Faruk Cankaya farukcankaya

Mount S3 Bucket to AWS EC2 Instance

[Connect][ec2-connect] to the EC2 instance.
Update package manager to be able to find s3fs: sudo apt-get update
Install s3fs and awscli: sudo apt install s3fs awscli -y
```
which s3fs
/usr/bin/s3fs
```
Setup access(if you do NOT have credentials please check the [Give permission to EC2 to access S3][permission] section below ):

	import copy
	import logging

	import numpy as np
	import torch
	from PIL import Image, ImageDraw
	from detectron2.data import MetadataCatalog
	from detectron2.evaluation import DatasetEvaluator
	from detectron2.structures import Instances
	from detectron2.utils import comm

	@ROI_MASK_HEAD_REGISTRY.register()
	class CustomMaskRCNNConvUpsampleHead(MaskRCNNConvUpsampleHead):

	@configurable
	def __init__(self, input_shape: ShapeSpec, , num_classes, conv_dims, conv_norm="", *kwargs):
	super().__init__(input_shape, num_classes=num_classes, conv_dims=conv_dims, conv_norm=conv_norm, **kwargs)
	self.vis_period = 0

	logger.info("Starting training from iteration {}".format(start_iter))
	with EventStorage(start_iter) as storage:
	previous_val_map = 0 # ADDED NEW
	no_improvement_for_epochs = 0 # ADDED NEW
	for data, iteration in zip(data_loader, range(start_iter, max_iter)):
	storage.iter = iteration
	...
	if (
	cfg.TEST.EVAL_PERIOD > 0
	and (iteration + 1) % cfg.TEST.EVAL_PERIOD == 0

	# Only 'ADDED NEW' commented statements are added for validation loss calculation.
	validation_data_loader = prepare_validation_loader(cfg) # ADDED NEW

	logger.info("Starting training from iteration {}".format(start_iter))
	with EventStorage(start_iter) as storage:
	for data, iteration in zip(data_loader, range(start_iter, max_iter)):
	storage.iter = iteration

	loss_dict = model(data)
	losses = sum(loss_dict.values())

	def calculate_validation_loss(validation_dataset_loader, model, storage):
	data = next(validation_dataset_loader)
	with torch.no_grad():
	loss_dict = model(data)

	losses = sum(loss_dict.values())
	assert torch.isfinite(losses).all(), loss_dict

	loss_dict_reduced = {"val_" + k: v.item() for k, v in
	comm.reduce_dict(loss_dict).items()}

	def prepare_validation_loader(cfg):
	val_cfg = cfg.clone()
	val_cfg.defrost()
	val_cfg.DATASETS.TRAIN = val_cfg.DATASETS.TEST
	val_cfg.freeze()
	val_mapper = DatasetMapper(val_cfg, True, augmentations=[])
	return iter(build_detection_train_loader(val_cfg, mapper=val_mapper))


	#!/bin/bash

	# Usage
	# Make install_kafka.sh and check_consumers.sh executable via `chmod +x _file_`.
	# Then, run the script:
	# ./check_consumers.sh -g 'consumer.group.retry' -b 'localhost:9092,localhost:9093'

	while getopts g:b: flag
	do

	"use strict";

	const ImageData = require("./ImageData");
	const ImageConverterOperator = require("./ImageConverterOperator");
	const gm = require("gm").subClass({imageMagick: true})

	/**
	* Get enable to use memory size in ImageMagick
	* Typically we determine to us 90% of max memory size
	* @see https://docs.aws.amazon.com/lambda/latest/dg/lambda-environment-variables.html