Shantanu's Blog

Corporate Consultant

September 16, 2019


Secure FTP access to your S3 bucket in 4 easy steps

1) Visit SFTP transfer home page and create a new server:

Endpoint configuration: Public
Identity provider: Service managed

2) Create required role (my_sftp_role) and policy (my_sftp_policy) using this documentation:

3) Create required SSH keys using this guide:

4) Create a new user:
a) Provide a name like 'test'
b) access role (my_sftp_role) and select policy (my_sftp_policy) that we created in step 2
c) Choose the same S3 bucket as home directory that we mentioned in the policy
d) Upload the SSH public key, we created in step 3

You can now connect to your SFTP server using private key that was created in step 3:

sftp -i /home/ec2-user/transfer-key

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September 10, 2019


import aws command line output to pandas

Here is how we can get the API gateway report into pandas dataframe. The first command will download the api gateway REST apis in JSON format. Python pandas module has json_normalize function that will convert the json data into dataframe. json.load is used to read the document from file.

# !aws --region=us-east-1 apigateway get-rest-apis > /tmp/to_file.json

import pandas as pd
import json
from import json_normalize

with open("/tmp/to_file.json") as f:
    data = json.load(f)

df = json_normalize(data, "items")

df["createdDate"] = pd.to_datetime(df["createdDate"], unit="s")
df["type"] = df["endpointConfiguration.types"].str[0]

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Cloudformation template of 3 lines

These 3 lines of cloudformation code will create a SNS topic. Since the name is not defined in the template, a new name will be created automatically.

    Type: AWS::SNS::Topic

When you remove this template, the topic is also removed. If you want to keep the resources even after the template is deleted, then update the stack with the following template...

    DeletionPolicy: Retain
      Type: AWS::SNS::Topic

Once the stack is updated, your resources will not be removed even if you delete the stack that created it.

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Using pre-trained resnet model after modifying layers

pytorch is the package developed by facebook to help computer vision and Natural Language Processing. TorchVision package consists of popular datasets, model architectures, and common image transformations for computer vision. We use the "models" class and import pre-trained model from resnet group.

from torchvision import models
import torch
res_mod = models.resnet34(pretrained=True)

It is possible to print the layers that the pre-trained model is using. The numpy array data passes through all this trouble to give birth to final dataset that will represent the given class.

for name, child in res_mod.named_children():

In some cases we may want to selectively unfreeze layers and have the gradients computed for just a few chosen layers.
for e.g. in this case layer3 and layer4 should be made available for training while re-using rest of the slabs.

for name, child in res_mod.named_children():
    if name in ["layer3", "layer4"]:
        print(name + " has been unfrozen.")
        for param in child.parameters():
            param.requires_grad = True
        for param in child.parameters():
            param.requires_grad = False

The change in the sequence should be communicated back to torch so that it will be used for the training.

optimizer_conv = torch.optim.SGD(
    filter(lambda x: x.requires_grad, res_mod.parameters()), lr=0.001, momentum=0.9

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Autonormalize using featuretools

This is how a typical pandas data frame look like. How do I know the relations between the columns? Is it possible to normalize the data into 2 or 3 tables?

import pandas as pd
rows = [['tigers', 'boston', 'MA', 20],
       ['elephants', 'chicago', 'IL', 21],
       ['foxes', 'miami', 'FL', 20],
       ['snakes', 'austin', 'TX', 20],
       ['dolphins', 'honolulu', 'HI', 19],
       ['eagles', 'houston', 'TX', 21]]
df = pd.DataFrame(rows, columns=['team', 'city', 'state', 'roster_size'])

This is just 2 lines of code that will show the relations and build complex relations automatically.

from featuretools.autonormalize import autonormalize as an
print (an.find_dependencies(df))

featuretools module has auto normalize class that will do all this and more!

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September 09, 2019


Pandas dataframe to athena

Here are 5 steps to save your pandas dataframe to Athena table.

1) Create a sample dataframe.

from io import StringIO
import pandas as pd

u_cols = ["page_id", "web_id"]
audit_trail = StringIO(

df = pd.read_csv(audit_trail, sep="|", names=u_cols)

2) Convert all columns to string.

df = df.astype(str)

3) Create a new bucket. You can also use rb --force to empty the bucket before re-creating.

!aws s3 mb s3://todel162/

4) Save the pandas dataframe as parquet files to S3

import awswrangler
session = awswrangler.Session()
session.pandas.to_parquet(dataframe=df, path="s3://todel162")

5) Login to console and create a new table in Athena.

   `page_id` string,
  `web_id` string
  'serialization.format' = '1'
) LOCATION 's3://todel162/'
TBLPROPERTIES ('has_encrypted_data'='false');

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Using pre-trained models

Here is how you can use pre-trained model in just 5 or 6 lines of code using imageAI library.

#!pip install -q opencv-python tensorflow keras imageAI


from imageai.Detection import ObjectDetection
detector = ObjectDetection()
detector.detectObjectsFromImage(input_image="test.jpeg", output_image_path="out.jpeg")


from imageai.Prediction import ImagePrediction
predictor = ImagePrediction()

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September 08, 2019


Pandas transform inconsistent behavior for list

There is a serious bug in pandas aggregation using transform method.

df = pd.DataFrame(data={'label': ['a', 'b', 'b', 'c'], 'wave': [1, 2, 3, 4], 'y': [0,0,0,0]})

The following does not return a list as we would expect.

df['new'] = df.groupby(['label'])[['wave']].transform(list)

I can use tuple instead of list to get the correct results. But that is a work-around. The bug looks very annoying because we do not know if any other functions will also misbehave.

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September 07, 2019


Docker security check

Running the security check on docker server, is easy.

git clone
cd docker-bench-security
sudo sh

You may get a few warnings like this...

[WARN] 1.2.4  - Ensure auditing is configured for Docker files and directories - /var/lib/docker

Open this file and add the log files paths. Do not forget to restart audit deamon.

# vi  /etc/audit/audit.rules

-w /usr/bin/docker -p wa
-w /var/lib/docker -p wa
-w /etc/docker -p wa
-w /etc/default/docker -p wa
-w /etc/docker/daemon.json -p wa
-w /usr/bin/docker-containerd -p wa
-w /usr/bin/docker-runc -p wa
-w /etc/sysconfig/docker -p wa

# restart auditd service

Another file to be added for security purpose:

vi /etc/docker/daemon.json

    "icc": false,
    "log-driver": "syslog",
    "disable-legacy-registry": true,
    "live-restore": true,
    "userland-proxy": false,
    "no-new-privileges": true

Add this environment variable:

echo "DOCKER_CONTENT_TRUST=1" | sudo tee -a /etc/environment

# restart docker



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