get_py_MAF_handle {mafR} | R Documentation |
Utilities to manage Python environment and torch tensors
Description
Utility initializing a Python environment for running zuko.flows.MAF
and retrieving it.
Usage
get_py_MAF_handle(envir, reset=FALSE, torch_device="cpu", GPU_mem=NULL,
verbose = TRUE)
Arguments
envir |
An environment (in the R sense) initialized as shown in the Examples. |
reset |
Boolean: Whether to reinitialize the Python session or not. |
torch_device |
Character: |
GPU_mem |
For development purposes (effect is complicated). An amount of (dedicated) GPU memory, in bytes. |
verbose |
Boolean. Whether to print some messages or not. |
Value
If successful, get_py_MAF_handle
returns the modified input environment.
If sourcing the Python code provided by mafR failed (presumably from trying to use an improperly set-up Python environment), the error condition message is returned.
Examples
# Initialization of Python session:
my_env <- list2env(list(is_set=FALSE),parent = emptyenv())
my_env <- get_py_MAF_handle(my_env, reset=FALSE, torch_device="cpu")
if (inherits(my_env,"environment")) {
# => provides access to:
my_env$torch # Imported Python package (result of reticulate::import("torch"))
my_env$device # the torch_device
# and to internal definitions for MAF training
}
[Package mafR version 1.1.6 Index]