Registry / ai-ml / model-archiver

model-archiver

library1.0.3pypypiunverified

Model Archiver is a Python library and command-line tool used for creating archives of trained neural network models. These archives, typically with a .mar extension, are specifically structured to be consumed by the MXNet-Model-Server for efficient inference. The PyPI package (version 1.0.3) serves as a standalone tool, although its core functionality has largely evolved into 'torch-model-archiver' for the 'TorchServe' (formerly 'Multi-Model-Server') ecosystem.

ai-ml devops

pip install model-archiver

github ↗package ↗homepage ↗

INSTALL

IMPORT

SIG · MODEL-ARCHIVER

model-archiver

ai-mlpythonv1.0.3

Install

1.9s avg

Import

—

Disk

20MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v1.0.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

installs and imports cleanly · install 0.0s · import 0.000s · 21.9MB

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 1.9s · import 0.000s · 22MB

20MB installed

● package 20MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

ModelArchiver

✓ from model_archiver import ModelArchiver

✗ from model_archiver import ModelArchiver

Create a model archive (.mar file) for an MXNet model. This command packages your model artifacts, a custom handler script (`my_handler.py`), and any extra files (like `requirements.txt` or configuration files) into a single archive. The generated .mar file is then placed in the specified `export-path`. Ensure your handler script correctly implements the `initialize`, `preprocess`, `inference`, and `postprocess` methods for your specific model.

model-archiver --model-name my_mxnet_model \ --version 1.0 \ --model-path ./path_to_model_artifacts/ \ --handler my_handler.py \ --extra-files requirements.txt,config.json \ --export-path ./model_store/ # Example `my_handler.py` (simplified structure): # class MXNetHandler: # def __init__(self): # self.model = None # def initialize(self, context): # properties = context.system_properties # model_dir = properties.get("model_dir") # # Load model, e.g., using MXNet's gluon.nn # # self.model = gluon.nn.load_param(os.path.join(model_dir, 'model_params.params')) # def preprocess(self, data): # # Preprocess input data # return data # def inference(self, data): # # Perform inference using self.model # return self.model(data) # def postprocess(self, data): # # Postprocess inference results # return data

model-archiver --version

Debug

Known issues

breakingThe `model-archiver` PyPI package (version 1.0.3) is primarily designed for the legacy MXNet-Model-Server. For modern deep learning model serving, particularly with PyTorch, the actively maintained and recommended tool is `torch-model-archiver`, which is part of the `TorchServe` ecosystem (evolved from Multi-Model-Server).

fix

For new projects or PyTorch models, consider using `pip install torch-model-archiver` and the associated `torch-model-archiver` CLI. This provides access to the latest features, bug fixes, and active community support.

affects: <=1.0.3

gotchaThe `model-archiver` is predominantly a command-line interface tool. While it exposes Python modules, its intended usage for creating model archives is through its CLI. Programmatic invocation of its internal functions is less common and may have limited dedicated documentation.

fix

For packaging models, rely on the `model-archiver` command-line tool. If programmatic integration is essential, consult the source code on GitHub for direct API usage patterns, focusing on the `model_archiver.mar` module.

affects: <=1.0.3

gotchaWhen packaging ONNX models, additional dependencies (`protobuf` compiler, `onnx`, `mxnet`) are not installed by default with `model-archiver`. Without these, ONNX model packaging will fail.

fix

Before archiving ONNX models, manually install the required libraries: `pip install protobuf onnx mxnet` (or the specific MXNet variant like `mxnet-cu101`). Ensure a protobuf compiler is also available in your environment.

affects: <=1.0.3

gotchaHandling complex model project structures (e.g., multiple interdependent Python files in subdirectories) within the handler can lead to import errors. The archiving process might flatten directory structures, causing Python's import mechanism to fail.

fix

For complex dependencies, either package them as a Python egg/wheel and include it via `--extra-files`, or adjust your handler to manually manage the Python path or use relative imports carefully. Alternatively, provide a `requirements.txt` with your `model-archiver` command for dependencies available on PyPI.

affects: <=1.0.3

Upgrade

Version history

1.0.3latest on PyPI · released May 16, 2019

Audit

Dependencies

numpyrequiredFundamental package for scientific computing.

PillowrequiredImage processing capabilities, common in vision models.

pyyamlrequiredUsed for configuration file parsing.

boto3optionalAWS SDK for Python, often used for S3 integration (optional).

protobufrequiredUsed for data serialization, especially with ONNX models.

requestsrequiredHTTP client for various internal communications.

grpciorequiredgRPC Python utilities for communication with model servers.

grpcio-toolsrequiredTools for gRPC, often used in conjunction with grpcio.

Agent activity

20 hits · last 30 days

node

Resources

githubhttps://github.com/awslabs/mxnet-model-server ↗

packagehttps://pypi.org/project/model-archiver/ ↗

homepagehttps://pytorch.org/serve/ ↗