Tag: cloud

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Restructuring / refactoring my Mastodon ZorkBot

Now my ZorkBot is working over at @zorkbot@botsin.space I’ve restructured the 3 Lambdas so it’s easier to follow what calls what.

In it’s current/initial working state, I built it with 3 AWS Lambdas:

mastodon-zorkbot

This Lambda runs every 5 minutes, checking for players messaging the bot

  • checks for last replied to messages so it only replies to a message once, using a DynamoDB table
  • if a new reply is needed, sends the game move command from the player to the next Lamdba, mastodonbot-aws-sdk2
  • replies to the player using the Mastodon APIs

The Lambda is deployed as: mastodonbot-zorkbot-dev-mastodon-reply

mastodonbot-aws-sdk2

This Lambda is called by mastodon-zorkbot, and provides parsing of the text responses from the frotz Custom Runtime Lambda, which is calls next

custom-lambda-zork

This is a Docker image for a Custom Lambda Runtime that invokes the frotz binary. This is the zmachine game runtime that runs the Zork game. It could be updated to run any game supported by frotz. The Custom Runtime has a shell script that pipes echo commands into frotz, captures the response and retuns to mastodonbot-aws-sdk2

The Refactoring

For first steps I’m just going to rename the folders of each of the Lamdbas and the serverless.yml service names so it’s easier to track what calls what in sequence:

mastodon-zorkbot -> zorkbot-1-mastodonreplies

mastodonbot-aws-sdk2 -> zorkbot-2-responseparser

custom-lambda-zork -> zorkbot-3-frotzcustomruntime

Will update later with progress.

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New AWS Console with ‘favorites’

I just got prompted to switch to the new AWS Console and found this new feature that I’ve always though would be useful, and now here it it 🙂 You can now flag specific services as your ‘favorites’ and they appear in a ribbon bar across the top of your console and in a widget on the console page. Here’s my 3 ‘favorited’ services in the bar at the top of the console:

If you go to ‘Action’ then ‘Add widgets’ you can also add a favorites section and drag it to a location on your console page:

Here it is at the top of my console page:

Previously we had the ‘Recently visited’ section on the Console, but the items in the list would move around depending which you most recently used. Now with favorites you can pin the services you’re most often using so you can more easily access them.

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Using AWS Organizations for learning and personal projects

If you work on many projects deployed to AWS over time, it can become more difficult to track what resources are where and what relates to what. Tagging can help a lot, so can regions. For example I can deploy one project to us-west-1 and another to us-west-2.

Another idea is to take advantages of multiple AWS accounts and manage them as an Organization. There’s no additional cost for each account or setting up the Organization, the costs are still only for the resources you are using.

Now you have multiple accounts to segregate various projects or other things you’re working on, instead of logging off one account and logging on to the other and switching back a forth, you can assume a role within other accounts from the Account dropdown and ‘Switch Roles’. This option is only visible if you are signed on as an IAM user and not the root account user.

Before you get to this step, in the account you want to switch to, create a new IAM role with the permissions you need to use, and in the Trust section, add the account id for the other account where you want to assume the role from. The complete the fields above and insert the ARN id for the role.

After the first time you’ve used this switch role feature, you’ll see the role in the Account dropdown to reuse later.

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Running aitextgen model training in a Docker container

I’m setting up an approach to run text generation model training jobs on demand with aitextgen, and the first approach I’m looking at is to run the training in a Docker container. Later I may move this to an AWS service like ECS, but this is my first step.

I’ve built a Docker image with the following dockerfile:

FROM amazonlinux
RUN yum update -y
RUN yum install -y python3
RUN pip3 install aitextgen
ADD source-file-for-fine-tuning.txt .
ADD generate.py .
ADD train.py .

.. and then built my image with:

docker build -t aitextgen .

I then run a container passing in the cmd I want to run, in this case ‘python3 train.py’:

docker run --volume /data/trained_model:/trained_model:rw -d aitextgen sh -c "cd / && python3 train.py && mv aitextgen.tokenizer.json /trained_model"

I’m also attaching a bind point where the model output is being written to during the run, and -d to run the container in the background. The last step in the run command copies the token file to the mounted EBS volume so it can be reused by the generation.

To generate text from the model, run:

docker run --volume /data/trained_model:/trained_model:rw -d aitextgen sh -c "cd / && python3 generate.py"
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