{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":56537,"databundleVersionId":8877088,"sourceType":"competition"}],"dockerImageVersionId":30761,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-09-25T16:57:18.778312Z","iopub.execute_input":"2024-09-25T16:57:18.778762Z","iopub.status.idle":"2024-09-25T16:57:19.238171Z","shell.execute_reply.started":"2024-09-25T16:57:18.778723Z","shell.execute_reply":"2024-09-25T16:57:19.236909Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Upload Data to S3","metadata":{}},{"cell_type":"code","source":"import boto3\nimport os\n\nos.environ['AWS_ACCESS_KEY_ID'] = \"*****\"\nos.environ['AWS_SECRET_ACCESS_KEY'] = \"*****\"\nos.environ['AWS_DEFAULT_REGION'] = \"*****\"\n\ndef upload_to_s3(file_path, bucket_name, s3_key):\n    # Create an S3 client\n    s3 = boto3.client('s3')\n\n    try:\n        # Upload the file\n        s3.upload_file(file_path, bucket_name, s3_key)\n        print(f\"Successfully uploaded {file_path} to {bucket_name}/{s3_key}\")\n    except Exception as e:\n        print(f\"Error uploading file: {str(e)}\")\n\nlocal_train_file_path = '../input/leap-atmospheric-physics-ai-climsim/train.csv'\nlocal_test_file_path = '../input/leap-atmospheric-physics-ai-climsim/test.csv'\ns3_bucket_name = 'capstone-project-ucsd-mle-bootcamp-final'\ns3_train_file = 'train_data.csv'\ns3_test_file = 'test_data.csv'","metadata":{"execution":{"iopub.status.busy":"2024-09-25T16:57:22.56939Z","iopub.execute_input":"2024-09-25T16:57:22.569948Z","iopub.status.idle":"2024-09-25T16:57:22.922449Z","shell.execute_reply.started":"2024-09-25T16:57:22.569905Z","shell.execute_reply":"2024-09-25T16:57:22.921225Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"upload_to_s3(local_train_file_path, s3_bucket_name, s3_train_file)\nupload_to_s3(local_test_file_path, s3_bucket_name, s3_test_file)","metadata":{"execution":{"iopub.status.busy":"2024-09-19T20:28:27.613476Z","iopub.execute_input":"2024-09-19T20:28:27.61393Z","iopub.status.idle":"2024-09-19T20:53:38.636257Z","shell.execute_reply.started":"2024-09-19T20:28:27.613889Z","shell.execute_reply":"2024-09-19T20:53:38.632468Z"},"trusted":true},"outputs":[],"execution_count":null}]}