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.
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.
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.
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.
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.
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.
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.
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.
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.
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.