Intel® Geti™ 1.1.0#

Release Summary#

Intel® Geti™ 1.1.0 contains several updates and feature enhancements:

Dataset import option - Easily import data and annotations to an existing project
Switch to adjust auto-training - Follow your desired workflow by enabling or disabling automatic training via a simple switch
Export model analytics graphs - Leverage training or testing plots in both PDF and CSV format for further analysis
Dataset rename option - Now you can easily rename a newly created dataset

Release Details#

This section covers additional details on the new functionality available with Intel Geti 1.1.0.

Dataset import option#

Import datasets by clicking on + next to the dataset name and pressing Import testing set. You can also create an empty testing set and press + next to the name of the newly created set and import datasets. This feature enables you to reuse previously created datasets without the need to reupload.

The respective project task must support the dataset format.

Switch to adjust auto-training#

Now you can easily disable and enable auto-training by clicking on from the header of the screen in each project panel or the annotator screen. When creating projects, the auto-training option is enabled by default.

Exportable model analytics graphs#

You can export all the metrics graphs in a PDF format or individually selected metrics graphs in a PDF or CSV format. To download all metrics graphs, click on Download all graphs in the top right-hand corner of the Model metrics panel. To download an individual graph, click on in the in the top right-hand corner of the graph box.

Dataset rename option#

Rename existing datasets by clicking on next to the name of the newly created dataset.

System Requirements for Intel® Geti™ 1.1.0#

The platform can be installed on a machine with the following minimum hardware requirements:

CPU for workstations: Intel® Core™ i7, Intel® Core™ i9 or Intel® Xeon® scalable processors family capable of running 20 concurrent threads (in case of using the default K3s) or 48 concurrent threads (in case of using pre-installed K8s).

Note

From Intel® Core™ family, we recommend the following CPU series:

13th gen (Raptor Lake): Intel® Core™ i7 13700 series and Intel® Core™ i9 13900 series
12th gen (Alder Lake): Intel® Core™ i9 12900 series

CPU for cloud deployments: CPUs capable of running min. 24 concurrent threads for K3s or min. 48 concurrent threads for K8s (so for example, on AWS EC2 instances, this requirement would be translated to min. 24 vCPUs for K3s or min. 48 vCPUs for K8s).
GPU: min. one NVIDIA GPU with min. 16GB of memory (e.g. RTX 3090, RTX 6000, RTX 8000, Tesla A100, Tesla V100, Tesla P100, or Tesla T4), other NVIDIA GPUs in a similar series are likely also compatible if they meet minimum memory requirements. However, the full range of devices is not fully tested and not specifically supported. We recommend 24GB of memory for the stable training & optimization.
Memory: min. 64 GB RAM (128 GB recommended)
Disk Space: min. 500 GB (1 TB recommended) for a root partition

Installation of the platform on a multinode configuration of Kubernetes cluster is not supported.