# Installation ## Pip Pip installation flow has been validated on Ubuntu only at this stage. ```shell apt-get update && apt-get -y install python3.10 python3-pip openmpi-bin libopenmpi-dev python -m pip install --pre --extra-index-url https://pypi.nvidia.com optimum-nvidia ``` For developers who want to target the best performances, please look at the installation methods below. ## Docker container You can use a Docker container to try Optimum-NVIDIA today. Images are available on the Hugging Face Docker Hub. ```bash docker pull huggingface/optimum-nvidia ``` ## Building from source Instead of using the pre-built docker container, you can build Optimum-NVIDIA from source: ```bash TARGET_SM="90-real;89-real" git clone --recursive --depth=1 https://github.com/huggingface/optimum-nvidia.git cd optimum-nvidia/third-party/tensorrt-llm make -C docker release_build CUDA_ARCHS=$TARGET_SM cd ../.. && docker build -t : -f docker/Dockerfile . ``` # Quickstart Guide ## Pipelines Hugging Face pipelines provide a simple yet powerful abstraction to quickly set up inference. If you already have a pipeline from transformers, you can unlock the performance benefits of Optimum-NVIDIA by just changing one line. ```diff - from transformers.pipelines import pipeline + from optimum.nvidia.pipelines import pipeline pipe = pipeline('text-generation', 'meta-llama/Llama-2-7b-chat-hf', use_fp8=True) pipe("Describe a real-world application of AI in sustainable energy.") ``` ## Generate If you want control over advanced features like quantization and token selection strategies, we recommend using the `generate()` API. Just like with `pipelines`, switching from existing transformers code is super simple. ```diff - from transformers import AutoModelForCausalLM + from optimum.nvidia import AutoModelForCausalLM from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", padding_side="left") model = AutoModelForCausalLM.from_pretrained( "meta-llama/Llama-2-7b-chat-hf", + use_fp8=True, + max_prompt_length=1024, + max_output_length=2048, # Must be at least size of max_prompt_length + max_new_tokens + max_batch_size=8, ) model_inputs = tokenizer(["How is autonomous vehicle technology transforming the future of transportation and urban planning?"], return_tensors="pt").to("cuda") generated_ids = model.generate( **model_inputs, top_k=40, top_p=0.7, repetition_penalty=10, ) tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` To learn more about text generation with LLMs, check out [this guide](https://huggingface.co/docs/transformers/llm_tutorial)! # Support Matrix We test Optimum-NVIDIA on 4090, L40S, and H100 Tensor Core GPUs, though it is expected to work on any GPU based on the following architectures: * Ampere (A100/A30 are supported. Experimental support for A10, A40, RTX Ax000) * Hopper * Ada-Lovelace Note that FP8 support is only available on GPUs based on Hopper and Ada-Lovelace architectures. Optimum-NVIDIA works on Linux will support Windows soon. Optimum-NVIDIA currently accelerates text-generation with LLaMAForCausalLM, and we are actively working to expand support to include more model architectures and tasks. # Contributing Check out our [Contributing Guide](./CONTRIBUTING.md)