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kkt\nkkt is a tool for kaggle kernel management.\n\n[![Actions Status](https://github.com/ar90n/kkt/workflows/Python%20package/badge.svg)](https://github.com/ar90n/kkt/actions)\n[![PyPI](https://img.shields.io/pypi/v/kkt.svg)](https://pypi.python.org/pypi/kkt)\n[![PythonVersions](https://img.shields.io/pypi/pyversions/kkt.svg)](https://pypi.python.org/pypi/kkt)\n\n## Feature\n* Show the status of the latest version\n* Push your script or notebook to the Kaggle Kernels\n* Pack and emmbedded your library codes into the generated bootstrap codes\n* Create a dataset containing your dependent packages\n* Add bootstrap codes into the head of your script or notebook automatically\n* Add git tags whose name is corresponding kernel version\n* Set environment variable for your kernels\n\n## Installation\nFor now, kkt is designed to be used with poetry. So kkt can be installed by the following.\n\n```bash\n$ poetry add kkt --dev\n```\n\n## Usage\n\n### Set username and token of kaggle-api\nPlease setup your kaggle-api credentials as following this [article](https://github.com/Kaggle/kaggle-api#api-credentials)\n\n### Setup kkt in your project\nSetup this project for [digit-recognizer competition](https://www.kaggle.com/c/digit-recognizer).\nIn this configuration, we use script.py. If you want to use notebook, kkt also support it.\n\n```bash\n$ poetry run kkt init\nAppending Kkt section into your pyproject.toml config.\ncompetition: digit\n0 digit-recognizer\n\u003e 0\nslug: kkt-example\ncode_file [script.py]: script.py\nkernel_type [script]: script\nis_private [Y/n]: n\nenable_gpu [y/N]: n\nenable_internet [y/N]: y\nWould you like to add dataset sources? [y/N]: n\nenable_git_tag [y/N]: n\n```\n\n### Create kkt_example package and its driver code.\nkkt_example provides random choice solver for digit-recognizer competition.\n```bash\n$ tree\n.\n├── kkt_example\n│   └── __init__.py\n├── poetry.lock\n├── pyproject.toml\n└── script.py\n\n1 directory, 4 files\n```\n\n__init__.py\n```python\nfrom pathlib import Path\nimport random\n\nimport pandas as pd\n\ndef choice():\n    return random.randint(0, 9)\n\ndef load_sample_submission():\n    path = Path(\"..\") / \"input\" / \"digit-recognizer\" / \"sample_submission.csv\"\n    return pd.read_csv(path,  index_col=\"ImageId\")\n```\n\nscript.py\n```python\nimport kkt_example\n\nsubmission = kkt_example.load_sample_submission()\nfor _, row in submission.iterrows():\n    row[\"Label\"] = kkt_example.choice()\n\n    submission.to_csv(\"submission.csv\")\n```\n\npyproject.toml\n```toml\n[tool.poetry]\nname = \"kkt-example\"\nversion = \"0.1.0\"\ndescription = \"\"\nauthors = [\"Masahiro Wada \u003cargon.argon.argon@gmail.com\u003e\"]\n\n[tool.poetry.dependencies]\npython = \"^3.7\"\npandas = \"^1.0.0\"\n\n[tool.poetry.dev-dependencies]\nkkt = \"^0.3.1\"\n\n\n[tool.kkt]\nenable_git_tag = false\n\n[tool.kkt.meta_data]\ncode_file = \"script.py\"\ncompetition = \"digit-recognizer\"\ncompetition_sources = [\"digit-recognizer\"]\ndataset_sources = []\nenable_gpu = false\nenable_internet = true\nis_private = false\nkernel_type = \"script\"\nslug = \"kkt-example\"\n\n[build-system]\nrequires = [\"poetry\u003e=0.12\"]\nbuild-backend = \"poetry.masonry.api\"\n```\n\nIf you want run script.py in local environmet, please run the following.\n\n```bash\n$ poetry run python script.py\n$ head submission.csv\nImageId,Label\n1,1\n2,1\n3,0\n4,2\n5,4\n6,8\n7,5\n8,3\n9,2\n```\n\n### Create a dataset containing dependent packages if need\nIn this example, there aren't extra required packages. So `kkt install` displays the following. And this step is not mandatory.\n\n```bash\n$ poetry run kkt install\nref: /ar90ngas/kkt-example-install\nurl: https://www.kaggle.com/ar90ngas/kkt-example-install\nversion: 1\nPushing install kernel successed.\nWait for install kernel completion...\nWait for install kernel completion...\nWait for install kernel completion...\nExtra required packages are nothing.\n```\n\nBut in the little complicated project such as mnist_efficientnet example, `kkt install` displays the following. This means that a new dataset whose slug is `ar90ngas/mnist-efficientnet-requirements` is created. And it contains an extra package which is required by this example. And this package will be installed automatically in the bootstrap code.\n\n```bash\n$ poetry run kkt install\nref: /ar90ngas/mnist-efficientnet-install\nurl: https://www.kaggle.com/ar90ngas/mnist-efficientnet-install\nversion: 1\nPushing install kernel successed.\nWait for install kernel completion...\nWait for install kernel completion...\nWait for install kernel completion...\nOutput file downloaded to /tmp/tmpq6m9iq9p/timm-0.1.30-py3-none-any.whl\nStarting upload for file timm-0.1.30-py3-none-any.whl\n100%|█████████████████████████████████████████████████████████| 203k/203k [00:03\u003c00:00, 53.7kB/s]\nUpload successful: timm-0.1.30-py3-none-any.whl (203KB)\nref: ar90ngas/mnist-efficientnet-requirements\nurl: https://www.kaggle.com/ar90ngas/mnist-efficientnet-requirements\n```\n\n### Push notebook to Kaggle Kernels\n```bash\n$ poetry run kkt push\nref: /ar90ngas/kkt-example\nurl: https://www.kaggle.com/ar90ngas/kkt-example\nversion: 1\n```\nPlease visit [the result](https://www.kaggle.com/ar90ngas/kkt-example).\n\n### Show the status\n```bash\n$ poetry run kkt status\nstatus: complete\n```\n\n## Configuration\nPlease see [examples](https://github.com/ar90n/kkt/tree/master/examples)\n\n\n## License\nThis software is released under the Apache License, see [LICENSE](LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Far90n%2Fkkt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Far90n%2Fkkt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Far90n%2Fkkt/lists"}