data_manager module

class data_manager.CUDADataManager(num_agents: Optional[int] = None, num_envs: Optional[int] = None, episode_length: Optional[int] = None)

Bases: object

CUDA Data Manager: manages the data initialization of GPU, and data transfer between CPU host and GPU device

Example

cuda_data_manager = CUDADataManager( num_agents=10, num_envs=5, episode_length=100 )

data1 = DataFeed() data1.add_data(name=”X”, data=np.array([[1, 2, 3, 4, 5],

[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]])

)

data1.add_data(name=”a”, data=100) cuda_data_manager.push_data_to_device(data)

data2 = DataFeed() data2.add_data(name=”Y”, data=[[0.1,0.2,0.3,0.4,0.5],

[0.0,0.0,0.0,0.0,0.0], [0.0,0.0,0.0,0.0,0.0]]

)

cuda_data_manager.push_data_to_device(data2, torch_accessible=True)

X_copy_at_host = cuda_data_manager.pull_data_from_device(name=”X”) Y_copy_at_host = cuda_data_manager.pull_data_from_device(name=”Y”)

if cuda_data_manager.is_data_on_device_via_torch(“Y”):

Y_tensor_accessible_by_torch = cuda_data_manager.data_on_device_via_torch(“Y”)

# cuda_function here assumes a compiled CUDA C function cuda_function(cuda_data_manager.device_data(“X”),

cuda_data_manager.device_data(“Y”), block=(10,1,1), grid=(5,1))

add_meta_info(meta: Dict)

Add meta information to the data manager, only accepts scalar integer or float

Parameters

meta – for example, {“episode_length”: 100, “num_agents”: 10}

add_shared_constants(constants: Dict)

Add shared constants to the data manager

Parameters

constants – e.g., {“action_mapping”: [[0,0], [1,1], [-1,-1]]}

data_on_device_via_torch(name: str) → torch.Tensor

The data on the device. This is used for Pytorch default access within GPU. To fetch the tensor back to the host, call pull_data_from_device()

Parameters

name – name of the device array

returns: the tensor itself at the device.

device_data(name: str)

Parameters

name – name of the device data

returns: the data pointer in the device for CUDA to access

get_dtype(name: str)

get_shape(name: str)

property host_data

is_data_on_device(name: str) → bool

is_data_on_device_via_torch(name: str) → bool

This is used to check if the data exist and accessible via Pytorch default access within GPU. name: name of the device

property log_data_list

meta_info(name: str)

pull_data_from_device(name: str)

Fetch the values of device array back to the host

Parameters

name – name of the device array

returns: a host copy of scalar data or numpy array fetched back from the device array

push_data_to_device(data: Dict, torch_accessible: bool = False)

Register data to the host, and push to the device (1) register at self._host_data (2) push to device and register at self._device_data_pointer, CUDA program can directly access those data via pointer (3) if save_copy_and_apply_at_reset or log_data_across_episode as instructed by the data, register and push to device using step (1)(2) too

Parameters

data – e.g., {“name”: {“data”: numpy array,

“attributes”: {“save_copy_and_apply_at_reset”: True, “log_data_across_episode”: True}}}. This data dictionary can be constructed by warp_drive.utils.data_feed.DataFeed :param torch_accessible: if True, the data is directly accessible by Pytorch

property reset_data_list

reset_device(name: Optional[str] = None)

Reset the device array values back to the host array values Note: this reset is not a device-only execution, but incurs data transfer from host to device

Parameters

name – (optional) reset a device array by name, if None, reset all arrays

property scalar_data_list

shared_constant(name: str)

class data_manager.CudaTensorHolder(t)

Bases: pycuda._driver.PointerHolderBase

A class that facilitates casting tensors to pointers.