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Kubeflow vs Pachyderm: What are the differences?

Key Differences between Kubeflow and Pachyderm

Kubeflow and Pachyderm are both popular tools in the field of data science and machine learning. While they share some similarities, there are significant differences between them. In this article, we will explore six key differences between Kubeflow and Pachyderm.

  1. Workflow Orchestration: Kubeflow provides a comprehensive platform for building and deploying ML workflows on Kubernetes. It offers features like pipeline orchestration, model versioning, and hyperparameter tuning. On the other hand, Pachyderm focuses more on data versioning and provides a data lineage system. It is designed to track changes in data as it flows through ML pipelines.

  2. Collaboration and Deployment: Kubeflow supports a collaborative environment where multiple users can work on the same ML workflow simultaneously. It offers features like version control for pipeline definitions and manages resources efficiently for distributed training. Pachyderm, on the other hand, emphasizes data collaboration and reproducibility. It allows teams to easily share, version, and reproduce datasets as they evolve over time.

  3. Data Versioning: Pachyderm excels in data versioning by using a Git-like approach. It allows users to track changes to data and reproduce ML pipelines using specific dataset versions. Kubeflow also supports versioning of training code, but its data versioning capabilities are not as robust as Pachyderm's.

  4. Job Orchestration: Kubeflow provides a flexible framework for running ML jobs on Kubernetes. It supports various execution engines like TensorFlow, PyTorch, and Apache Spark. Pachyderm, however, focuses more on data processing jobs. It provides a distributed version-controlled file system that enables reproducible data processing workflows.

  5. Model Deployment: Kubeflow offers a scalable and distributed approach to model serving. It provides tools like TensorFlow Serving and Kubernetes-based model serving pipelines. Pachyderm, on the other hand, does not have built-in model serving capabilities. It mainly focuses on data versioning and data processing rather than model deployment.

  6. Ecosystem Integration: Kubeflow integrates well with other tools in the Kubernetes ecosystem, such as Istio for service mesh, Prometheus for monitoring, and Grafana for visualization. Pachyderm, although it can be used alongside Kubernetes, is less tightly integrated with the Kubernetes ecosystem.

In summary, Kubeflow is a comprehensive ML workflow platform with features like pipeline orchestration and model versioning, while Pachyderm specializes in data versioning and provides a data lineage system. Kubeflow emphasizes collaboration, deployment, and model serving, whereas Pachyderm focuses on data collaboration, job orchestration, and reproducibility.

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Pros of Kubeflow
Pros of Pachyderm
  • 9
    System designer
  • 3
    Google backed
  • 3
  • 3
    Kfp dsl
  • 0
  • 3
  • 1
  • 1
    Can run on GCP or AWS

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Cons of Kubeflow
Cons of Pachyderm
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    • 1
      Recently acquired by HPE, uncertain future.

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    What is Kubeflow?

    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.

    What is Pachyderm?

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

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    What companies use Kubeflow?
    What companies use Pachyderm?
    See which teams inside your own company are using Kubeflow or Pachyderm.
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    What tools integrate with Kubeflow?
    What tools integrate with Pachyderm?

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    What are some alternatives to Kubeflow and Pachyderm?
    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
    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 is an open source platform for managing the end-to-end machine learning lifecycle.
    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.
    An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.
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