The Algorithmia Java client provides a native Java interface to the Algorithmia API, letting developers manage and call algorithms, work with data in object stores using Algorithmia Data Sources, and access other features of the Algorithmia platform.
This guide will cover setting up the client, calling an algorithm using direct user input, calling an algorithm that accesses data through Algorithmia Data Sources, and using Algorithmia’s Hosted Data service. For complete details about the Algorithmia API, please refer to the API Docs. Reference documentation for the Java Client can be found in the Algorithmia Client JavaDocs.
To follow along you can create a new Java file in the IDE of your choice.
Set Up the Client
The Algorithmia Java Client is published to Maven central. To get started, the Algorithmia Java Client can be added as a library through Maven using your IDE of choice or you can download the JAR file and add it as a dependency in your POM file:
Using version range [,1.1.0) is recommended as it implies using the latest backward-compatible bugfixes.
To use the client you’ll need an API key, which Algorithmia uses for fine-grained authentication across the platform. For this example, we’ll use the
default-key that was created along with your account, which has a broad set of permissions. Log in to Algorithmia and navigate to Home > API Keys to find your key, or read the API keys documentation for more information.
Once the client is installed, you can import it into your code and instantiate the client object:
Specifying an On-Premises or Private Cloud Endpoint
This feature is available to Algorithmia Enterprise users only.
If you are running Algorithmia Enterprise, you can specify the API endpoint when you create the client object:
Alternately, you can ensure that each of your servers interacting with your Algorithmia Enterprise instance have an environment variable named
ALGORITHMIA_API and the client will use it. The fallback API endpoint is always the hosted Algorithmia marketplace service at https://api.algorithmia.com/
Calling an Algorithm
Algorithms take three basic types of input whether they are invoked directly through the API or by using a client library: strings, JSON, and binary data. In addition, individual algorithms might have their own I/O requirements, such as using different data types for input and output, or accepting multiple types of input, so consult the input and output sections of an algorithm’s documentation for specifics.
The first algorithm we’ll call is a demo version of the algorithm used in the Algorithm Development Getting Started guide, which is available at demo/Hello. Looking at the algorithm’s documentation, it takes a string as input and returns a string.
In order to call an Algorithm from Java, we need to first create an algorithm object. With the client already instantiated, we can run the following code to create an object:
Then, we can use the
.pipe() method to call the algorithm. We’ll provide our input as the argument to the function, and then print the output using the
result attribute and the
Which should print the phrase,
Hello HAL 9000.
JSON and Java
The Java client provides some ease-of-use abstractions for working with algorithms with JSON inputs and outputs. Call an algorithm with JSON input by passing in a type that can be serialized to JSON, including most plain old java objects and collection types. If the algorithm output is JSON, call the
as method on the response with a
TypeToken containing the type that it should be deserialized into.
Let’s look at an example using JSON and the nlp/LDA algorithm. The algorithm docs tell us that the algorithm takes a list of documents and returns a number of topics that are relevant to those documents. The documents can be a list of strings, a Data API file path, or a URL. We’ll first create a variable called
inputJson with a HashMap. Add a single entry with a key of
docsList, and the documents to be analyzed as an array of strings. We can then call the algorithm by passing
The output will be a list of relevant topics and their number of occurrences, which will look something like:
Alternatively, you may work with raw JSON input by calling
pipeJson(), and raw JSON output by calling
asJsonString() on the response. We’ll use this approach later in this guide.
You might have noticed that in this example we included a version number when instantiating the algorithm. Pinning your code to a specific version of the algorithm can be especially important in a production environment where the underlying implementation might change from version to version.
The client exposes options that can configure algorithm requests. This includes support for changing the timeout or indicating that the API should include stdout in the response. In the following example, we set the timeout to 60 seconds and disable
stdout in the response:
To be able to better develop across languages, Algorithmia has created a set of standardized errors that can be returned by either the platform or by the algorithm being run. In Java, API errors and Algorithm exceptions will result in calls to
Your account can make up to 80 Algorithmia requests at the same time (this limit can be raised if needed).
Algorithm requests have a payload size limit of 10MB for input and 15MB for output. If you need to work with larger amounts of data, you can make use of the Algorithmia Data API.
Working with Algorithmia Data Sources
For some algorithms, passing input to the algorithm at request time is sufficient, while others might have larger data requirements or need to preserve state between calls. Application developers can use Algorithmia’s Hosted Data to store data as text, JSON, or binary, and access it via the Algorithmia Data API.
The Data API defines connectors to a variety of storage providers, including Algorithmia Hosted Data, Amazon S3, Google Cloud Storage, Azure Storage Blobs, and Dropbox. After creating a connection in Data Sources, you can use the API to create, update, and delete directories and files and manage permissions across providers by making use of Data URIs in your code.
In this example, we’ll upload an image to Algorithmia’s Hosted Data storage provider, and use the dlib/FaceDetection algorithm to detect any faces in the image. The algorithm will create a new copy of the image with bounding boxes drawn around the detected faces, and then return a JSON object with details about the dimensions of the bounding boxes and a URI where you can download the resulting image.
Create a Data Collection
The documentation for “Face Detection” says that it takes a URL or a Data URI of the image to be processed, and a Data URI where the algorithm can store the result. We’ll create a directory to host the input image, then update its permissions so that its publicly accessible:
Instead of your username you can also use ‘.my’ when calling algorithms. For more information about the ‘.my’ pseudonym check out the Hosted Data Guide.
Upload Data to your Data Collection
Now we’re ready to upload an image file for processing. For this example, we’ll use this photo of a group of friends. Download the image and save it locally as
Next, create a variable that holds the location where you would like to upload the image as a URI:
Then upload your local file to the data collection using the
This method call will replace a file if it already exists at the specified location. If you wish to avoid replacing a file, check if the file exists before using this method.
Confirm that the file was created by navigating to Algorithmia’s Hosted Data Source and finding your data collection and file.
Call the Algorithm
Once the file has been uploaded, you are ready to call the algorithm. Create the algorithm object, then pass the required inputs—a JSON object, encoded as a string, with the image URI (which is stored in
img_file in the code above) and a URI for the image output—to
Once the algorithm has completed,
result will contain the dimensions of the bounding boxes for any detected faces and the URI for the resulting file, which you can then download (or provide as input to another algorithm in a pipeline). We can print these by using the
asJsonString() method on the
Algorithms can create and store data in folders named with the algorithm name in the Algorithm Data collection. To access this folder from within an executing algorithm, the
.algo shortcut can be used, as in the input example above. When accessing data from a client context, the algorithm author and name can be used along with the
.algo shortcut to download data, in the format
Download the resulting file
The URI included in the algorithm output uses the
.algo shortcut, so we’ll need to modify it slightly to download the file by adding the algorithm name and author. Create a new
DataDirectory object using the path to the data collection where the algorithm has written it’s output.
Verify that the file exists, and try downloading it to a new local file location:
This copies the file from your data collection and saves it as a file on your local machine, with details about the file in the variable
Alternately, if you just need the binary content of the file to be stored in a variable, you can retrieve the remote file’s content without saving the actual file:
This will get the image as binary data, saving it to the variable
image_data, which might be useful when writing algorithms that are part of an image processing pipeline.
If the file was text (an image, etc.), you could instead use the function
.getString() to retrieve the file’s content as a string. For more methods on how to get a file from a data collection using the Data API go to the API Specification.
Publishing Algorithmia Insights
This feature is available to Algorithmia Enterprise users only.
Inference-related metrics (a feature of Algorithmia Insights) can be reported via using the
report_insights method of the Algorithmia client.
Depending on your algorithm, you might want to report on the algorithm payload for each API call (such as the features or number of features), the output of the algorithm to monitor data distributions of predictions, or probability of each inference.
In the case of an example credit scoring model, shown in this demo for Algorithmia Insights, reported metrics include the algorithm predictions:
In addition to the functionality covered in this guide, the Java Client Library provides a complete interface to the Algorithmia platform, including managing algorithms, administering organizations, and working with source control. You can also visit the API Docs to view the complete API specification.
If you’re a data scientist or developer who will be building and deploying new algorithms, you can move on to the Algorithm Development > Getting Started guide.