{"id":45366,"date":"2022-04-14T23:11:58","date_gmt":"2022-04-14T23:11:58","guid":{"rendered":"https:\/\/www.thepicpedia.com\/faq\/best-answer-aws-emr-how-to-upload-a-notebook-to-use-on-the-cluster\/"},"modified":"2022-04-14T23:11:58","modified_gmt":"2022-04-14T23:11:58","slug":"best-answer-aws-emr-how-to-upload-a-notebook-to-use-on-the-cluster","status":"publish","type":"post","link":"https:\/\/www.thepicpedia.com\/faq\/best-answer-aws-emr-how-to-upload-a-notebook-to-use-on-the-cluster\/","title":{"rendered":"Best answer: Aws emr how to upload a notebook to use on the cluster ?"},"content":{"rendered":"

The<\/strong> most common way is to upload<\/strong> the data to Amazon S3 and use the built-in features of Amazon EMR to load the data onto your cluster. You can also use the<\/strong> DistributedCache feature of Hadoop to transfer files from a distributed file system to the<\/strong> local file system.<\/p>\n

You asked, how do you use<\/strong> Jupyter notebook<\/strong> for EMR cluster<\/strong>? <\/p>\n

    \n
  1. Python.<\/li>\n
  2. R.<\/li>\n
  3. Scala.<\/li>\n
  4. Apache Toree (which provides the Spark, PySpark, SparkR, and SparkSQL kernels)<\/li>\n
  5. Julia.<\/li>\n
  6. Ruby.<\/li>\n
  7. JavaScript.<\/li>\n
  8. CoffeeScript.<\/li>\n<\/ol>\n

    You asked, how do I create a cluster notebook<\/strong>? <\/p>\n

      \n
    1. Choose Notebooks, Create notebook.<\/li>\n
    2. Enter a Notebook name and an optional Notebook<\/strong> description.<\/li>\n
    3. If you have an active cluster<\/strong> to which you want to attach the notebook, leave the default Choose an existing cluster<\/strong> selected, click Choose, select a cluster from the list, and then click Choose cluster.<\/li>\n<\/ol>\n

      Considering this, where are EMR<\/strong> notebooks saved? Each EMR<\/strong> notebook is saved to Amazon S3 as a file named NotebookName . ipynb . As long as a notebook file is compatible with the same version of Jupyter Notebook that EMR Notebooks is based on, you can open the notebook as an EMR<\/strong> notebook<\/strong>.<\/p>\n

      Subsequently, how do I transfer data from S3 to EMR? <\/p>\n

        \n
      1. Open the<\/strong> Amazon EMR console, and then choose Clusters.<\/li>\n
      2. Choose the Amazon EMR cluster from the<\/strong> list, and then choose Steps.<\/li>\n
      3. Choose Add step, and then choose the following options: <\/li>\n
      4. Choose Add.<\/li>\n
      5. When the<\/strong> step Status changes to Completed, verify that the files were copied to the cluster:<\/li>\n<\/ol>\n<\/ol>\n

        Can EMR read data from S3?<\/h2>\n<\/p>\n

        EMRFS is an implementation of the Hadoop file system used for reading and writing regular files from Amazon EMR directly to Amazon S3.<\/p>\n<\/p>\n

        How do I access AWS Jupyter notebook?<\/h2>\n<\/p>\n
          \n
        1. Create an AWS account. An EC2 instance requires an AWS account. <\/li>\n
        2. Navigate to EC2. Log into AWS and go to the EC2 main page. <\/li>\n
        3. Launch a new instance.<\/li>\n
        4. Select Ubuntu.<\/li>\n
        5. Select t2.micro.<\/li>\n
        6. Check out your new instance.<\/li>\n
        7. Create a new security group.<\/li>\n
        8. Create and download a new key pair.<\/li>\n<\/ol>\n<\/p>\n

          How do I run a Jupyter notebook on AWS GPU?<\/h2>\n<\/p>\n
            \n
          1. 1 – Navigate to the EC2 control panel and follow the “launch instance” link.<\/li>\n
          2. 2 – Select the official AWS deep learning Ubuntu AMI. <\/li>\n
          3. 3 – Select the p2. <\/li>\n
          4. 4 – Configure instance details. <\/li>\n
          5. 5 – Launch your instance and connect to it. <\/li>\n
          6. 6 – Set up SSL certificates. <\/li>\n
          7. 6 – Configure Jupyter. <\/li>\n
          8. 7 – Update Keras.<\/li>\n<\/ol>\n<\/p>\n

            How do you run a PySpark Jupyter notebook on EMR?<\/h2>\n<\/p>\n
              \n
            1. Step 1: Launch an EMR Cluster. To start off, Navigate to the EMR section from your AWS Console. <\/li>\n
            2. Step 2: Connecting to your EMR Cluster. <\/li>\n
            3. Step 3: Install Anaconda. <\/li>\n
            4. Step 3: Launch pyspark.<\/li>\n<\/ol>\n<\/p>\n

              How do you attach a notebook to a cluster?<\/h2>\n<\/p>\n
                \n
              1. Click. Create in the sidebar and select Notebook from the menu. The Create Notebook dialog appears.<\/li>\n
              2. Enter a name and select the notebook’s default language.<\/li>\n
              3. If there are running clusters, the Cluster drop-down displays. Select the cluster you want to attach the notebook to.<\/li>\n
              4. Click Create.<\/li>\n<\/ol>\n<\/p>\n

                How do you attach a cluster in Databricks?<\/h2>\n<\/p>\n

                To attach a cluster to a pool using the cluster creation UI, select the pool from the Driver Type or Worker Type drop-down when you configure the cluster. Available pools are listed at the top of each drop-down list. You can use the same pool or different pools for the driver node and worker nodes.<\/p>\n<\/p>\n

                How do I run a Databricks notebook?<\/h2>\n<\/p>\n

                Click the triangle on the right side of a folder to open the folder menu. Select Create > Notebook. Enter the name of the notebook, the language (Python, Scala, R or SQL) for the notebook, and a cluster to run it on.<\/p>\n<\/p>\n

                Is it possible to compress the output from the EMR cluster?<\/h2>\n<\/p>\n

                Output data compression This can be enabled by setting the configuration setting mapred. output. compress to true. If you are running a streaming job you can enable this by passing the streaming job these arguments.<\/p>\n<\/p>\n

                What is notebook in EMR?<\/h2>\n<\/p>\n

                An EMR notebook is a “serverless” notebook that you can use to run queries and code. Unlike a traditional notebook, the contents of an EMR notebook itself\u2014the equations, queries, models, code, and narrative text within notebook cells\u2014run in a client.<\/p>\n<\/p>\n

                How do I view EMR logs?<\/h2>\n<\/p>\n

                To view cluster logs using the console Open the Amazon EMR console at https:\/\/console.aws.amazon.com\/elasticmapreduce\/ . From the Cluster List page, choose the details icon next to the cluster you want to view. This brings up the Cluster Details page.<\/p>\n<\/p>\n

                What is AWS data Sync?<\/h2>\n<\/p>\n

                Transfer data between on premises and AWS. AWS DataSync is a secure, online service that automates and accelerates moving data between on premises and AWS storage services.<\/p>\n<\/p>\n

                How do I access EMR Hdfs?<\/h2>\n<\/p>\n

                To access EMR Local, use only linux cli commands while to access EMR HDFS we need to add \u201chadoop fs\u201d and \u201c-\u201d as shown above. In AWS, \u201chive\u201d command is used in EMR to launch Hive CLI as shown. Also we can work with Hive using Hue. Please follow the link to launch Hue and access Hive.<\/p>\n<\/p>\n

                What is Amazon AMR?<\/h2>\n<\/p>\n

                Amazon EMR (previously called Amazon Elastic MapReduce) is a managed cluster platform that simplifies running big data frameworks, such as Apache Hadoop and Apache Spark , on AWS to process and analyze vast amounts of data.<\/p>\n<\/p>\n

                What must be created for output location before launching an EMR cluster?<\/h2>\n<\/p>\n

                The most common output format of an Amazon EMR cluster is as text files, either compressed or uncompressed. Typically, these are written to an Amazon S3 bucket. This bucket must be created before you launch the cluster. You specify the S3 bucket as the output location when you launch the cluster.<\/p>\n<\/p>\n

                What is the default input format for an EMR cluster?<\/h2>\n<\/p>\n

                The default input format for a cluster is text files with each line separated by a newline (n) character, which is the input format most commonly used. If your input data is in a format other than the default text files, you can use the Hadoop interface InputFormat to specify other input types.<\/p>\n<\/p>\n

                Is S3 based on HDFS?<\/h2>\n<\/p>\n

                While Apache Hadoop has traditionally worked with HDFS, S3 also meets Hadoop’s file system requirements. Companies such as Netflix have used this compatibility to build Hadoop data warehouses that store information in S3, rather than HDFS.<\/p>\n<\/p>\n

                How do I use AWS CLI in Jupyter notebook?<\/h2>\n<\/p>\n
                  \n
                1. Install and Configure the AWS CLI. Follow the steps in the official AWS docs to install and then configure the AWS CLI. <\/li>\n
                2. Install Jupyter Notebooks \/ Jupyter Lab. <\/li>\n
                3. (Optional) Setup shortcuts to launch Jupyter from Win10 Start Menu. <\/li>\n
                4. Create a Jupyter Notebook for CLI. <\/li>\n
                5. Conclusion. <\/li>\n
                6. References.<\/li>\n<\/ol>\n<\/p>\n

                  How do I run a Jupyter notebook on a different port?<\/h2>\n<\/p>\n
                    \n
                  1. Launch Jupyter Notebook from remote server, selecting a port number for : # Replace with your selected port number jupyter notebook –no-browser –port= <\/li>\n
                  2. You can access the notebook from your remote machine over SSH by setting up a SSH tunnel.<\/li>\n<\/ol>\n<\/p>\n

                    What is AWS Jupyter notebook?<\/h2>\n<\/p>\n

                    Jupyter Notebook is an open-source web application that you can use to create and share documents that contain live code, equations, visualizations, and narrative text.<\/p>\n<\/p>\n

                    How do I access my Jupyter notebook remotely?<\/h2>\n<\/p>\n
                      \n
                    1. On the remote machine, start jupyter notebook from your current directory and specify the port: jupyter notebook –no-browser –port=9999.<\/li>\n
                    2. On the local machine, catch the forwarded port: ssh -NfL localhost:9999:localhost:9999 your_user_name@remote_ip_address.<\/li>\n<\/ol>\n<\/p>\n

                      How do you create a Jupyter notebook in AWS?<\/h2>\n<\/p>\n

                      To create a Jupyter notebook Sign in to the SageMaker console at https:\/\/console.aws.amazon.com\/sagemaker\/ . On the Notebook instances page, open your notebook instance by choosing either Open JupyterLab for the JupyterLab interface or Open Jupyter for the classic Jupyter view.<\/p>\n<\/p>\n

                      How do I use an AWS notebook?<\/h2>\n<\/p>\n