Commit 298465f
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|---|---|---|---|
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| 9 | 9 | | Windows | [](http://ci.arrayfire.org/view/All/job/arrayfire-wrappers/job/python-windows/) | | |
| 10 | 10 | | OSX | [](http://ci.arrayfire.org/view/All/job/arrayfire-wrappers/job/python-osx/) | | |
| 11 | 11 | ||
| 12 | - ## Example | ||
| 13 | - | ||
| 14 | - ```python | ||
| 15 | - import arrayfire as af | ||
| 16 | - | ||
| 17 | - # Display backend information | ||
| 18 | - af.info() | ||
| 19 | - | ||
| 20 | - # Generate a uniform random array with a size of 5 elements | ||
| 21 | - a = af.randu(5, 1) | ||
| 22 | - | ||
| 23 | - # Print a and its minimum value | ||
| 24 | - af.display(a) | ||
| 25 | - | ||
| 26 | - # Print min and max values of a | ||
| 27 | - print("Minimum, Maximum: ", af.min(a), af.max(a)) | ||
| 28 | - ``` | ||
| 29 | - | ||
| 30 | - ## Sample outputs | ||
| 12 | + ## Documentation | ||
| 31 | 13 | ||
| 32 | - On an AMD GPU: | ||
| 14 | + Documentation for this project can be found [over here](http://arrayfire.org/arrayfire-python/). | ||
| 33 | 15 | ||
| 34 | - ``` | ||
| 35 | - Using opencl backend | ||
| 36 | - ArrayFire v3.0.1 (OpenCL, 64-bit Linux, build 17db1c9) | ||
| 37 | - [0] AMD : Spectre | ||
| 38 | - -1- AMD : AMD A10-7850K Radeon R7, 12 Compute Cores 4C+8G | ||
| 39 | - | ||
| 40 | - [5 1 1 1] | ||
| 41 | - 0.4107 | ||
| 42 | - 0.8224 | ||
| 43 | - 0.9518 | ||
| 44 | - 0.1794 | ||
| 45 | - 0.4198 | ||
| 46 | - | ||
| 47 | - Minimum, Maximum: 0.17936542630195618 0.9517996311187744 | ||
| 48 | - ``` | ||
| 49 | - | ||
| 50 | - On an NVIDIA GPU: | ||
| 16 | + ## Example | ||
| 51 | 17 | ||
| 18 | + ```python | ||
| 19 | + # Monte Carlo estimation of pi | ||
| 20 | + def calc_pi_device(samples): | ||
| 21 | + # Simple, array based API | ||
| 22 | + # Generate uniformly distributed random numers | ||
| 23 | + x = af.randu(samples) | ||
| 24 | + y = af.randu(samples) | ||
| 25 | + # Supports Just In Time Compilation | ||
| 26 | + # The following line generates a single kernel | ||
| 27 | + within_unit_circle = (x * x + y * y) < 1 | ||
| 28 | + # Intuitive function names | ||
| 29 | + return 4 * af.count(within_unit_circle) / samples | ||
| 52 | 30 | ``` | |
| 53 | - Using cuda backend | ||
| 54 | - ArrayFire v3.0.0 (CUDA, 64-bit Linux, build 86426db) | ||
| 55 | - Platform: CUDA Toolkit 7, Driver: 346.46 | ||
| 56 | - [0] Tesla K40c, 12288 MB, CUDA Compute 3.5 | ||
| 57 | - -1- GeForce GTX 750, 1024 MB, CUDA Compute 5.0 | ||
| 58 | - | ||
| 59 | - Generate a random matrix a: | ||
| 60 | - [5 1 1 1] | ||
| 61 | - 0.7402 | ||
| 62 | - 0.9210 | ||
| 63 | - 0.0390 | ||
| 64 | - 0.9690 | ||
| 65 | - 0.9251 | ||
| 66 | - | ||
| 67 | - Minimum, Maximum: 0.039020489901304245 0.9689629077911377 | ||
| 68 | - ``` | ||
| 69 | - | ||
| 70 | - Fallback to CPU when CUDA and OpenCL are not availabe: | ||
| 71 | 31 | ||
| 72 | - ``` | ||
| 73 | - Using cpu backend | ||
| 74 | - ArrayFire v3.0.0 (CPU, 64-bit Linux, build 86426db) | ||
| 75 | - | ||
| 76 | - Generate a random matrix a: | ||
| 77 | - [5 1 1 1] | ||
| 78 | - 0.0000 | ||
| 79 | - 0.1315 | ||
| 80 | - 0.7556 | ||
| 81 | - 0.4587 | ||
| 82 | - 0.5328 | ||
| 83 | - | ||
| 84 | - Minimum, Maximum: 7.825903594493866e-06 0.7556053400039673 | ||
| 85 | - ``` | ||
| 86 | 32 | ||
| 87 | 33 | Choosing a particular backend can be done using `af.backend.set( backend_name )` where backend_name can be one of: "_cuda_", "_opencl_", or "_cpu_". The default device is chosen in the same order of preference. | |
| 88 | 34 | ||
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