Mount Artifacts to Build instances

This feature makes it easier for you to access your existing Artifacts. With this update, you can mount your Artifacts directly to your build instances and access files from your buckets without downloading them!

You can add multiple artifacts to the build instance by going to the instance settings and under artifact mounting. The data is only downloaded when you or a program reads the file.

The mounted Artifacts behave as standard directories, so anything you could do with a regular file or directory is directly applicable here too. We've integrated the Artifacts mounting feature now into Projects. Hence, any build instance you create inside your project has the project artifact mounted by default on the path /home/ubuntu/project/bundle-<project-id>. This feature simplifies your life by providing a single place for your data, which is always available whenever you create an instance.

GUI for Jobs

We're also releasing a new GUI for Jobs and Deploy! This upgrade simplifies setting up and deploying code, so you can get your projects up and running faster.

With this update, you can now use a NimbleBox-designed pre-set job template to quickly set up a Job or Deploy. To use this feature, connect your GitHub account to NimbleBox, and select a repository with its branch and the file in the repository you would like to run. You can then choose your requirements file and save. A job is triggered that runs your programmed function.

Improvements & Fixes

• Persistent logging for Models
• Feature gates support for Models
• Supporting GPU choices in Jobs & Deploy including A10G and A100 GPUs
• Un-mounting artifacts when instance is stopped
• Added project logs when artifact is deleted
• Added auto shutdown notification
• Deploy: access key for each model
• Deploy: new model states
• Deploy: updated log streaming for models, added pagination

Traffic Distribution

Testing multiple models is critical but challenging when building an ML service. Worry not; we just launched traffic distribution- the precursor to a full-fledged deployment testing dashboard.

With traffic distribution, you can dynamically allocate traffic to different versions of your models based on various criteria. This feature gives you greater control over your ML deployments, allowing you to optimize your services for multiple use cases and freely experiment with your models.

Artifacts Integrations: AWS S3, Azure Blob, and Google Cloud Storage

At NimbleBox, we believe managing data should be easy and efficient, regardless of where it's stored. That's why Artifacts are a crucial component of our platform, designed to help you manage your data and models in a scalable and reproducible way. With Artifacts, you can store, version, and share your datasets, models, and other artifacts with your team, making collaborating on ML projects, reproducing experiments, and scaling your workflows easier.

With this exciting update, you can seamlessly integrate your data stored in AWS S3, Azure Blob, or Google Cloud Storage with Artifacts, allowing you to work with your data across different cloud storage services without any manual transfer between services.

Improvements & Fixes

• Create Projects from GitHub and BitBucket repositories
• Fixed instance not starting with GPU for non-default regions
• APIs for monitoring live deployments
• Instance: backup and template bug fix
• Integration: prod integration list fix
• Project: BYOC project and delete API
• Added workspace status update API
• Added a networking tab for traffic distribution
• Enabled line smoothening in the monitoring dashboard
• Added status to templates card
• Changed menu order in instance card
• Add sorting and animation in the build list
• Changed Instance Settings -> Configure
• Updated saving & exiting Hardware Choice
• Changed the order of tabs in Build
• Added project image feature
• Created a new onboarding flow
• RuleBuilder: Add restrictions in the rule builder input
• Build: Update Sort icon
• Project: Add Sorting in the projects list
• Project: Cards animations added to the projects list
• Project: Add search in the projects list
• Removed personal workspace details from the workspaces list
• Traffic distribution: Slider issue fixed
• Fixed update hardware issue in Build
• Fixed projects bug where the same tag was getting added twice
• Fixed upload to GCS issue in relics

Better Build Dashboard with search, sort & filters

Can't find the instance you were working on yesterday? We understand, and well now it's easier than ever to find it.

You can now directly search, sort, or filter your instances using tags such as "Created by me", "Shared with me", and "Running instances". By applying one or more of these options, you can quickly locate the instance you need.

Add Alerts to your Experiments

In a world where it is harder than ever to keep track of failed models, we bring you- Alerts!

You can create alerts of varying complexity by using logical operators such as "and" and "or".

To set up an alert, go to the Experiments section and select the Alert tab. From there, click on "New Rule" and provide a name for the rule. You can then select the logical operation you want to use from the drop-down menus and write a message that will be sent to your team via email whenever a rule is broken.

Added follow-tail to logs

You can now follow the tail in logs! Simply enable the "Follow Trail" toggle located in the top-right corner of the page.

This will cause the screen to automatically scroll down as new logs are generated, allowing you to easily keep track of the most recent logs as they appear.

Naming & re-naming modules

Like any other fast-moving development team, we're not perfect. We named Build Instances as Projects and then realised we have a new feature that could be perhaps more-aptly named Projects. So we are renaming Build Projects to Build Instances.

Sadly, our much-loved internal name for s3 bucket integrations → Relics, is being renamed to Artifacts. It was much harder for beginners in MLOps to relate to the term Relics, and Artifacts seemed a more apt name for the feature.

We're always listening to you, so let us know how you feel about this. (tag NimbleBoxAI on twitter)

Improvements & Fixes

• Model deployment with S3 was implemented
• Added a tooltip to the sidebar menu when in the collapsed state
• Build's instance card is now clickable
• Launch Instance UI improvements
• Not allowing special characters in Projects tag names
• Fixed double scroll issue in Artifacts
• Fixed Frontend not showing some logs in Monitoring
• Fixed upload file bug in Artifacts
• Deploy: A bug was fixed where the resource was not being persisted in the database for models
• Datadog plugin was enabled for APISix
• A refresh token was added for models,
• Jobs: _add_refresh_token method was added
• Jobs & Deploy: Circular import was fixed
• Jobs & Deploy: Bug fixes in the init container
• Jobs & Deploy: Bug fixes in termination helper
• Jobs & Deploy: updated vulnerable packages
• Implemented the ability to close logs modal on escape
• Added Activity logs tab inside deployment
• Added "view curl command" for models in a ready state in Deploy
• Deploy: status field mask for a model update call
• Deploy: unique model_access_key
• Deploy: implemented SetModelMetadata FeatureGate
• Model Deployment Failure UI bug fixed
• Added workspace id to delete experiment API in Monitoring
• Deploy: not allowing overriding of access_key for pinned models
• Deploy: add auth_info-related environment variables for models
• Add access tokens to models in Deploy

Job Resources UI

We have introduced a new user interface for viewing and modifying Job Resources.

You can change the CPU, GPU, RAM and storage resources when creating or editing a job, allowing you to fine-tune your job settings and optimize your resource usage.

You can also see the timeout set for a job and modify the max retries if it fails.

Improved Logging

Introducing a new and improved logging system for Jobs and Deployments. Run Logs and Model Logs now have beautiful and intuitive interfaces, making it easier for users to log and analyze their data.

You can now filter and search logs based on severity level and keywords.

Improvements & Fixes

• Relics naming issue fixed
• Launch Instance UI improvements
• Dependency updates to fix vulnerabilities
• Switching servers to multiple sidecars for better performance and latencies
• Better Internal authentication between our internal services (Relics, LMAO)
• BYOC Jobs: Added functionality for [un]pause, reschedule, logs
• BYOC Jobs: Fixed job creation issue
• Infrastructure upgrades for faster execution of BYOC tasks
• Fixed Relics bug: Conflicting name lead to 500 error
• Fixed Relics permission issue: Workspace Admins could not modify Relics
• Fixed BYOC Templates bug: Deleting an issue
• Fixed BYOC Backups bug: Backups listing was incorrect
• Fixed Join workspace bug

General Availability of Monitoring

Experiment Tracking is the key to successful MLOps.

So with this feature, you can now run multiple experiments in parallel and track any metric, visualizing the results with beautiful charts. In addition, this new tool provides a centralized platform for managing all information related to experiments, making it easier for your research and engineering team to work together and achieve your targets.

Read more about this launch on our blog.

Improved Jobs Scheduling

You can now Schedule Expressions for Job scheduling.

Set a schedule like 45 9 1 * * to schedule the job at "9:45am on the 1st of every month".

You can also see the next 2 jobs that would be scheduled once you save.

This allows you to focus on the task at hand, without having to worry about your job schedules.

Jobs Resources

Now you have the option to configure Kubernetes-style hardware resources for a Job directly from the UI. This feature is available on NimbleBox cloud as well as for your GCP/AWS/Azure cloud integration.

With this feature, you can easily adjust parameters such as CPU and GPU allocation (with the option to even add multiple GPUs), memory usage, disk space, max retries in case of a failed job and network timeout.

To use this feature, you can simply navigate to the Jobs dashboard. Select the job you wish to modify, go to Settings and click on "Edit Resources".

You can use the resource allocation fields and dropdown menus to adjust the desired parameters. Once you've made the changes and saved them, the job will be allocated the newly configured resources for the next execution.

Relics Activity Logs, MIME type support & RBAC

Now Relics comes with activity logs. You can go to your Relic and click on the "Logs" tab to see all relic activity with timestamps.

With MIME type support, you can now see your images and file content directly in the Relics dashboard.

Relics now also comes with Role-Based access control allowing you to choose which team members you want to share your Relics with.

Click on the corresponding Relic and click on the "Manage Access" tab to add or remove access.

BYOC Instance Backup

You can create snapshot backups of the instances on your cloud easily from the Build Dashboard.

Navigate to the "Backup now" option in your instance settings.

Improvements & Fixes

• Improved backend for Monitoring Dashboard
• Fixed Jobs logs not showing up in some cases
• Fixed GPU machine start issue in AWS (BYOC)
• Fixed hardware configuration issues in GCP Integration (BYOC)
• Fixed the incorrect deployment URLs that were shown in UI for BYOC Deploy
• Fixed a bug in the wallet system which led to credits not showing up properly

Log streaming & GPU in Deploy

In this release, we've added log streaming to Deploy, both in the UI and nbox, for improved visibility and tracking. In addition, deploy now has GPU compatibility, allowing for more efficient processing. Additionally, you can now specify hardware resources for model deployment.

Phasing out personal workspaces

We have decided to phase out personal workspaces and are saying goodbye to the Personal Pro plan. However, we will continue to support existing plans for the time being. As part of these changes, we have also removed the trial of workspaces and are introducing - a wallet for workspaces. These updates are aimed at streamlining our product offerings.

BYOC Upgrades

• BYOC Templates are now supported
• BYOC Build now has GPU Compatibility
• BYOC Deploy now has GPU Compatibility

Improvements & Fixes

• Build Projects are now called Instances
• Asking for cookie consent - Blocking user cookies if the user denies consent
• Displaying error message on instance card if storage is full
• Onboarding changes & bug fixes
• Added bottom margin to allow for HubSpot container
• Fixed increasing storage in Build
• Fixed displaying incorrect storage percentage in Build
• Fixed regions not rendering for the "all" option in Build
• Model deployment can now be stopped while it's in progress
• Disabled model logs when the model is stopped

Bring Your Own Cloud (BYOC)

We are excited to introduce our Bring Your Own Cloud (BYOC) feature on the NimbleBox.ai platform. With this new feature, you can now integrate your own private cloud environment on our platform for easy management and scaling. You can use your cloud integration with our existing products- Build, Jobs, Deploy, and Relics.

We have additional security features to ensure your data remains secure and private. We support a wide range of cloud providers, including Amazon Web Services, Microsoft Azure, and Google Cloud Platform. We have added additional tools and resources to help you set up and manage your private cloud environment.

Fixes and Improvements

• We implemented the ability for you to create instances in any region
• You can now set custom subdomains for your Build instances
• BYOC Integrations list has all option to list all instances, jobs, and deployments from all cloud integrations.
• We implemented the ability for you to update hardware configurations easily while using BYOC
• Display of instance details modified for a better user experience
• Improved functionality of the instance list feature
• Better logging for Deployments based on errors
• We made various minor improvements and enhancements to the system