| Name | nemo-eval JSON |
| Version |
0.1.0rc1
JSON |
| download |
| home_page | None |
| Summary | NeMo Eval: Evaluation Utilities for LLM and VLM models |
| upload_time | 2025-08-16 08:43:39 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
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| keywords |
nlp
nemo
deep
gpu
language
learning
machine
nvidia
pytorch
torch
|
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<div align="center">
# NeMo Eval
[](https://codecov.io/github/NVIDIA-NeMo/Eval)
[](https://github.com/NVIDIA-NeMo/Eval/actions/workflows/cicd-main.yml)
[](https://github.com/NVIDIA-NeMo/Eval/blob/main/pyproject.toml)
[](https://github.com/NVIDIA-NeMo/)
[Documentation](https://docs.nvidia.com/nemo/eval/latest/index.html) | [Examples](https://github.com/NVIDIA-NeMo/Eval?tab=readme-ov-file#-usage-examples) | [Contributing](https://github.com/NVIDIA-NeMo/Eval/blob/main/CONTRIBUTING.md)
</div>
## Overview
The NeMo Framework is NVIDIA’s GPU-accelerated, end-to-end training platform for large language models (LLMs), multimodal models, and speech models. It enables seamless scaling of both pretraining and post-training workloads, from a single GPU to clusters with thousands of nodes, supporting Hugging Face/PyTorch and Megatron models. NeMo includes a suite of libraries and curated training recipes to help users build models from start to finish.
The Eval library ("NeMo Eval") is a comprehensive evaluation module within the NeMo Framework for LLMs. It offers streamlined deployment and advanced evaluation capabilities for models trained using NeMo, leveraging state-of-the-art evaluation harnesses.
## 🚀 Features
- **Multi-Backend Deployment**: Supports PyTriton and multi-instance evaluations using the Ray Serve deployment backend
- **Comprehensive Evaluation**: Includes state-of-the-art evaluation harnesses for academic benchmarks, reasoning benchmarks, code generation, and safety testing
- **Adapter System**: Features a flexible architecture with chained interceptors for customizable request and response processing
- **Production-Ready**: Supports high-performance inference with CUDA graphs and flash decoding
- **Multi-GPU and Multi-Node Support**: Enables distributed inference across multiple GPUs and compute nodes
- **OpenAI-Compatible API**: Provides RESTful endpoints aligned with OpenAI API specifications
## 🔧 Install NeMo Eval
### Prerequisites
- Python 3.10 or higher
- CUDA-compatible GPU(s) (tested on RTX A6000, A100, H100)
- NeMo Framework container (recommended)
### Use pip
For quick exploration of NeMo Eval, we recommend installing our pip package:
```bash
pip install nemo-eval
```
### Use Docker
For optimal performance and user experience, use the latest version of the [NeMo Framework container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags). Please fetch the most recent $TAG and run the following command to start a container:
```bash
docker run --rm -it -w /workdir -v $(pwd):/workdir \
--entrypoint bash \
--gpus all \
nvcr.io/nvidia/nemo:${TAG}
```
### Use uv
To install NeMo Eval with uv, please refer to our [Contribution guide](https://github.com/NVIDIA-NeMo/Eval/blob/main/CONTRIBUTING.md).
## 🚀 Quick Start
### 1. Deploy a Model
```python
from nemo_eval.api import deploy
# Deploy a NeMo checkpoint
deploy(
nemo_checkpoint="/path/to/your/checkpoint",
serving_backend="pytriton", # or "ray"
server_port=8080,
num_gpus=1,
max_input_len=4096,
max_batch_size=8
)
```
### 2. Evaluate the Model
```python
from nvidia_eval_commons.core.evaluate import evaluate
from nvidia_eval_commons.api.api_dataclasses import ApiEndpoint, EvaluationConfig, EvaluationTarget
# Configure evaluation
api_endpoint = ApiEndpoint(
url="http://0.0.0.0:8080/v1/completions/",
model_id="megatron_model"
)
target = EvaluationTarget(api_endpoint=api_endpoint)
config = EvaluationConfig(type="gsm8k", output_dir="results")
# Run evaluation
results = evaluate(target_cfg=target, eval_cfg=config)
print(results)
```
## 📊 Support Matrix
| Checkpoint Type | Inference Backend | Deployment Server | Evaluation Harnesses Supported |
|----------------|-------------------|-------------|--------------------------|
| NeMo FW checkpoint via Megatron Core backend | [Megatron Core in-framework inference engine](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core/inference) | PyTriton (single and multi node model parallelism), Ray (single node model parallelism with multi instance evals) | lm-evaluation-harness, simple-evals, BigCode, BFCL, safety-harness, garak |
## 🏗️ Architecture
### Core Components
#### 1. Deployment Layer
- **PyTriton Backend**: Provides high-performance inference through the NVIDIA Triton Inference Server, with OpenAI API compatibility via a FastAPI interface. Supports model parallelism across single-node and multi-node configurations. Note: Multi-instance evaluation is not supported.
- **Ray Backend**: Enables multi-instance evaluation with model parallelism on a single node using Ray Serve, while maintaining OpenAI API compatibility. Multi-node support is coming soon.
#### 2. Evaluation Layer
- **NVIDIA Eval Factory**: Provides standardized benchmark evaluations using packages from NVIDIA Eval Factory, bundled in the NeMo Framework container. The `lm-evaluation-harness` is pre-installed by default, and additional tools listed in the [support matrix](#-support-matrix) can be added as needed. For more information, see the [documentation](https://github.com/NVIDIA-NeMo/Eval/tree/main/docs).
- **Adapter System**: Flexible request/response processing pipeline with **Interceptors** that provide modular processing:
- **Available Interceptors**: Modular components for request/response processing
- **SystemMessageInterceptor**: Customize system prompts
- **RequestLoggingInterceptor**: Log incoming requests
- **ResponseLoggingInterceptor**: Log outgoing responses
- **ResponseReasoningInterceptor**: Process reasoning outputs
- **EndpointInterceptor**: Route requests to the actual model
## 📖 Usage Examples
### Basic Deployment with PyTriton as the Serving Backend
```python
from nemo_eval.api import deploy
# Deploy model
deploy(
nemo_checkpoint="/path/to/checkpoint",
serving_backend="pytriton",
server_port=8080,
num_gpus=1,
max_input_len=8192,
max_batch_size=4
)
```
### Basic Evaluation
```Python
from nvidia_eval_commons.core.evaluate import evaluate
from nvidia_eval_commons.api.api_dataclasses import ApiEndpoint, ConfigParams, EvaluationConfig, EvaluationTarget
# Configure Endpoint
api_endpoint = ApiEndpoint(
url="http://0.0.0.0:8080/v1/completions/",
model_id="megatron_model"
)
# Evaluation target configuration
target = EvaluationTarget(api_endpoint=api_endpoint)
# Configure EvaluationConfig with type, number of samples to evaluate on, etc.
config = EvaluationConfig(type="gsm8k",
output_dir="results",
params=ConfigParams(
limit_samples=10
))
# Run evaluation
results = evaluate(target_cfg=target, eval_cfg=config)
```
### Use Adapters
The example below demonstrates how to configure an Adapter to provide a custom system prompt. Requests and responses are processed through interceptors, which are automatically selected based on the parameters defined in `AdapterConfig`.
```python
from nemo_eval.utils.api import AdapterConfig
# Configure adapter for reasoning
adapter_config = AdapterConfig(
api_url="http://0.0.0.0:8080/v1/completions/",
use_reasoning=True,
end_reasoning_token="</think>",
custom_system_prompt="You are a helpful assistant that thinks step by step.",
max_logged_requests=5,
max_logged_responses=5
)
# Run evaluation with adapter
results = evaluate(
target_cfg=target,
eval_cfg=config,
adapter_cfg=adapter_config
)
```
### Deploy with Multiple GPUs
```python
# Deploy with tensor parallelism or pipeline parallelism
deploy(
nemo_checkpoint="/path/to/checkpoint",
serving_backend="pytriton",
num_gpus=4,
tensor_parallelism_size=4,
pipeline_parallelism_size=1,
max_input_len=8192,
max_batch_size=8
)
```
### Deploy with Ray
```python
# Deploy using Ray Serve
deploy(
nemo_checkpoint="/path/to/checkpoint",
serving_backend="ray",
num_gpus=2,
num_replicas=2,
num_cpus_per_replica=8,
server_port=8080,
include_dashboard=True,
cuda_visible_devices="0,1"
)
```
## 📁 Project Structure
```
Eval/
├── src/nemo_eval/ # Main package
│ ├── api.py # Main API functions
│ ├── package_info.py # Package metadata
│ ├── adapters/ # Adapter system
│ │ ├── server.py # Adapter server
│ │ ├── utils.py # Adapter utilities
│ │ └── interceptors/ # Request/response interceptors
│ └── utils/ # Utility modules
│ ├── api.py # API configuration classes
│ ├── base.py # Base utilities
│ └── ray_deploy.py # Ray deployment utilities
├── tests/ # Test suite
│ ├── unit_tests/ # Unit tests
│ └── functional_tests/ # Functional tests
├── tutorials/ # Tutorial notebooks
├── scripts/ # Reference nemo-run scripts
├── docs/ # Documentation
├── docker/ # Docker configuration
└── external/ # External dependencies
```
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](https://github.com/NVIDIA-NeMo/Eval/blob/main/CONTRIBUTING.md) for details on development setup, testing, and code style guidelines
## 📄 License
This project is licensed under the Apache License 2.0. See the [LICENSE](https://github.com/NVIDIA-NeMo/Eval/blob/main/LICENSE) file for details.
## 📞 Support
- **Issues**: [GitHub Issues](https://github.com/NVIDIA-NeMo/Eval/issues)
- **Discussions**: [GitHub Discussions](https://github.com/NVIDIA-NeMo/Eval/discussions)
- **Documentation**: [NeMo Documentation](https://nemo-framework-documentation.gitlab-master-pages.nvidia.com/eval-build/)
## 🔗 Related Projects
- [NeMo Export Deploy](https://github.com/NVIDIA-NeMo/Export-Deploy) - Model export and deployment
---
**Note**: This project is actively maintained by NVIDIA. For the latest updates and features, please check our [releases page](https://github.com/NVIDIA-NeMo/Eval/releases).
Raw data
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"description": "<div align=\"center\">\n\n# NeMo Eval\n\n[](https://codecov.io/github/NVIDIA-NeMo/Eval)\n[](https://github.com/NVIDIA-NeMo/Eval/actions/workflows/cicd-main.yml)\n[](https://github.com/NVIDIA-NeMo/Eval/blob/main/pyproject.toml)\n[](https://github.com/NVIDIA-NeMo/)\n\n[Documentation](https://docs.nvidia.com/nemo/eval/latest/index.html) | [Examples](https://github.com/NVIDIA-NeMo/Eval?tab=readme-ov-file#-usage-examples) | [Contributing](https://github.com/NVIDIA-NeMo/Eval/blob/main/CONTRIBUTING.md)\n</div>\n\n## Overview\nThe NeMo Framework is NVIDIA\u2019s GPU-accelerated, end-to-end training platform for large language models (LLMs), multimodal models, and speech models. It enables seamless scaling of both pretraining and post-training workloads, from a single GPU to clusters with thousands of nodes, supporting Hugging Face/PyTorch and Megatron models. NeMo includes a suite of libraries and curated training recipes to help users build models from start to finish.\n\nThe Eval library (\"NeMo Eval\") is a comprehensive evaluation module within the NeMo Framework for LLMs. It offers streamlined deployment and advanced evaluation capabilities for models trained using NeMo, leveraging state-of-the-art evaluation harnesses.\n\n\n\n## \ud83d\ude80 Features\n\n- **Multi-Backend Deployment**: Supports PyTriton and multi-instance evaluations using the Ray Serve deployment backend\n- **Comprehensive Evaluation**: Includes state-of-the-art evaluation harnesses for academic benchmarks, reasoning benchmarks, code generation, and safety testing\n- **Adapter System**: Features a flexible architecture with chained interceptors for customizable request and response processing\n- **Production-Ready**: Supports high-performance inference with CUDA graphs and flash decoding\n- **Multi-GPU and Multi-Node Support**: Enables distributed inference across multiple GPUs and compute nodes\n- **OpenAI-Compatible API**: Provides RESTful endpoints aligned with OpenAI API specifications\n\n## \ud83d\udd27 Install NeMo Eval\n\n### Prerequisites\n\n- Python 3.10 or higher\n- CUDA-compatible GPU(s) (tested on RTX A6000, A100, H100)\n- NeMo Framework container (recommended)\n\n### Use pip\n\nFor quick exploration of NeMo Eval, we recommend installing our pip package:\n\n```bash\npip install nemo-eval\n```\n\n### Use Docker\n\nFor optimal performance and user experience, use the latest version of the [NeMo Framework container](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/nemo/tags). Please fetch the most recent $TAG and run the following command to start a container:\n\n```bash\ndocker run --rm -it -w /workdir -v $(pwd):/workdir \\\n --entrypoint bash \\\n --gpus all \\\n nvcr.io/nvidia/nemo:${TAG}\n```\n\n### Use uv\n\nTo install NeMo Eval with uv, please refer to our [Contribution guide](https://github.com/NVIDIA-NeMo/Eval/blob/main/CONTRIBUTING.md).\n\n## \ud83d\ude80 Quick Start\n\n### 1. Deploy a Model\n\n```python\nfrom nemo_eval.api import deploy\n\n# Deploy a NeMo checkpoint\ndeploy(\n nemo_checkpoint=\"/path/to/your/checkpoint\",\n serving_backend=\"pytriton\", # or \"ray\"\n server_port=8080,\n num_gpus=1,\n max_input_len=4096,\n max_batch_size=8\n)\n```\n\n### 2. Evaluate the Model\n\n```python\nfrom nvidia_eval_commons.core.evaluate import evaluate\nfrom nvidia_eval_commons.api.api_dataclasses import ApiEndpoint, EvaluationConfig, EvaluationTarget\n\n# Configure evaluation\napi_endpoint = ApiEndpoint(\n url=\"http://0.0.0.0:8080/v1/completions/\",\n model_id=\"megatron_model\"\n)\ntarget = EvaluationTarget(api_endpoint=api_endpoint)\nconfig = EvaluationConfig(type=\"gsm8k\", output_dir=\"results\")\n\n# Run evaluation\nresults = evaluate(target_cfg=target, eval_cfg=config)\nprint(results)\n```\n\n## \ud83d\udcca Support Matrix\n\n| Checkpoint Type | Inference Backend | Deployment Server | Evaluation Harnesses Supported |\n|----------------|-------------------|-------------|--------------------------|\n| NeMo FW checkpoint via Megatron Core backend | [Megatron Core in-framework inference engine](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core/inference) | PyTriton (single and multi node model parallelism), Ray (single node model parallelism with multi instance evals) | lm-evaluation-harness, simple-evals, BigCode, BFCL, safety-harness, garak |\n\n## \ud83c\udfd7\ufe0f Architecture\n### Core Components\n#### 1. Deployment Layer\n\n- **PyTriton Backend**: Provides high-performance inference through the NVIDIA Triton Inference Server, with OpenAI API compatibility via a FastAPI interface. Supports model parallelism across single-node and multi-node configurations. Note: Multi-instance evaluation is not supported.\n- **Ray Backend**: Enables multi-instance evaluation with model parallelism on a single node using Ray Serve, while maintaining OpenAI API compatibility. Multi-node support is coming soon.\n\n#### 2. Evaluation Layer\n\n- **NVIDIA Eval Factory**: Provides standardized benchmark evaluations using packages from NVIDIA Eval Factory, bundled in the NeMo Framework container. The `lm-evaluation-harness` is pre-installed by default, and additional tools listed in the [support matrix](#-support-matrix) can be added as needed. For more information, see the [documentation](https://github.com/NVIDIA-NeMo/Eval/tree/main/docs).\n\n- **Adapter System**: Flexible request/response processing pipeline with **Interceptors** that provide modular processing:\n - **Available Interceptors**: Modular components for request/response processing\n - **SystemMessageInterceptor**: Customize system prompts\n - **RequestLoggingInterceptor**: Log incoming requests\n - **ResponseLoggingInterceptor**: Log outgoing responses\n - **ResponseReasoningInterceptor**: Process reasoning outputs\n - **EndpointInterceptor**: Route requests to the actual model\n\n## \ud83d\udcd6 Usage Examples\n\n### Basic Deployment with PyTriton as the Serving Backend\n\n```python\nfrom nemo_eval.api import deploy\n\n# Deploy model\ndeploy(\n nemo_checkpoint=\"/path/to/checkpoint\",\n serving_backend=\"pytriton\",\n server_port=8080,\n num_gpus=1,\n max_input_len=8192,\n max_batch_size=4\n)\n```\n\n### Basic Evaluation\n\n```Python\nfrom nvidia_eval_commons.core.evaluate import evaluate\nfrom nvidia_eval_commons.api.api_dataclasses import ApiEndpoint, ConfigParams, EvaluationConfig, EvaluationTarget\n# Configure Endpoint\napi_endpoint = ApiEndpoint(\n url=\"http://0.0.0.0:8080/v1/completions/\",\n model_id=\"megatron_model\"\n)\n# Evaluation target configuration\ntarget = EvaluationTarget(api_endpoint=api_endpoint)\n# Configure EvaluationConfig with type, number of samples to evaluate on, etc.\nconfig = EvaluationConfig(type=\"gsm8k\",\n output_dir=\"results\",\n params=ConfigParams(\n limit_samples=10\n ))\n\n# Run evaluation\nresults = evaluate(target_cfg=target, eval_cfg=config)\n```\n\n### Use Adapters\n\nThe example below demonstrates how to configure an Adapter to provide a custom system prompt. Requests and responses are processed through interceptors, which are automatically selected based on the parameters defined in `AdapterConfig`.\n\n```python\nfrom nemo_eval.utils.api import AdapterConfig\n\n# Configure adapter for reasoning\nadapter_config = AdapterConfig(\n api_url=\"http://0.0.0.0:8080/v1/completions/\",\n use_reasoning=True,\n end_reasoning_token=\"</think>\",\n custom_system_prompt=\"You are a helpful assistant that thinks step by step.\",\n max_logged_requests=5,\n max_logged_responses=5\n)\n\n# Run evaluation with adapter\nresults = evaluate(\n target_cfg=target,\n eval_cfg=config,\n adapter_cfg=adapter_config\n)\n```\n\n### Deploy with Multiple GPUs\n\n```python\n# Deploy with tensor parallelism or pipeline parallelism\ndeploy(\n nemo_checkpoint=\"/path/to/checkpoint\",\n serving_backend=\"pytriton\",\n num_gpus=4,\n tensor_parallelism_size=4,\n pipeline_parallelism_size=1,\n max_input_len=8192,\n max_batch_size=8\n)\n```\n\n### Deploy with Ray\n\n```python\n# Deploy using Ray Serve\ndeploy(\n nemo_checkpoint=\"/path/to/checkpoint\",\n serving_backend=\"ray\",\n num_gpus=2,\n num_replicas=2,\n num_cpus_per_replica=8,\n server_port=8080,\n include_dashboard=True,\n cuda_visible_devices=\"0,1\"\n)\n```\n\n## \ud83d\udcc1 Project Structure\n\n```\nEval/\n\u251c\u2500\u2500 src/nemo_eval/ # Main package\n\u2502 \u251c\u2500\u2500 api.py # Main API functions\n\u2502 \u251c\u2500\u2500 package_info.py # Package metadata\n\u2502 \u251c\u2500\u2500 adapters/ # Adapter system\n\u2502 \u2502 \u251c\u2500\u2500 server.py # Adapter server\n\u2502 \u2502 \u251c\u2500\u2500 utils.py # Adapter utilities\n\u2502 \u2502 \u2514\u2500\u2500 interceptors/ # Request/response interceptors\n\u2502 \u2514\u2500\u2500 utils/ # Utility modules\n\u2502 \u251c\u2500\u2500 api.py # API configuration classes\n\u2502 \u251c\u2500\u2500 base.py # Base utilities\n\u2502 \u2514\u2500\u2500 ray_deploy.py # Ray deployment utilities\n\u251c\u2500\u2500 tests/ # Test suite\n\u2502 \u251c\u2500\u2500 unit_tests/ # Unit tests\n\u2502 \u2514\u2500\u2500 functional_tests/ # Functional tests\n\u251c\u2500\u2500 tutorials/ # Tutorial notebooks\n\u251c\u2500\u2500 scripts/ # Reference nemo-run scripts\n\u251c\u2500\u2500 docs/ # Documentation\n\u251c\u2500\u2500 docker/ # Docker configuration\n\u2514\u2500\u2500 external/ # External dependencies\n```\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](https://github.com/NVIDIA-NeMo/Eval/blob/main/CONTRIBUTING.md) for details on development setup, testing, and code style guidelines\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the Apache License 2.0. See the [LICENSE](https://github.com/NVIDIA-NeMo/Eval/blob/main/LICENSE) file for details.\n\n## \ud83d\udcde Support\n\n- **Issues**: [GitHub Issues](https://github.com/NVIDIA-NeMo/Eval/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/NVIDIA-NeMo/Eval/discussions)\n- **Documentation**: [NeMo Documentation](https://nemo-framework-documentation.gitlab-master-pages.nvidia.com/eval-build/)\n\n## \ud83d\udd17 Related Projects\n\n- [NeMo Export Deploy](https://github.com/NVIDIA-NeMo/Export-Deploy) - Model export and deployment\n\n---\n\n**Note**: This project is actively maintained by NVIDIA. For the latest updates and features, please check our [releases page](https://github.com/NVIDIA-NeMo/Eval/releases).\n",
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