![]() As an option, you can vent the top to release the heat rising from the GPUs, but I like the case because it has very little venting. The great part of this design is all the heat ends up in the top. The PSU mounts in the bottome front as well. The CPU is now at the bottom back of the case and with the rear fan acting as an intake and not exhaust, you get great CPU cooling. The motherboard is upside down, so that GPUs (In my case an axial fan gpu) faces towards the top. Consequently, you get inefficient operation from the PSU, whereby maximum power and efficiency are almost impossible to reach at temperatures exceeding 40☌ (as they are normally based on an operating environment of around 25☌.) The longevity of components inside the power supply also suffer.Īfter reading the charts of PSU placement and the GPU cooling diagrams, I'm even more convinced that my Lian Li PCA05-NB is a great solution. However, it also results in the power supply absorbing much of the waste heat generated by the graphics cards and processor. Supposedly, this improves dissipation and prevents heat from building up. Air is sucked in from inside the chassis and then expelled. Older PC cases manufactured according to the ATX specification put the power supply just under the case's top. Otherwise, you're fighting the forces of convection, and possibly creating a situation where a screw or other loose part could fall into the power supply. You should go this route only with passively-cooled "silent" PSUs, so that the warm air can rise. Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.Don’t install the PSU with its opening facing up in the chassis. 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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