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Release Notes


Q1 2020 Enterprise Release Notes


2020 is going to be a big year at Algorithmia. We are working on features that empower our customers in tooling flexibility, connectivity, security, and ML management so they can focus on extracting value from ML.

Our first release of 2020 comprises a diverse feature set that increases the options our customers have for frictionless ML deployment and model auditing. Several of these features are iterations on previously released features because we want to continuously strive for more for our customers.

Q1 Features

  • Source Code Management – GitHub Integration
  • Newest on-premises offering – VMWare
  • Platform Usage Reports

Source Code Management

We are pleased to announce that we have expanded our source code management offering to include GitHub, adding to the benefits of a centralized code repository and increasing ML portability! By connecting your Algorithmia and GitHub accounts, you can store your source code on GitHub and deploy it directly to Algorithmia. It’s that simple.

Once enabled by your administrator, you will be able to select a GitHub (or GitHub Enterprise) instance when creating an algorithm, which will create a new GitHub repository for your algorithm.

All updates to the repository’s default branch will automatically precipitate new builds for your algorithm. By leveraging GitHub with Algorithmia, algorithm developers can leverage existing GitHub workflows they already have and access the entire suite of GitHub features, including GitHub Actions, and still ensure that source code visibility is restricted to those with proper GitHub permissions.

Where to learn more

Algorithmia administrators can learn more about integrating GitHub with Algorithmia here. Once configured, users need simply select the GitHub repository host when creating an algorithm:

Source code management

This feature is available now in our deployment and will ship to users during this release.

Newest on-premises offering: Algorithmia on VMWare

Algorithmia’s newest on-premises offering, VMWare, means multi-cloud ML deployment across public and private clouds is a reality. Algorithmia Enterprise for VMWare allows customers to run Algorithmia in their own VMWare infrastructure.

Enterprise customers can benefit from serving and running models in their data centers for compliance, regulatory, IT policy, or security use cases where AE for AWS or Azure are not viable. Our VMWare edition of Algorithmia Enterprise is deployable on either existing or new VMWare compute and storage infrastructure. Customers bring their VMs, raw storage, and network connectivity, and we provide the rest.

This feature is ideal for customers who want to use Algorithmia Enterprise in their own datacenter, especially if they are existing VMWare users.

Talk to us about how AE for VMWare fits into your organization’s data center strategy!

Algorithmia Enterprise for VMWare is available to all enterprise customers now.

Platform Usage Reports

Given a customizable date range (which defaults to the past 7 days), users can now view platform usage from three different perspectives: all usage, algorithms, and users.

You asked and we delivered.

We are now empowering our customers with increased model auditing tools. Usage Reports provide clear visibility into how the ML platform is used.

Select a preset date range or input a custom date range to display all activities/usage of the platform, including:

  • model consumption
  • compute duration
  • caller information
  • hardware (CPU/GPU) use

Reports are presented in the UI, and selecting a metric provides a detailed list of the model execution results. Administrators can also download the reports as a CSV file.

Platform usage date range

All Usage

This tab shows information grouped by the username/algorithm tuple. That is, it shows which users called which algorithms and the number of times they did so (along with hardware, computer time, and other metrics).

Platform usage all usage view

When you click on a row in the all usage table, you will see the complete set of columns:

  • algorithm: algorithm display name
  • version: hash version of the exact algorithm called
  • caller: which user called the algorithm
  • owner: user who created and owns the algorithm
  • total calls: total number of calls the calling user made to this algorithm in the date range
  • total duration: total number of seconds the algorithm ran for caller in the date range
  • total errors: total number of failed calls to this algorithm the caller made in the date range
  • error duration: total number of seconds the algorithm ran for calls in which it returned an error (for this caller)
  • hardware: whether the algorithm runs on a CPU or GPU instance

One caveat to the total compute time: each call is rounded up to the nearest second. For instance, if in the given date range, a user calls a certain algorithm twice, and the first call took 2.04 seconds and the second call took 2.08 seconds, the total time for those two calls is 4.12 seconds, which the table will round up and display as 5 seconds.

Also note that if a call takes less than one second, the call’s duration will be rounded up to a full second. So calling Hello World 23 times would most likely result in a total duration of 23 seconds being displayed. The motivation for this is that in the public version of Algorithmia we only charge by credits. One credit = one second, so if a user makes a call that lasts less than a second we still charge for the full second.


This tab shows usage information grouped at the algorithm level. That is, it shows which algorithms were called and how many times (total across all users) they were called.

Platform usage algorithm view

When you click on a row in the algorithms table, you will see this complete set of columns:

  • algorithm: algorithm display name
  • version: hash version of the exact algorithm called
  • owner: user who created and owns the algorithm
  • total calls: total number of calls that users of the platform made to this algorithm
  • total duration: total number of seconds the algorithm ran summed up across all users
  • total errors: total number of errors this algorithm returned summed up across all users
  • error duration: total number of seconds the algorithm ran calls in which it returned an error (summed up across all users)
  • hardware: whether the algorithm runs on a CPU or GPU instance


This tab shows usage information grouped at the user level, so you can see how many total calls each user made (across all algorithms).

Platform users view

When you click on a row in the users table, you will see this complete set of columns:

  • caller: the username of the user this row represents
  • total calls: total number of calls the user made summed up across all algorithms
  • total duration: total number of seconds the user’s calls spent computing summed up across all algorithms
  • total errors: total number of errors returned to the user summed up across all algorithms
  • error duration: total number of seconds computed by calls that returned errors to the user

Users wanting to perform more advanced operations on the tables (like sorting, customized grouping and summing) can download the full tables as CSV files.

Other quarterly update

API Key Terminology

There are two updates to API key terminology this release.

First, we no longer refer to our standard API keys as “Simple API Keys.” They are now simply “API Keys.”

Second, “Management API Keys” has been changed to “Admin API Keys.” This naming better fits the purpose for which these keys were designed, and is intended to minimize confusion for our enterprise administrators.

Enterprise Release Notes 19.10

Algorithmia’s 19.10 release featured a platform performance update and bug fixes for specific enterprise users. For more information, contact your Customer Success Manager.

Enterprise Release Notes 19.08

Algorithmia’s 19.08 release featured security updates for enterprise users. For more information, contact your Customer Success Manager.

Enterprise Release Notes 19.05

New this quarter:
  • Jupyter Notebook integration
  • Event-driven algorithm runs
  • Additional APIs
  • Additional IPA package sets
  • Organization-hosted data
  • Enterprise UI customization

Jupyter Notebook Integration

Data scientists can now deploy to the AI Layer directly from a Jupyter Notebook completing the entire data science life cycle—model training, visualization, and deployment—without ever leaving their Notebook.

Our updated APIs enable easier integrations like this and Zapier (from the last release), and we will be offering more endpoints moving forward. Please don’t hesitate to suggest tools you’d like to integrate.

Release Notes

Event-Driven Algorithm Runs

Event listeners allow external services to trigger actions such as model runs within the AI Layer. The first of our supported event sources is Amazon SQS, with a tutorial available in our Developer Center.

Additional APIs

New APIs allow you to create, recompile, and publish models directly from CI/CD pipelines, without using the Web API.

Release Notes

Release Notes

Additional IPA Package Sets

As introduced in the 19.01 release, We now enable more flexible instances and support new machine learning frameworks with many different combinations. New package sets execute faster and support a wider range of developer choices.

Our shift towards package sets allows the platform to iterate faster and increases the performance of algorithm runtimes. This allows the Algorithmia cluster to target package sets toward tailored scenarios.

We’re delivering a continuous stream of new sets, so check back for the latest versions often.

Organization-Hosted Data

A new data-browsing UX makes it even easier to browse and share data and models within your organization.

Organizations allow teams of customers to work in a private subset of models, moderate model publishing, and organize models into logical groups based on teams. This feature makes an organization more powerful by coupling the algorithms with the data necessary for models.

Release Notes

To view your hosted data, click on Data Sources in the main toolbar, then select an organiation. From here, browse the organization’s collections as you would your own data collections.

Release Notes

Organization data collections have the same permissions as user data collections, including:

Private Read: Only members of the organization may access the data, either directly or via an algorithm.

My Algorithms Read: Any caller may access the data of organization-owned algorithms, but only organization members may directly view the data.

Public Read: Anyone may interact with the data.

Private Write: Only members of the organization may write to the collection. This is the only available write setting.

Enterprise UI Customization

Enterprise customers now have the ability to tailor the UI of their AI Layer instances to their corporate brands.

What can now be customized:

  • Brand Color
  • Logo
  • Favicon
  • Navigation Title (Title that appears in browser tab)
  • Header Title (Title that appears beneath logo)
  • Title Of Sidenav Buttons

For questions and requests, please contact us at

Enterprise Release Notes 19.01

Algorithmia is moving into the new year with a lot of great improvements to the platform. We are always looking to increase performance in production model serving and infrastructure efficiency in every release while making the day-to-day developer and data scientist experience even more seamless.

Projects for this release focused on providing greater flexibility and data connectivity options and enabling queuing for user calls. These updates are part of our dedication to continuously improve our platform for our users. We’ve made a number of changes under the hood that will provide tangible boosts to speed, flexibility, and supportability.

New this quarter:
  • Image per algorithm
  • Algorithm APIs
  • Support for .NET Core and .NET Standard
  • Update algorithm settings
  • New data storage connectors
  • User work limits and queuing

Image Per Algorithm

We are really happy to announce the release of a major evolution to the architecture of how algorithm containers are created and loaded when requested on the Algorithmia platform. We now enable more flexible instances and support new machine learning frameworks with many different combinations.

New framework releases will be quickly added to allow data scientists and developers to take advantage of the latest technologies available to them.

Image Per Algorithm changes how algorithm version container images are created and shifts the creation of containers to when the algorithm changes instead of when a request comes in for the algorithm and a slot needs to be loaded. This shift towards package sets enables the platform to iterate faster and support emerging technology, and it increases the performance of algorithm runtimes.

This allows the Algorithmia cluster to target package sets toward tailored scenarios, like running a specific version of Tensorflow with Python 3.7. There is a package set available that is pre-configured for that scenario that will work in GPU and CPU environments.

The Algorithmia team will be creating new package sets in future releases and over time will transition all class algorithm base templates to new Image Per Algorithm-based package sets.

Release Notes IPA

New package sets will be released to the Hosted Service on Algorithmia first and then rolled up together in future quarterly releases for Algorithmia Enterprise.

If you are interested in any package sets that have been released to the Hosted Service, please let your Deployment Architect know to work on getting it imported before your next upgrade.

Initial Package Sets for This Release

This release includes the following package sets that can be used for newly created algorithms going forward. New package sets will be created and shipped to the Algorithmia Hosted Service and bundled into future releases of Algorithmia Enterprise.

Release Notes CPU, GPU python table

We will enable Enterprise Admins to create custom package sets to help provide best practices and templates for their data science teams specific to their organization.

Algorithm Management

Building on the previous release, we are expanding the available set of APIs that developers and system administrators can use to interact with the Algorithmia platform through automation. The main scenarios we heard customers want to automate with these newly available APIs are to enable continuous integration and continuous delivery pipelines.

We’re happy to say that the last two steps in an automated pipeline are no longer exclusive to the Algorithmia UI.

The additional APIs that have been added in this release are:

  • Create algorithm
  • Get algorithm information
  • Update algorithm
  • Publish algorithm

Release Notes algorithmia management APIs

These algorithm APIs do not require admin API keys to use. Non-administrator users of an Algorithmia platform will be able to use their API keys to interact with algorithms they have permissions to work with. The use of admin API keys will not be supported by these algorithm APIs.

You can find the details for each of these APIs available in the OpenAPI spec.

The OpenAPI spec allows developers to generate language-specific clients through code generators, test APIs, interact with APIs more easily. It can even generate a Postman collection that can be imported into your Postman workspace. You can find many of these tools on the OpenAPI website.

Update Algorithm Settings

Administrators and data scientists asked for a way to be able to update an algorithm’s settings without going through a full publish. For example, many users wanted to update their algorithm’s network access permissions or change the source code visibility after creating it.

Each of these settings and more is now available on the Manage tab of each algorithm. Most of the settings will apply immediately so data scientists can test private, unpublished versions.

However, to make settings changes available to developers consuming your algorithm, you’ll need to go through a full publish.

The two immediate settings that don’t require a publish are Source Visibility and License:

Release Notes algorithm settings

How to Update Algorithm Settings

Release Notes how to update algorithm settings

During the publishing process, we’ll detect which settings will be changed for the next published version and inform the publisher the type of Semantic Versioning increase that will be required.

Note: All previous versions of a published algorithm are immutable and unable to be changed in order to help developers who are already using a specific version in their production apps.

New Data Storage Connectors

This release includes two new data storage connectors to allow data scientists and developers to leverage additional input and output locations for algorithms.

The data connector system allows an Algorithmia instance to securely handle brokering connections between an algorithm and a data source provided by the developers without needing to provide connection credentials.

Additionally, since it’s a single data API for the algorithm developer, access to an underlying data source is abstracted, allowing for algorithm developers to support many types of data sources without needing to implement provider-specific logic.

The two data connectors that are included in this release are:

To find out more information about the Data API available in each Algorithmia instance, including the Hosted Data Service, you can visit the overview topic in the Developer Center.

Release Notes how to update algorithm settings

Algorithmia .NET SDK Supports .NET Core and .NET Standard

Customers have been adopting and transitioning to .NET Core and have been asking us to transition from only supporting the .NET framework. We’re happy to now include support for the Algorithmia .NET Client in all of the frameworks supported by .NET Standard 2.0, which includes .NET Core. This means that many more application types can run across Windows, Linux, MacOS, and mobile, and other devices will be able to directly make calls to algorithms.

Supporting .NET Standard 2.0 provides support for the following frameworks:

  • .NET Core
  • ASP.NET Core
  • .NET Framework (>4.6.1)
  • Mono
  • Xamarin.iOS
  • Xamarin.Mac
  • Xamarin.Android
  • Universal Windows Platform Apps
  • Unity

All of the Algorithmia Clients are open-sourced on GitHub. You can follow the .NET Client on the GitHub website.

To get going, you can install the updated NuGet package for the Algorithmia .NET Client and follow the walkthrough available in the Developer Center.

Install-Package Algorithmia.Client

Flexibility to Enable User Work Limits and Queuing

Administrators need the flexibility to tune an algorithm’s usage of infrastructure while still running in production. Algorithmia has had the ability to provide “sticky slots” as one such flexibility example. This release introduces a new flexibility option to specify work limits for a particular user running production workloads.

This option will set limits to accept calls from a user but will begin queueing requests that are above the maximum active jobs limit until the maximum concurrent sessions are reached. The setting is available on the Advanced options page of the Admin Panel by clicking the “Update User’s Work Limits” button.

Administrators now have the flexibility to manage an algorithm’s use of their cluster. In one Algorithmia cluster, administrators were able to see a 30-percent decrease in monthly infrastructure costs by being able to finely tune production algorithms.

How to Update a User’s Work Limits

Release Notes set work limits

User to Update: Enter the username of the user you want to limit. Max Concurrent Sessions: Input a max number of concurrent requests a user can make at one time to ensure resources are not being over allocated. Max Active Jobs: Insert a max number of active jobs a user can have before requests begin queueing to ensure other users have resources available to them.

Release Notes max work limits

For questions and requests, please contact us at