Metadata-Version: 2.1
Name: sap-computer-vision-package
Version: 1.0.38
Summary: SAP Computer Vision Package
Home-page: https://www.sap.com/
Author: SAP SE
License: SAP DEVELOPER LICENSE AGREEMENT
Download-URL: https://pypi.python.org/pypi/sap-computer-vision-package
Keywords: SAP Computer Vision,SAP Computer Vision Package,SAP AI Core
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Legal Industry
Classifier: Intended Audience :: Manufacturing
Classifier: Intended Audience :: Science/Research
Classifier: License :: Other/Proprietary License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: setuptools (==58.0.4)
Requires-Dist: numpy (~=1.21.0)
Requires-Dist: scipy (~=1.7.0)
Requires-Dist: torch (~=1.10.0)
Requires-Dist: torchvision (~=0.11.0)
Requires-Dist: timm (~=0.5.4)
Requires-Dist: Pillow (~=9.0.0)
Requires-Dist: click (~=8.0.0)
Requires-Dist: fvcore
Requires-Dist: Jinja2
Requires-Dist: opencv-python
Requires-Dist: slugify
Requires-Dist: termcolor
Requires-Dist: tabulate
Requires-Dist: click (~=8.0.1)
Requires-Dist: pyyaml (~=5.4.1)
Requires-Dist: jinja2 (~=3.0.1)
Requires-Dist: slugify (~=0.0.1)
Requires-Dist: environs (==9.0.0)
Requires-Dist: ujson (==4.0.0)
Requires-Dist: psutil (==5.7.2)
Requires-Dist: shortuuid (==1.0.1)
Requires-Dist: requests-toolbelt (==0.9.1)

# SAP Computer Vision Package

This package helps with the implementation of Computer Vision use-cases on top of AI Core.
It extends [detectron2](https://detectron2.readthedocs.io/en/latest/), a state-of-the-art library for object detection and image segmentation. Our package adds image classification and feature extraction (eg., for image retrieval) capabilities. For a fast development of Computer Vision solutions, the package offers training and evaluation methods and other helpful components, such as a large set of augmentation functions. The package can also be used stand-alone without AI Core, and AI Core integration can be added later to the project.

The functionalities of the package can be used on AI Core without any programming. For this purpose the package offers a command line interface to create AI Core templates for training and serving. From our experience it reduces the time for implementing a Computer Vision use-case on AI Core from several days to several hours.

## Supported use-cases

* Object Detection
* Image Classification
* Image Feature Extraction
* Model Training and Deployment on SAP AI Core

## Installation
### Prerequisites

Before installation, make sure that PyTorch and detectron2 are installed. Details on how to install PyTroch can be found [here](https://pytorch.org/get-started/locally/). After the installation of PyTorch the matching version of `detectron2` has to be installed. Please check the [detectron2 installation guide](https://detectron2.readthedocs.io/en/latest/tutorials/install.html) to select the proper version. The package is tested with `detectron2=0.6`.

#### Mac OS

On MacOS follwing commands can be used to install both:
```
pip install torch==1.10 torchvision
pip install https://github.com/facebookresearch/detectron2/archive/refs/tags/v0.6.zip
```

#### Linx

For linux pre-builds of `detectron2` are available:
```
pip install torch==1.10 torchvision
pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.10/index.html
```
Make sure to select the url matching your `torch` version and `cuda` when GPU support is needed. Details can be found in the [detectron2 installation guide](https://detectron2.readthedocs.io/en/latest/tutorials/install.html).


### Installation from Source

When building from source normally `setup_without_centaur.py` is the suitable setup file. It skips the process of building the model serving binary locally, which only works on linux systems. The binary is only needed in the docker images to serve models.

To include local code changes to the installation run:
```
python setup_with_centaur.py develop
```
This is similar to `pip install -e .`, except that `setup_without_centaur.py` is used instead of `setup.py`.


### Installation using `pip`

To install this package from `pypi` run:

```
pip install sap-computer-vision-package
```

### Installation of `metaflow` with Argo Support

To use the CLI part of the package `metaflow` (with the argo plugin) and `awscli` are needed. To install the correct metaflow version run:
```
pip install 'git+https://github.com/sappier/metaflow' awscli
```
This works on MacOS and linux systems.

## Getting Started

### Using the Python Library Part

If you are interested to use our package as a simple extension to `detectron2`, we recommend running `sap_cv examples <target_dir>` to copy our example notebooks to `<target_dir>` and take a look at those.

### Using the Package on AI Core

Before testing the pipelines on AI Core, make sure that the items in the following checklist are fulfilled.

#### Configure AWS credentials and `metaflow`

When templates are created metaflow pushes tarballs to the S3 bucket. Those tarballs are loaded during pipeline execution. For this to work metaflow needs writing permissions to the S3 bucket onboarded to AI Core and `metaflow` has to configured to use this bucket as its datastore.

Details on how to configure an aws profile can be found [here](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-profiles.html). In order to enable `metaflow` to copy the tarballs into the bucket, the `awscli` must not ask for a password when starting a copy process. To achieve this either give the `default` profile the permissions to access the bucket or run `export AWS_PROFILE=<profile-with-bucket-access>` before creating templates.

Full documentation on how to configure `metaflow` can be found in the [metaflow documentation](https://admin-docs.metaflow.org/overview/configuring-metaflow). We only need to configure the S3 as the storage backend and **do not need** the configuration for AWS Batch. A mininmal configuration file (`~/.metaflowconfig/config.json`) looks like this:

```
{
    "METAFLOW_DATASTORE_SYSROOT_S3": "<path-in-bucket>",
    "METAFLOW_DATATOOLS_SYSROOT_S3": "<path-in-bucket>/data",
    "METAFLOW_DEFAULT_DATASTORE": "s3"
}
```

#### AI Core Checklist

- [ ] Complete [AI Core Onboarding](https://help.sap.com/viewer/2d6c5984063c40a59eda62f4a9135bee/LATEST/en-US/8ce24833036d481cb3113a95e3a39a07.html)
- [ ] Access to the Git repository, Docker repository and S3 bucket onboarded to AI Core
- [ ] Install [prerequisites](#prerequisites) including [the correct `metaflow` version](#installation-of-metaflow-with-argo-support)
- [ ] Install `sap_computer_vision_package` [locally](#installation)
- [ ] Configure [aws credentials and `metaflow`](#configure-aws-credentials-and-metaflow)

#### Basic Usage

To show all available templates run `sap_cv show`. The command `sap_cv show <pipeline_name>` shows detailed information about a specific pipeline and its parameters.

The training pipelines are templates for AI Core execution. To run it under your tenant you need the template and the matching docker image:

- To create a template execute `sap_cv create-template <pipeline_name> -o/--output-file=<choose_name>.json`. The template contains several tenant specific entries like `imagePullSecrets` etc. Please adjust them by hand or use a pipeline config YAML (see section below).
- To create a docker image execute `sap_cv build-docker <pipeline_name> -t <tag/docker-image-target>` and push it using `docker push <tag/docker-image-target>`

The template has to be pushed into the onboarded git repo (consult AI Core documentation to set it up) and the container to the onboarded docker repository.

Templates are built using `metaflow` using a plugin to create Argo templates. Make sure that a proper `metaflow` version (for the argo plugin install this fork: https://github.com/sappier/metaflow) is installed and that the storage is configured correctly (check section ["Configure AWS credentials and `metaflow`"](#configure-aws-credentials-and-metaflow)).


#### Pipeline Config .yaml

Tenant specific values for the template can either be provided through the CLI through additional options. For more information execute `sap_cv create-template <pipeline_name> --argo-help`. To simplify the command and make the creation of the template trackable in git it is possible to use a .yaml containing the values.

Example:
```
labels:
  scenarios.ai.sap.com/id: "<scenario-id>"
  ai.sap.com/version: "<version-number>"
annotations:
  scenarios.ai.sap.com/name: "<scenario-name>"
  executables.ai.sap.com/name: "<executable-name>"
image: <tag/docker-image-target>`
imagePullSecrets:
  - name: "<docker-repo-secret>"
envFrom:
  - secretRef:
      name: "<object-store-secret>"
```

To use the pipeline config during the creation process use the `--pipeline-config` options, e.g. `sap_cv create-template <pipeline_name> -o/--output-file=<choose_name>.json --pipeline-config=pipeline_cfg.yaml`.

#### Common Issues

**Impossible to have multiple templates for the same pipeline in a tenant.**

The name for `executable` specified in the template has to be unique. To overwrite the default name of a pipeline use the `--name` option when creating the template: `sap_cv create-template <pipeline_name> -o/--output-file=<choose_name>.json --name=<executable-name>`.

**Template creations gets stucked without an error.**

When the template creation process gets stuck in this step:
```
$ sap_cv create-template batch_processing -o test.json
Metaflow 2.4.4 executing BatchProcessing for user:I545048
Validating your flow...
    The graph looks good!
Running pylint...
    Pylint is happy!
Deploying batchprocessing to Argo Workflow Templates...
```
it is most that the permissions to write to the bucket are missing. Make sure to select the correct AWS profile by running `export AWS_PROFILE=<profile-with-bucket-access>`. More details can be found in the section ["Configure AWS credentials and `metaflow`"](#configure-aws-credentials-and-metaflow).


## Giving Feedback and Reporting Bugs

If you are an SAP customer you can give feedback or report bugs by creating an incident via the [SAP ONE Support Launchpad](https://launchpad.support.sap.com/#incident/create)
using the component ID "CA-ML-CV".

If you are not an SAP customer yet, you can give feedback or report bugs by registering with [SAP Community](https://community.sap.com/) and asking a [question](https://answers.sap.com/questions/ask.html) using the tag "SAP AI Core" in the field "SAP Managed Tags".


## License

This package is distributed under the SAP Developers License, see LICENSE file in the package. The package uses several third party open source components. Please see file DISCLAIMER for more details on their licensing.


