Hi, DEV community 🙋‍♀️

In this post, I will abord some pytorch codes to help with your journey. Most of my knowledge comes from Freecodecamp tutorial, TechwithTim. The Cherno, Programing with Mosh and Fireship (all from youtube). They deserve credit for teaching me a lot of cool stuff.🥰

1. Imports

• General:

imports the root package, imports dataset representation and loading

import torch from torch.utils.data import Dataset, DataLoader 

• Neural Network API:

imports the computation graph, puts the tensor node in the computation graph, imports neural networks, layers ..., import optimizers and the hybrid frontend decorator plus tracing jit.

import torch.autograd as autograd from torch import Tensor import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torch.jit import script, trace 

• Torchscript and JIT

torch.jit.trace() @script 

-ONNX

the last line prints a human-readable representation

torch.onnx.export(model,dummy data, xxxx.proto) model= onnx.load("ModelName.proto") onnx.checker.check_model(model) onnx.helper.printable_graph(model.graph) #this one 

-Vision

imports vision datasets. architect and transforms that can be composed

from torchvision import datasets, models, transforms import torchvision.transforms as transforms 

• Distributed Training

it will distribute the communication and the memory sharing processes

import torch.distributed as dist from torch.multiprocessing import Process 

1. Data Utilities

• Datasets abstract class representing datasets, labelling the datasets in form of tensor and concatenation of them

Dataset TensorDataset Concat Dataset 

• Data loaders and data samplers

DataLoader(dataset, batch_size=1, ...) sampler.Sampler(dataset, ...) sampler.XSampler where # Sequencial || Random || SubsetRandom || WeightedRandom || Batch || Distributed 

Stay tuned for the part 2 ☆: .｡. o(≧▽≦)o .｡.:☆