tesseract-sdk


Nametesseract-sdk JSON
Version 0.8.3 PyPI version JSON
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SummaryPython SDK for Tesseract Models
upload_time2023-10-20 20:27:08
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docs_urlNone
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requires_python>=3.8
licenseApache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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keywords geodesic analysis seerai data science tesseract
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            ## Tesseract Python SDK

[![Build](https://github.com/seerai/tesseract-python-sdk/actions/workflows/build_and_test.yml/badge.svg)](https://github.com/seerai/tesseract-python-sdk/actions/workflows/build_and_test.yml)
[![codecov](https://codecov.io/gh/seerai/tesseract-python-sdk/branch/main/graph/badge.svg?token=Czn2HFCymj)](https://codecov.io/gh/seerai/tesseract-python-sdk)
This is an SDK for developing Tesseract models in Python.


## Test a Tesseract Image

To run tests that will ensure that your model container will run correctly in tesseract you can
use the validation cli. To run with a basic setup you just need to run:
```bash
tesseract-sdk validate <image-name>:<tag>
```
This will look at the model info in your model code and generate a random array for input. It will
then spin up the container and attempt to send data into the model. If data is returned from the
model then it will validate the the shape and dtypes are correct. Thats all you need for simple models.

For more complicated models or models where you would like to test with real data you need to 
create a configuration file for testing. The configuration file just lets the validator know
about things like where the local data to be loaded is, and which bands should be included. The 
resulting arrays or features will be written out to PNG and geojson respectively. An example config
is shown below:
```json
{
    "image": "my-tesseract-model:v0.0.1",
    "test_data": {
        "job_id": "my-job-id",
        "project": "my-project",
    },
    "asset_bands": [
        {
            "asset_name": "modis",
            "bands": [0,1,2,5],
        },
        {
            "asset_name": "sentinel",
            "bands": [2,4]
        }
    ],
    "args": {
        "model-arg-1": "value1",
        "model-arg-2": "value2"
    },
    "output_asset_bands": [
        {
            "asset_name": "model_output_1",
            "bands": [0, 1, 2]
        },
        {
            "asset_name": "model_output_2",
            "bands": [0]
        }
    ],
    "save_output": false
}
```

**image**: The docker image to validate.

**test_data**: This can either be a dictionary with a job id and project to get data directly from
    a Tesseract job, or path to a zarr file. If reading directly from a Tesseract job, the dict
    should have only the keys `job_id` and `project`. To read from a zarr file directly, pass in the 
    path or URL as a string. This can be a local file or a remote zarr file (for example in google 
    storage) so long as the credentials are available. __Optional__: if not provided, random data 
    will be created. 

**asset_bands**: A list of asset bands like the inputs to a Tesseract Job. Each asset_band in the
    list should contain the keys "asset_name" and "bands". The "asset_name" must exist in the input
    zarr file or Tesseract job and "bands" should be a list of integers corresponding to bands in 
    the asset. __Optional__: If not provided, will use all bands from all input datsets.

**args**: Any arguments that need to be passed to the model inference function. __Optional__: If not
provided no args are supplied to the model.

**output_asset_bands**: For each model output, the bands that should be used to output an image. 
    This should be either 1 or 3 bands. For each item in the list, a PNG image will be created so 
    that the model outputs can be quickly inspected to ensure that the model looks like it is 
    working correctly. Unlike `asset_bands`, you can have multiple outputs here with the same name.
    This can be useful if you want to output several images for one asset i.e. 3 images with one
    band each instead of one 3 band image. __Optional__: If not provided, no output images will 
    be generated.

**save_output**: If True, will write the model output as bytes that can be read in with numpy. Files
    will be named by the name of the output with a '.dat' extension. __Optional__: Defaults to 
    `false`.

To run the validator with a configuration file, simply pass it to the utility:
```bash
tesseract-sdk validate -f valid_config.json
```

## Contributing
To contribute to the project you must first install the package using the `dev` option.
```bash
pip install .[dev]
```
**IMPORTANT:** Before creating a PR make sure to update the protobuf files. The PR checks will
fail if you do not. To update the protobuf files run the following commands:
```bash
make protoc-python
make copy-protos
make check-protos
```

            

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    "author": "",
    "author_email": "The SeerAI Team <contact@seer.ai>",
    "download_url": "",
    "platform": null,
    "description": "## Tesseract Python SDK\n\n[![Build](https://github.com/seerai/tesseract-python-sdk/actions/workflows/build_and_test.yml/badge.svg)](https://github.com/seerai/tesseract-python-sdk/actions/workflows/build_and_test.yml)\n[![codecov](https://codecov.io/gh/seerai/tesseract-python-sdk/branch/main/graph/badge.svg?token=Czn2HFCymj)](https://codecov.io/gh/seerai/tesseract-python-sdk)\nThis is an SDK for developing Tesseract models in Python.\n\n\n## Test a Tesseract Image\n\nTo run tests that will ensure that your model container will run correctly in tesseract you can\nuse the validation cli. To run with a basic setup you just need to run:\n```bash\ntesseract-sdk validate <image-name>:<tag>\n```\nThis will look at the model info in your model code and generate a random array for input. It will\nthen spin up the container and attempt to send data into the model. If data is returned from the\nmodel then it will validate the the shape and dtypes are correct. Thats all you need for simple models.\n\nFor more complicated models or models where you would like to test with real data you need to \ncreate a configuration file for testing. The configuration file just lets the validator know\nabout things like where the local data to be loaded is, and which bands should be included. The \nresulting arrays or features will be written out to PNG and geojson respectively. An example config\nis shown below:\n```json\n{\n    \"image\": \"my-tesseract-model:v0.0.1\",\n    \"test_data\": {\n        \"job_id\": \"my-job-id\",\n        \"project\": \"my-project\",\n    },\n    \"asset_bands\": [\n        {\n            \"asset_name\": \"modis\",\n            \"bands\": [0,1,2,5],\n        },\n        {\n            \"asset_name\": \"sentinel\",\n            \"bands\": [2,4]\n        }\n    ],\n    \"args\": {\n        \"model-arg-1\": \"value1\",\n        \"model-arg-2\": \"value2\"\n    },\n    \"output_asset_bands\": [\n        {\n            \"asset_name\": \"model_output_1\",\n            \"bands\": [0, 1, 2]\n        },\n        {\n            \"asset_name\": \"model_output_2\",\n            \"bands\": [0]\n        }\n    ],\n    \"save_output\": false\n}\n```\n\n**image**: The docker image to validate.\n\n**test_data**: This can either be a dictionary with a job id and project to get data directly from\n    a Tesseract job, or path to a zarr file. If reading directly from a Tesseract job, the dict\n    should have only the keys `job_id` and `project`. To read from a zarr file directly, pass in the \n    path or URL as a string. This can be a local file or a remote zarr file (for example in google \n    storage) so long as the credentials are available. __Optional__: if not provided, random data \n    will be created. \n\n**asset_bands**: A list of asset bands like the inputs to a Tesseract Job. Each asset_band in the\n    list should contain the keys \"asset_name\" and \"bands\". The \"asset_name\" must exist in the input\n    zarr file or Tesseract job and \"bands\" should be a list of integers corresponding to bands in \n    the asset. __Optional__: If not provided, will use all bands from all input datsets.\n\n**args**: Any arguments that need to be passed to the model inference function. __Optional__: If not\nprovided no args are supplied to the model.\n\n**output_asset_bands**: For each model output, the bands that should be used to output an image. \n    This should be either 1 or 3 bands. For each item in the list, a PNG image will be created so \n    that the model outputs can be quickly inspected to ensure that the model looks like it is \n    working correctly. Unlike `asset_bands`, you can have multiple outputs here with the same name.\n    This can be useful if you want to output several images for one asset i.e. 3 images with one\n    band each instead of one 3 band image. __Optional__: If not provided, no output images will \n    be generated.\n\n**save_output**: If True, will write the model output as bytes that can be read in with numpy. Files\n    will be named by the name of the output with a '.dat' extension. __Optional__: Defaults to \n    `false`.\n\nTo run the validator with a configuration file, simply pass it to the utility:\n```bash\ntesseract-sdk validate -f valid_config.json\n```\n\n## Contributing\nTo contribute to the project you must first install the package using the `dev` option.\n```bash\npip install .[dev]\n```\n**IMPORTANT:** Before creating a PR make sure to update the protobuf files. The PR checks will\nfail if you do not. To update the protobuf files run the following commands:\n```bash\nmake protoc-python\nmake copy-protos\nmake check-protos\n```\n",
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    "license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/  TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION  1. Definitions.  \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.  \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.  \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.  4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:  (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and  (b) You must cause any modified files to carry prominent notices stating that You changed the files; and  (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and  (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives.  Copyright [yyyy] [name of copyright owner]  Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at  http://www.apache.org/licenses/LICENSE-2.0  Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.",
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