semvec/pypi-packages huggingface dataset:
evaluate-service 1.8.5
AutoML Toolkit
License: Apache License 2.0
Requires Python: >=3.6

**Evaluate Service ver1.8.5 released:**

**Introduction**

The evaluation service is a tool used to evaluate the performance of a 
model on specific hardware  developed by Noah's Ark Laboratory, the main features are as follows:
1. Multi-Backend: PyTorch, TensorFlow, MindSpore and Caffe. The input model can come form Pytorch, Tensorflow,
Mindspore and caffe.
2. Multi-hardware: Mobile Phone, Ascend 310, Kirinri 990 and etc. The model can be evaluated on multiple types of hardware.
3. Online real-time evaluation and offline evaluation. The evaluate service can combine with [Vega](https://github.com/huawei-noah/vega)
to implement network architecture search with hardware in the ring. One can also use the evaluate service independently.
4. Supports secure communication encryption. In security mode, communications are encrypted to secure the model and data.

**Installation**

Install evaluate service and the open source softwares that evaluate service depends on:

`pip3 install --user --upgrade evaluate-service`

**Cooperation and Contribution**

Welcome to use evaluate-service. If you have any questions or suggestions, need help, fix bugs, contribute new algorithms, 
or improve the documentation, submit an issue in the community. We will reply to and communicate with you in a timely manner.
noah-vega 1.8.5
AutoML Toolkit
License: Apache License 2.0
Requires Python: >=3.6

**Vega ver1.8.5 released:**

**Introduction**

Vega is an AutoML algorithm tool chain developed by Noah's Ark Laboratory, the main features are as follows:

1. Full pipeline capabilities: The AutoML capabilities cover key functions such as Hyperparameter Optimization, Data Augmentation, Network Architecture Search (NAS), Model Compression, and Fully Train. These functions are highly decoupled and can be configured as required, construct a complete pipeline.
2. Industry-leading AutoML algorithms: Provides Noah's Ark Laboratory's self-developed **industry-leading algorithms (Benchmark)** and **Model Zoo** to download the State-of-the-art (SOTA) models.
3. Fine-grained network search space: The network search space can be freely defined, and rich network architecture parameters are provided for use in the search space. The network architecture parameters and model training hyperparameters can be searched at the same time, and the search space can be applied to Pytorch, TensorFlow and MindSpore.
4. High-concurrency neural network training capability: Provides high-performance trainers to accelerate model training and evaluation.
5. Multi-Backend: PyTorch, TensorFlow, MindSpore.

**Installation**

Install Vega and the open source softwares that Vega depends on:

`pip3 install --user --upgrade noah-vega`

**Cooperation and Contribution**

Welcome to use Vega. If you have any questions or suggestions, need help, fix bugs, contribute new algorithms, or improve the documentation, submit an issue in the community. We will reply to and communicate with you in a timely manner.