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  1. docs/chroot/README.md

    Chroot allows user based namespace isolation on many standard Linux deployments.
    
    ## 1. Prerequisites
    
    - Familiarity with [chroot](http://man7.org/linux/man-pages/man2/chroot.2.html)
    - Chroot installed on your machine.
    
    ## 2. Install MinIO in Chroot
    
    ```sh
    mkdir -p /mnt/export/${USER}/bin
    wget https://dl.min.io/server/minio/release/linux-amd64/minio -O /mnt/export/${USER}/bin/minio
    chmod +x /mnt/export/${USER}/bin/minio
    ```
    
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  2. docs/integrations/veeam/README.md

    ### Create a backup job
    
    #### Backup Virtual Machines with Veeam Backup and Replication
    
    - Under Home > Jobs > Backup in Navigation Pane, click on Backup Job button in the ribbon and choose Virtual Machine. Follow the on screen wizard.
    
    - On the Storage screen, choose the Scale-out Backup Repository that was configured previously.
    
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  3. README.md

    MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.
    
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  4. docs/docker/README.md

    See our web documentation on [Deploying MinIO in Standalone Mode](Deploy Standalone MinIO in a Container) for a more structured tutorial on deploying MinIO in a container.
    
    ## Prerequisites
    
    Docker installed on your machine. Download the relevant installer from [here](https://www.docker.com/community-edition#/download).
    
    ## Run Standalone MinIO on Docker
    
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  5. helm/minio/README.md

    MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.
    
    | IMPORTANT |
    | -------------------------- |
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  6. README.md

    researchers push the state-of-the-art in ML and developers easily build and
    deploy ML-powered applications.
    
    TensorFlow was originally developed by researchers and engineers working within
    the Machine Intelligence team at Google Brain to conduct research in machine
    learning and neural networks. However, the framework is versatile enough to be
    used in other areas as well.
    
    TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)
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  7. docs/multi-tenancy/README.md

    1. [Standalone Deployment](#standalone-deployment)
    2. [Distributed Deployment](#distributed-deployment)
    3. [Cloud Scale Deployment](#cloud-scale-deployment)
    
    ## 1. Standalone Deployment
    
    To host multiple tenants on a single machine, run one MinIO Server per tenant with a dedicated HTTPS port, configuration, and data directory.
    
    ### 1.1 Host Multiple Tenants on a Single Drive
    
    Use the following commands to host 3 tenants on a single drive:
    
    ```sh
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  8. docs/lambda/README.md

    cat > testobject << EOF
    MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.
    EOF
    ```
    
    Upload this object to the bucket via `mc cp`
    ```
    mc cp testobject myminio/functionbucket/
    ```
    
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  9. docs/orchestration/docker-compose/README.md

    ## 1. Prerequisites
    
    * Familiarity with [Docker Compose](https://docs.docker.com/compose/overview/).
    * Docker installed on your machine. Download the relevant installer from [here](https://www.docker.com/community-edition#/download).
    
    ## 2. Run Distributed MinIO on Docker Compose
    
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  10. docs/compression/README.md

    MinIO uses [`klauspost/compress/s2`](https://github.com/klauspost/compress/tree/master/s2)
    streaming compression due to its stability and performance.
    
    This algorithm is specifically optimized for machine generated content.
    Write throughput is typically at least 500MB/s per CPU core,
    and scales with the number of available CPU cores.
    Decompression speed is typically at least 1GB/s.
    
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