| Name | teradatamodelops JSON |
| Version |
7.1.0
JSON |
| download |
| home_page | None |
| Summary | Python client for Teradata ModelOps (TMO) |
| upload_time | 2024-06-25 14:45:21 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
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| keywords |
teradata
vantage
data science
devops
vantagecloud
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# Teradata ModelOps Client
- [Installation](#installation)
- [CLI](#cli)
- [Authentication](#authentication)
- [SDK](#sdk)
- [Release Notes](#release-notes)
# Installation
The teradatamodelops is available from [pypi](https://pypi.org/project/teradatamodelops/)
```bash
pip install teradatamodelops
```
_Note: previously Teradata ModelOps was known as AnalyticsOps Accelerator (AOA), so you might encounter mentions of AOA or aoa, it's referring to an older version of ModelOps_
# CLI
## configuration
By default, the CLI looks for configuration stored in `~/.aoa/config.yaml`. Copy the config from ModelOps UI -> Session Details -> CLI Config. This will provide the command to create or update the `config.yaml`. If required, one can override this configuration at runtime by specifying environment variables (see `api_client.py`)
## help
The cli can be used to perform a number of interactions and guides the user to perform those actions.
```bash
> tmo -h
usage: tmo [-h] [--debug] [--version] {list,add,run,init,clone,link,connection,feature,doctor} ...
TMO CLI
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
--version Display the version of this tool
actions:
valid actions
{list,add,run,init,clone,link,connection,feature,doctor}
list List projects, models, local models or datasets
add Add model to working dir
run Train and Evaluate model locally
init Initialize model directory with basic structure
clone Clone Project Repository
link Link local repo to project
connection Manage local connections
feature Manage feature statistics
doctor Diagnose configuration issues
```
## clone
The `clone` command provides a convenient way to perform a git clone of the repository associated with a given project. The command can be run interactively and will allow you to select the project you wish to clone. Note that by default it clones to the current working directory so you either need to make sure you create an empty folder and run it from within there or else provide the `--path ` argument.
```bash
> tmo clone -h
usage: tmo clone [-h] [--debug] [-id PROJECT_ID] [-p PATH]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-id PROJECT_ID, --project-id PROJECT_ID
Id of Project to clone
-p PATH, --path PATH Path to clone repository to
```
### init
When you create a git repository, its empty by default. The `init` command allows you to initialize the repository with the structure required by the AOA. It also adds a default README.md and HOWTO.md.
```bash
> tmo init -h
usage: tmo init [-h] [--debug]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
```
## list
Allows to list the aoa resources. In the cases of listing models (pushed / committed) and datasets, it will prompt the user to select a project prior showing the results. In the case of local models, it lists both committed and non-committed models.
```bash
> tmo list -h
usage: tmo list [-h] [--debug] [-p] [-m] [-lm] [-t] [-d] [-c]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-p, --projects List projects
-m, --models List registered models (committed / pushed)
-lm, --local-models List local models. Includes registered and non-registered (non-committed / non-pushed)
-t, --templates List dataset templates
-d, --datasets List datasets
-c, --connections List local connections
```
All results are shown in the format
```
[index] (id of the resource) name
```
for example:
```
List of models for project Demo:
--------------------------------
[0] (03c9a01f-bd46-4e7c-9a60-4282039094e6) Diabetes Prediction
[1] (74eca506-e967-48f1-92ad-fb217b07e181) IMDB Sentiment Analysis
```
## add
Add a new model to a given repository based on a model template. A model in any other existing ModelOps git repository (specified via the `-t <giturl>`) can be used.
```bash
> tmo add -h
usage: tmo add [-h] [--debug] -t TEMPLATE_URL -b BRANCH
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-t TEMPLATE_URL, --template-url TEMPLATE_URL
Git URL for template repository
-b BRANCH, --branch BRANCH
Git branch to pull templates (default is main)
```
Example usage
```bash
> tmo add -t https://github.com/Teradata/modelops-demo-models -b master
```
## run
The cli can be used to validate the model training and evaluation logic locally before committing to git. This simplifies the development lifecycle and allows you to test and validate many options. It also enables you to avoid creating the dataset definitions in the AOA UI until you are ready and have a finalised version.
```bash
> tmo run -h
usage: tmo run [-h] [--debug] [-id MODEL_ID] [-m MODE] [-d DATASET_ID] [-t DATASET_TEMPLATE_ID] [-ld LOCAL_DATASET] [-lt LOCAL_DATASET_TEMPLATE] [-c CONNECTION]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-id MODEL_ID, --model-id MODEL_ID
Id of model
-m MODE, --mode MODE Mode (train or evaluate)
-d DATASET_ID, --dataset-id DATASET_ID
Remote datasetId
-t DATASET_TEMPLATE_ID, --dataset-template-id DATASET_TEMPLATE_ID
Remote datasetTemplateId
-ld LOCAL_DATASET, --local-dataset LOCAL_DATASET
Path to local dataset metadata file
-lt LOCAL_DATASET_TEMPLATE, --local-dataset-template LOCAL_DATASET_TEMPLATE
Path to local dataset template metadata file
-c CONNECTION, --connection CONNECTION
Local connection id
```
You can run all of this as a single command or interactively by selecting some optional arguments, or none of them.
For example, if you want to run the cli interactively you just select `tmo run` but if you wanted to run it non interactively to train a given model with a given datasetId you would expect
```bash
> tmo run -id <modelId> -m <mode> -d <datasetId>
```
## task add
Add a new feature engineering task to a given repository based on a task template. A task in any other existing ModelOps git repository (specified via the `-t <giturl>`) can be used.
```bash
> tmo task add -h
usage: tmo task add [-h] [--debug] -t TEMPLATE_URL -b BRANCH
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-t TEMPLATE_URL, --template-url TEMPLATE_URL
Git URL for template repository
-b BRANCH, --branch BRANCH
Git branch to pull templates (default is main)
```
Example usage
```bash
> tmo task add -t https://github.com/Teradata/modelops-demo-models -b feature_engineering
```
## task run
The cli can be used to run feature engineering tasks locally before committing to git.
```bash
> tmo task run -h
usage: tmo task run [-h] [--debug] [-f FUNCTION_NAME] [-c CONNECTION]
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-f FUNCTION_NAME, --function-name FUNCTION_NAME
Function name of the task
-c CONNECTION, --connection CONNECTION
Local connection id
```
You can run all of this as a single command or interactively by selecting some optional arguments, or none of them.
For example, if you want to run the cli interactively you just select `tmo task run` but if you wanted to run it non interactively to run a given task function you would expect
```bash
> tmo task run -f <functionName> -c <connectionId>
```
## connection
The connection credentials stored in the ModelOps service cannot be accessed remotely through the CLI for security reasons. Instead, users can manage connection information locally for the CLI. These connections are used by other CLI commands which access Vantage.
```bash
> tmo connection -h
usage: tmo connection [-h] {list,add,remove,export,test,create-byom-table} ...
optional arguments:
-h, --help show this help message and exit
actions:
valid actions
{list,add,remove,export,test,create-byom-table}
list List all local connections
add Add a local connection
remove Remove a local connection
export Export a local connection to be used as a shell script
test Test a local connection
create-byom-table Create a table to store BYOM models
```
## feature
Manage feature metadata by creating and populating feature metadata table(s). The feature metadata tables contain information required when computing statistics during training, scoring etc. This metadata depends on the feature type (categorical or continuous).
As this metadata can contain sensitive profiling information (such as categories), it is recommended to treat this metadata in the same manner as you treat the features for a given use case. That is, the feature metadata should live in a project or use case level database.
```bash
> tmo feature -h
usage: tmo feature [-h] {compute-stats,list-stats,create-stats-table,import-stats} ...
optional arguments:
-h, --help show this help message and exit
action:
valid actions
{compute-stats,list-stats,create-stats-table,import-stats}
compute-stats Compute feature statistics
list-stats List available statistics
create-stats-table Create statistics table
import-stats Import column statistics from local JSON file
```
### tmo feature compute metadata
Compute the feature metadata information required when computing statistics during training, scoring etc. This metadata depends on the feature type (categorical or continuous).
Continuous: the histograms edges
Categorical: the categories
```bash
> tmo feature compute-stats -h
usage: tmo feature compute-stats [-h] [--debug] -s SOURCE_TABLE -m METADATA_TABLE [-t {continuous,categorical}] -c COLUMNS
optional arguments:
-h, --help show this help message and exit
--debug Enable debug logging
-s SOURCE_TABLE, --source-table SOURCE_TABLE
Feature source table/view
-m METADATA_TABLE, --metadata-table METADATA_TABLE
Metadata table for feature stats, including database name
-t {continuous,categorical}, --feature-type {continuous,categorical}
Feature type: continuous or categorical (default is continuous)
-c COLUMNS, --columns COLUMNS
List of feature columns
```
Example usage
```bash
tmo feature compute-stats \
-s <feature-db>.<feature-data> \
-m <feature-metadata-db>.<feature-metadata-table> \
-t continuous -c numtimesprg,plglcconc,bloodp,skinthick,twohourserins,bmi,dipedfunc,age
```
# Authentication
A number of authentication methods are supported for both the CLI and SDK.
- device_code (interactive)
- client_credentials (service-service)
- bearer (raw bearer token)
When working interactively, the recommended auth method for the CLI is `device_code`. It will guide you through the auth automatically. For the SDK, use `bearer` if working interactively.
For both CLI and SDK, if working in an automated service-service manner, use `client_credentials`.
# SDK
The SDK for ModelOps allows users to interact with ModelOps APIs from anywhere they can execute python such as notebooks, IDEs etc. It can also be used for devops to automate additional parts of the process and integrate into the wider organization.
## Create Client
By default, creating an instance of the `AoaClient` looks for configuration stored in `~/.aoa/config.yaml`. When working with the SDK, we recommend that you specify (and override) all the necessary configuration as part of the `AoaClient` invocation.
An example to create a client using a bearer token for a given project is
```python
from aoa import AoaClient
client = AoaClient(
aoa_url="<modelops-endpoint>",
auth_mode="bearer",
auth_bearer="<bearer-token>",
project_id="23e1df4b-b630-47a1-ab80-7ad5385fcd8d",
)
```
To get the values to use for bearer token and aoa_url, go to the ModelOps UI -> Session Details -> SDK Config.
## Read Entities
We provide an extensive sdk implementation to interact with the APIs. You can find, create, update, archive, etc any entity that supports it via the SDK. In addition, most if not all search endpoints are also implemented in the sdk. Here are some examples
```python
from aoa import AoaClient, DatasetApi, JobApi
import pprint
client = AoaClient(project_id="23e1df4b-b630-47a1-ab80-7ad5385fcd8d")
dataset_api = DatasetApi(aoa_client=client)
datasets = dataset_api.find_all()
pprint.pprint(datasets)
dataset = dataset_api.find_by_id("11e1df4b-b630-47a1-ab80-7ad5385fcd8c")
pprint.pprint(dataset)
job_api = JobApi(aoa_client=client)
jobs = job_api.find_by_id("21e1df4b-b630-47a1-ab80-7ad5385fcd1c")
pprint.pprint(jobs)
```
## Deploy Model Version
Let's assume we have a model version `4131df4b-b630-47a1-ab80-7ad5385fcd15` which we want to deploy In-Vantage and schedule it to execute once a month at midnight of the first day of the month using dataset connection `11e1df4b-b630-47a1-ab80-7ad5385fcd8c` and dataset template `d8a35d98-21ce-47d0-b9f2-00d355777de1`. We can use the SDK as follows to perform this.
```python
from aoa import AoaClient, TrainedModelApi, JobApi
client = AoaClient(project_id="23e1df4b-b630-47a1-ab80-7ad5385fcd8d")
trained_model_api = TrainedModelApi(aoa_client=client)
job_api = JobApi(aoa_client=client)
trained_model_id = "4131df4b-b630-47a1-ab80-7ad5385fcd15"
deploy_request = {
"engineType": "IN_VANTAGE",
"publishOnly": False,
"language": "PMML",
"cron": "0 0 1 * *",
"byomModelLocation": {
"database": "<db-name>",
"table": "<table-name>"
},
"datasetConnectionId": "11e1df4b-b630-47a1-ab80-7ad5385fcd8c",
"datasetTemplateId": "d8a35d98-21ce-47d0-b9f2-00d355777de1",
"engineTypeConfig": {
"dockerImage": "",
"engine": "byom",
"resources": {
"memory": "1G",
"cpu": "1"
}
}
}
job = trained_model_api.deploy(trained_model_id, deploy_request)
# wait until the job completes (if the job fails it will raise an exception)
job_api.wait(job['id'])
```
## Import Model Version
Let's assume we have a PMML model which we have trained in another data science platform. We want to import the artefacts for this version (model.pmml and data_stats.json) against a BYOM model `f937b5d8-02c6-5150-80c7-1e4ff07fea31`.
```python
from aoa import (
AoaClient,
ModelApi,
TrainedModelApi,
TrainedModelArtefactsApi,
UserAttributesApi,
JobApi
)
import uuid
client = AoaClient(project_id="23e1df4b-b630-47a1-ab80-7ad5385fcd8d")
model_api = ModelApi(aoa_client=client)
trained_model_api = TrainedModelApi(aoa_client=client)
trained_model_artefacts_api = TrainedModelArtefactsApi(aoa_client=client)
user_attributes_api = UserAttributesApi(aoa_client=client)
job_api = JobApi(aoa_client=client)
# set metadata for your import request
model_id = '<model-uuid>'
filename = './pima.pmml'
language = 'PMML'
dataset_connection_id = '<dataset-connection-uuid>'
train_dataset_id = 'train-dataset-uuid'
# first, upload the artefacts which we want to associate with the BYOM model version
import_id = trained_model_artefacts_api.upload_byom_model(language,filename)
import_request = {
"artefactImportId": import_id,
"externalId": "my_model_external_id",
"modelMonitoring": {
"language": language,
"useDefaultEvaluation": True,
"evaluationEnabled": True,
"modelType": "CLASSIFICATION",
"byomColumnExpression": "CAST(CAST(json_report AS JSON).JSONExtractValue('$.predicted_HasDiabetes') AS INT)",
"driftMonitoringEnabled": True,
"datasetId": train_dataset_id,
"datasetConnectionId": default_connection_id,
},
}
job = model_api.import_byom(model_id, import_request)
# wait until the job completes (if the job fails it will raise an exception)
job_api.wait(job["id"])
# now you can list the artefacts which were uploaded and linked to the model version
trained_model_id = job["metadata"]["trainedModel"]["id"]
artefacts = trained_model_artefacts_api.list_artefacts(trained_model_id)
```
## Release Notes
### 7.1.0
- Explicitly ask for `logmech` when defining a local Vantage connection
- Fixed `tmo connection create-byom-table` logging error
- Added support for `access_token` when using OAuth2 Device code grant type.
- Updated `requests` dependency
- Client hardening efforts
- Added Feature Engineering Tasks functions `tmo task add` and `tmo task run`
### 7.0.6
- Fixed compatibility issue with `teradataml>=20.0.0.0`
- Minor formatting updates
- Added user attributes API
- Updated programmatic BYOM import examples
### 7.0.5
**WARNING** Please recreate statistics metadata if you are using this version. It will produce more accurate results and correct behaviour.
- Fixed computing statistics metadata for continuous features with all NULLs
- Fixed training/eval/scoring statistics on columns with all NULLs
- Added a workaround for VAL Histograms failure due to SP argument being longer than 31000 characters
- Rounding all Decimal and Float bin edges to Teradata-supported FLOAT literals (15 digits max)
- Fixed CLI error computing continuous statistics metadata related to mixed case column names
### 7.0.4
**WARNING** Please recreate statistics metadata if you are using this version. It will produce more accurate results and correct behaviour.
- Fixed STO helper functions to pick up correct python environment
- Computing statistics metadata:
- Allow less than 10 bins for continuous integer features
- Ignore continuous features that have single value for all rows
- Allow different precision for decimal and float continuous features
- Fix a bug with repeating edges for decimal/float continuous features
- Using rounded edges for integer continuous features
- Ignore NULLs for categorical features
- Ignore categorical features that have NULL value for all rows
- Allow collecting training statistics without providing a target (unsupervised learning)
- Report right and left outliers when computing statistics on continuous features
- Allow collecting training/evaluation/scoring statistics when statistics metadata is missing for some columns (missing columns are reported in data_stats.json)
- For categorical features, report outlier categories in "unmonitored_frequency"
- Assume statistics metadata is empty, in case it could not be read from the database
- Minor fixes and improvements
### 7.0.3
- Fixed statistics computation for symmetric distributions
- Added automatic retry for device_code expired session or refresh token
### 7.0.2
- Updated dependencies
### 7.0.1
- Updates to new name
- Changes to support both 1.x and 2.x version of SQLAlchemy
- Added a command to create BYOM table
- Various quality of life and performance improvements
### 7.0.0.0
- Refactor: Refactor data statistics API / internals to be simpler (breaking changes)
- Feature: Switch CLI authentication to use `device_code` grant flow
- Feature: Add raw Bearer token support for authentication (SDK)
### 6.1.4
- Feature: Document user facing stats functions
- Feature: Improve end user error messaging related to stats
- Bug: Fix `aoa init` and `aoa add` not working due to refactor in 6.1.3
### 6.1.3
- Feature: Improve error messages for statistics calculations and validation
- Feature: Use [aia](https://pypi.org/project/aia/) for AIA chasing for missing intermediate certificates
- Bug: No defaults for set when BYOM and VAL DBs not configured on connections
- Bug: Fixed requirement versions to ensure more stability across python versions
- Bug: Fixed slow CLI for some commands due to repeated server initialization
### 6.1.2
- Bug: Work around problems with special character in passwords for teradataml
### 6.1.0
- Cleanup: Remove all non OAuth2 (JWT) authentication methods
- Cleanup: Remove `aoa configure`
- Feature: Improve error messages to user on CLI
- Feature: Add `aoa link` for linking project to repo locally
- Bug: Don't show archived datasets
- Bug: Fix issue with `aoa feature create-table`
### 6.0.0
- Feature: Support API changes on ModelOps 06.00
- Feature: CLI DX improvements
- Feature: Add Telemetry query bands to session
- Feature: `aoa feature` support for managing feature metadata
- Feature: `aoa add` uses reference git repository for model templates
- Feature: Improve DX from Notebooks
### 5.0.0
- Feature: Add simpler teradataml context creation via aoa_create_context
- Feature: Add database to connections
- Feature: Support for human-readable model folder names
- Feature: Improve UX of aoa run
- Feature: Improved error messages for users related to auth and configure
- Refactor: Package refactor of aoa.sto.util to aoa.util.sto
- Bug: cli listing not filtering archived entities
- Cleanup: Remove pyspark support from CLI
-
### 4.1.12
- Bug: aoa connection add now hides password symbols
- Bug: sto.util.cleanup_cli() used hardcoded models table
- Feature: sto.util.check_sto_version() checks In-Vantage Python version compatibility
- Feature: sto.util.collect_sto_versions() fetches dict with Python and packages versions
### 4.1.11
- Bug: aoa run (evaluation) for R now uses the correct scoring file
### 4.1.10
- Bug: aoa init templates were out of date
- Bug: aoa run (score) didn't read the dataset template correctly
- Bug: aoa run (score) tried to publish to prometheus
- Bug: aoa run (score) not passing model_table kwargs
### 4.1.9
- Bug: Histogram buckets incorrectly offset by 1 for scoring metrics
### 4.1.7
- Bug: Quoted and escaped exported connection environmental variables
- Bug: aoa clone with `path` argument didn't create .aoa/config.yaml in correct directory
- Feature: aoa clone without `path` now uses repository name by default
- Feature: update BYOM import to upload artefacts before creating version
### 4.1.6
- Feature: Added local connections feature with Stored Password Protection
- Feature: Self creation of .aoa/config.yaml file when cloning a repo
- Bug: Fix argparse to use of common arguments
- Feature: Support dataset templates for listing datasets and selecting dataset for train/eval
- Bug: Fix aoa run for batch scoring, prompts for dataset template instead of dataset
- Bug: Fix batch scoring histograms as cumulative
### 4.1.5
- Bug: Fix computing stats
- Feature: Autogenerate category labels and support for overriding them
- Feature: Prompt for confirmation when retiring/archiving
### 4.1.4
- Feature: Retiring deployments and archiving projects support
- Feature: Added support for batch scoring monitoring
### 4.1.2
- Bug: Fix computing stats
- Bug: Fix local SQL model training and evaluation
### 4.1
- Bug: CLI shows archived entities when listing datasets, projects, etc
- Bug: Adapt histogram bins depending on range of integer types.
### 4.0
- Feature: Extract and record dataset statistics for Training, Evaluation
### 3.1.1
- Feature: `aoa clone` respects project branch
- Bug: support Source Model ID from the backend
### 3.1
- Feature: ability to separate evaluation and scoring logic into separate files for Python/R
### 3.0
- Feature: Add support for Batch Scoring in run command
- Feature: Added STO utilities to extract metadata for micro-models
### 2.7.2
- Feature: Add support for OAUTH2 token refresh flows
- Feature: Add dataset connections api support
### 2.7.1
- Feature: Add TrainedModelArtefactsApi
- Bug: pyspark cli only accepted old resources format
- Bug: Auth mode not picked up from environment variables
### 2.7.0
- Feature: Add support for dataset templates
- Feature: Add support for listing models (local and remote), datasets, projects
- Feature: Remove pykerberos dependency and update docs
- Bug: Fix tests for new dataset template api structure
- Bug: Unable to view/list more than 20 datasets / entities of any type in the cli
### 2.6.2
- Bug: Added list resources command.
- Bug: Remove all kerberos dependencies from standard installation, as they can be now installed as an optional feature.
- Feature: Add cli support for new artefact path formats
### 2.6.1
- Bug: Remove pykerberos as an installation dependency.
Raw data
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"maintainer_email": "Anton Zadorozhniy <anton.zadorozhniy@teradata.com>, Sheheryar Ali Bhatti <sheheryar.bhatti@teradata.com>, Christian Axel Schmidt Dick <christianaxel.schmidt@teradata.com>, \"Mo, Mengyu\" <mengyu.mo@teradata.com>",
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"author_email": "Anton Zadorozhniy <anton.zadorozhniy@teradata.com>, Sheheryar Ali Bhatti <sheheryar.bhatti@teradata.com>, Christian Axel Schmidt Dick <christianaxel.schmidt@teradata.com>, Will Fleury <will.fleury@teradata.com>",
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"description": "# Teradata ModelOps Client\n\n- [Installation](#installation)\n- [CLI](#cli)\n- [Authentication](#authentication)\n- [SDK](#sdk)\n- [Release Notes](#release-notes)\n\n\n# Installation\n\nThe teradatamodelops is available from [pypi](https://pypi.org/project/teradatamodelops/)\n\n```bash\npip install teradatamodelops\n```\n\n_Note: previously Teradata ModelOps was known as AnalyticsOps Accelerator (AOA), so you might encounter mentions of AOA or aoa, it's referring to an older version of ModelOps_\n\n# CLI \n\n## configuration\n\nBy default, the CLI looks for configuration stored in `~/.aoa/config.yaml`. Copy the config from ModelOps UI -> Session Details -> CLI Config. This will provide the command to create or update the `config.yaml`. If required, one can override this configuration at runtime by specifying environment variables (see `api_client.py`)\n\n## help\n\nThe cli can be used to perform a number of interactions and guides the user to perform those actions.\n\n```bash\n> tmo -h\nusage: tmo [-h] [--debug] [--version] {list,add,run,init,clone,link,connection,feature,doctor} ...\n\nTMO CLI\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n --version Display the version of this tool\n\nactions:\n valid actions\n\n {list,add,run,init,clone,link,connection,feature,doctor}\n list List projects, models, local models or datasets\n add Add model to working dir\n run Train and Evaluate model locally\n init Initialize model directory with basic structure\n clone Clone Project Repository\n link Link local repo to project\n connection Manage local connections\n feature Manage feature statistics\n doctor Diagnose configuration issues\n```\n\n\n## clone\n\nThe `clone` command provides a convenient way to perform a git clone of the repository associated with a given project. The command can be run interactively and will allow you to select the project you wish to clone. Note that by default it clones to the current working directory so you either need to make sure you create an empty folder and run it from within there or else provide the `--path ` argument.\n\n```bash\n> tmo clone -h\nusage: tmo clone [-h] [--debug] [-id PROJECT_ID] [-p PATH]\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n -id PROJECT_ID, --project-id PROJECT_ID\n Id of Project to clone\n -p PATH, --path PATH Path to clone repository to\n```\n\n### init\n\nWhen you create a git repository, its empty by default. The `init` command allows you to initialize the repository with the structure required by the AOA. It also adds a default README.md and HOWTO.md.\n\n```bash\n> tmo init -h\nusage: tmo init [-h] [--debug]\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n```\n\n## list\n\nAllows to list the aoa resources. In the cases of listing models (pushed / committed) and datasets, it will prompt the user to select a project prior showing the results. In the case of local models, it lists both committed and non-committed models.\n```bash\n> tmo list -h\nusage: tmo list [-h] [--debug] [-p] [-m] [-lm] [-t] [-d] [-c]\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n -p, --projects List projects\n -m, --models List registered models (committed / pushed)\n -lm, --local-models List local models. Includes registered and non-registered (non-committed / non-pushed)\n -t, --templates List dataset templates\n -d, --datasets List datasets\n -c, --connections List local connections\n```\nAll results are shown in the format\n```\n[index] (id of the resource) name\n```\nfor example:\n```\nList of models for project Demo:\n--------------------------------\n[0] (03c9a01f-bd46-4e7c-9a60-4282039094e6) Diabetes Prediction\n[1] (74eca506-e967-48f1-92ad-fb217b07e181) IMDB Sentiment Analysis\n```\n\n## add\n\nAdd a new model to a given repository based on a model template. A model in any other existing ModelOps git repository (specified via the `-t <giturl>`) can be used.\n\n```bash\n> tmo add -h\nusage: tmo add [-h] [--debug] -t TEMPLATE_URL -b BRANCH\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n -t TEMPLATE_URL, --template-url TEMPLATE_URL\n Git URL for template repository\n -b BRANCH, --branch BRANCH\n Git branch to pull templates (default is main)\n```\n\nExample usage \n```bash\n> tmo add -t https://github.com/Teradata/modelops-demo-models -b master\n```\n\n## run\n\nThe cli can be used to validate the model training and evaluation logic locally before committing to git. This simplifies the development lifecycle and allows you to test and validate many options. It also enables you to avoid creating the dataset definitions in the AOA UI until you are ready and have a finalised version.\n\n```bash\n> tmo run -h\nusage: tmo run [-h] [--debug] [-id MODEL_ID] [-m MODE] [-d DATASET_ID] [-t DATASET_TEMPLATE_ID] [-ld LOCAL_DATASET] [-lt LOCAL_DATASET_TEMPLATE] [-c CONNECTION]\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n -id MODEL_ID, --model-id MODEL_ID\n Id of model\n -m MODE, --mode MODE Mode (train or evaluate)\n -d DATASET_ID, --dataset-id DATASET_ID\n Remote datasetId\n -t DATASET_TEMPLATE_ID, --dataset-template-id DATASET_TEMPLATE_ID\n Remote datasetTemplateId\n -ld LOCAL_DATASET, --local-dataset LOCAL_DATASET\n Path to local dataset metadata file\n -lt LOCAL_DATASET_TEMPLATE, --local-dataset-template LOCAL_DATASET_TEMPLATE\n Path to local dataset template metadata file\n -c CONNECTION, --connection CONNECTION\n Local connection id\n```\n\nYou can run all of this as a single command or interactively by selecting some optional arguments, or none of them.\n\nFor example, if you want to run the cli interactively you just select `tmo run` but if you wanted to run it non interactively to train a given model with a given datasetId you would expect\n```bash\n> tmo run -id <modelId> -m <mode> -d <datasetId>\n```\n\n\n## task add\n\nAdd a new feature engineering task to a given repository based on a task template. A task in any other existing ModelOps git repository (specified via the `-t <giturl>`) can be used.\n\n```bash\n> tmo task add -h\nusage: tmo task add [-h] [--debug] -t TEMPLATE_URL -b BRANCH\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n -t TEMPLATE_URL, --template-url TEMPLATE_URL\n Git URL for template repository\n -b BRANCH, --branch BRANCH\n Git branch to pull templates (default is main)\n```\n\nExample usage \n```bash\n> tmo task add -t https://github.com/Teradata/modelops-demo-models -b feature_engineering\n```\n\n## task run\n\nThe cli can be used to run feature engineering tasks locally before committing to git.\n\n```bash\n> tmo task run -h\nusage: tmo task run [-h] [--debug] [-f FUNCTION_NAME] [-c CONNECTION]\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n -f FUNCTION_NAME, --function-name FUNCTION_NAME\n Function name of the task\n -c CONNECTION, --connection CONNECTION\n Local connection id\n```\n\nYou can run all of this as a single command or interactively by selecting some optional arguments, or none of them.\n\nFor example, if you want to run the cli interactively you just select `tmo task run` but if you wanted to run it non interactively to run a given task function you would expect\n```bash\n> tmo task run -f <functionName> -c <connectionId>\n```\n\n\n## connection\n\nThe connection credentials stored in the ModelOps service cannot be accessed remotely through the CLI for security reasons. Instead, users can manage connection information locally for the CLI. These connections are used by other CLI commands which access Vantage. \n\n\n```bash\n> tmo connection -h\nusage: tmo connection [-h] {list,add,remove,export,test,create-byom-table} ...\n\noptional arguments:\n -h, --help show this help message and exit\n\nactions:\n valid actions\n\n {list,add,remove,export,test,create-byom-table}\n list List all local connections\n add Add a local connection\n remove Remove a local connection\n export Export a local connection to be used as a shell script\n test Test a local connection\n create-byom-table Create a table to store BYOM models\n```\n\n\n## feature \n\nManage feature metadata by creating and populating feature metadata table(s). The feature metadata tables contain information required when computing statistics during training, scoring etc. This metadata depends on the feature type (categorical or continuous). \n\nAs this metadata can contain sensitive profiling information (such as categories), it is recommended to treat this metadata in the same manner as you treat the features for a given use case. That is, the feature metadata should live in a project or use case level database. \n\n```bash\n> tmo feature -h\nusage: tmo feature [-h] {compute-stats,list-stats,create-stats-table,import-stats} ...\n\noptional arguments:\n -h, --help show this help message and exit\n\naction:\n valid actions\n\n {compute-stats,list-stats,create-stats-table,import-stats}\n compute-stats Compute feature statistics\n list-stats List available statistics\n create-stats-table Create statistics table\n import-stats Import column statistics from local JSON file\n```\n\n### tmo feature compute metadata\n\nCompute the feature metadata information required when computing statistics during training, scoring etc. This metadata depends on the feature type (categorical or continuous).\n\nContinuous: the histograms edges \nCategorical: the categories\n\n```bash\n> tmo feature compute-stats -h\nusage: tmo feature compute-stats [-h] [--debug] -s SOURCE_TABLE -m METADATA_TABLE [-t {continuous,categorical}] -c COLUMNS\n\noptional arguments:\n -h, --help show this help message and exit\n --debug Enable debug logging\n -s SOURCE_TABLE, --source-table SOURCE_TABLE\n Feature source table/view\n -m METADATA_TABLE, --metadata-table METADATA_TABLE\n Metadata table for feature stats, including database name\n -t {continuous,categorical}, --feature-type {continuous,categorical}\n Feature type: continuous or categorical (default is continuous)\n -c COLUMNS, --columns COLUMNS\n List of feature columns\n```\n\nExample usage\n\n```bash\ntmo feature compute-stats \\\n -s <feature-db>.<feature-data> \\\n -m <feature-metadata-db>.<feature-metadata-table> \\\n -t continuous -c numtimesprg,plglcconc,bloodp,skinthick,twohourserins,bmi,dipedfunc,age\n```\n\n\n# Authentication\n\nA number of authentication methods are supported for both the CLI and SDK. \n\n- device_code (interactive)\n- client_credentials (service-service)\n- bearer (raw bearer token)\n\nWhen working interactively, the recommended auth method for the CLI is `device_code`. It will guide you through the auth automatically. For the SDK, use `bearer` if working interactively. \nFor both CLI and SDK, if working in an automated service-service manner, use `client_credentials`. \n\n# SDK\n\nThe SDK for ModelOps allows users to interact with ModelOps APIs from anywhere they can execute python such as notebooks, IDEs etc. It can also be used for devops to automate additional parts of the process and integrate into the wider organization.\n\n## Create Client\n\nBy default, creating an instance of the `AoaClient` looks for configuration stored in `~/.aoa/config.yaml`. When working with the SDK, we recommend that you specify (and override) all the necessary configuration as part of the `AoaClient` invocation. \n\nAn example to create a client using a bearer token for a given project is\n\n```python\nfrom aoa import AoaClient\n\nclient = AoaClient(\n aoa_url=\"<modelops-endpoint>\",\n auth_mode=\"bearer\",\n auth_bearer=\"<bearer-token>\",\n project_id=\"23e1df4b-b630-47a1-ab80-7ad5385fcd8d\",\n)\n```\n\nTo get the values to use for bearer token and aoa_url, go to the ModelOps UI -> Session Details -> SDK Config. \n\n## Read Entities\n\nWe provide an extensive sdk implementation to interact with the APIs. You can find, create, update, archive, etc any entity that supports it via the SDK. In addition, most if not all search endpoints are also implemented in the sdk. Here are some examples\n\n```python\nfrom aoa import AoaClient, DatasetApi, JobApi\nimport pprint\n\nclient = AoaClient(project_id=\"23e1df4b-b630-47a1-ab80-7ad5385fcd8d\")\n\ndataset_api = DatasetApi(aoa_client=client)\n\ndatasets = dataset_api.find_all()\npprint.pprint(datasets)\n\ndataset = dataset_api.find_by_id(\"11e1df4b-b630-47a1-ab80-7ad5385fcd8c\")\npprint.pprint(dataset)\n\njob_api = JobApi(aoa_client=client)\njobs = job_api.find_by_id(\"21e1df4b-b630-47a1-ab80-7ad5385fcd1c\")\npprint.pprint(jobs)\n```\n\n## Deploy Model Version\n\nLet's assume we have a model version `4131df4b-b630-47a1-ab80-7ad5385fcd15` which we want to deploy In-Vantage and schedule it to execute once a month at midnight of the first day of the month using dataset connection `11e1df4b-b630-47a1-ab80-7ad5385fcd8c` and dataset template `d8a35d98-21ce-47d0-b9f2-00d355777de1`. We can use the SDK as follows to perform this.\n\n```python\nfrom aoa import AoaClient, TrainedModelApi, JobApi\n\nclient = AoaClient(project_id=\"23e1df4b-b630-47a1-ab80-7ad5385fcd8d\")\ntrained_model_api = TrainedModelApi(aoa_client=client)\njob_api = JobApi(aoa_client=client)\n\ntrained_model_id = \"4131df4b-b630-47a1-ab80-7ad5385fcd15\"\ndeploy_request = {\n \"engineType\": \"IN_VANTAGE\",\n \"publishOnly\": False,\n \"language\": \"PMML\",\n \"cron\": \"0 0 1 * *\",\n \"byomModelLocation\": {\n \"database\": \"<db-name>\",\n \"table\": \"<table-name>\"\n },\n \"datasetConnectionId\": \"11e1df4b-b630-47a1-ab80-7ad5385fcd8c\",\n \"datasetTemplateId\": \"d8a35d98-21ce-47d0-b9f2-00d355777de1\",\n \"engineTypeConfig\": {\n \"dockerImage\": \"\",\n \"engine\": \"byom\",\n \"resources\": {\n \"memory\": \"1G\",\n \"cpu\": \"1\"\n }\n }\n}\n\njob = trained_model_api.deploy(trained_model_id, deploy_request)\n\n# wait until the job completes (if the job fails it will raise an exception)\njob_api.wait(job['id'])\n```\n\n## Import Model Version\n\nLet's assume we have a PMML model which we have trained in another data science platform. We want to import the artefacts for this version (model.pmml and data_stats.json) against a BYOM model `f937b5d8-02c6-5150-80c7-1e4ff07fea31`.\n\n```python\nfrom aoa import (\n AoaClient, \n ModelApi,\n TrainedModelApi, \n TrainedModelArtefactsApi,\n UserAttributesApi, \n JobApi\n)\nimport uuid\n\nclient = AoaClient(project_id=\"23e1df4b-b630-47a1-ab80-7ad5385fcd8d\")\nmodel_api = ModelApi(aoa_client=client)\ntrained_model_api = TrainedModelApi(aoa_client=client)\ntrained_model_artefacts_api = TrainedModelArtefactsApi(aoa_client=client)\nuser_attributes_api = UserAttributesApi(aoa_client=client)\n\njob_api = JobApi(aoa_client=client)\n\n# set metadata for your import request\nmodel_id = '<model-uuid>'\nfilename = './pima.pmml'\nlanguage = 'PMML'\ndataset_connection_id = '<dataset-connection-uuid>'\ntrain_dataset_id = 'train-dataset-uuid'\n\n# first, upload the artefacts which we want to associate with the BYOM model version\nimport_id = trained_model_artefacts_api.upload_byom_model(language,filename)\n\nimport_request = {\n \"artefactImportId\": import_id,\n \"externalId\": \"my_model_external_id\",\n \"modelMonitoring\": {\n \"language\": language,\n \"useDefaultEvaluation\": True,\n \"evaluationEnabled\": True,\n \"modelType\": \"CLASSIFICATION\",\n \"byomColumnExpression\": \"CAST(CAST(json_report AS JSON).JSONExtractValue('$.predicted_HasDiabetes') AS INT)\",\n \"driftMonitoringEnabled\": True,\n \"datasetId\": train_dataset_id,\n \"datasetConnectionId\": default_connection_id,\n },\n}\n\njob = model_api.import_byom(model_id, import_request)\n\n# wait until the job completes (if the job fails it will raise an exception)\njob_api.wait(job[\"id\"])\n\n# now you can list the artefacts which were uploaded and linked to the model version\ntrained_model_id = job[\"metadata\"][\"trainedModel\"][\"id\"]\nartefacts = trained_model_artefacts_api.list_artefacts(trained_model_id)\n```\n\n\n## Release Notes\n\n### 7.1.0\n\n- Explicitly ask for `logmech` when defining a local Vantage connection\n- Fixed `tmo connection create-byom-table` logging error\n- Added support for `access_token` when using OAuth2 Device code grant type.\n- Updated `requests` dependency\n- Client hardening efforts\n- Added Feature Engineering Tasks functions `tmo task add` and `tmo task run`\n\n### 7.0.6\n\n- Fixed compatibility issue with `teradataml>=20.0.0.0`\n- Minor formatting updates\n- Added user attributes API\n- Updated programmatic BYOM import examples\n\n### 7.0.5\n\n**WARNING** Please recreate statistics metadata if you are using this version. It will produce more accurate results and correct behaviour.\n\n- Fixed computing statistics metadata for continuous features with all NULLs\n- Fixed training/eval/scoring statistics on columns with all NULLs\n- Added a workaround for VAL Histograms failure due to SP argument being longer than 31000 characters\n- Rounding all Decimal and Float bin edges to Teradata-supported FLOAT literals (15 digits max)\n- Fixed CLI error computing continuous statistics metadata related to mixed case column names\n\n### 7.0.4\n\n**WARNING** Please recreate statistics metadata if you are using this version. It will produce more accurate results and correct behaviour.\n\n- Fixed STO helper functions to pick up correct python environment\n- Computing statistics metadata:\n - Allow less than 10 bins for continuous integer features\n - Ignore continuous features that have single value for all rows\n - Allow different precision for decimal and float continuous features\n - Fix a bug with repeating edges for decimal/float continuous features\n - Using rounded edges for integer continuous features\n - Ignore NULLs for categorical features\n - Ignore categorical features that have NULL value for all rows\n- Allow collecting training statistics without providing a target (unsupervised learning)\n- Report right and left outliers when computing statistics on continuous features\n- Allow collecting training/evaluation/scoring statistics when statistics metadata is missing for some columns (missing columns are reported in data_stats.json)\n- For categorical features, report outlier categories in \"unmonitored_frequency\"\n- Assume statistics metadata is empty, in case it could not be read from the database\n- Minor fixes and improvements\n\n### 7.0.3\n\n- Fixed statistics computation for symmetric distributions\n- Added automatic retry for device_code expired session or refresh token\n\n### 7.0.2\n\n- Updated dependencies\n\n### 7.0.1\n\n- Updates to new name\n- Changes to support both 1.x and 2.x version of SQLAlchemy\n- Added a command to create BYOM table\n- Various quality of life and performance improvements\n\n### 7.0.0.0\n\n- Refactor: Refactor data statistics API / internals to be simpler (breaking changes)\n- Feature: Switch CLI authentication to use `device_code` grant flow\n- Feature: Add raw Bearer token support for authentication (SDK)\n\n\n### 6.1.4\n\n- Feature: Document user facing stats functions\n- Feature: Improve end user error messaging related to stats\n- Bug: Fix `aoa init` and `aoa add` not working due to refactor in 6.1.3\n\n### 6.1.3\n\n- Feature: Improve error messages for statistics calculations and validation\n- Feature: Use [aia](https://pypi.org/project/aia/) for AIA chasing for missing intermediate certificates\n- Bug: No defaults for set when BYOM and VAL DBs not configured on connections\n- Bug: Fixed requirement versions to ensure more stability across python versions\n- Bug: Fixed slow CLI for some commands due to repeated server initialization\n\n### 6.1.2\n\n- Bug: Work around problems with special character in passwords for teradataml\n\n### 6.1.0\n\n- Cleanup: Remove all non OAuth2 (JWT) authentication methods\n- Cleanup: Remove `aoa configure` \n- Feature: Improve error messages to user on CLI\n- Feature: Add `aoa link` for linking project to repo locally\n- Bug: Don't show archived datasets\n- Bug: Fix issue with `aoa feature create-table`\n\n\n### 6.0.0\n\n- Feature: Support API changes on ModelOps 06.00\n- Feature: CLI DX improvements \n- Feature: Add Telemetry query bands to session\n- Feature: `aoa feature` support for managing feature metadata\n- Feature: `aoa add` uses reference git repository for model templates\n- Feature: Improve DX from Notebooks \n\n\n### 5.0.0\n\n- Feature: Add simpler teradataml context creation via aoa_create_context\n- Feature: Add database to connections\n- Feature: Support for human-readable model folder names\n- Feature: Improve UX of aoa run\n- Feature: Improved error messages for users related to auth and configure\n- Refactor: Package refactor of aoa.sto.util to aoa.util.sto\n- Bug: cli listing not filtering archived entities\n- Cleanup: Remove pyspark support from CLI\n- \n\n### 4.1.12\n\n- Bug: aoa connection add now hides password symbols\n- Bug: sto.util.cleanup_cli() used hardcoded models table\n- Feature: sto.util.check_sto_version() checks In-Vantage Python version compatibility\n- Feature: sto.util.collect_sto_versions() fetches dict with Python and packages versions\n\n### 4.1.11\n\n- Bug: aoa run (evaluation) for R now uses the correct scoring file\n\n### 4.1.10\n\n- Bug: aoa init templates were out of date\n- Bug: aoa run (score) didn't read the dataset template correctly\n- Bug: aoa run (score) tried to publish to prometheus \n- Bug: aoa run (score) not passing model_table kwargs\n\n### 4.1.9\n\n- Bug: Histogram buckets incorrectly offset by 1 for scoring metrics\n\n### 4.1.7\n\n- Bug: Quoted and escaped exported connection environmental variables\n- Bug: aoa clone with `path` argument didn't create .aoa/config.yaml in correct directory\n- Feature: aoa clone without `path` now uses repository name by default\n- Feature: update BYOM import to upload artefacts before creating version \n\n### 4.1.6\n\n- Feature: Added local connections feature with Stored Password Protection\n- Feature: Self creation of .aoa/config.yaml file when cloning a repo\n- Bug: Fix argparse to use of common arguments\n- Feature: Support dataset templates for listing datasets and selecting dataset for train/eval\n- Bug: Fix aoa run for batch scoring, prompts for dataset template instead of dataset\n- Bug: Fix batch scoring histograms as cumulative\n\n### 4.1.5\n\n- Bug: Fix computing stats\n- Feature: Autogenerate category labels and support for overriding them\n- Feature: Prompt for confirmation when retiring/archiving\n\n### 4.1.4\n\n- Feature: Retiring deployments and archiving projects support\n- Feature: Added support for batch scoring monitoring\n\n### 4.1.2\n\n- Bug: Fix computing stats\n- Bug: Fix local SQL model training and evaluation\n\n### 4.1\n\n- Bug: CLI shows archived entities when listing datasets, projects, etc\n- Bug: Adapt histogram bins depending on range of integer types.\n\n### 4.0\n\n- Feature: Extract and record dataset statistics for Training, Evaluation\n\n### 3.1.1\n\n- Feature: `aoa clone` respects project branch\n- Bug: support Source Model ID from the backend\n\n### 3.1\n\n- Feature: ability to separate evaluation and scoring logic into separate files for Python/R\n\n### 3.0\n\n- Feature: Add support for Batch Scoring in run command\n- Feature: Added STO utilities to extract metadata for micro-models\n\n### 2.7.2\n\n- Feature: Add support for OAUTH2 token refresh flows\n- Feature: Add dataset connections api support\n\n### 2.7.1\n\n- Feature: Add TrainedModelArtefactsApi\n- Bug: pyspark cli only accepted old resources format\n- Bug: Auth mode not picked up from environment variables \n\n### 2.7.0 \n\n- Feature: Add support for dataset templates\n- Feature: Add support for listing models (local and remote), datasets, projects\n- Feature: Remove pykerberos dependency and update docs\n- Bug: Fix tests for new dataset template api structure\n- Bug: Unable to view/list more than 20 datasets / entities of any type in the cli \n\n### 2.6.2\n - Bug: Added list resources command.\n - Bug: Remove all kerberos dependencies from standard installation, as they can be now installed as an optional feature.\n - Feature: Add cli support for new artefact path formats\n \n### 2.6.1 \n - Bug: Remove pykerberos as an installation dependency. \n",
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"license": "Copyright (c) 2024 Teradata. All rights reserved. LICENSE AGREEMENT PRODUCT: teradatamodelops IMPORTANT - READ THIS AGREEMENT CAREFULLY BEFORE INSTALLING OR USING THE SOFTWARE. TERADATA WILL LICENSE THE SOFTWARE TO YOU ONLY IF YOU ACCEPT THE TERMS AND CONDITIONS OF THIS AGREEMENT AND MEET THE CONDITIONS FOR USING THE SOFTWARE DESCRIBED BELOW. BY DOWNLOAING, INSTALLING OR USING THE SOFTWARE, YOU (1) AGREE TO THE TERMS AND CONDITIONS OF THIS AGREEMENT, AND (2) REPRESENT AND WARRANT THAT YOU POSSESS THE AUTHORITY TO ENTER INTO THIS AGREEMENT ON BEHALF OF YOU, YOUR EMPLOYER (WHEN ACTING ON BEHALF OF YOUR EMPLOYER), AND/OR A TERADATA-AUTHORIZED LICENSEE (WHEN YOU AND YOUR EMPLOYER ARE ACTING ON BEHALF OF A TERADATA-AUTHORIZED LICENSEE). IF YOU DO NOT ACCEPT THE TERMS AND CONDITIONS OF THIS AGREEMENT, DO NOT DOWNLOAD, INSTALL OR USE THE SOFTWARE. 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This Agreement constitutes the entire understanding of the parties with respect to the Software and Services, and supersede all other prior agreements and understandings whether oral or written. No oral representation or change to this Agreement will be binding upon either party unless agreed to in writing and signed by authorized representatives of all parties. You will not assign this Agreement or your rights, nor will you delegate your obligations under this Agreement. Failure by either party to enforce any term or condition of this Agreement will not be deemed a waiver of future enforcement of that or any other term or condition. The provisions of this Agreement are severable. \"Include\", \"includes\", and \"including\" shall be interpreted as introducing a list of examples which do not limit the generality of any preceding words or any words in the list of examples.",
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