I have the task of optimizing search on the website. The search should be for pictures and for text by text query. I have already developed, trained, tested and selected a machine learning model that transforms images and text into a feature vector (Python, based on OpenAI CLIP). This feature vector will be transferred to Elastic Search. Elastic Search will be configured by another specialist.
The model will be used first to determine the feature vector on all existing images and texts, and then be used whenever new content is added or existing content is changed.
There is a lot of existing content (approximately several tens of millions of pictures and texts together). About 100-500 pieces of content are added and changed per day.
I haven't worked much with AWS, but in this case the model needs to be deployed to AWS somehow. Of course, I have the model and the entire project locally, I can write an API app and make a Docker container.
The question is, what is the best method to deploy this application on AWS? The best in terms of speed and ease of implementation (for me as an AWS beginner), as well as cost optimization, taking into account the number of requests for the application.
I've seen different possibilities, from simply deploying the application on EC2 (probably the easiest option) to using SageMaker. Also Kubernetes and ECS...
I'd recommend using SageMaker Hosting endpoint if you need to be able to run vectorization in near-real time any time of the day, or in a SageMaker Training job if you can run vectorization batched, for example once every few hour.
For both systems you can use pre-defined Framework containers and SDK to which you pass a Python code and optionally requirements.txt, or you can create your own image.
Related
Apologies in advance for my little knowledge of AWS
I'm trying to draw parallels between my current setup on Heroku to a move to AWS. I've run into some memory issues on Heroku because of some machine learning models I'm running and Heroku seems too expensive for my needs.
I was recommenced to move to aws using fargate which would be a better fit for my app. Below is my whole architecture, I'm hoping for some guidance on my direction of what I have and where I plan to go.
A django application running on heroku.
The base of functionality is the user uploads a video from their mobile device and uploads it to s3. A message from SNS is sent to my Heroku server that the upload is completed. The server kicks off a celery task that downloads the video from s3 and uses a machine learning model to do some natural language processing, then saves the results to my postresql database. Obviously this is very compute intensive, so I've run into some memory issues and can for-see scaling issues to come.
After lots of tweaking and attempts to no avail, I've decided to move over to AWS and leverage some of the cost benefits that I've seen in comparison to heroku of running more memory intensive tasks.
I should also mention there is a web interface involved with this django project and it isn't just a REST Api.
As far as AWS goes, I'm looking for a bit of direction. Possibly just a rough outline of the architecture I should look deeper into.
My first plan is to dockerize my application and go from there...but I'm a bit stuck on how my application fits (website, rest api, worker threads) into the AWS ecosystem.
AWS is a great fit for the application you describe. AWS Fargate / RDS will host your Django application. You have the option of using AWS Batch to handle your processing. One huge advantage is the ability to scale according to the needs of your application.
This image is one possible way to structure your application. It's a lot of work to get to this point, but AWS offers a lot of power and flexibility for reasonable costs IMO.
I've been reading some articles regarding this topic and have preliminary thoughts as what I should do with it, but still want to see if anyone can share comments if you have more experience with running machine learning on AWS. I was doing a project for a professor at school, and we decided to use AWS. I need to find a cost-effective and efficient way to deploy a forecasting model on it.
What we want to achieve is:
read the data from S3 bucket monthly (there will be new data coming in every month),
run a few python files (.py) for custom-built packages and install dependencies (including the files, no more than 30kb),
produce predicted results into a file back in S3 (JSON or CSV works), or push to other endpoints (most likely to be some BI tools - tableau etc.) - but really this step can be flexible (not web for sure)
First thought I have is AWS sagemaker. However, we'll be using "fb prophet" model to predict the results, and we built a customized package to use in the model, therefore, I don't think the notebook instance is gonna help us. (Please correct me if I'm wrong) My understanding is that sagemaker is a environment to build and train the model, but we already built and trained the model. Plus, we won't be using AWS pre-built models anyways.
Another thing is if we want to use custom-built package, we will need to create container image, and I've never done that before, not sure about the efforts to do that.
2nd option is to create multiple lambda functions
one that triggers to run the python scripts from S3 bucket (2-3 .py files) every time a new file is imported into S3 bucket, which will happen monthly.
one that trigger after the python scripts are done running and produce results and save into S3 bucket.
3rd option will combine both options:
- Use lambda function to trigger the implementation on the python scripts in S3 bucket when the new file comes in.
- Push the result using sagemaker endpoint, which means we host the model on sagemaker and deploy from there.
I am still not entirely sure how to put pre-built model and python scripts onto sagemaker instance and host from there.
I'm hoping whoever has more experience with AWS service can help give me some guidance, in terms of more cost-effective and efficient way to run model.
Thank you!!
I would say it all depends on how heavy your model is / how much data you're running through it. You're right to identify that Lambda will likely be less work. It's quite easy to get a lambda up and running to do the things that you need, and Lambda has a very generous free tier. The problem is:
Lambda functions are fundamentally limited in their processing capacity (they timeout after max 15 minutes).
Your model might be expensive to load.
If you have a lot of data to run through your model, you will need multiple lambdas. Multiple lambdas means you have to load your model multiple times, and that's wasted work. If you're working with "big data" this will get expensive once you get through the free tier.
If you don't have much data, Lambda will work just fine. I would eyeball it as follows: assuming your data processing step is dominated by your model step, and if all your model interactions (loading the model + evaluating all your data) take less than 15min, you're definitely fine. If they take more, you'll need to do a back-of-the-envelope calculation to figure out whether you'd leave the Lambda free tier.
Regarding Lambda: You can literally copy-paste code in to setup a prototype. If your execution takes more than 15min for all your data, you'll need a method of splitting your data up between multiple Lambdas. Consider Step Functions for this.
SageMaker is a set of services that each is responsible for a different part of the Machine Learning process. What you might want to use is the hosted version of Jupyter notebooks in SageMaker. You get a lot of freedom in the size of the instance that you are using (CPU/GPU, memory, and disk), and you can install various packages on that instance (such as FB Prophet). If you need it once a month, you can stop and start the notebook instances between these times and "Run all" the cells in your notebooks on this instance. It will only cost you the minutes of execution.
regarding the other alternatives, it is not trivial to run FB Prophet in Lambda due to the size limit of the libraries that you can install on Lambda (to avoid too long cold start). You can also use ECS (container Service) where you can have much larger images, but you need to know how to build a Docker image of your code and endpoint to be able to call it.
I got some data, which is 3.2 million entries in a csv file. I'm trying to use CNN estimator in tensorflow to train the model, but it's very slow. Everytime I run the script, it got stuck, like the webpage(localhost) just refuse to respond anymore. Any recommendations? (I've tried with 22 CPUs and I can't increase it anymore)
Can I just run it and use a thread, like the command line python xxx.py & to keep the process going? And then go back to check after some time?
Google offers serverless machine learning with TensorFlow for precisely this reason. It is called Cloud ML Engine. Your workflow would basically look like this:
Develop the program to train your neural network on a small dataset that can fit in memory (iron out the bugs, make sure it works the way you want)
Upload your full data set to the cloud (Google Cloud Storage or BigQuery or &c.) (documentation reference: training steps)
Submit a package containing your training program to ML Cloud (this will point to the location of your full data set in the cloud) (documentation reference: packaging the trainer)
Start a training job in the cloud; this is serverless, so it will take care of scaling to as many machines as necessary, without you having to deal with setting up a cluster, &c. (documentation reference: submitting training jobs).
You can use this workflow to train neural networks on massive data sets - particularly useful for image recognition.
If this is a little too much information, or if this is part of a workflow that you'll be doing a lot and you want to get a stronger handle on it, Coursera offers a course on Serverless Machine Learning with Tensorflow. (I have taken it, and was really impressed with the quality of the Google Cloud offerings on Coursera.)
I am sorry for answering even though I am completely igonorant to what datalab is, but have you tried batching?
I am not aware if it is possible in this scenario, but insert maybe only 10 000 entries in one go and do this in so many batches that eventually all entries have been inputted?
I am working on a project that will presumably have a lot of user uploaded content and also a fairly large user base. I am now looking for deploying this app to the Google Compute Engine.
I have looked up for the possible options and nginx+gunicorn seems to be a good option. In the beginning I am going to be using a single ns-1 instance with 100 GB persistent hard drive and google cloud sql for serving my database.
But I want to make things scalable so that I can add more instances and disk storage without any hustle in the future. But I am very confused how to do that. So the main concern is.
I want such setup so that I can extend my disk space and no. of Google Compute Instances whenever I want.
In order to have a fully scalable architecture, a good approach is to separate computation / serving, from file storage, and both from data storage. Going part by part:
file storage - Google Cloud Storage - by storing common service files in a GCS bucket, you get a central repository that is both highly-redundant, and scalable;
data storage - Google Cloud SQL - gives you a highly reliable, scalable MySQL-like database back-end, which can be resized at will to accommodate increasing database usage;
front-ends - GCE instance group - template-generated web / computation front-ends, setting up a resource pool into which a forwarding rule (load balancer) distributes incoming connections.
In a nutshell, this is one of the most adaptable set-ups I can think of, while you keep control over every aspect of the service and underlying infrastructure.
A simple approach would be to run a Python app on Google App Engine, which will auto-scale your instances (both up and down) and it supports Django, as mentioned by #spirulence in the comments.
Here are some starting points:
Django and Cloud SQL support on App Engine
Running Pure Django Projects on Google App Engine
Third-party Libraries in Python 2.7
The last link shows which versions of Django are currently supported.
I am writing a webapp thats runs on AWS. My app requires users to upload their pdf files. I will convert them into Images using the "convert" utility in linux.
Here is my setup on Ubuntu 12.04:
Django
Celery
Django Celery
Boto
I am using apache as my webserver.
The work flow is as follows:
Three are three asynchronous tasks and two queues for handling all the processing and S3 for storing input and Output files.
A user uploads a pdf then:
accept_file_task is called: This task takes the user uploaded pdf and stores it in my S3 storage and then inserts a message into the input_queue(SQS)
check_queue_and_launch_instance_task: A periodic task that keeps monitoring the number of messages in the input_queue and launches instances whenever the queue has more messages than the no of Ec2 instances
The instances have a bootstrap script which is a while True: loop. Any of the instances can pick the message from the input_queue and do a Subprocess.Popen("convert "+input+ouput) and write the processed stated to output_queue and also upload the image generated into S3 output bucket and make it available as a download link
output_process_task: another periodic task that keeps polling the output_queue and whenever a message is available it will update the status in the table mentioned below.
I am using a model called Document to store all the status information. I also have users registering and hence a table to store all user information. Also Celery created a lot of tables to store all its task information. Right now I am using a single instance and the sqlite3 database (that comes with python) on that instance.
I am unsure about the following things
How do I scale up the database? Should I go for a RDS or a simpleDB or AmazonDB. If not celery, I could have easily used simpleDB. I am really stuck on this one
How do I get rid of the two periodic tasks check_queue_and_launch_instance_task and output_process_task. My idea is that Autoscaling must be used in some way so that if need at a later stage an Elastic Load Balancer can be used.
If any of you have designed something similar please help me on how to go about it
How do I scale up the database? Should I go for a RDS or a simpleDB or AmazonDB. If not celery, I could have easily used simpleDB. I am really stuck on this one
Keep in mind that premature optimization is the root of all evil. The question of RDS (which is really just MySQL, Oracle, or MS SQL) vs. SimpleDB is more of an application design decision than one based on scalability. SimpleDB is just a simple key-value store. RDS, on the other hand, will give you full ACID functionality. If your data is relational, then you should be using a relational database. If you just need a place to store simple strings or integers, then something like SimpleDB would make more sense.
Right now I am using a single instance and the sqlite3 database (that comes with python) on that instance.
Make sure that you understand the consequences of a) creating a single point-of-failure in your design and b) SQLite's limitations compared to using a standalone RDBMS in this application. (You can use it, but it's really intended for single-user applications).
How do I get rid of the two periodic tasks check_queue_and_launch_instance_task and output_process_task. My idea is that Autoscaling must be used in some way so that if need at a later stage an Elastic Load Balancer can be used.
If you're willing to replace Celery with SQS, you can tie together SQS + SNS + Cloudwatch to simplify this portion of your app. Though what you're doing doesn't sound like a bad choice, especially if it's working well already. Your time is probably better spent working on the problems in front of you rather than those that might occur down the road.