HAMi vs HTCondor

HAMi is open-source middleware for sharing and scheduling GPUs and other accelerators among AI workloads on Kubernetes. Workloads can request GPU memory and core limits through Kubernetes resource controls. Scheduling options include binpack, spread and numa-first, with binpack and spread available at node or GPU scope. The supported-device list spans NVIDIA, AMD, AWS Neuron, Huawei Ascend, Cambricon, Enflame, Hygon, Iluvatar, Kunlunxin, MetaX, Moore Threads, Vastai and Biren; isolation and partitioning capabilities vary by device. HAMi works with Kubernetes APIs, DRA and CDI, and documents scheduling integrations including Volcano, Kueue, Koordinator and NVIDIA KAI Scheduler. Its node metrics endpoint exposes device memory and core utilization for Prometheus. Classic HAMi requires Kubernetes v1.23 or later. HAMi-DRA requires Kubernetes v1.34 or later, the DRA Consumable Capacity feature gate, CDI and NVIDIA driver 440 or later. NVIDIA limits use user-space library interception, which does not cover applications that bypass CUDA. Enforcement is described as soft and best-effort; the FAQ recommends MIG when hardware-enforced isolation is required.

HTCondor is a free high-throughput computing system that coordinates networked machines to run submitted jobs. It finds available machines, matches their resource offers to job requirements and preferences through ClassAds, and can restart a job elsewhere if its current machine becomes unavailable. It is suited to submitting large batches, including thousands of runs across different data sets, and can use otherwise idle machines in a pool. Users do not need login accounts on machines running their jobs; file transfers and split execution support that model. HTCondor can draw on clusters, cloud resources, and international grids, and flocking can connect multiple installations. It supports transfers to and from S3-compatible storage, including Google Cloud Storage through its S3 interoperability API, but not entire buckets or directories. The software is licensed under Apache License 2.0, includes Python bindings, and offers user and administrator guides. Community support is available through a mailing list; paid contract support offers guaranteed resolution and faster turnaround. Security fixes cover current feature and LTS versions, with the previous LTS supported for approximately twelve months after a new LTS release.