dpnp.frombuffer¶

dpnp.frombuffer(buffer, dtype=<class 'float'>, count=-1, offset=0, *, like=None, device=None, usm_type='device', sycl_queue=None)[source]¶

Interpret a buffer as a 1-dimensional array.

For full documentation refer to numpy.frombuffer.

Parameters:

bufferbuffer_like

An object that exposes the buffer interface.

dtype{None, str, dtype object}, optional

Data-type of the returned array. If None, uses a default floating point data type for the device on which the returned array is allocated.

Default: float.

countint, optional

Number of items to read. -1 means all data in the buffer.

Default: -1.

offsetint, optional

Start reading the buffer from this offset (in bytes).

Default: 0.

device{None, string, SyclDevice, SyclQueue, Device}, optional

An array API concept of device where the output array is created. device can be None, a oneAPI filter selector string, an instance of dpctl.SyclDevice corresponding to a non-partitioned SYCL device, an instance of dpctl.SyclQueue, or a dpnp.tensor.Device object returned by dpnp.ndarray.device.

Default: None.

usm_type{None, "device", "shared", "host"}, optional

The type of SYCL USM allocation for the output array.

Default: "device".

sycl_queue{None, SyclQueue}, optional

A SYCL queue to use for output array allocation and copying. The sycl_queue can be passed as None (the default), which means to get the SYCL queue from device keyword if present or to use a default queue.

Default: None.

Returns:

outdpnp.ndarray: A 1-dimensional array created from input buffer object.

Limitations

Parameter like is supported only with default value None. Otherwise, the function raises NotImplementedError exception.

See also

dpnp.fromfile: Construct array from data in a text or binary file.
dpnp.fromiter: Construct array from an iterable object.
dpnp.fromstring: Construct array from the text data in a string.
ndarray.tobytes: Construct Python bytes from the raw data bytes in the array.

Notes

This uses numpy.frombuffer and coerces the result to a DPNP array.

Examples

>>> import dpnp as np
>>> s = b'\x01\x02\x03\x04'
>>> np.frombuffer(s, dtype=np.int32)
array([67305985], dtype=int32)
>>> np.frombuffer(b'\x01\x02\x03\x04\x05', dtype='u1', count=3)
array([1, 2, 3], dtype=uint8)

Creating an array on a different device or with a specified usm_type

>>> x = np.frombuffer(s, dtype=np.int32) # default case
>>> x.device, x.usm_type
(Device(level_zero:gpu:0), 'device')

>>> y = np.frombuffer(s, dtype=np.int32, device='cpu')
>>> y.device, y.usm_type
(Device(opencl:cpu:0), 'device')

>>> z = np.frombuffer(s, dtype=np.int32, usm_type="host")
>>> z.device, z.usm_type
(Device(level_zero:gpu:0), 'host')