imflash217

from IPython.display import HTML, Image
from google.colab.output import eval_js
from base64 import b64decode

# setup the canvas
canvas_html = """
<canvas width=%d height=%d></canvas>
<button>Done!</button>
<script>

imflash217

##########################################################################################
import torch
import torch.nn.Functional as F
import pytorch_lightning as pl

##########################################################################################
class FlashModel(pl.LightningModule):
    def __init__(self, model):
        super().__init__()
        self.model = model

imflash217

Transfer a gist to a GitHub repository

clone the gist

git clone https://gist.github.com/ishu3101/6fb35afd237e42ef25f9

rename the directory

mv 6fb35afd237e42ef25f9 ConvertTo-Markdown

change the working directory to the newly renamed directory

cd ConvertTo-Markdown

imflash217

import torch
import torch.nn.Functional as F
import pytorch_lightning as pl

##########################################################################################

class FlashModel(pl.LightningModule):
    """DOCSTRING"""
    def __init__(self, model):
        super().__init__()

imflash217

import torch
import torch.nn.Functional as F
import pytorch_lightning as pl

###########################################################################################

class FlashModel(pl.LightningModule):
    """DOCSTRING"""
    def __init__(self, model):
        super().__init__()

imflash217

import torch
import torch.nn.Functional as F
import pytorch_lightning as pl

###########################################################################################
## Pytorch_Lightning version
##
class FlashModel(pl.LightningModule):
    """DOCSTRING"""
    def __init__(self, model):

imflash217

import os

import torch
import torch.nn.Functional as F
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

import pytorch_lightning as pl

###########################################################################################

imflash217

# A LightningModule ORGANIZES the PyTorch code into the following modules:
# 1. Computations (init)
# 2. Training loop (training_step)
# 3. Validation loop (validation_step)
# 4. Test loop (test_step)
# 5. Optimizers (configure_optimizers)

##############################################################################
model = FlashModel()
trainer = Trainer()

imflash217

### DATALOADERS ##################################################################
# When building DataLoaders. Set `num_workers>0` and `pin_memory=True`
DataLoader(dataset, num_workers=8, pin_memory=True)

### num_workers ##################################################################
# num_workers depends on the batch size and the machine
# A general place to start is to set num_workers = number of CPUs in the machine.
# Increasing num_workers all increases the CPU usage
# BEST TIP: Increase num_workers slowly and stop when there is no performance increase.

imflash217

import torch as pt
import pytorch_lightning as pl

#######################################################################
class FlashModel(pl.LightningModule):
    """This defines a MODEL"""
    def __init__(self, num_layers: int = 3):
        super().__init__()
        self.layer1 = pt.nn.Linear()
        self.layer2 = pt.nn.Linear()