Alternatives to Kubeflow logo

Alternatives to Kubeflow

TensorFlow, Apache Spark, MLflow, Airflow, and Polyaxon are the most popular alternatives and competitors to Kubeflow.
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What is Kubeflow and what are its top alternatives?

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
Kubeflow is a tool in the Machine Learning Tools category of a tech stack.
Kubeflow is an open source tool with 10K GitHub stars and 1.6K GitHub forks. Here鈥檚 a link to Kubeflow's open source repository on GitHub

Top Alternatives to Kubeflow

  • TensorFlow

    TensorFlow

    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. ...

  • Apache Spark

    Apache Spark

    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning. ...

  • MLflow

    MLflow

    MLflow is an open source platform for managing the end-to-end machine learning lifecycle. ...

  • Airflow

    Airflow

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed. ...

  • Polyaxon

    Polyaxon

    An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications. ...

  • Argo

    Argo

    Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition). ...

  • Kubernetes

    Kubernetes

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions. ...

  • Pachyderm

    Pachyderm

    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations. ...

Kubeflow alternatives & related posts

TensorFlow logo

TensorFlow

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Open Source Software Library for Machine Intelligence
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PROS OF TENSORFLOW
  • 23
    High Performance
  • 16
    Connect Research and Production
  • 13
    Deep Flexibility
  • 9
    Auto-Differentiation
  • 9
    True Portability
  • 2
    Easy to use
  • 2
    High level abstraction
  • 1
    Powerful
CONS OF TENSORFLOW
  • 8
    Hard
  • 5
    Hard to debug
  • 1
    Documentation not very helpful

related TensorFlow posts

Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 1.2M views

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

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Apache Spark logo

Apache Spark

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Fast and general engine for large-scale data processing
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PROS OF APACHE SPARK
  • 58
    Open-source
  • 47
    Fast and Flexible
  • 7
    One platform for every big data problem
  • 6
    Easy to install and to use
  • 6
    Great for distributed SQL like applications
  • 3
    Works well for most Datascience usecases
  • 2
    Machine learning libratimery, Streaming in real
  • 2
    In memory Computation
  • 0
    Interactive Query
CONS OF APACHE SPARK
  • 2
    Speed

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Eric Colson
Chief Algorithms Officer at Stitch Fix | 20 upvotes 路 1.7M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 7 upvotes 路 870.6K views

Why we built Marmaray, an open source generic data ingestion and dispersal framework and library for Apache Hadoop :

Built and designed by our Hadoop Platform team, Marmaray is a plug-in-based framework built on top of the Hadoop ecosystem. Users can add support to ingest data from any source and disperse to any sink leveraging the use of Apache Spark . The name, Marmaray, comes from a tunnel in Turkey connecting Europe and Asia. Similarly, we envisioned Marmaray within Uber as a pipeline connecting data from any source to any sink depending on customer preference:

https://eng.uber.com/marmaray-hadoop-ingestion-open-source/

(Direct GitHub repo: https://github.com/uber/marmaray Kafka Kafka Manager )

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MLflow logo

MLflow

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An open source machine learning platform
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PROS OF MLFLOW
  • 3
    Code First
  • 3
    Simplified Logging
CONS OF MLFLOW
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    related MLflow posts

    Airflow logo

    Airflow

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    A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb
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    PROS OF AIRFLOW
    • 39
      Features
    • 12
      Task Dependency Management
    • 11
      Beautiful UI
    • 9
      Cluster of workers
    • 9
      Extensibility
    • 5
      Open source
    • 4
      Python
    • 3
      Complex workflows
    • 2
      K
    • 2
      Dashboard
    • 2
      Custom operators
    • 1
      Good api
    • 1
      Apache project
    CONS OF AIRFLOW
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      related Airflow posts

      Shared insights
      on
      JenkinsJenkinsAirflowAirflow

      I am looking for an open-source scheduler tool with cross-functional application dependencies. Some of the tasks I am looking to schedule are as follows:

      1. Trigger Matillion ETL loads
      2. Trigger Attunity Replication tasks that have downstream ETL loads
      3. Trigger Golden gate Replication Tasks
      4. Shell scripts, wrappers, file watchers
      5. Event-driven schedules

      I have used Airflow in the past, and I know we need to create DAGs for each pipeline. I am not familiar with Jenkins, but I know it works with configuration without much underlying code. I want to evaluate both and appreciate any advise

      See more

      I am looking for the best tool to orchestrate #ETL workflows in non-Hadoop environments, mainly for regression testing use cases. Would Airflow or Apache NiFi be a good fit for this purpose?

      For example, I want to run an Informatica ETL job and then run an SQL task as a dependency, followed by another task from Jira. What tool is best suited to set up such a pipeline?

      See more
      Polyaxon logo

      Polyaxon

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      An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
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      PROS OF POLYAXON
      • 2
        Cli
      • 2
        API
      • 2
        Streamlit integration
      • 2
        Python Client
      • 2
        Notebook integration
      • 2
        Tensorboard integration
      • 2
        VSCode integration
      CONS OF POLYAXON
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        related Polyaxon posts

        Argo logo

        Argo

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        Container-native workflows for Kubernetes
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        PROS OF ARGO
          Be the first to leave a pro
          CONS OF ARGO
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            related Argo posts

            Kubernetes logo

            Kubernetes

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            Manage a cluster of Linux containers as a single system to accelerate Dev and simplify Ops
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            PROS OF KUBERNETES
            • 151
              Leading docker container management solution
            • 121
              Simple and powerful
            • 95
              Open source
            • 70
              Backed by google
            • 55
              The right abstractions
            • 24
              Scale services
            • 16
              Replication controller
            • 9
              Permission managment
            • 6
              Simple
            • 5
              Supports autoscaling
            • 5
              Cheap
            • 3
              Promotes modern/good infrascture practice
            • 3
              No cloud platform lock-in
            • 3
              Self-healing
            • 3
              Open, powerful, stable
            • 3
              Scalable
            • 3
              Reliable
            • 2
              A self healing environment with rich metadata
            • 2
              Captain of Container Ship
            • 2
              Quick cloud setup
            • 1
              Custom and extensibility
            • 1
              Expandable
            • 1
              Easy setup
            • 1
              Gke
            • 1
              Golang
            • 1
              Backed by Red Hat
            • 1
              Everything of CaaS
            • 1
              Runs on azure
            • 1
              Cloud Agnostic
            • 1
              Sfg
            CONS OF KUBERNETES
            • 13
              Poor workflow for development
            • 10
              Steep learning curve
            • 4
              Orchestrates only infrastructure
            • 2
              High resource requirements for on-prem clusters

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            Conor Myhrvold
            Tech Brand Mgr, Office of CTO at Uber | 37 upvotes 路 3.4M views

            How Uber developed the open source, end-to-end distributed tracing Jaeger , now a CNCF project:

            Distributed tracing is quickly becoming a must-have component in the tools that organizations use to monitor their complex, microservice-based architectures. At Uber, our open source distributed tracing system Jaeger saw large-scale internal adoption throughout 2016, integrated into hundreds of microservices and now recording thousands of traces every second.

            Here is the story of how we got here, from investigating off-the-shelf solutions like Zipkin, to why we switched from pull to push architecture, and how distributed tracing will continue to evolve:

            https://eng.uber.com/distributed-tracing/

            (GitHub Pages : https://www.jaegertracing.io/, GitHub: https://github.com/jaegertracing/jaeger)

            Bindings/Operator: Python Java Node.js Go C++ Kubernetes JavaScript OpenShift C# Apache Spark

            See more
            Yshay Yaacobi

            Our first experience with .NET core was when we developed our OSS feature management platform - Tweek (https://github.com/soluto/tweek). We wanted to create a solution that is able to run anywhere (super important for OSS), has excellent performance characteristics and can fit in a multi-container architecture. We decided to implement our rule engine processor in F# , our main service was implemented in C# and other components were built using JavaScript / TypeScript and Go.

            Visual Studio Code worked really well for us as well, it worked well with all our polyglot services and the .Net core integration had great cross-platform developer experience (to be fair, F# was a bit trickier) - actually, each of our team members used a different OS (Ubuntu, macos, windows). Our production deployment ran for a time on Docker Swarm until we've decided to adopt Kubernetes with almost seamless migration process.

            After our positive experience of running .Net core workloads in containers and developing Tweek's .Net services on non-windows machines, C# had gained back some of its popularity (originally lost to Node.js), and other teams have been using it for developing microservices, k8s sidecars (like https://github.com/Soluto/airbag), cli tools, serverless functions and other projects...

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            Pachyderm logo

            Pachyderm

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            MapReduce without Hadoop. Analyze massive datasets with Docker.
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            PROS OF PACHYDERM
            • 3
              Containers
            CONS OF PACHYDERM
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              related Pachyderm posts