{
  "id": 128646,
  "title": "Which Instance type Should i use ?",
  "url": "/competitions/deepfake-detection-challenge/discussion/128646",
  "author_name": "",
  "post_date": "2020-02-02T03:58:28.669860700Z",
  "votes": 1,
  "comment_count": 6,
  "views": 0,
  "content": "<p>Hi <strong>Deep Fake Detection  Challengers</strong>, I've received $750 credits to my aws account but i don't know how to utilize those credits in an efficient way. Could any one guide me ?</p>\n\n<p>I would like to know...\n1.  <strong>Which instance type should i use for this competition ?</strong>\n2.  <strong>How much speed i require to process the data  ?</strong></p>\n\n<p>And There are many required fields to get benefited by those credits. if anyone make a notebook with step by step process it's very helpful.</p>\n\n<p><strong>Thanks in Advance</strong> !</p>",
  "messages": [
    {
      "id": "734825",
      "postDate": "02/02/2020 03:58:28",
      "content": "<p>Hi <strong>Deep Fake Detection  Challengers</strong>, I've received $750 credits to my aws account but i don't know how to utilize those credits in an efficient way. Could any one guide me ?</p>\n\n<p>I would like to know...\n1.  <strong>Which instance type should i use for this competition ?</strong>\n2.  <strong>How much speed i require to process the data  ?</strong></p>\n\n<p>And There are many required fields to get benefited by those credits. if anyone make a notebook with step by step process it's very helpful.</p>\n\n<p><strong>Thanks in Advance</strong> !</p>",
      "rawMarkdown": "Hi **Deep Fake Detection  Challengers**, I've received $750 credits to my aws account but i don't know how to utilize those credits in an efficient way. Could any one guide me ?\n\nI would like to know...\n1.  **Which instance type should i use for this competition ?**\n2.  **How much speed i require to process the data  ?**\n\nAnd There are many required fields to get benefited by those credits. if anyone make a notebook with step by step process it's very helpful.\n\n**Thanks in Advance** !",
      "votes": null
    },
    {
      "id": "734861",
      "postDate": "02/02/2020 05:27:48",
      "content": "<p>You can check AWS instance pricing at <a href=\"https://aws.amazon.com/ec2/pricing/on-demand/\">https://aws.amazon.com/ec2/pricing/on-demand/</a></p>\n\n<p>Recommendation is to always start with a lower cost instance such as m5.large (or for faster CPU c5.large), and later go for a higher instance only if the smaller instance is unable to handle the load (require more memory or more threads).</p>",
      "rawMarkdown": "You can check AWS instance pricing at https://aws.amazon.com/ec2/pricing/on-demand/\n\nRecommendation is to always start with a lower cost instance such as m5.large (or for faster CPU c5.large), and later go for a higher instance only if the smaller instance is unable to handle the load (require more memory or more threads).",
      "votes": null
    },
    {
      "id": "734872",
      "postDate": "02/02/2020 05:46:01",
      "content": "<p>Thanks for the response. <a href=\"/sirishks\">@sirishks</a> </p>\n\n<p>I've another question to get understand,  how to transfer the data from kaggle (470 GB) to aws storage ?</p>",
      "rawMarkdown": "Thanks for the response. @sirishks \n\nI've another question to get understand,  how to transfer the data from kaggle (470 GB) to aws storage ?",
      "votes": null
    },
    {
      "id": "737536",
      "postDate": "02/05/2020 13:37:46",
      "content": "<p>do the AWS credits include vm with GPU?</p>",
      "rawMarkdown": "do the AWS credits include vm with GPU?",
      "votes": null
    },
    {
      "id": "738534",
      "postDate": "02/06/2020 16:52:35",
      "content": "<p><a href=\"/optimalfit\">@optimalfit</a> Yes.</p>",
      "rawMarkdown": "optimalfit Yes.",
      "votes": null
    },
    {
      "id": "738890",
      "postDate": "02/07/2020 06:30:47",
      "content": "<ol>\n<li>Start a EC2 instance with an EBS instance. It should have enough space to store the unzipped files</li>\n<li>SSH into your EC2 </li>\n<li>Download the data using wget. There is a separate discussion thread on the forum ( check Abhishek's response) </li>\n<li>Add permissions to access AWS storage</li>\n<li>cp from the command line. </li>\n</ol>\n\n<p>Done. 💯 </p>",
      "rawMarkdown": "1. Start a EC2 instance with an EBS instance. It should have enough space to store the unzipped files\n2. SSH into your EC2 \n3. Download the data using wget. There is a separate discussion thread on the forum ( check Abhishek's response) \n4. Add permissions to access AWS storage\n5. cp from the command line. \n\nDone. 💯",
      "votes": null
    },
    {
      "id": "738898",
      "postDate": "02/07/2020 06:39:09",
      "content": "<p>Thanks for the response..\n I'm using Amazon sagemaker  with ml.c5.xlarge instance having 500 GB volume size  and i am downloading it through <strong>wget --load-cookies cookies.txt <a href=\"https://www.kaggle.com/c/16880/datadownload/dfdc_train_all.zip\">https://www.kaggle.com/c/16880/datadownload/dfdc_train_all.zip</a></strong>  </p>\n\n<p>Initially i was trying to transfer the data directly from kaggle to AWS S3 storage but i couldn't do.</p>",
      "rawMarkdown": "Thanks for the response..\n I'm using Amazon sagemaker  with ml.c5.xlarge instance having 500 GB volume size  and i am downloading it through **wget --load-cookies cookies.txt https://www.kaggle.com/c/16880/datadownload/dfdc_train_all.zip**  \n\nInitially i was trying to transfer the data directly from kaggle to AWS S3 storage but i couldn't do.",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 734861,
      "author_name": "sirishks",
      "author_url": "",
      "post_date": "02/02/2020 05:27:48",
      "content": "<p>You can check AWS instance pricing at <a href=\"https://aws.amazon.com/ec2/pricing/on-demand/\">https://aws.amazon.com/ec2/pricing/on-demand/</a></p>\n\n<p>Recommendation is to always start with a lower cost instance such as m5.large (or for faster CPU c5.large), and later go for a higher instance only if the smaller instance is unable to handle the load (require more memory or more threads).</p>",
      "votes": null,
      "replies": [
        {
          "id": 734872,
          "author_name": "manojkumar03",
          "author_url": "",
          "post_date": "02/02/2020 05:46:01",
          "content": "<p>Thanks for the response. <a href=\"/sirishks\">@sirishks</a> </p>\n\n<p>I've another question to get understand,  how to transfer the data from kaggle (470 GB) to aws storage ?</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 737536,
          "author_name": "optimalfit",
          "author_url": "",
          "post_date": "02/05/2020 13:37:46",
          "content": "<p>do the AWS credits include vm with GPU?</p>",
          "votes": null,
          "replies": []
        },
        {
          "id": 738534,
          "author_name": "saisrinivasreddy",
          "author_url": "",
          "post_date": "02/06/2020 16:52:35",
          "content": "<p><a href=\"/optimalfit\">@optimalfit</a> Yes.</p>",
          "votes": null,
          "replies": []
        }
      ]
    },
    {
      "id": 738890,
      "author_name": "skylord",
      "author_url": "",
      "post_date": "02/07/2020 06:30:47",
      "content": "<ol>\n<li>Start a EC2 instance with an EBS instance. It should have enough space to store the unzipped files</li>\n<li>SSH into your EC2 </li>\n<li>Download the data using wget. There is a separate discussion thread on the forum ( check Abhishek's response) </li>\n<li>Add permissions to access AWS storage</li>\n<li>cp from the command line. </li>\n</ol>\n\n<p>Done. 💯 </p>",
      "votes": null,
      "replies": [
        {
          "id": 738898,
          "author_name": "manojkumar03",
          "author_url": "",
          "post_date": "02/07/2020 06:39:09",
          "content": "<p>Thanks for the response..\n I'm using Amazon sagemaker  with ml.c5.xlarge instance having 500 GB volume size  and i am downloading it through <strong>wget --load-cookies cookies.txt <a href=\"https://www.kaggle.com/c/16880/datadownload/dfdc_train_all.zip\">https://www.kaggle.com/c/16880/datadownload/dfdc_train_all.zip</a></strong>  </p>\n\n<p>Initially i was trying to transfer the data directly from kaggle to AWS S3 storage but i couldn't do.</p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "734825": "Hi **Deep Fake Detection  Challengers**, I've received $750 credits to my aws account but i don't know how to utilize those credits in an efficient way. Could any one guide me ?\n\nI would like to know...\n1.  **Which instance type should i use for this competition ?**\n2.  **How much speed i require to process the data  ?**\n\nAnd There are many required fields to get benefited by those credits. if anyone make a notebook with step by step process it's very helpful.\n\n**Thanks in Advance** !",
    "734861": "You can check AWS instance pricing at https://aws.amazon.com/ec2/pricing/on-demand/\n\nRecommendation is to always start with a lower cost instance such as m5.large (or for faster CPU c5.large), and later go for a higher instance only if the smaller instance is unable to handle the load (require more memory or more threads).",
    "734872": "Thanks for the response. @sirishks \n\nI've another question to get understand,  how to transfer the data from kaggle (470 GB) to aws storage ?",
    "737536": "do the AWS credits include vm with GPU?",
    "738534": "optimalfit Yes.",
    "738890": "1. Start a EC2 instance with an EBS instance. It should have enough space to store the unzipped files\n2. SSH into your EC2 \n3. Download the data using wget. There is a separate discussion thread on the forum ( check Abhishek's response) \n4. Add permissions to access AWS storage\n5. cp from the command line. \n\nDone. 💯",
    "738898": "Thanks for the response..\n I'm using Amazon sagemaker  with ml.c5.xlarge instance having 500 GB volume size  and i am downloading it through **wget --load-cookies cookies.txt https://www.kaggle.com/c/16880/datadownload/dfdc_train_all.zip**  \n\nInitially i was trying to transfer the data directly from kaggle to AWS S3 storage but i couldn't do."
  },
  "source": "meta"
}