What payment methods does AWS accept?Issue Which payment methods can I use to pay my AWS bill?Resolution AWS accepts most major credit cards.If you're an AISPL customer, you can use Visa, MasterCard, and American Express.Note: Some credit cards might require additional authorization from your bank in order to be used successfully to pay AWS or AISPL bills.Check with the issuing institution of your card for more details.Keywords payment method, credit card, AISPL Managing Your AWS Payment Methods Managing Your AISPL Payment Methods Did this page help you?Yes | No Back to the AWS Support Knowledge Center Need help?Visit the AWS Support Center Published: 2016-02-18In this tutorial we'll set up a bitcoin-payable API for a deep learning algorithm using Amazon Web Services (AWS) for the computational back end.The algorithm we'll serve is an example of an artistic style transfer algorithm that applies the artistic style of one image to another image.

Although the 21 tools allow anyone to set up a bitcoin-payable API from any computer, running our algorithm on AWS means it won't slow down your work machine by serving user requests.Even if you own a powerful supercomputer, you probably don't want to let public demand monopolize your machine's resources.The final Django app of this tutorial is available in a github repository.Amazon Web Services provides a large number of services for on-demand cloud computing and storage.In this tutorial we'll primarily be using S3 for storage and EC2 for our compute-heavy back end.Both of these services cost money, but for most hobby purposes S3 is free.The pricing details show that you get 5GB of storage and thousands of requests for free for the first year of usage.If you do end up using a lot of space, 1TB works out to around $12/month.EC2 offers a large number of machine types at varying prices.They have a free tier, where a "t2.micro" machine with 1GB of memory and a single CPU can be used for 750 hours per month for free for the first year.

In this tutorial we'll be using a "g2.2xlarge" machine that has 15 GB of memory, 8 CPUs and access to a NVIDIA GRID GPU with 1,536 CUDA cores and 4 GB of video memory.This machine costs $0.65 USD per hour, with the minimum billing interval being one hour.The configuration above is well suited for a GPU computation that takes between 15 minutes and 1 hour.bitcoin block proof of workSimilarly powerful machines that don't have GPU access are just under $0.50 USD per hour.sweden bitcoin exchangeIf you're a spendthrift, for $4.00 USD per hour you can get an X1 instance with 128 CPUs with almost 2TB of memory.bitcoin etoroWe'll use Django for this tutorial.wird bitcoin steigen

If you aren't familiar with Django, see the 21 Django and Heroku tutorial.The basic flow will be a Django app which handles requests from the user and launches an EC2 instance for each buy request.We'll store any input data that the user provides in an S3 bucket, and configure the EC2 instance to read to and write from that bucket.The server will check to see if the EC2 instance is done by checking that its outputs are stored in the S3 bucket.wird bitcoin steigenBecause a single invocation of the algorithm can take time, in this tutorial we'll send the client a token when the EC2 instance is successfully launched, and the client can use that token to redeem the output at a later time.buy bitcoin ottawaA more complicated API might ask the user for an email address and send them an email when the computation is finished.

Our basic client-server interaction is described by the following diagram So a client makes the initial request.The server generates a token, pushes the inputs to S3, and spins up an EC2 instance which talks to S3.The server then gives the token to the client.The client can then poll the server to see if the computation is done, and when it is, the server returns the outputs.This isn't an ideal customer experience, but it's a simple template one can iterate and improve on.In the first half of this tutorial we'll explain how to programmatically manage EC2 instances and S3 buckets from Python using the boto3library.The second half of this tutorial will incorporate this into a Django app with the behavior of the above diagram.The crux of this endpoint is launching and monitoring an EC2 instance.We'll use the python boto3 library for this.The crucial section of the boto3 documentation is the EC2 create_instances function.The basic usage looks like this: = .()

Most of the difficulty in using boto3 is in providing the correct arguments to the create_instances function to ensure the instance has the correct access permissions and termination behavior.The first argument, ImageId, specifies what AWS calls an "Amazon Machine Image" (AMI) for your EC2 instance.An AMI is a snapshot of a machine, and it includes things like This is convenient because if the algorithm you want to sell has a complex set of dependencies, you can configure those dependencies once on AWS, create a snapshot AMI, and use that AMI for new instances and perfect reproducibility.We'll walk through how to create a custom AMI later in this tutorial.The first thing we need to do is create an AWS account and get credentials.Sign up for AWS, and then create an access key at the IAM home.This should consist of a 20-character access key and a 40-character secret key.We'll be recording these as environment variables, but remember that they should be kept secret.

You should also pick a default AWS region, and note that all of your AWS configurations are specific to a region.Boto3 fetches the two secret keys from the environment.In the second part we'll use a proper method for storing these secrets.But in the first part we can just include them in our python program.= We'll start by giving our instance access to S3.Create a new bucket at the S3 console and record its name.AWS uses what they call "Instance Access Management (IAM) Instance Profiles" to define permissions for an EC2 instance to interact with other AWS services.Browse to the IAM console and click on "Roles."Here you can create an instance profile, to which we'll attach the 'AmazonS3FullAccess' policy.You can create a more restrictive access profile if you want.First give the role a name.Then select 'AmazonS3FullAccess' and click "Next Step."Finally, select the newly created role from the list and copy down the "Instance Profile ARN(s)" field value.

It should look roughly like arn:aws:iam:::instance-profile/.You will likely want to SSH into a running instance to debug problems or perform configuration during the initial setup.So this step will create an SSH key-pair and configure our EC2 instances to allow SSH access from a specific IP address.On the EC2 dashboard under "Network and Security", click on "Security Groups," and create a new security group.The most basic way to fill out the fields is to give SSH access to your IP only, but you might also reasonably open the HTTP port to all IPs, and have a nice landing page with a description of how to use your API.The name of the security group will be passed to create_instances.In the same "Network and Security" section, click "Key Pairs" and then "Create Key Pair."Give it a name and upon clicking "Create" your browser will automatically download a .pem file.Save this .pem file in an appropriate place like ~/.ssh.

If you lose this file you'll have to generate another one from the AWS console.Important: You must change the permissions on your .pem file to 400, or else it will be rejected by AWS when you try to SSH into an instance, and you'll have to generate a new key.Record the name of the key (the part before .pem), as we will pass it to create_instances.We'll give commands to our EC2 instance via a "cloud-config" script.This is a script that a newly created EC2 instance will run after booting up, and will allow us to install packages and run commands.This script is passed to create_instances via the UserData keyword argument.Here is an example of a very simple cloud-config script that installs the AWS command line tools and writes a simple file to S3.Be sure to replace YOUR_REGION and YOUR_BUCKET_NAME with your actual region and bucket name strings.Here's an example one-off python script that creates a t2.micro instance with all of the security settings we described, and runs the "hello world" userdata script from the previous section.

= = = .()=[= ) :()..., ., ., .Let's inspect the keyword arguments one by one.Run the script above (after pip installing boto3) and then observe on your EC2 dashboard that the instance is running.Once it's finished launching (and running some initialization checks), check to make sure that your S3 bucket is populated with a hello world text file.Before we terminate this instance, let's SSH into it.Recall where you saved your .pem file, note the public DNS in the output above, and run ubuntu is the default user for this AMI.From here, the remaining work involves changing the cloud-config script.For example, here is a cloud-config script which (if your IAM profile allows HTTP access), launches a PHP web server.You will notice that the commands in a cloud-config script are run by root, not by the ubuntu user.This has some important consequences.In particular, if you launch your EC2 instance with a custom AMI --- perhaps because you need a certain GPU library as we will shortly --- you need to make sure that root has the appropriate environment variables set.

Perhaps the quickest way to do this is add them as export commands to the cloud-config script.Putting and getting files on S3 is much simpler than launching EC2 instances.The following python snippet defines functions for uploading and downloading files from your S3 bucket using boto3., ) (, = .().(,, ) AWS has a publicly searchable list of AMIs for you to choose from.For example, there are many pre-existing deep learning AMIs.To find them, from the EC2 dashboard under "Images" click on "AMIs."Click the filter that says "Owned by me," and change it to "Public images."Then put in your search term.Making a custom AMI allows you to save the preconfigured state of an EC2 instance so that you don't have to re-install libraries every time you launch an instance.The process for doing this is: In part 2 we'll use a custom AMI ami-1ab24377 with Torch and cuDNN to enable deep learning on a GPU.This AMI also has a set of deep-learning models pre-downloaded.

Note that AMIs are tied to a region, so to use this custom AMI you need to set your region to us-east-1.If you make a custom AMI, there is one pitfall you should be aware of.When creating a custom AMI there's an option to attach various kinds of volumes to your instance.This specifies what sort of storage your AMI has access to.There is also a checkbox that tells AWS to delete the volume when the instance terminates.This is important because AWS charges you for volume usage, and EC2 creates a new volume for each instance you launch.Neglecting to delete unused volumes can be a costly oversight.A more sophisticated AWS endpoint might have a queue and coorindate the relationship between EC2 instances and volumes, starting and stopping instances instead of terminating them.Managing such a queue is beyond the scope of this tutorial.The last bit of configuration needed is to register an API key with imgur.We'll use imgur to upload the output images, and send the user a url as the final product.

You can register your application to get imgur API keys here.Note that you don't need to include a callback URL.At this point, if you've configured AWS, installed 21, made a Heroku account, and registered for imgur API keys, then you have enough information to use the Heroku quick-deploy button at the open source repository.The rest of this tutorial will detail the internals of the django app.Note that you won't be able to publish a quick-deployed app using the Heroku command (as explained in the Django Heroku tutorial unless you modify the manifest.yaml template to use your specific information.In this part we'll build a simple Django app for handling requests and spinning up EC2 instances.If you're new to Django apps, see the 21 tutorial on writing and deploying Django apps with Heroku.The app will launch an EC2 instance that performs artistic style transfer using deep learning, and the code we'll use is based off of Justin Johnson's Torch implementation.

We're going to provide a rough overview of the endpoint.You can see all the details by browsing the git repository for this tutorial.Let's start by putting all of our secrets and configuration variables in a .env file in the Django project's base directory.I have left all of my secrets blank, and populated some defaults for local debugging purposes.We'll also be using hashids to generate tokens from our database ids.The Django/Heroku tutorial has details on how to load these environment variables into your Django app, as well as how to use hashids.Once this is set up, you can wrap any views you write with the @payment.required decorator as follows.=) () The required POST data parameters are: The _execute_buy function creates a new instance of a simple Request Django model, detailed below.=, =) = .(=,Then _execute_buy does the following: ( = ... = .(.)) e: : e.) e: : .((e=) The aws.launch function is our previous script from Part 1 for launching instances using boto3.

We put it in a separate module called aws to separate it from the view logic.The complete details can be found at this tutorial's github repository The cloud config script involves setting special environment variables to point to the installed Torch and CUDA libraries.Then it Here's the userdata script.Note that we've left the filenames and parameters as python format-string arguments.So in a more complicated app, one could expose more parameters to the API.As a warning, there are limits to these parameters.For example, increasing the -image_size parameter drastically increases the amount of memory used on the EC2 instance.If it exceeds the maximum allowed memory, the instance will crash and the customer will never get their image.In the above, note we used the region us-east-1 which would change if you're using a different region.Further note that in making our custom AMI ami-1ab24377, we pre-cloned a git respository in the root directory called style-transfer-torch that has the needed files inside it to run the style transfer algorithm.

If you're designing an algorithm that you maintain in a git repository, it may be reasonable to clone that git respository as part of the cloud-config script so that bug fixes are instantly deployed to your endpoint.Now we can allow the user to redeem a token.The redeem API endpoint checks to see if the EC2 instance has pushed the desired output file to S3.If there's no such file, our API responds to the caller with "not done yet."If there is a file, it uploads that file to imgur, and returns an imgur url to the user, and marks the token as redeemed.: : (: = ..(=).: () .. e:.(.((e: , : =) :.(.(: =) = .(.)= .. = .: , : , : =) (: = () : : .=) () As per the Django/Heroku tutorial, you can deploy this endpoint to heroku.The github repository includes a quick-deploy button so you can quickly and easily test it out.Here is an example usage that styles Dorian Nakamoto as an ancient Roman mosaic.In these examples, replace APP_NAME with your deployed heroku app name.