#python #cloud_computing #cloud_management #data_science #deep_learning #distributed_training #gpu #hyperparameter_tuning #job_queue #job_scheduler #machine_learning #ml_infrastructure #multicloud #serverless #spot_instances #tpu
https://github.com/skypilot-org/skypilot
https://github.com/skypilot-org/skypilot
GitHub
GitHub - skypilot-org/skypilot: Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage…
Run, manage, and scale AI workloads on any AI infrastructure. Use one system to access & manage all AI compute (Kubernetes, 20+ clouds, or on-prem). - skypilot-org/skypilot
#python #amd #cuda #gpt #inference #inferentia #llama #llm #llm_serving #llmops #mlops #model_serving #pytorch #rocm #tpu #trainium #transformer #xpu
vLLM is a library that makes it easy, fast, and cheap to use large language models (LLMs). It is designed to be fast with features like efficient memory management, continuous batching, and optimized CUDA kernels. vLLM supports many popular models and can run on various hardware including NVIDIA GPUs, AMD CPUs and GPUs, and more. It also offers seamless integration with Hugging Face models and supports different decoding algorithms. This makes it flexible and easy to use for anyone needing to serve LLMs, whether for research or other applications. You can install vLLM easily with `pip install vllm` and find detailed documentation on their website.
https://github.com/vllm-project/vllm
vLLM is a library that makes it easy, fast, and cheap to use large language models (LLMs). It is designed to be fast with features like efficient memory management, continuous batching, and optimized CUDA kernels. vLLM supports many popular models and can run on various hardware including NVIDIA GPUs, AMD CPUs and GPUs, and more. It also offers seamless integration with Hugging Face models and supports different decoding algorithms. This makes it flexible and easy to use for anyone needing to serve LLMs, whether for research or other applications. You can install vLLM easily with `pip install vllm` and find detailed documentation on their website.
https://github.com/vllm-project/vllm
GitHub
GitHub - vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine for LLMs
A high-throughput and memory-efficient inference and serving engine for LLMs - vllm-project/vllm
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