py_matrice SDK — Comprehensive Reference

Purpose: Python SDK for creating projects and inference pipelines using the Matrice packages. This document is a precise, scannable reference for modules, files, functions, and testing/use-case mapping.

1. Overview

Item	Description
Package name	`matrice`
Description	Common server utilities for Matrice.ai services; SDK for projects, datasets, models, annotations, deployments, and inference pipelines
Python	>=3.8, <4
Source layout	`src/matrice/` (flat package with many modules)
External deps	`matrice_common` (session, RPC, `handle_response`, `dependencies_check`), optional: `matrice_data_processing`, `matrice_streaming`, `requests`, `Pillow`

2. Repository Layout

py_matrice/
├── src/matrice/           # Main package
│   ├── __init__.py        # Entry: dependency check (requests, Pillow)
│   ├── action.py          # Action lifecycle & account-level action APIs
│   ├── action_tracker.py  # LocalActionTracker, ActionTracker (BYOM/local runs)
│   ├── actionTracker.py  # (Legacy/duplicate naming; see action_tracker)
│   ├── annotation.py     # Annotation CRUD, files, items, labels, create_dataset
│   ├── application.py    # Application CRUD, add/delete model versions
│   ├── app_store.py      # AppStore: list public applications, get by ID
│   ├── camera_management.py      # Cameras, locations, groups, streams, topics
│   ├── compute.py        # ComputeInstance, ComputeType, list/add instances
│   ├── dataset.py        # Dataset CRUD, versions, items, splits, upload helpers
│   ├── docker_utils.py   # Docker install/check/start/pull (local env)
│   ├── drift_monitor.py  # DriftMonitoring (inference drift)
│   ├── exported_model.py # ExportedModel (export lifecycle, eval, download)
│   ├── inference_pipeline_management.py  # Pipelines, timing, cameras, start/stop
│   ├── local_test.py     # LocalTest (local run harness)
│   ├── metrics_calculator.py    # Detection/classification metrics (mAP, accuracy, etc.)
│   ├── metrics_calculator_oop.py # ObjectDetectionMetrics, ClassificationMetrics classes
│   ├── model_store.py    # Model store APIs, ModelArch, ModelFamily, BYOM
│   ├── models.py         # Model (trained model lifecycle, eval, logs, plots)
│   ├── pipeline.py      # Pipeline (high-level pipeline abstraction)
│   ├── projects.py      # Projects: project CRUD, datasets, models, deployments, inference pipelines
│   ├── scaling.py       # _Scaling (internal scaling helpers)
│   ├── streaming_automation.py   # StreamingAutomation (gateways, cameras, pipelines)
│   ├── streaming_benchmarking.py # StreamingBenchmarking (load testing)
│   ├── streaming_gateway_management.py # StreamingGatewayManagement
│   ├── testing.py        # TestingActionTracker, ModelDownloadMock, TestingMatriceDeploy
│   └── ...
├── examples/
│   └── benchmark_example.py   # StreamingBenchmarking usage examples
├── stub_generation.py    # EnhancedStubGenerator: generates .pyi stubs for src/matrice
├── setup.py              # setuptools + optional mypyc build
├── pyproject.toml        # Build config, mypy overrides
├── build-config.json     # ENABLE_MYPYC fallback
├── API_DOCUMENTATION.md  # Backend inference API (HTTP endpoints)
├── DEPLOYMENT-GUIDE.md   # Deployment instructions
└── src/MISSING_APIS.md   # Notes on missing/planned APIs

3. Build and Setup

Task	How
Version	`PACKAGE_VERSION` env var, else `0.0.0.dev0`
Pure Python build	Default; `ENABLE_MYPYC` false or unset
Compiled build	`ENABLE_MYPYC=true` or `build-config.json` `build.enable_mypyc: true`
Stubs	`setup.py` runs `stub_generation.py` before build; writes `src/matrice/__init__.pyi` (and optionally per-module .pyi)
Install	`pip install -e .` from repo root (or from `py_matrice/`)

4. Module and Function Reference

4.1 `init.py`

Role: Package entry; ensures optional deps available.
Behavior: matrice_common.utils.dependencies_check(["requests", "Pillow"]).

4.2 `projects.py` — Projects

Class: Projects(session, project_name=None, project_id=None)

Constructor: Requires project_name or project_id; fetches project and sets project_id, project_name, project_input, output_type, status, etc.

Key methods (high level):

Method	Purpose
`_get_project_by_name()`	GET project by name
`_get_a_project_by_id()`	GET project by ID
`get_service_action_logs(service_id, service_name)`	Service action logs
`get_latest_action_record(service_id)`	Latest action for service
`import_local_dataset(...)`	Create dataset from local path (via `_create_dataset`)
`import_cloud_dataset(...)`	Create dataset from URL/cloud
`create_annotation(project_type, ann_title, dataset_id, ...)`	Create annotation task; returns `(Annotation, Action)`
`add_models_for_training(model_train_configs, primary_metric, dataset_id=None, dataset_name=None, ...)`	Submit training jobs (single or list of configs)
`create_model_export(model_train_id, export_formats, model_config, ...)`	Add export config → returns `(ExportedModel, Action)`
`create_fastapi_deployment(...)`	Create FastAPI deployment (wraps `_create_deployment`)
`create_triton_deployment(...)`	Create Triton deployment with batching options
`_create_deployment(...)`	Generic deployment (trained or exported model)
`create_inference_pipeline(name, description, applications)`	Create inference pipeline from application IDs
`list_inference_pipelines(page, limit, status, search)`	List project inference pipelines
`delete()`	Delete project
`change_status(enable=True)`	Enable/disable project
`get_actions_logs(action_id)`	Action logs by record ID
`list_collaborators()`	Project collaborators
`invite_user_to_project(email, permissions)`	Invite user with permissions
`update_permissions(collaborator_id, permissions)`	Update collaborator permissions
`list_deployments(page_size, page_number)`	List deployments (returns dict of Deployment by name)
`list_datasets(status, page_size, page_number)`	List datasets
`list_annotations(...)`	List annotations
`list_trained_models(...)`	List trained models
`list_exported_models(...)`	List exported models
`list_drift_monitorings(...)`	List drift monitorings (keyed by deployment name)
`get_dataset(dataset_id, dataset_name)`	Return `Dataset` instance
`get_annotation(dataset_id, annotation_id, annotation_name)`	Return `Annotation` instance
`get_model(model_id, model_name)`	Return `Model` instance
`get_exported_model(model_export_id, model_export_name)`	Return `ExportedModel` instance
`get_deployment(deployment_id, deployment_name)`	Return `Deployment` (from matrice_streaming)
`get_dataset_status_summary()`	Dataset status counts (OrderedDict)
`get_annotations_status_summary()`	Annotation status counts
`get_model_status_summary()`	Model status counts
`get_model_export_status_summary()`	Export status counts
`get_deployment_status_summary()`	Deployment status counts
`_validate_labellers_and_reviewers(users_list)`	Resolve collaborators to IDs for annotation

4.3 `dataset.py` — Datasets

Standalone functions:

get_dataset_size_in_mb_from_url(session, url, project_id) — Dataset size from URL.
upload_file(session, file_path) — Upload ZIP; returns {success, data, message}.
_get_upload_path(session, file_name) — Get upload URL for file.

Class: Dataset(session, dataset_id=None, dataset_name=None)

Constructor: Resolves by name or ID; loads details and sets dataset_id, dataset_name, version_status, latest_version, no_of_samples, no_of_classes, etc.

Method	Purpose
`_get_details()`, `_get_dataset()`, `_get_dataset_by_name()`	Internal fetch by ID/name
`_get_summary(dataset_version)`	Version summary (counts, histogram)
`get_categories(dataset_version)`	Categories for version
`list_items(dataset_version, page_size, page_number)`	Paginated items (v2 API)
`get_processed_versions()`	Processed version list
`check_valid_spilts(dataset_version)`	Validate train/val/test splits
`rename(updated_name)`	Rename dataset
`update_item_label(dataset_version, item_id, label_id)`	Set item label
`add_data(source, source_url, new_dataset_version, old_dataset_version, ...)`	Import new version from URL
`split_data(old_version, new_version, is_random_split, train_num, val_num, test_num, transfers, ...)`	Split/transfer between splits
`delete_item(dataset_version, dataset_item_ids)`	Delete items (classification or detection)
`delete_version(dataset_version)`	Delete a version
`delete()`	Delete entire dataset

4.4 `models.py` — Trained Models

Class: Model(session, model_id=None, model_name="")

Constructor: Fetches details by ID or name; populates attributes (e.g. model_id, dataset_id, model_arch_id, model_inputs, model_outputs, status, created_at, etc.).

Method	Purpose
`get_details()`	(data, error, message)
`rename(name)`	Update model train name
`delete()`	Delete trained model
`get_prediction(input_path)`	Test model on file (POST file)
`get_eval_result(dataset_id, dataset_version, split_type)`	Fetch eval result
`plot_eval_results()`	Matplotlib eval plots
`add_evaluation(dataset_id, dataset_version, split_types, ...)`	Add eval job
`get_download_path()`	Presigned download URL (trained)
`download_model(file_name)`	Download to local file
`get_model_training_logs()`	Epoch logs
`plot_epochs_losses()`, `plot_epochs_metrics()`	Training curves
`model_test(model_type)`	Deployment server info for model

4.5 `model_store.py` — Model Store & BYOM

Module-level functions:

Function	Purpose
`list_public_model_families(session, project_type, page_size, page_num)`	Public families
`list_private_model_families(session, project_id=None, project_name=None, ...)`	Private families
`list_public_model_archs(session, project_type, ...)`	Public architectures
`list_private_model_archs(session, project_id=None, project_name=None, ...)`	Private architectures
`get_all_models(session, project_id, project_type)`	All models for project
`get_all_model_families(session, project_id, project_type)`	All families
`byom_status_summary(session, project_id, project_name)`	BYOM summary
`check_family_exists_by_name(session, family_name)`	Bool existence
`fetch_supported_runtimes_metrics(session, project_id, model_inputs, model_outputs)`	Runtimes & metrics
`get_automl_config(session, project_id, model_count, recommended_runtime, performance_tradeoff, tuning_type)`	AutoML configs → (model_archs, configs, model_counts)

Class: ModelArch(session, model_family_name=None, model_key=None, model_family_id=None, model_arch_id=None)

Fetches arch, train config, export formats; exposes model_arch_id, model_name, params_millions, export_formats, default_model_config, model_family (ModelFamily).
Methods: _get_model_arch(), get_export_formats(), get_train_config(tuning_type, model_checkpoint), get_export_config(export_format), get_export_action_config(export_format), get_train_action_config().

Class: ModelFamily(session, model_family_name=None, model_family_id=None)

Methods: get_model_family_details(), get_model_archs(model_name, model_key), get_model_arch(model_key).

Class: BYOM(session)

Methods: add_model_family, update_model_family, delete_model_family, get_model_family, delete_model_arch, add_train_action_config, add_export_action_config, delete_model_action_config, get_public_model_families_docker, use_docker_image_from_public_model_family, add_family_requirement_file, add_family_docker_file, upload_model_family_codebase, download_model_family_codebase, wait_for_codebase_upload, get_model_family_codebase_details, get_test_cases_by_type, start_test_cases, get_started_test_cases, get_model_family_actions, integrate_model_actions, publish_model_family, get_model_family_benchmark_results, update_model_family_benchmark_results.

4.6 `exported_model.py` — Exported Models

Class: ExportedModel(session, model_export_id=None, model_export_name="")

Constructor: Loads summary and details; sets model_export_id, model_train_id, model_inputs, model_outputs, export_format, status, etc.
Methods: get_details(), rename(updated_name), delete(), add_evaluation(...), get_trained_model(), get_evaluation_result(...), get_prediction(input_path), get_download_path(), download_model(file_name).

4.7 `annotation.py` — Annotations

Class: Annotation(session, annotation_id=None, annotation_name=None)

Constructor: Resolves by ID or name; sets version_status, latest_version, project_type, etc.
Methods: _get_details(), _get_annotation_by_id(), _get_annotation_by_name(), rename(annotation_title), delete(), get_annotation_files(page_size, page_number), get_item_history(annotation_item_id), list_items(...), update_classification_item(...), annotate_classification_item(...), get_categories(), create_dataset(is_create_new, old_dataset_version, new_dataset_version, new_version_description), add_label(labelname), delete_item(annotation_item_id).

4.8 `action.py` — Actions

Class: Action(session, action_id)

Loads action details into action_type, project_id, user_id, step_code, status, created_at, service_name, action_details, job_params.

Standalone functions (all return (data, error)):

get_project_id_by_service_id(session, service_id)
list_all_account_action_details(session)
get_recent_actions(session)
get_action_record_for_account_number(session)
get_action_logs_from_record_id(session, action_record_id)
get_service_action_logs(session, service_id)
get_action_logs_from_action_record_id(session, action_record_id)
get_action_details(session, action_id)
get_action_docker_logs(session, action_record_id)
get_action_graph(session, granularity, start_date, end_date)
clone_project(session, source_project_id, new_project_name)
enable_disable_project(session, type, project_id)

4.9 `application.py` & `app_store.py`

application.py — Class: Application(session)

create_application(name, projectID, coverImage, notebookLink, blogLink)
delete_application(applicationID)
add_model_version(application_id, model_name, model_id, project_id, model_type, blog_link)
delete_model(model_id)

app_store.py — Class: AppStore(session)

get_all_applications(page, limit)
get_public_application_by_id(application_id)

4.10 `inference_pipeline_management.py` — Inference Pipelines

Class: InferencePipelineManagement(session)

Pipeline: create_inference_pipeline(name, project_id, cameras, user_id, description, access_scale, deploy_type, server_type, ...), get_inference_pipeline_dashboard(page, limit), get_inference_pipeline_by_id(pipeline_id), list_inference_pipelines(project_id, page, limit, sort_by, sort_order), get_applications_by_pipeline(pipeline_id), get_cameras_by_streaming_gateway(pipeline_id), get_cameras_without_streaming_gateway(pipeline_id), start_inference_pipeline(pipeline_id, compute_alias, cluster_name), stop_inference_pipeline(pipeline_id), rename_inference_pipeline(pipeline_id, name), update_aggregator_status(pipeline_id, aggregator_id, status, is_running), add_cameras_and_applications_to_pipeline(pipeline_id, cameras, ...), delete_inference_pipeline(pipeline_id).
Timing: create_inference_pipeline_timing(...), get_inference_pipeline_timing_by_id(timing_id), get_inference_pipeline_timing_by_pipeline(pipeline_id), get_latest_active_timing_by_pipeline(pipeline_id), update_inference_pipeline_timing(...), delete_inference_pipeline_timing(timing_id).
Query: get_inference_pipelines_by_account(), get_streaming_gateways_by_pipeline(pipeline_id), get_compute_alias_by_pipeline(pipeline_id).

4.11 `camera_management.py` — Cameras

Class: CameraManagement(session)

Groups: create_camera_group(...), create_camera_group_vms(camera_ids, group_name), plus list/update/delete/get for groups.
Locations: create, list, update, delete, get for locations.
Cameras: create, list, update, delete, get; stream/topic helpers.
Streams/Topics: Methods for camera streams and topics (create, list, update, delete, etc.).

(Exact method names follow the same pattern as other management classes; see docstrings in file.)

4.12 `streaming_gateway_management.py` — Streaming Gateways

Class: StreamingGatewayManagement(session)

Gateways: create_streaming_gateway(gateway_name, description, account_type, status, server_type, network_settings, compute_alias, cluster_name, video, user_id), plus list, get, update, delete, start, stop, heartbeat, etc.

4.13 `streaming_automation.py` — Streaming Automation

Class: StreamingAutomation(account_number, access_key=None, secret_key=None, project_id=None, project_name=None)

Builds Session, StreamingGatewayManagement, CameraManagement, InferencePipelineManagement.
Purpose: End-to-end automation: gateways, locations, camera groups, cameras (including video upload), and inference pipelines.
Helpers: _generate_tag(prefix), _generate_id(), _is_valid_id(value).
Main flows: Complete setup (gateway + cameras + pipeline), auto-setup with tag, teardown, add cameras to pipeline, etc. (see class docstring and method names in file).

4.14 `streaming_benchmarking.py` — Streaming Benchmarking

Class: StreamingBenchmarking(video_path, compute_alias, app_name, account_number, access_key=None, secret_key=None, project_id=None, project_name=None, min_cameras=1, max_cameras=20, interval_minutes=5.0, step_size=1, metrics_interval_minutes=0.5, output_file="benchmark_results.json", ...)

Purpose: Load testing by incrementally adding cameras and collecting metrics (gateway + pipeline).
Key methods: initialize_setup(), start_benchmark(duration_minutes=None), stop_benchmark(), add_camera(), collect_metrics(), export_results().
Supports mock metrics and real API; state file for resume.

4.15 `testing.py` — Testing Harness

SplitMetricStruct: Pydantic-like struct for split metrics (splitType, metricName, metricValue).
dotdict: Dict with dot access.
TestingActionTracker(model_family_info_path, model_info_path, config_path): Loads model family/model info and action config (train/export/eval); prepares dataset; mock checkpoint/download; job params; logging to testing_logs/; epoch/eval result logging; dataset prep (classification, detection, YOLO from MSCOCO).
ModelDownloadMock: Mock model download to testing_logs folder.
TestingMatriceDeploy(load_model, predict): Runs load_model + predict on synthetic image; logs to testing_logs/deploy.json.

4.16 `action_tracker.py` — Action Tracker

LocalActionTracker / ActionTracker: Track action state and steps for BYOM/local runs (interface to report progress/logs).

4.17 `metrics_calculator.py` & `metrics_calculator_oop.py`

metrics_calculator.py: Functions for detection (mAP, mAR, precision/recall, IoU) and classification (accuracy, precision, recall, F1, specificity, confusion matrix, MCC, AUC-ROC, AUC-PR, Cohen's kappa, log loss). Entry points: get_object_detection_evaluation_results, get_classification_evaluation_results, calculate_metrics, accuracy.
metrics_calculator_oop.py: ObjectDetectionMetrics, ClassificationMetrics classes; plus get_object_detection_evaluation_results, get_classification_evaluation_results, calculate_metrics, accuracy.

4.18 `compute.py` — Compute

ComputeInstance: Represents a compute instance (attributes from API).
ComputeType: Instance type metadata.
Functions: list_instance_types(...), list_account_compute(session, status="all"), get_compute_status_summary(session, lease_type="on-demand"), add_on_demand_instance(...).

4.19 `docker_utils.py` — Docker

Functions: pull_docker_image(...), check_docker(), test_docker(), start_docker(), try_host_docker(), install_docker(), uninstall_docker(), _reinstall_docker().
Purpose: Local Docker availability for running jobs/containers.

4.20 `drift_monitor.py` — DriftMonitoring

Class: DriftMonitoring(session, ...) — Inference drift monitoring (see class in file for methods).

4.21 `pipeline.py` — Pipeline

Class: Pipeline — High-level pipeline abstraction (see file for current API).

4.22 `scaling.py` — _Scaling

Class: _Scaling — Internal scaling helpers (used by deployment/infra).

4.23 `local_test.py` — LocalTest

Class: LocalTest — Local execution harness for tests (see file for methods).

5. Root Scripts

5.1 `stub_generation.py`

Class: EnhancedStubGenerator(source_dir, target_dir, recursive=False)
Role: Parses src/matrice Python files, extracts imports/definitions (constants, functions, classes), generates __init__.pyi (and optionally per-module .pyi) with type hints and docstrings.
Invocation: Run by setup.py before build; or python stub_generation.py (defaults: source_dir=src/matrice, target_dir=src/matrice, recursive=False).
Output: src/matrice/__init__.pyi; if recursive, one .pyi per module.

6. Examples and Notebooks

File	Purpose
`examples/benchmark_example.py`	Demonstrates `StreamingBenchmarking`: basic run, fast load test, stress test, real API, manual control, analyze results (JSON). Run with optional example number: `python benchmark_example.py 1` … `6`.
`streaming_benchmarking_examples.ipynb`	Notebook for streaming benchmarking examples.
`streaming_automation_examples.ipynb`	Notebook for streaming automation examples.

7. Quick Reference — Where to Look for Use Cases

Use case	Primary module(s)	Entry points
Create/list projects, get project-scoped lists	`projects.py`	`Projects(session, project_name=...)`, then e.g. `list_datasets`, `list_trained_models`, `create_inference_pipeline`
Import/create datasets (local or cloud)	`projects.py`, `dataset.py`	`Projects.import_local_dataset`, `Projects.import_cloud_dataset`; `Dataset` for version/items/splits
Create annotation task	`projects.py`	`Projects.create_annotation(...)` → `Annotation`, `Action`
Configure and submit training	`model_store.py`, `projects.py`	`ModelArch.get_train_config`, `Projects.add_models_for_training`
Export trained model	`projects.py`, `exported_model.py`	`Projects.create_model_export` → `ExportedModel`
Deploy model (FastAPI / Triton)	`projects.py`	`Projects.create_fastapi_deployment`, `Projects.create_triton_deployment`
Create/list inference pipelines (app-based)	`projects.py`	`Projects.create_inference_pipeline`, `Projects.list_inference_pipelines`
Full inference pipeline (cameras, gateways, start/stop)	`inference_pipeline_management.py`, `camera_management.py`, `streaming_gateway_management.py`	`InferencePipelineManagement`, `CameraManagement`, `StreamingGatewayManagement`
Automate gateways + cameras + pipelines	`streaming_automation.py`	`StreamingAutomation`
Load test streaming (add cameras, collect metrics)	`streaming_benchmarking.py`, `examples/benchmark_example.py`	`StreamingBenchmarking`
BYOM (bring your own model family/arch)	`model_store.py`	`BYOM`, `ModelFamily`, `ModelArch`
Action status and logs	`action.py`, `projects.py`	`Action(session, action_id)`, `Projects.get_actions_logs`
Evaluation metrics (detection/classification)	`metrics_calculator.py`, `metrics_calculator_oop.py`	`get_*_evaluation_results`, `calculate_metrics`, `accuracy`, or OOP classes
Local/testing run (dataset prep, mock upload/download, logs)	`testing.py`, `action_tracker.py`	`TestingActionTracker`, `LocalActionTracker` / `ActionTracker`
Compute and Docker (local/env)	`compute.py`, `docker_utils.py`	`list_account_compute`, `add_on_demand_instance`; `check_docker`, `start_docker`

8. Quick Reference — Testing and Validation

Goal	File / class to use	Notes
Benchmark streaming (mock or real)	`streaming_benchmarking.py` + `examples/benchmark_example.py`	Set `use_mock=True` for no real resources; run example 1–6
Train/export/eval test harness (local)	`testing.py` → `TestingActionTracker`	Needs model family/model JSON and config path (train/export/eval)
Deploy test (load model + predict)	`testing.py` → `TestingMatriceDeploy`	Pass `load_model` and `predict` callables
Dataset size from URL	`dataset.py` → `get_dataset_size_in_mb_from_url`	Before import
Upload dataset file	`dataset.py` → `upload_file`	ZIP only
Check Docker for local runs	`docker_utils.py` → `check_docker`, `test_docker`	Optional for jobs
List/add compute	`compute.py` → `list_account_compute`, `add_on_demand_instance`	For deployment/inference

9. API Response Convention

Most SDK methods that call the backend return a tuple (data, error, message):

data: Response payload (dict/list) or None on failure.
error: Error string or None if success.
message: Human-readable status.

Example: data, err, msg = project.list_datasets(); if err: ....

10. Dependencies (runtime)

matrice_common: Session, RPC, handle_response, get_summary, dependencies_check.
Optional: matrice_data_processing (e.g. create_dataset in projects._create_dataset), matrice_streaming (e.g. Deployment in projects.list_deployments / get_deployment).
Standard: requests, Pillow (checked in __init__.py).

Use this document as the single place to locate modules, classes, and functions for implementing or testing flows without re-scanning the codebase.

Metadata

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Download URL	matrice-1.1.9-py3-none-any.whl
Size	245.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`7ea791e5135306d3b66259b42154b98db2f4c796ef3d09c2aeeb7fdc250d8d79`
BLAKE2b-256 checksum How to use checksums	`02ca8cf02d2f0312174ed269e94ca6eed3039a0536e24cf07a480539de34b8a3`
Upload date	Jul 29, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.13`