cloudrift-ai
Official@cloudrift-ai
Optimizes GPU kernel performance and model deployment configurations for high-demand neural network architectures on cloud infrastructure.
Agent Skills by cloudrift-ai
Showing 6 vetted skills indexed across 1 GitHub repositories.
cloudrift-aitune-model
Autotune compiled LLM kernels and generate root-cause performance findings reports.
cloudrift-aicollect-node-data
Automate per-GPU kernel node data collection and merging for LLM compiler autotuning.
cloudrift-aistart-remote-server
Provision cloud GPU VMs on GCP or CloudRift for emmy compiler workloads.
cloudrift-aibenchmark-new-model
Automate onboarding of HuggingFace models into emmy with deployment validation and benchmarking.
cloudrift-aitune-golden
Re-tune and validate GPU matmul golden configuration YAMLs via the emmy CLI.
cloudrift-aidiscover-new-models
Rank newly released open-weight LLMs by demand and VRAM compatibility.
Frequently Asked Questions About cloudrift-ai
FAQPage SchemaWhat specific performance tasks does this organization enable?▼
These capabilities enable the autotuning of compiled kernels, generation of root-cause performance reports, and the systematic benchmarking of models. Users can validate deployment configurations and perform granular data collection for GPU node performance analysis.
Which technical personas benefit from these capabilities?▼
These functions are designed for machine learning engineers, infrastructure architects, and performance researchers focused on hardware-level optimization. Professionals managing high-compute environments and model deployment pipelines will find these utilities essential for maximizing throughput on GPU-accelerated infrastructure.
What are the primary infrastructure prerequisites for these operations?▼
Operations require access to GCP or CloudRift environments for provisioning GPU virtual machines. Additionally, the emmy compiler environment must be configured to support the matmul golden configuration validation and the ingestion of HuggingFace model architectures.