NDArray API¶
NDArray package (mxnet.ndarray) contains tensor operations similar to numpy.ndarray. The syntax is similar except for some additional calls to deal with I/O and multi-devices.
Create NDArray¶
Like numpy, you could create mxnet.ndarray like followings:
>>> import mxnet as mx
>>> # all-zero array of dimension 100x50
>>> a = mx.nd.zeros((100, 50))
>>> # all-one array of dimension 256x32x128x1
>>> b = mx.nd.ones((256, 32, 128, 1))
>>> # initialize array with contents
>>> c = mx.nd.array([[1, 2, 3], [4, 5, 6]])
NDArray operations¶
We provide some basic ndarray operations like arithmetic and slice operations. More operations are coming in handy!
Arithmetic operations¶
>>> import mxnet as mx
>>> a = mx.nd.zeros((100, 50))
>>> a.shape
(100L, 50L)
>>> b = mx.nd.ones((100, 50))
>>> # c and d will be calculated in parallel here!
>>> c = a + b
>>> d = a - b
>>> # inplace operation, b's contents will be modified, but c and d won't be affected.
>>> b += d
Slice operations¶
>>> import mxnet as mx
>>> a = mx.nd.zeros((100, 50))
>>> a[0:10] = 1 # first 10 rows will become 1
Conversion from/to numpy.ndarray¶
MXNet NDArray supports pretty nature way to convert from/to mxnet.ndarray to/from numpy.ndarray:
>>> import mxnet as mx
>>> import numpy as np
>>> a = np.array([1,2,3])
>>> b = mx.nd.array(a) # convert from numpy array
>>> b
<mxnet.ndarray.NDArray object at ...>
>>> b.asnumpy() # convert to numpy array
array([ 1., 2., 3.], dtype=float32)
Save Load NDArray¶
You can always use pickle to save and load NDArrays. We also provide functions to help save and load list or dictionary of NDArrays from file systems.
>>> import mxnet as mx
>>> a = mx.nd.zeros((100, 200))
>>> b = mx.nd.zeros((100, 200))
>>> # save list of NDArrays
>>> mx.nd.save("/path/to/array/file", [a, b])
>>> # save dictionary of NDArrays to AWS S3
>>> mx.nd.save("s3://path/to/s3/array", {'A' : a, 'B' : b})
>>> # save list of NDArrays to hdfs.
>>> mx.nd.save("hdfs://path/to/hdfs/array", [a, b])
>>> from_file = mx.nd.load("/path/to/array/file")
>>> from_s3 = mx.nd.load("s3://path/to/s3/array")
>>> from_hdfs = mx.nd.load("hdfs://path/to/hdfs/array")
The good thing about using the above save and load interface is that:
- You could use the format across all
mxnetlanguage bindings. - Already support S3 and HDFS.
Multi-device Support¶
The device information is stored in mxnet.Context structure. When creating ndarray in mxnet, user could either use the context argument (default is CPU context) to create arrays on specific device or use the with statement as follows:
>>> import mxnet as mx
>>> cpu_a = mx.nd.zeros((100, 200))
>>> cpu_a.context
cpu(0)
>>> with mx.Context(mx.gpu(0)):
>>> gpu_a = mx.nd.ones((100, 200))
>>> gpu_a.context
gpu(0)
>>> ctx = mx.Context(mx.gpu(0))
>>> gpu_b = mx.nd.zeros((100, 200), ctx)
>>> gpu_b.context
gpu(0)
Currently, we DO NOT allow operations among arrays from different contexts. To allow this, use copyto member function to copy the content to different devices and continue computation:
>>> import mxnet as mx
>>> x = mx.nd.zeros((100, 200))
>>> with mx.Context(mx.gpu(0)):
>>> y = mx.nd.zeros((100, 200))
>>> z = x + y
mxnet.base.MXNetError: [13:29:12] src/ndarray/ndarray.cc:33: Check failed: lhs.ctx() == rhs.ctx() operands context mismatch
>>> cpu_y = mx.nd.zeros((100, 200))
>>> y.copyto(cpu_y)
>>> z = x + cpu_y