Metadata-Version: 2.1
Name: tesseract-sdk
Version: 0.9.0
Summary: Python SDK for Tesseract Models
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Project-URL: Homepage, https://docs.seer.ai/geodesic-web
Keywords: geodesic,analysis,seerai,data,science,tesseract
Classifier: Programming Language :: Python
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: GIS
Classifier: Topic :: Scientific/Engineering :: Visualization
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License-File: LICENSE
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Provides-Extra: extract
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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.

## Writing a Tesseract Image

To use the Tesseract SDK to create a Tesseract image, you need to implement two functions: an
inference function and a get model info function. The names of these functions dont actually matter,
since you need to pass both of these functions into the `tesseract.serve` function. However, their
call signatures do matter. The inference function should take at least one dictionary as an argument
along with a logger and any **kwargs. The dictionaries available to the inference function are:
`assets` and `grids`. The `assets` dictionary contains the input data for the model, and the `grids`
dictionary contains the grid information for the model such as timestamps and x,y coords. an example
funciton signature is shown below:
```python
def inference(assets: Dict[str, np.ndarray], grids: Dict[str, np.ndarray], logger: Logger, **kwargs) -> Dict[str, np.ndarray]:
    pass
```
You can also leave off grids if you dont need that information in the inference function:
```python
def inference(assets: Dict[str, np.ndarray], logger: Logger, **kwargs) -> Dict[str, np.ndarray]:
    pass
```
For some examples of how to write an inference function, see the `examples` directory.


## 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
```
