![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3307250%2Fe9fa93c14012b812d0fab26f5e08579e%2Fkagglecli.png?generation=1590989529063086&alt=media\")
!pip install kaggle
\n\nfrom google.colab import files\nfiles.upload()
\n\n!mkdir -p ~/.kaggle\n!cp kaggle.json ~/.kaggle\n!chmod 600 ~/.kaggle/kaggle.json
\n\nimport kaggle\nUPDATE:kaggle.api.competition_download_file('siim-isic-melanoma-classification','jpeg')
Copy this and this will extract the files you specified
\n\nUPDATE : \nif the above method doesnt work do this\n1 ) Click the download button of the folder you want download\n2) Cancel the download\n3) Go the Chrome Dev Tools (Ctrl + Shift + I)\n4) In the network section you will see a file called \"jpeg.zip?GoogleAccessID=...\"\n5) Right click there and copy the url as \"copy as CURL(bash)\" or \"copy as POSIX\" in uc-browser\n6) In colab paste this and add -o <some_file>.zip to download the specified folder zip files\nVoila...!!!!!\n7) (OPTIONAL) you can add --progress-bar to view the downloading progress
!pip install kaggle
\n\nfrom google.colab import files\nfiles.upload()
\n\n!mkdir -p ~/.kaggle\n!cp kaggle.json ~/.kaggle\n!chmod 600 ~/.kaggle/kaggle.json
\n\nimport kaggle\nUPDATE:kaggle.api.competition_download_file('siim-isic-melanoma-classification','jpeg')
Copy this and this will extract the files you specified
\n\nUPDATE : \nif the above method doesnt work do this\n1 ) Click the download button of the folder you want download\n2) Cancel the download\n3) Go the Chrome Dev Tools (Ctrl + Shift + I)\n4) In the network section you will see a file called \"jpeg.zip?GoogleAccessID=...\"\n5) Right click there and copy the url as \"copy as CURL(bash)\" or \"copy as POSIX\" in uc-browser\n6) In colab paste this and add -o <some_file>.zip to download the specified folder zip files\nVoila...!!!!!\n7) (OPTIONAL) you can add --progress-bar to view the downloading progress
Thank you
\n\nDo you know where there is information about these commands? I would like to download the data in a certain folder
\n\nRegards
", "rawMarkdown": "Thank you\n\nDo you know where there is information about these commands? I would like to download the data in a certain folder\n\nRegards", "votes": null }, { "id": "869397", "postDate": "06/01/2020 01:52:33", "content": "Did you try it? If yes, how long does it take to download the entire jpeg folder?
\n\nUPDATE: It looks like it is downloading the entire 100 Gb dataset. I ran out of space in my Colab session.
", "rawMarkdown": "Did you try it? If yes, how long does it take to download the entire jpeg folder?\n\nUPDATE: It looks like it is downloading the entire 100 Gb dataset. I ran out of space in my Colab session.", "votes": null }, { "id": "869563", "postDate": "06/01/2020 05:32:53", "content": "
From their github page, this function returns the file we specified from the competition name
", "rawMarkdown": "\n\nFrom their github page, this function returns the file we specified from the competition name", "votes": null }, { "id": "869576", "postDate": "06/01/2020 05:43:58", "content": "So, did you try it? Did it work for you? If yes then how long did it take?
", "rawMarkdown": "So, did you try it? Did it work for you? If yes then how long did it take?", "votes": null }, { "id": "870033", "postDate": "06/01/2020 12:56:20", "content": "I also think that it is downloading the 100 GB, I have tried it from colab and the machine runs out of memory. But in theory it has more than 30 GB free
", "rawMarkdown": "I also think that it is downloading the 100 GB, I have tried it from colab and the machine runs out of memory. But in theory it has more than 30 GB free", "votes": null }, { "id": "870194", "postDate": "06/01/2020 14:50:25", "content": "So did you figure it out? I am also trying to do in Colab.
", "rawMarkdown": "So did you figure it out? I am also trying to do in Colab.", "votes": null }, { "id": "871331", "postDate": "06/02/2020 09:17:02", "content": "@graf10a @himanshuagarwal190 yes that second method works better
", "rawMarkdown": "graf10a @himanshuagarwal190 yes that second method works better", "votes": null }, { "id": "871682", "postDate": "06/02/2020 15:10:33", "content": "Here is a method that works https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/155624
", "rawMarkdown": "Here is a method that works https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/155624", "votes": null }, { "id": "871704", "postDate": "06/02/2020 15:24:51", "content": "UPDATE: I created TFRecords that contain both the images and tabular data. Downloading the 512x512 here is only 3 GB. And downloading the 256x256 here is only 700MB. These files contain all the meta data and test data. From these downloads only, you can make a submission to this competition. (You don't need to download train.csv and test.csv from competition's 100GB file).
\n\nAnd I'm currently uploading 768x768 which is 6GB. And 384x384 which is 1.5GB. And 192x192 which is 300MB and 128x128 which is 200MB.
", "rawMarkdown": "UPDATE: I created TFRecords that contain both the images and tabular data. Downloading the 512x512 [here][1] is only 3 GB. And downloading the 256x256 [here][2] is only 700MB. These files contain all the meta data and test data. From these downloads only, you can make a submission to this competition. (You don't need to download train.csv and test.csv from competition's 100GB file).\n\nAnd I'm currently uploading 768x768 which is 6GB. And 384x384 which is 1.5GB. And 192x192 which is 300MB and 128x128 which is 200MB.\n\n[1]: https://www.kaggle.com/cdeotte/melanoma-512x512\n[2]: https://www.kaggle.com/cdeotte/melanoma-256x256", "votes": null } ], "comments": [ { "id": 868978, "author_name": "maximofn", "author_url": "", "post_date": "05/31/2020 15:56:54", "content": "Thank you
\n\nDo you know where there is information about these commands? I would like to download the data in a certain folder
\n\nRegards
", "votes": null, "replies": [] }, { "id": 869397, "author_name": "graf10a", "author_url": "", "post_date": "06/01/2020 01:52:33", "content": "Did you try it? If yes, how long does it take to download the entire jpeg folder?
\n\nUPDATE: It looks like it is downloading the entire 100 Gb dataset. I ran out of space in my Colab session.
", "votes": null, "replies": [ { "id": 869563, "author_name": "msharuk589", "author_url": "", "post_date": "06/01/2020 05:32:53", "content": "
From their github page, this function returns the file we specified from the competition name
", "votes": null, "replies": [] }, { "id": 869576, "author_name": "graf10a", "author_url": "", "post_date": "06/01/2020 05:43:58", "content": "So, did you try it? Did it work for you? If yes then how long did it take?
", "votes": null, "replies": [] }, { "id": 870033, "author_name": "maximofn", "author_url": "", "post_date": "06/01/2020 12:56:20", "content": "I also think that it is downloading the 100 GB, I have tried it from colab and the machine runs out of memory. But in theory it has more than 30 GB free
", "votes": null, "replies": [] }, { "id": 870194, "author_name": "himanshuagarwal190", "author_url": "", "post_date": "06/01/2020 14:50:25", "content": "So did you figure it out? I am also trying to do in Colab.
", "votes": null, "replies": [] }, { "id": 871331, "author_name": "msharuk589", "author_url": "", "post_date": "06/02/2020 09:17:02", "content": "@graf10a @himanshuagarwal190 yes that second method works better
", "votes": null, "replies": [] } ] }, { "id": 871682, "author_name": "maximofn", "author_url": "", "post_date": "06/02/2020 15:10:33", "content": "Here is a method that works https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/155624
", "votes": null, "replies": [] }, { "id": 871704, "author_name": "cdeotte", "author_url": "", "post_date": "06/02/2020 15:24:51", "content": "UPDATE: I created TFRecords that contain both the images and tabular data. Downloading the 512x512 here is only 3 GB. And downloading the 256x256 here is only 700MB. These files contain all the meta data and test data. From these downloads only, you can make a submission to this competition. (You don't need to download train.csv and test.csv from competition's 100GB file).
\n\nAnd I'm currently uploading 768x768 which is 6GB. And 384x384 which is 1.5GB. And 192x192 which is 300MB and 128x128 which is 200MB.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "868336": "!pip install kaggle\n\nfrom google.colab import files\nfiles.upload()\n\n!mkdir -p ~/.kaggle\n!cp kaggle.json ~/.kaggle\n!chmod 600 ~/.kaggle/kaggle.json\n\nimport kaggle\nUPDATE:`kaggle.api.competition_download_file('siim-isic-melanoma-classification','jpeg')`\n\n\nCopy this and this will extract the files you specified\n\nUPDATE : \nif the above method doesnt work do this\n1 ) Click the download button of the folder you want download\n2) Cancel the download\n3) Go the Chrome Dev Tools (Ctrl + Shift + I)\n4) In the network section you will see a file called \"jpeg.zip?GoogleAccessID=...\"\n5) Right click there and copy the url as \"copy as CURL(bash)\" or \"copy as POSIX\" in uc-browser\n6) In colab paste this and add `-o", "868978": "Thank you\n\nDo you know where there is information about these commands? I would like to download the data in a certain folder\n\nRegards", "869397": "Did you try it? If yes, how long does it take to download the entire jpeg folder?\n\nUPDATE: It looks like it is downloading the entire 100 Gb dataset. I ran out of space in my Colab session.", "869563": "\n\nFrom their github page, this function returns the file we specified from the competition name", "869576": "So, did you try it? Did it work for you? If yes then how long did it take?", "870033": "I also think that it is downloading the 100 GB, I have tried it from colab and the machine runs out of memory. But in theory it has more than 30 GB free", "870194": "So did you figure it out? I am also trying to do in Colab.", "871331": "graf10a @himanshuagarwal190 yes that second method works better", "871682": "Here is a method that works https://www.kaggle.com/c/siim-isic-melanoma-classification/discussion/155624", "871704": "UPDATE: I created TFRecords that contain both the images and tabular data. Downloading the 512x512 [here][1] is only 3 GB. And downloading the 256x256 [here][2] is only 700MB. These files contain all the meta data and test data. From these downloads only, you can make a submission to this competition. (You don't need to download train.csv and test.csv from competition's 100GB file).\n\nAnd I'm currently uploading 768x768 which is 6GB. And 384x384 which is 1.5GB. And 192x192 which is 300MB and 128x128 which is 200MB.\n\n[1]: https://www.kaggle.com/cdeotte/melanoma-512x512\n[2]: https://www.kaggle.com/cdeotte/melanoma-256x256" }, "source": "meta" }