Skip to main content

Optimum RBLN is the interface between the HuggingFace Transformers and Diffusers libraries and RBLN accelerators. It provides a set of tools enabling easy model loading and inference on single and multiple rbln device settings for different downstream tasks.

Project description

Optimum RBLN

PyPI version License Documentation Contributor Covenant

🤗 Optimum RBLN provides an interface between HuggingFace libraries (Transformers, Diffusers) and RBLN NPUs, including ATOM and REBEL.

This library enables seamless integration between the HuggingFace ecosystem and RBLN NPUs through a comprehensive toolkit for model loading and inference across single and multi-NPU environments. While we maintain a list of officially validated models and tasks, users can easily adapt other models and tasks with minimal modifications.

Key Features

🚀 High Performance Inference

  • Optimized model execution on RBLN NPUs through RBLN SDK compilation
  • Support for both single and multi-NPU inference
  • Integrated with RBLN Runtime for optimal performance

🔧 Easy Integration

  • Seamless compatibility with HuggingFace Model Hub
  • Drop-in replacement for existing HuggingFace pipelines
  • Minimal code changes required for NPU acceleration

Seamless Replacement for Existing HuggingFace Code

- from diffusers import StableDiffusionXLPipeline
+ from optimum.rbln import RBLNStableDiffusionXLPipeline

# Load model
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
- pipe = StableDiffusionXLPipeline.from_pretrained(model_id)
+ pipe = RBLNStableDiffusionXLPipeline.from_pretrained(model_id, export=True)

# Generate image
image = pipe(prompt).images[0]

# Save image result
image.save("image.png")

+ # (Optional) Save compiled artifacts to skip the compilation step in future runs
+ pipe.save_pretrained("compiled_sdxl")

Documentation

Check out the documentation of Optimum RBLN for more advanced usage.

Getting Started

Note: The rebel-compiler library, which is required for running optimum-rbln, is only available for approved users. Please refer to the installation guide for instructions on accessing and installing rebel-compiler.

Install from PyPI

To install the latest release of this package:

pip install optimum-rbln --extra-index-url https://download.pytorch.org/whl/cpu

Install from source

Prerequisites

  • Install uv (refer to this link for detailed commands)

The below command installs optimum-rbln along with its dependencies.

git clone https://github.com/rbln-sw/optimum-rbln.git
cd optimum-rbln
./scripts/uv-sync.sh

Need Help?

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

optimum_rbln-0.11.1a3.tar.gz (545.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

optimum_rbln-0.11.1a3-py3-none-any.whl (626.1 kB view details)

Uploaded Python 3

File details

Details for the file optimum_rbln-0.11.1a3.tar.gz.

File metadata

  • Download URL: optimum_rbln-0.11.1a3.tar.gz
  • Upload date:
  • Size: 545.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for optimum_rbln-0.11.1a3.tar.gz
Algorithm Hash digest
SHA256 e497e1d40e00c85be35612d89eed88caeb531eb8ca2c5dd7045fac4fe1b93927
MD5 ac04d4b59be5a3a0579900ad1ae6f83b
BLAKE2b-256 734c9470bf8a4293e91d19b56b2f4da9342e7aba4c75668577bdaaa5af21a5e8

See more details on using hashes here.

File details

Details for the file optimum_rbln-0.11.1a3-py3-none-any.whl.

File metadata

  • Download URL: optimum_rbln-0.11.1a3-py3-none-any.whl
  • Upload date:
  • Size: 626.1 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.12

File hashes

Hashes for optimum_rbln-0.11.1a3-py3-none-any.whl
Algorithm Hash digest
SHA256 408b50f4cc9f81a520c11a610e27772868efab4a1b3c13271cf9597c95769059
MD5 2445c98f4f155a19ea35f0f8765f284e
BLAKE2b-256 9f0c04de97f8da3d801ca8d581245035a18329ccbb19c1c434573ea5c7794b18

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page