This changelog provides an overview of user-visible updates, including new features, improvements, bug fixes, and deprecations, for the tensorkube CLI.

As the CLI is still in its alpha development stage, breaking changes may occur. We strive to minimize these disruptions and provide the tensorkube upgrade.

We thank you for your understanding and patience as we progress towards a stable release of the CLI.

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Latest

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0.0.13 (August 19, 2024)

  • Added support for GitHub actions. You can configure your CD pipelines using the guide here.
  • Now your container builds will get interrupted if one of the intermediate command fails. Earlier you had to wait for all the resources to be executed before you can retry your builds.
  • Added support for multiple concurrent clusters: Now you can run Tensorfuse alongside other kubernetes clusters without changing context
  • Migration from sslip.io domains: Sslip domains are notorious for misconfigurations and outages. We have finally modified our stack to directly support your deployments using AWS load balancer urls. Using tensorkube list deployments will give the new urls. However, the older way of using sslip.io urls as host headers will continue to work. To view older header urls use —old flag: tensorkube list deployments --old
  • S3 bug:- Earlier there was a bug which caused redundant files to be available in your containers in the case of simultaneous runs. This has been resolved now. Each deployment run would begin from a clean slate and only the latest deployment would prevail.
  • Added Required Login:- We are continuously trying to improve the support we can give to our customers. To further aid this we will be releasing a dashboard in the coming weeks. As a prerequisite you will have to log-in to tensorfuse once for running your commands. This means running tensorkube login just once.

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0.0.12 (August 12, 2024)

  • Fixed build failures during deployments

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0.0.11 (August 5, 2024)

  • Fixed the bug where long environment names prevented proper resource creation

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0.0.10 (July 29, 2024)

  • Added support for directly ssh-ing into your deployments using the tensorkube deployment ssh <deployment-name> command. This command will open an interactive shell in the pod and allow you to debug issues.
  • Added support for environments so that there is segregation between prod, staging etc. You can run tensorkube environment create --env-name <env-name> to create a new environment and then use --env <env-name> flag with all other command.
  • Added support to stream logs from your deployments to your terminal with the help of tensorkube deployment logs <deployment-name> command.
  • Added support for getting detailed information around your deployments (such as latest version, status etc) using tensorkube deployment describe <deployment-name> command.
  • A better UI for interacing with the CLI. Now you can see the progress of your commands in a more interactive way.
  • Added cleanup jobs to remove outdated root filesystems.

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0.0.9 (July 22, 2024)

  • Released the Tensorfuse Container Runtime that enables ~3s cold starts for your containers.
  • Made installation instructions independent of the package manager. Now you no longer have to depend on snap to install Tensorfuse prerequisites.
  • Added support for cloudwatch logging and container insights on cloudwatch for you Tensorfuse runtime. This enables you to browse and debug logs from the Cloudwatch console and view usage metrics dashboards.
  • Fixed the s3 mountpoint error that keeps popping up randomly during container starts.
  • Modified the runtime to work with Bottlerocket AMIs and the Nvidia container runtime.

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0.0.7 (June 26, 2024)

  • Added support for T4 and L4 gpu types.
  • Added support for ignoring files and directories specified in a .dockerignore file during the image build process.
  • Added the tensorkube upgrade command to bring your tensorfuse runtime to the latest version
  • Added flags --min-scale and --max-scale to control the number of pods running in your tensorfuse app. By default the minimum scale is 0 and the maximum scale is 3.
  • Non gpu pods will no longer be scheduled on gpu machines.

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0.0.5 (June 24, 2024)

  • The CLI now works seamlessly on Linux machines. Earlier, the CLI was supported only on Macs.
  • Added support for Network File System within your Tensorkube cluster. Network File System enables faster cold starts and reduces Image build times as well.
  • Upgraded the Image build engine to use less resources during an Image build process. The new build engine consumes less RAM and hence can run on cheaper instances.
  • You can now use tensorkube install-prerequisites to install and check all the prerequisite packages required to run tensorkube before configuring it with tensorkube configure.
  • tensorkube configure now resumes from the last installation checkpoint. You now don’t have to remove and reset the cluster in case your configuration runs into errors.

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0.0.4 (June 17, 2024)

  • Logging support added for streaming logs during the container image build process and while a service is getting started. Every intermediate process from building the image to submitting a nodeclaim to actually starting a pod can be understood and debug using logs now.
  • Support for versioning deployments is now live. Everytime you run tensorkube deploy, a new version of your service is created and the older version is retired.
  • tensorkube deploy now accepts --cpu and --memory optional parameters which allow you to specify the number of CPU millicores and the amount of RAM you want your servers to run on.
  • Your pods now remain active upto 5 minutes after they receive their last HTTP request.
  • Your pods now have a hard upscale limit of 3 pods.

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0.0.3 (June 11, 2024)

  • Tensorkube CLI is now available on pypi and can be installed using pip install tensorkube
  • tensorkube deploy now supports --gpus and --gpu-type parameters. --gpus defines the number of GPUs each pod requires and the --gpu-type parameter defines the type of GPU you want your system to run on.
  • You can now list all your service deployments using the tensorkube list deployments command
  • Your file uploads now come with progress bars so that you can optimise your images during deployment.
tensorkube deploy