - generated pipeline
            • Add a pod annotation
            • Set security context
            • Add a pod label
            • Build a builtin implementation of builtin - in builtin
            • Create a tabnet - hyperparameter tuning job
            • Creates a WSGI pipeline pipeline
            • Get an autocoml feature selection pipeline
            • Return default pipeline params
            • Factory for kubeflow
            • Update task_spec
            • Constructs a DistillSkip pipeline
            • Get skip evaluation pipeline
            • Create a ContainerOp from a component specification
            • Hyperov MNIST experiment
            • Create TensorBoard
            • Creates a list of suggested parameter sets from the provided metrics
            • Train a WML model
            • Get the inputs for all the tasks in the pipeline
            • Wrapper for pytorch Cifar10
            • Deploy model parameters
            • Rewrite data to use volumes
            • Update an op
            • Creates a dataset from a csv file
            • Build a python component using the given function
            • Generate an automl_tabular pipeline
            Get all kandi verified functions for this library.

            pipelines Key Features

            No Key Features are available at this moment for pipelines.

            pipelines Examples and Code Snippets

            Tutorial 3: Customize Data Pipelines-Design of Data pipelines
            Pythondot img1Lines of Code : 27dot img1License : Permissive (Apache-2.0)
            copy iconCopy
            img_norm_cfg = dict(
                mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
            train_pipeline = [
                dict(type='LoadImageFromFile'),
                dict(type='LoadAnnotations', with_bbox=True),
                dict(type='Resize', img_scale=(1333, 800),  
            Pipelines
            pypidot img2Lines of Code : 6dot img2no licencesLicense : No License
            copy iconCopy
            >>> pipe = r.pipeline()
            >>> pipe.set('foo', 5)
            >>> pipe.set('bar', 18.5)
            >>> pipe.set('blee', "hello world!")
            >>> pipe.execute()
            [True, True, True]
            
              
            Pipelines
            pypidot img3Lines of Code : 6dot img3no licencesLicense : No License
            copy iconCopy
            >>> pipe = r.pipeline()
            >>> pipe.set('foo', 5)
            >>> pipe.set('bar', 18.5)
            >>> pipe.set('blee', "hello world!")
            >>> pipe.execute()
            [True, True, True]
            
              
            three.js - Web GPURender Pipelines
            JavaScriptdot img4Lines of Code : 173dot img4License : Permissive (MIT License)
            copy iconCopy
            import WebGPURenderPipeline from './WebGPURenderPipeline.js';
            import WebGPUProgrammableStage from './WebGPUProgrammableStage.js';
            
            class WebGPURenderPipelines {
            
            	constructor( device, nodes, utils ) {
            
            		this.device = device;
            		this.nodes = nodes;
            		  
            three.js - Web GPUCompute Pipelines
            JavaScriptdot img5Lines of Code : 43dot img5License : Permissive (MIT License)
            copy iconCopy
            import WebGPUProgrammableStage from './WebGPUProgrammableStage.js';
            
            class WebGPUComputePipelines {
            
            	constructor( device, nodes ) {
            
            		this.device = device;
            		this.nodes = nodes;
            
            		this.pipelines = new WeakMap();
            		this.stages = {
            			compute: new W  
            Vertex AI Pipeline Failed Precondition
            Pythondot img6Lines of Code : 7dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
                dataset_create_op = gcc_aip.TabularDatasetCreateOp(
                project=project,
                display_name=display_name, 
                bq_source=bq_source,
                location = gcp_region
            )
            
            ModelUploadOp step failing with custom prediction container
            Pythondot img7Lines of Code : 13dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            model_upload_op = gcc_aip.ModelUploadOp(
                project="my-project",
                location="us-west1",
                display_name="session_model",
                serving_container_image_uri="gcr.io/my-project/pred:latest",
                serving_container_environment_variables=[
              
            Sharing secrets in Kubeflow pipeline
            Pythondot img8Lines of Code : 25dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            def build_get_data():
                component = kfp.components.load_component_from_file(os.path.join(COMPONENTS_PATH, 'get-data-component.yaml'))()
                component.add_volume(k8s_client.V1Volume(
                    name="get-data-volume",
                    secret=k8s_clie
            Sharing secrets in Kubeflow pipeline
            Pythondot img9Lines of Code : 7dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            config.load_incluster_config()
            v1 = client.CoreV1Api()
            sec = v1.read_namespaced_secret(, ).data
            
            YOUR_SECRET_1 = base64.b64decode(sec.get()).decode('utf-8')
            YOUR_SECRET_2 = base64.b64decode(sec.get()).decode('utf-8')
            
            Issue when trying to pass data between Kubeflow components using files
            Pythondot img10Lines of Code : 39dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            import kfp
            from kfp import dsl
            from kfp import components as comp
            
            
            def add(a: float, b: float, f: comp.OutputTextFile()):
                '''Calculates sum of two arguments'''
                sum_ = a + b
                f.write(str(sum_)) # cast to str
                return sum_
            
            
            de

            Community Discussions

            QUESTION

            How to read an individual items of an array in bash for loop
            Asked 2021-Jun-15 at 14:32

            I have a code snippet below

            ...

            ANSWER

            Answered 2021-Jun-15 at 14:26
            ctr=0
            for ptr in "${values[@]}"
            do
                az pipelines variable-group variable update --group-id 1543 --name "${ptr}" --value "${az_create_options[$ctr]}" #First element read and value updated
                az pipelines variable-group variable update --group-id 1543 --name "${ptr}" --value "${az_create_options[$ctr]}" #Second element read and value updated
                ctr=$((ctr+1))
            done
            
            

            Source https://stackoverflow.com/questions/67987940

            QUESTION

            Apache Beam SIGKILL
            Asked 2021-Jun-15 at 13:51

            The Question

            How do I best execute memory-intensive pipelines in Apache Beam?

            Background

            I've written a pipeline that takes the Naemura Bird dataset and converts the images and annotations to TF Records with TF Examples of the required format for the TF object detection API.

            I tested the pipeline using DirectRunner with a small subset of images (4 or 5) and it worked fine.

            The Problem

            When running the pipeline with a bigger data set (day 1 of 3, ~21GB) it crashes after a while with a non-descriptive SIGKILL. I do see a memory peak before the crash and assume that the process is killed because of a too high memory load.

            I ran the pipeline through strace. These are the last lines in the trace:

            ...

            ANSWER

            Answered 2021-Jun-15 at 13:51

            Multiple things could cause this behaviour, because the pipeline runs fine with less Data, analysing what has changed could lead us to a resolution.

            Option 1 : clean your input data

            The third line of the logs you provide might indicate that you're processing unclean data in your bigger pipeline mmap(NULL, could mean that | "Get Content" >> beam.Map(lambda x: x.read_utf8()) is trying to read a null value.

            Is there an empty file somewhere ? Are your files utf8 encoded ?

            Option 2 : use smaller files as input

            I'm guessing using the fileio.ReadMatches() will try to load into memory the whole file, if your file is bigger than your memory, this could lead to errors. Can you split your data into smaller files ?

            Option 3 : use a bigger infrastructure

            If files are too big for your current machine with a DirectRunner you could try to use an on-demand infrastructure using another runner on the Cloud such as DataflowRunner

            Source https://stackoverflow.com/questions/67684186

            QUESTION

            Verify Artifactory download in Jenkins pipeline
            Asked 2021-Jun-15 at 13:25

            I'm using the Jfrog Artifactory plugin in my Jenkins pipeline to pull some in-house utilities that the pipelines use. I specify which version of the utility I want using a parameter.

            After executing the server.download, I'd like to verify and report which version of the file was actually downloaded, but I can't seem to find any way at all to do that. I do get a buildInfo object returned from the server.download call, but I can find any way to pull information from that object. I just get an object reference if I try to print the buildInfo object. I'd like to abort the build and send a report out if the version of the utility downloaded is incorrect.

            The question I have is, "How does one verify that a file specified by a download spec is successfully downloaded?"

            ...

            ANSWER

            Answered 2021-Jun-15 at 13:25

            This functionality is only available on scripted pipeline at the moment, and is described in the documentation.

            For example:

            Source https://stackoverflow.com/questions/67973899

            QUESTION

            Updating multiple values of a Azure DevOps variable group from another variable group
            Asked 2021-Jun-15 at 13:07

            I have a requirement which is as follows:

            Variable Group A, has 7 set of key=value pairs Variable Group B, has 7 set of key=value pairs.

            In both cases keys are the same, values are only different.

            I am asking from the user, the value of be injected in variable group B, user provides me the variable group A name.

            Code snippet to perform such update is as below:

            ...

            ANSWER

            Answered 2021-Jun-15 at 13:07

            You wrongly used update command:

            Source https://stackoverflow.com/questions/67985912

            QUESTION

            ADX request throttling improvements
            Asked 2021-Jun-14 at 14:37

            I am getting {"code": "Too many requests", "message": "Request is denied due to throttling."} from ADX when I run some batch ADF pipelines. I have came across this document on workload group. I have a cluster where we did not configured work load groups. Now i assume all the queries will be managed by default workload group. I found that MaxConcurrentRequests property is 20. I have following doubts.

            1. Does it mean that this is the maximum concurrent requests my cluster can handle?

            2. If I create a rest API which provides data from ADX will it support only 20 requests at a given time?

            3. How to find the maximum concurrent requests an ADX cluster can handle?

            ...

            ANSWER

            Answered 2021-Jun-14 at 14:37

            for understanding the reason your command is throttled, the key element in the error message is this: Capacity: 6, Origin: 'CapacityPolicy/Ingestion'.

            this means - the number of concurrent ingestion operations your cluster can run is 6. this is calculated based on the cluster's ingestion capacity, which is part of the cluster's capacity policy.

            it is impacted by the total number of cores/nodes the cluster has. Generally, you could:

            • scale up/out in order to reach greater capacity, and/or
            • reduce the parallelism of your ingestion commands, so that only up to 6 are being run concurrently, and/or
            • add logic to the client application to retry on such throttling errors, after some backoff.

            additional reference: Control commands throttling

            Source https://stackoverflow.com/questions/67968146

            QUESTION

            How to "fully bind" a constant buffer view to a descriptor range?
            Asked 2021-Jun-14 at 06:33

            I am currently learning DirectX 12 and trying to get a demo application running. I am currently stuck at creating a pipeline state object using a root signature. I am using dxc to compile my vertex shader:

            ...

            ANSWER

            Answered 2021-Jun-14 at 06:33

            Long story short: shader visibility in DX12 is not a bit field, like in Vulkan, so setting the visibility to D3D12_SHADER_VISIBILITY_VERTEX | D3D12_SHADER_VISIBILITY_PIXEL results in the parameter only being visible to the pixel shader. Setting it to D3D12_SHADER_VISIBILITY_ALL solved my problem.

            Source https://stackoverflow.com/questions/67810702

            QUESTION

            where is Azure DevOps build artifact stored
            Asked 2021-Jun-14 at 04:32

            I am attempting to create a CI pipeline for a WCF project. I got the CI to successfully run but cannot determine where to look for the artifact. My intent is to have the CI pipeline publish this artifact in Azure and then have the CD pipeline run transformations on config files. Ultimately, we want to take that output and store it in blob storage (that will probably be another post since the WCF site is for an API).

            I also realize that I really do not want to zip the artifact since I will need to transform it anyway.

            Here are my questions:

            1. Where is the container that the artifact 'drop' is published to?
            2. How would I publish the site to the container without making it a single file.

            Thanks

            ...

            ANSWER

            Answered 2021-Jun-14 at 04:32

            You will find your artifacts here:

            You got single file because you have in VSBuild /p:PackageAsSingleFile=true

            Also you may consider using a newer task Publish Pipeline Artifact. If not please check DownloadBuildArtifacts task here

            Source https://stackoverflow.com/questions/67963655

            QUESTION

            Error while deploying release pipeline in Azure Devops
            Asked 2021-Jun-12 at 06:24

            I am trying to deploy an existing .Net Core application using Azure Devops by creating Build and release pipelines. The build pipeline worked fine, but I get the below error when running the release pipeline (under Deploy Azure App Service).

            Error: No package found with specified pattern: D:\a\r1\a***.zip
            Check if the package mentioned in the task is published as an artifact in the build or a previous stage and downloaded in the current job.

            What should be done to fix this?

            ...

            ANSWER

            Answered 2021-Jun-10 at 14:57

            This error is because the build task is not configured. You can try to put the below YAML code at the last to make it work.

            Source https://stackoverflow.com/questions/67917621

            QUESTION

            sklearn "Pipeline instance is not fitted yet." error, even though it is
            Asked 2021-Jun-11 at 23:28

            A similar question is already asked, but the answer did not help me solve my problem: Sklearn components in pipeline is not fitted even if the whole pipeline is?

            I'm trying to use multiple pipelines to preprocess my data with a One Hot Encoder for categorical and numerical data (as suggested in this blog).

            Here is my code, and even though my classifier produces 78% accuracy, I can't figure out why I cannot plot the decision-tree I'm training and what can help me fix the problem. Here is the code snippet:

            ...

            ANSWER

            Answered 2021-Jun-11 at 22:09

            You cannot use the export_text function on the whole pipeline as it only accepts Decision Tree objects, i.e. DecisionTreeClassifier or DecisionTreeRegressor. Only pass the fitted estimator of your pipeline and it will work:

            Source https://stackoverflow.com/questions/67943229

            QUESTION

            How can I tell a Microsoft-hosted agent in Azure Devops to preserve the workspace between jobs?
            Asked 2021-Jun-11 at 18:16

            I want to break down a large job, running on a Microsoft-hosted agent, into smaller jobs running sequentially, on the same agent. The large job is organized like this:

            ...

            ANSWER

            Answered 2021-Jun-11 at 18:16

            You can't ever rely on the workspace being the same between jobs, period -- jobs may run on any one of the available agents, which are spread across multiple working folders and possibly even on different physical machines.

            Have your jobs publish artifacts.

            i.e.

            Source https://stackoverflow.com/questions/67941754

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install pipelines

            Install Kubeflow Pipelines from choices described in Installation Options for Kubeflow Pipelines. :star: [Alpha] Starting from Kubeflow Pipelines 1.7, try out Emissary Executor. Emissary executor is Container runtime agnostic meaning you are able to run Kubeflow Pipelines on Kubernetes cluster with any Container runtimes. The default Docker executor depends on Docker container runtime, which will be deprecated on Kubernetes 1.20+.
            Install Kubeflow Pipelines from choices described in Installation Options for Kubeflow Pipelines.
            :star: [Alpha] Starting from Kubeflow Pipelines 1.7, try out Emissary Executor. Emissary executor is Container runtime agnostic meaning you are able to run Kubeflow Pipelines on Kubernetes cluster with any Container runtimes. The default Docker executor depends on Docker container runtime, which will be deprecated on Kubernetes 1.20+.

            Support

            Get started with your first pipeline and read further information in the Kubeflow Pipelines overview. See the various ways you can use the Kubeflow Pipelines SDK. See the Kubeflow Pipelines API doc for API specification. Consult the Python SDK reference docs when writing pipelines using the Python SDK. Refer to the versioning policy and feature stages documentation for more information about how we manage versions and feature stages (such as Alpha, Beta, and Stable).
            Find more information at:

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