1/27/2024 0 Comments Lies of P for windows download![]() ![]() When I'm playing without obs, the game takes more less 50% GPU usage. Memory : Kingston Fury Renegade, DDR5, 32 GB, 6000MHz, C元2 SSD disk Lexar NM790 1TB M.2 2280 PCI-E x4 Gen4 NVMe So I've set in OBS: Base (Canvas) Resolution 3840x2160 and Output (Scaled) Resolution 2560x1440.Ĭooler: CPU Endorfy Fortis 5 Dual Fan (EY3A009) (but it streches if I set FullHD but such low resolution is not acceptable for me). ![]() I have monitor 4k but unfortunately I can't set 1440 resolution in the game because the game does not stretch to the entire screen. I'm trying to record serie Lies of P and put to youtube. Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.I have strange problem. Python backend system that decouples API from implementation unumpy provides a NumPy API. Manipulate JSON-like data with NumPy-like idioms. Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.ĭeep learning framework that accelerates the path from research prototyping to production deployment.Īn end-to-end platform for machine learning to easily build and deploy ML powered applications.ĭeep learning framework suited for flexible research prototyping and production.Ī cross-language development platform for columnar in-memory data and analytics. Labeled, indexed multi-dimensional arrays for advanced analytics and visualization NumPy-compatible array library for GPU-accelerated computing with Python.Ĭomposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU. NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.ĭistributed arrays and advanced parallelism for analytics, enabling performance at scale. ![]() With this power comes simplicity: a solution in NumPy is often clear and elegant. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. Nearly every scientist working in Python draws on the power of NumPy. ![]()
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