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This was for the conda team to potentially incorporate into conda as a “marker” (I think that is the right term), so that conda could include a cuda package with a version given by this function in the dependency solver. The CUDA driver provides a C API to query what maximum version of CUDA is supported by the driver, so a few months ago I wrote a self-contained Python function for detecting what version of CUDA (if any) is present on the system: Nvidia cuda toolkit 9.2 installation failed install#This allows you to force a specific CUDA version this way: conda install pytorch cudatoolkit=8.0Īnd that will get you a PyTorch compiled with CUDA 8, rather than something else. So for packages in Anaconda that require CUDA, we make them depend on a specific cudatoolkit version. These two conflicting desires for compatibility and performance explain why it makes sense to compile packages with a range of CUDA versions (right now, I’d say 8.0-10 or 9.0 to 10.0 would be the best choice), but still leaves the burden on the user to know which CUDA version they need.īecause nearly all CUDA projects require the CUDA toolkit libraries, and Anaconda packages them, we use the cudatoolkit package as our CUDA version marker. As an example, TensorFlow compiled with CUDA 8 can take 10+ minutes to start up on a Volta GPU. A package compiled for CUDA 8 will not run on Volta GPUs without a lengthy JIT recompilation of all the CUDA functions in the project, which happens automatically, but can still be a bad user experience. Aside from new CUDA language features (which project may choose to ignore for compatibility reasons), building with newer CUDA versions can also improve performance as well as add native support for newer hardware. So, on one hand, there is motivation (much like glibc) to pick and arbitrary old CUDA and build everything with that, and rely on driver backward compatibility. This backward compatibility also extends to the cudatoolkit (the userspace libraries supplied by NVIDIA which Anaconda already packages), where a conda environment with cudatoolkit 8.0 would work just fine with a system that has the CUDA 9.2 drivers. So, for example, the CUDA 9.2 build of PyTorch would only require that CUDA >= 9.2 is present on the system. Nvidia cuda toolkit 9.2 installation failed drivers#CUDA drivers (the part that conda cannot install) are backward compatible with applications compiled with older versions of CUDA. Maxthon has plenty of other built-in tools. And you can extend this by creating your own mouse gestures for various common tasks. To move back a page, say, you hold down the right mouse button while moving the mouse from right to left. So once you've signed up for a free Maxthon Passport account, you can use "Cloud Push" to share web content like images, videos, text or links with other devices "Cloud Download" copies your downloads to the cloud so they're accessible from anywhere and "Cloud Tabs" keeps your tabs synchronised, so you can close a browsing session on one device, then carry on just where you left off on another.īuilt-in mouse gesture support allows you to navigate with a flick of the mouse. #Maxthon browser free download for windows vista androidMaxthon is available in Windows, Mac, iPhone and Android versions, too (with tablet support coming soon), and the new "Cloud Browser" features aim to make the most of that. Its use of the Webkit rendering engine (as used by Chrome and Safari) ensures excellent performance, but the program can also display pages using IE's Trident for compatibility. The program aims to make you feel at home right away by importing your favourites from IE, Firefox or Chrome, for instance. Maxthon Cloud Browser is an unusual tool which tries to combine the best features of the competition, with plenty of new additions of its own. |
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