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_nameorproject_id; fetches project and setsproject_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_logsfolder. - 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,ClassificationMetricsclasses; plusget_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/matricePython files, extracts imports/definitions (constants, functions, classes), generates__init__.pyi(and optionally per-module.pyi) with type hints and docstrings. - Invocation: Run by
setup.pybefore build; orpython stub_generation.py(defaults: source_dir=src/matrice, target_dir=src/matrice, recursive=False). - Output:
src/matrice/__init__.pyi; if recursive, one.pyiper 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
Noneon failure. - error: Error string or
Noneif 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_datasetinprojects._create_dataset),matrice_streaming(e.g.Deploymentinprojects.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.
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- Download URL: matrice-0.100.0-py3-none-any.whl
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- Size: 246.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/6.2.0 CPython/3.12.10
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