Mona Jalal monajalal

## cuda_11.7_installation_on_Ubuntu_22.04
#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

## snippet.py
import omni.usd
from pxr import UsdGeom, Usd, UsdGeom

def get_prims_startswith(stage, root_path, starts_with):
	matching_prims = []
	root_prim = stage.GetPrimAtPath(root_path)
	for prim in Usd.PrimRange(root_prim):
		if prim.GetName().startswith(starts_with):
			matching_prims.append(prim)
	return matching_prims

## datadistributedparallel.py
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import DataLoader

# Each process runs on 1 GPU device specified by the local_rank argument.
parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", type=int)
args = parser.parse_args()

# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')

## plot_confusion_matrix.png

      
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              1 star
            
          
                vikash512
                / plot_confusion_matrix.png
            
            
              Created
              July 31, 2018 12:47
                — forked from hitvoice/plot_confusion_matrix.png
            
              
                 Generate matrix plot for confusion matrix with pretty annotations.
              
          
## pytorch_image_folder_with_file_paths.py
import torch
from torchvision import datasets

class ImageFolderWithPaths(datasets.ImageFolder):
    """Custom dataset that includes image file paths. Extends
    torchvision.datasets.ImageFolder
    """

    # override the __getitem__ method. this is the method that dataloader calls
    def __getitem__(self, index):

## forecasting_metrics.py
import numpy as np

EPSILON = 1e-10


def _error(actual: np.ndarray, predicted: np.ndarray):
    """ Simple error """
    return actual - predicted


## Python Graph Traversal Fundamentals.ipynb

      
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                jrjames83
                / Python Graph Traversal Fundamentals.ipynb
            
            
              Created
              February 25, 2018 22:14
            
              
                Fundamentals of Graph Traversals in Python (explain it to me like i'm 10 years old)
              
          
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## gtav_object_ids.txt
prop_a4_pile_01
prop_a4_sheet_01
prop_a4_sheet_02
prop_a4_sheet_03
prop_a4_sheet_04
prop_a4_sheet_05
prop_abat_roller_static
prop_abat_slide
prop_acc_guitar_01
prop_acc_guitar_01_d1

## Multi-Part zip extraction in Ubuntu.txt
Download/Copy all related *.zip files in one directory.
Open terminal and change to that directory which has all zip files.
Enter command zip -s- FILE_NAME.zip -O COMBINED_FILE.zip
Enter unzip COMBINED_FILE.zip

## functional_keras.py
'''Functional Keras is a more functional replacement for the Graph API.
'''

###################
# 2 LSTM branches #
###################
a = Input(input_shape=(10, 32))  # output is a TF/TH placeholder, augmented with Keras attributes
b = Input(input_shape=(10, 32))
encoded_a = LSTM(32)(a)  # output is a TF/TH tensor
encoded_b = LSTM(32)(b)
	#!/bin/bash

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###

	### to verify your gpu is cuda enable check
	import omni.usd
	from pxr import UsdGeom, Usd, UsdGeom

	def get_prims_startswith(stage, root_path, starts_with):
	matching_prims = []
	root_prim = stage.GetPrimAtPath(root_path)
	for prim in Usd.PrimRange(root_prim):
	if prim.GetName().startswith(starts_with):
	matching_prims.append(prim)
	return matching_prims
	from torch.utils.data.distributed import DistributedSampler
	from torch.utils.data import DataLoader

	# Each process runs on 1 GPU device specified by the local_rank argument.
	parser = argparse.ArgumentParser()
	parser.add_argument("--local_rank", type=int)
	args = parser.parse_args()

	# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
	torch.distributed.init_process_group(backend='nccl')
	import torch
	from torchvision import datasets

	class ImageFolderWithPaths(datasets.ImageFolder):
	"""Custom dataset that includes image file paths. Extends
	torchvision.datasets.ImageFolder
	"""

	# override the __getitem__ method. this is the method that dataloader calls
	def __getitem__(self, index):
	import numpy as np

	EPSILON = 1e-10


	def _error(actual: np.ndarray, predicted: np.ndarray):
	""" Simple error """
	return actual - predicted
	prop_a4_pile_01
	prop_a4_sheet_01
	prop_a4_sheet_02
	prop_a4_sheet_03
	prop_a4_sheet_04
	prop_a4_sheet_05
	prop_abat_roller_static
	prop_abat_slide
	prop_acc_guitar_01
	prop_acc_guitar_01_d1
	Download/Copy all related *.zip files in one directory.
	Open terminal and change to that directory which has all zip files.
	Enter command zip -s- FILE_NAME.zip -O COMBINED_FILE.zip
	Enter unzip COMBINED_FILE.zip
	'''Functional Keras is a more functional replacement for the Graph API.
	'''

	###################
	# 2 LSTM branches #
	###################
	a = Input(input_shape=(10, 32)) # output is a TF/TH placeholder, augmented with Keras attributes
	b = Input(input_shape=(10, 32))
	encoded_a = LSTM(32)(a) # output is a TF/TH tensor
	encoded_b = LSTM(32)(b)