numba

How to pass data bigger than the VRAM size into the GPU?

由 ℡╲_俬逩灬. 提交于 2020-06-26 15:52:06
问题 I am trying to pass more data into my GPU than I have VRAM, which results in the following error. CudaAPIError: Call to cuMemAlloc results in CUDA_ERROR_OUT_OF_MEMORY I created this code to recreate the problem: from numba import cuda import numpy as np @cuda.jit() def addingNumbers (big_array, big_array2, save_array): i = cuda.grid(1) if i < big_array.shape[0]: for j in range (big_array.shape[1]): save_array[i][j] = big_array[i][j] * big_array2[i][j] big_array = np.random.random_sample(

How to pass data bigger than the VRAM size into the GPU?

由 最后都变了- 提交于 2020-06-26 15:51:38
问题 I am trying to pass more data into my GPU than I have VRAM, which results in the following error. CudaAPIError: Call to cuMemAlloc results in CUDA_ERROR_OUT_OF_MEMORY I created this code to recreate the problem: from numba import cuda import numpy as np @cuda.jit() def addingNumbers (big_array, big_array2, save_array): i = cuda.grid(1) if i < big_array.shape[0]: for j in range (big_array.shape[1]): save_array[i][j] = big_array[i][j] * big_array2[i][j] big_array = np.random.random_sample(

Turn off list reflection in Numba

由 落爺英雄遲暮 提交于 2020-06-13 04:58:40
问题 I'm trying to accelerate my code using Numba. One of the arguments I'm passing into the function is a mutable list of lists. When I try changing one of the sublists, I get this error: Failed in nopython mode pipeline (step: nopython mode backend) cannot reflect element of reflected container: reflected list(reflected list(int64)) I don't actually care about reflecting changes I make to the native list into the original Python list. How do I go about telling Numba not to reflect the changes?

Can Numba be used with Tensorflow?

由 笑着哭i 提交于 2020-06-10 02:53:26
问题 Can Numba be used to compile Python code which interfaces with Tensorflow? I.e. computations outside of the Tensorflow universe would run with Numba for speed. I have not found any resources on how to do this. 回答1: You can use tf.numpy_function, or tf.py_func to wrap a python function and use it as a TensorFlow op. Here is an example which I used: @jit def dice_coeff_nb(y_true, y_pred): "Calculates dice coefficient" smooth = np.float32(1) y_true_f = np.reshape(y_true, [-1]) y_pred_f = np

Numba: how i can count occurrences in an numba loop

由 断了今生、忘了曾经 提交于 2020-05-17 06:15:27
问题 I would like to count the occurrences of "1" in the sliced array of "col3". "y = (arr[row-x:row2]).sum()" is just an example that the codes run! Here i obtain something like: "y = (arr[row-x:row, 2]).count(1)" to count a dynamic array of row 2, how often "1" occurred. #Stackoverflow example from numba import njit import pandas as pd d = {'col1': [20, 23, 25, 44, 46, 47, 48, 49, 50, 50, 52, 55, 56, 69, 70], 'col2': [39, 32, 42, 50, 63, 67, 64, 68, 68, 74, 59, 75, 58, 71, 66], 'col3': [1, 1, 1,

How to pass array pointer to Numba function?

由 余生颓废 提交于 2020-05-16 22:05:16
问题 I'd like to create a Numba-compiled function that takes a pointer or the memory address of an array as an argument and does calculations on it, e.g., modifies the underlying data. The pure-python version to illustrate this looks like this: import ctypes import numba as nb import numpy as np arr = np.arange(5).astype(np.double) # create arbitrary numpy array def modify_data(addr): """ a function taking the memory address of an array to modify it """ ptr = ctypes.c_void_p(addr) data = nb.carray

How to pass array pointer to Numba function?

由 蓝咒 提交于 2020-05-16 22:04:37
问题 I'd like to create a Numba-compiled function that takes a pointer or the memory address of an array as an argument and does calculations on it, e.g., modifies the underlying data. The pure-python version to illustrate this looks like this: import ctypes import numba as nb import numpy as np arr = np.arange(5).astype(np.double) # create arbitrary numpy array def modify_data(addr): """ a function taking the memory address of an array to modify it """ ptr = ctypes.c_void_p(addr) data = nb.carray

使用Python玩转GPU

由 旧街凉风 提交于 2020-05-04 09:32:31
问题 随着机器学习对模型运算速度的需求越来越强烈， 一直想进行GPU编程，但一直以来这些都是c++的专利 一想到c++里的各种坑，就提不起劲来，毕竟这样来来回回填坑的投入产出，生产效率就会大打折扣 解决方案 让人欣喜的是，随着Python阵营的不断发展壮大，使用python进行GPU编程也越来越便捷了 那么具体有些什么样的包，能针对GPU做些啥事呢？ 看看一些具体的代码，就能大概明白： 首先是pycuda，这是它的一个例子： mod = SourceModule(""" __global__ void multiply_them(float *dest, float *a, float *b) { const int i = threadIdx.x; dest[i] = a[i] * b[i]; } """) 由上面的代码我们可以看出，pycuda将调用gpu的c++代码做了包装，可以在python里直接使用 再看看numba： @cuda.jit def increment_by_one(an_array): pos = cuda.grid(1) if pos < an_array.size: an_array[pos] += 1 我们可以发现，numba更进一步，直接使用装饰器的办法让调用GPU的过程更简洁方便 再看看cupy： import numpy as np

NotImplementedError: bounds checking is not supported for CUDA

由 我是研究僧i 提交于 2020-03-23 12:02:39
问题 I'm trying to run code on my GPU, and firstly I clashed with the problem of having a working CUDA on ubuntu 18.04. After some pain I was suggested to work in a Docker Image, where I have (I presume) a well working CUDA version (I'm starting learning pytorch, and I was able to obtain a torch.cuda.is_available() = True ). After that I tried to run a simple code copied here, in order to see if effectively my GPU was able to do its job. Unfortunately not... I receive the message:

Numba jit warnings interpretation in python

由 时间秒杀一切 提交于 2020-03-23 04:27:55
问题 I have defined the following recursive array generator and am using Numba jit to try and accelerate the processing (based on this SO answer) @jit("float32[:](float32,float32,intp)", nopython=False, nogil=True) def calc_func(a, b, n): res = np.empty(n, dtype="float32") res[0] = 0 for i in range(1, n): res[i] = a * res[i - 1] + (1 - a) * (b ** (i - 1)) return res a = calc_func(0.988, 0.9988, 5000) I am getting a bunch of warnings/errors that I do not quite get. Would appreciate help in
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