mlops | Use GitHub to facilitate automation | MLOps library

 by   github JavaScript Version: Current License: No License

kandi X-RAY | mlops Summary

kandi X-RAY | mlops Summary

mlops is a JavaScript library typically used in Data Preparation, MLOps, Deep Learning, Docker applications. mlops has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

Use GitHub to facilitate automation, collaboration and reproducibility in your machine learning workflows.
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              mlops has a low active ecosystem.
              It has 7 star(s) with 2 fork(s). There are 2 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 0 open issues and 2 have been closed. On average issues are closed in 3 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of mlops is current.

            kandi-Quality Quality

              mlops has no bugs reported.

            kandi-Security Security

              mlops has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              mlops does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
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              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              mlops releases are not available. You will need to build from source code and install.

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            mlops Key Features

            No Key Features are available at this moment for mlops.

            mlops Examples and Code Snippets

            No Code Snippets are available at this moment for mlops.

            Community Discussions

            QUESTION

            Get second last value in each row of dataframe, R
            Asked 2021-May-14 at 14:45

            I am trying to get the second last value in each row of a data frame, meaning the first job a person has had. (Job1_latest is the most recent job and people had a different number of jobs in the past and I want to get the first one). I managed to get the last value per row with the code below:

            first_job <- function(x) tail(x[!is.na(x)], 1)

            first_job <- apply(data, 1, first_job)

            ...

            ANSWER

            Answered 2021-May-11 at 13:56

            You can get the value which is next to last non-NA value.

            Source https://stackoverflow.com/questions/67486393

            QUESTION

            How to manage datasets in ClearML Web UI?
            Asked 2021-Mar-15 at 17:59

            Using a self-deployed ClearML server with the clearml-data CLI, I would like to manage (or view) my datasets in the WebUI as shown on the ClearML webpage (https://clear.ml/mlops/clearml-feature-store/):

            However, this feature does not show up in my Web UI. According to the pricing page, the feature store is not a premium feature. Do I need to configure my server in a special way to use this feature?

            ...

            ANSWER

            Answered 2021-Mar-15 at 17:59

            Disclaimer: I'm part of the ClearML (formerly Trains) Team

            I think this screenshot is taken from the premium version... The feature itself exists in the open-source version, but I "think" some of the dataset visualization capabilities are not available in the open-source self hosted version.

            Nonetheless, you have a fully featured feature-store, with the ability to add your own metrics / samples for every dataset/feature version. The open-source version also includes the advanced versioning & delta based storage for datasets/features (i.e. only the change set from the parent version is stored)

            Source https://stackoverflow.com/questions/66640850

            QUESTION

            Kubeflow vs other options
            Asked 2020-Apr-25 at 17:37

            I am trying to find when it makes sense to create your own Kubeflow MLOps platform:

            • If you are Tensorflow only shop, do you still need Kubeflow? Why not TFX only? Orchestration can be done with Airflow.
            • Why use Kubeflow if all you are using scikit-learn as it does not support GPU, distributed training anyways? Orchestration can be done with Airflow.
            • If you are convinced to use Kubeflow, cloud providers (Azure and GCP) are delivering ML pipeline concept (Google is using Kubeflow under the hood) as managed services. When it makes sense to deploy your own Kubeflow environment then? Even if you have a requirement to deploy on-prem, you have the option to use the cloud resources (nodes and data on cloud) to train your models, and only deploy the model to on-prem. Thus, using Azure or GCP AI Platform as managed service makes the most sense to deliver ML pipelines?
            ...

            ANSWER

            Answered 2020-Apr-25 at 17:37

            Building an MLOps platform is an action companies take in order to accelerate and manage the workflow of their data scientists in production. This workflow is reflected in ML pipelines, and includes the 3 main tasks of feature engineering, training and serving.

            Feature engineering and model training are tasks which require a pipeline orchestrator, as they have dependencies of subsequent tasks and that makes the whole pipeline prone to errors.

            Software building pipelines are different from data pipelines, which are in turn different from ML pipelines.

            A software CI/CD flow compiles the code to deploy-able artifacts and accelerates the software delivery process. So, code in, artifact out. It's being achieved by the invocation of compilation tasks, execution of tests and deployment of the artifact. Dominant orchestrators for such pipelines are Jenkins, Gitlab-CI, etc.

            A data processing flow gets raw data and performs transformation to create features, aggregations, counts, etc. So data in, data out. This is achieved by the invokation of remote distributed tasks, which perform data transformations by storing intermediate artifacts in data repositories. Tools for such pipelines are Airflow, Luigi and some hadoop ecosystem solutions.

            In the machine learning flow, the ML engineer writes code to train models, uses the data to evaluate them and then observes how they perform in production in order to improve them. So code and data in, model out. Hence the implementation of such a workflow requires a combination of the orchestration technologies we've discussed above.

            TFX present this pipeline and proposes the use of components that perform these subsequent tasks. It defines a modern, complete ML pipeline, from building the features, to running the training, evaluating the results, deploying and serving the model in production

            Kubernetes is the most advanced system for orchestrating containers, the defacto tool to run workloads in production, the cloud-agnostic solution to save you from a cloud vendor lock-in and hence optimize your costs.

            Kubeflow is positioned as the way to do ML in Kubernetes, by implementing TFX. Eventually it handling the code and data in, model out. It provides a coding environment by implementing jupyter notebooks in the form of kubernetes resources, called notebooks. All cloud providers are onboard with the project and implement their data loading mechanisms across KF's components. The orchestration is implemented via KF pipelines and the serving of the model via KF serving. The metadata across its components are specified in the specs of the kubernetes resources throughout the platform.

            In Kubeflow, the TFX components exist in the form of reusable tasks, implemented as containers. The management of the lifecycle of these components is achieved through Argo, the orchestrator of KF pipelines. Argo implements these workflows as kubernetes CRDs. In a workflow spec we define the dag tasks, the TFX components as containers, the metadata which will be written in the metadata store, etc. The execution of these workflows is happening nicely using standard kubernetes resources like pods, as well as custom resource definitions like experiments. That makes the implementation of the pipeline and the components language-agnostic, unline Airflow which implements the tasks in python only. These tasks and their lifecycle is then managed natively by kubernetes, without the need to use duct-tape solutions like Airflow's kubernetes-operator. Since everything is implemented as kubernetes resources, everything is a yaml and so the most Git friendly configuration you can find. Good luck trying to enforce version control in Airflow's dag directory.

            The deployment and management of the model in production is done via KF serving using the CRD of inferenceservice. It utilizes Istio's secure access to the models via its virtualservices, serverless resources using Knative Serving's scale-from-zero pods, revisions for versioning, prometheus metrics for observability, logs in ELK for debugging and more. Running models in production could not be more SRE friendly than that.

            On the topic of splitting training/serving between cloud and on-premise, the use of kubernetes is even more important, as it abstracts the custom infrastructure implementation of each provider, and so provides a unified environment to the developer/ml engineer.

            Source https://stackoverflow.com/questions/60787646

            QUESTION

            How to use requirements.txt or similar for a pickle object
            Asked 2020-Feb-28 at 02:21
            Problem

            How can I dump a pickle object with its own dependencies?

            The pickle object is generally generated from a notebook.

            I tried creating virtualenv for the notebook to track dependencies, however this way I don't get only the imports of the pickle object but many more that's used in other places of the application, which is fine enough but not the best solution.

            Background What I'm trying to achieve

            I'm trying to build a MLOps flow. Quick explanation: MLOps is a buzzword that's synonymous with DevOps for machine learning. There are different PaaS/SaaS solutions for it offered by different companies and they commonly solve following problems:

            • Automation of creating web API's from models
            • Handling requirements/dependencies
            • Storing & running scripts used for model generation, model binary and data sets.

            I'll skip the storage part and focus on the first two.

            How I'm trying to achieve

            In my case I'm trying to set up this flow using good old TeamCity where models are pickle objects generated by sk-learn. The requirements are:

            • The dependencies must be explicitly defined
            • Other pickle objects (rather than sk-learn) must be supported.
            • The workflow for a data scientists will look like:
              • Data scientist uploads the pickle model with requirements.txt.
              • Data scientist commits a definition file which look like this:
              ...

            ANSWER

            Answered 2020-Feb-28 at 02:21

            For such complex build steps I use a Makefile for on-prem system, and on cloud-based MLOps using something like AWS CodeBuild with sagemaker.

            An example would be as follows for packaging dependencies and executing the below build steps would require three files your main.py containing driver function of your code, Pipfile containing dependencies for your virtualenv and models:

            1. main.py

            Source https://stackoverflow.com/questions/56150740

            QUESTION

            pip search finds tensorflow, but pip install does not
            Asked 2020-Jan-23 at 06:55

            I am trying to build a Django app that would use Keras models to make recommendations. Right now I'm trying to use one custom container that would hold both Django and Keras. Here's the Dockerfile I've written.

            ...

            ANSWER

            Answered 2019-Jan-02 at 22:56

            It looks like tensorflow only publishes wheels (and only up to 3.6), and Alpine linux is not manylinux1-compatible due to its use of musl instead of glibc. Because of this, pip cannot find a suitable installation candidate and fails. Your best options are probably to build from source or change your base image.

            Source https://stackoverflow.com/questions/54014076

            QUESTION

            bluedata mlops prediction - 'NoneType' object does not support item assignment
            Asked 2019-Dec-03 at 04:39

            I'm calling the BlueData MLOPS prediction API to get a score for my model, but I'm getting the following response:

            ...

            ANSWER

            Answered 2019-Nov-23 at 12:20

            On closer inspection of my request, I noticed that I had the content type set to text/plain:

            Source https://stackoverflow.com/questions/59007538

            QUESTION

            BlueData MLOPS - PermissionError: [Errno 13] Permission denied
            Asked 2019-Dec-03 at 04:35

            I've created a MLOPS project on BlueData 4.0 and mounted the Project Repo (NFS) folder. I created the NFS service on Centos 7x as below:

            ...

            ANSWER

            Answered 2019-Dec-02 at 21:39

            It appears the project repo is created with root as owner and no write permissions on group level.

            To fix, you need to:

            • create a notebook cluster
            • open a Jupyter Terminal
            • sudo chmod -R 777 /bd-fs-mnt/nfsrepo (this only works if you create that cluster as tenant admin, as user you don't have sudo permission)

            Source https://stackoverflow.com/questions/59145582

            QUESTION

            Copying models between workspaces
            Asked 2019-Nov-11 at 10:35

            I am setting up deployment pipelines for our models and I wanted to support this scenario:

            1. User registers model in test AML workspace in test subscription, checks in deployment code/configs that references the model version (there is a requirements.txt-like file that specifies the model ID - name and version)
            2. Azure DevOps CI is triggered after code checkin to run az ml model deploy to a test environment.
            3. User decides after that endpoint works well, wants to deploy to prod. In Azure DevOps, manually invokes a prod pipeline that will use the same checked-in code/configs (with the same referenced model):
              • copy the model from the test AML workspace to a new registered model in the prod AML workspace in a different subscription, with the same version
              • run az ml model deploy with different variables corresponding to the prod env, but using the same checked-in AML code/configs

            I've looked at the MLOps references but can't seem to figure out how to support step 3 in the above scenario.

            I thought I could do an az ml model download to download the model from the test env and register it in the prod env. The registration process automatically sets the version number so, e.g. the config that references myModel:12 is no longer valid since in prod the ID is myModel:1

            How can I copy the model from one workspace in one subscription to another and preserve the ID?

            ...

            ANSWER

            Answered 2019-Aug-30 at 18:33

            You could use model tags to set up your own identifiers that are shared across workspace, and query models with specific tags:

            Source https://stackoverflow.com/questions/57716459

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

            Vulnerabilities

            No vulnerabilities reported

            Install mlops

            You can download it from GitHub.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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