MotusAI Achieves an Average Cluster Computing
Power Utilization Rate of Over 70% by Implementing Efficient and
Unified GPU Scheduling
KAYTUS, a leading IT infrastructure provider, has unveiled
MotusAI, an AI development platform now accessible for trial
worldwide. MotusAI is tailored for deep learning and AI
development, integrating GPU and data resources alongside AI
development environments to streamline computing resource
allocation, task orchestration, and centralized management. It
accelerates training data and manages AI model development
workflows seamlessly. This platform drastically reduces resource
investment, boosts development efficiency, elevates cluster
computing power utilization to over 70%, and significantly enhance
in large-scale training task scheduling performance.
Streamline AI Development for Cost-Effectiveness and
Efficiency
The rapid expansion of enterprise AI business and AI model
development brings forth challenges including low computing
efficiency, complexity in model development, varied requirements
for task orchestration across different scenarios, and unstable
computing resources. Ensuring efficient, flexible, and stable
operation of AI business is critical for enterprises to
consistently derive business insights, generate revenue, and
maintain competitiveness.
Optimize Resource Management for Maximum Computing Power
MotusAI efficiently allocates resources and workloads by
implementing intelligent and flexible GPU scheduling. It caters to
diverse AI workload demands for computing power by dynamically
allocating GPU resources based on demand. With multi-dimensional
and dynamic GPU resource allocation, including fine-grained GPU
scheduling and support for Multi-Instance GPU (MIG), MotusAI
effectively meets computing power requirements across various
scenarios such as model development, debugging, and training.
Streamline Task Orchestration for Versatile Support of Various
Scenario
MotusAI has revolutionized cloud-native scheduling systems. Its
scheduler surpasses the community version by dramatically improving
the scheduling performance of large-scale POD tasks. MotusAI
achieves rapid startup and environment readiness for hundreds of
PODs, boasting a five times increase in throughput and a five times
decrease in latency compared to the community scheduler. This
ensures efficient scheduling and utilization of computing resources
for large-scale training. Moreover, MotusAI enables dynamic scaling
of AI workloads for both training and inference services,
supporting burst tasks and fulfilling diverse scheduling needs
across various scenarios.
MotusAI empowers users to maximize computing resources, spanning
from fine-grained division of single-card multiple instances to
large-scale parallel computing across multiple machines and cards.
By integrating features like computing power pooling, dynamic
scaling, and GPU single-card reuse, MotusAI significantly enhances
computing power utilization, achieving an average utilization rate
of over 70%. Furthermore, it enhances computing efficiency by
leveraging cluster topology awareness and optimizing network
communication.
Data Transfer Acceleration for Three Times Efficiency
Improvement
MotusAI excels in data transfer acceleration through innovative
features such as supporting local loading and computing of remote
data, which eliminates delays caused by network I/O during
computation. Utilizing strategies like "zero-copy" data transfer,
multi-threaded retrieval, incremental data updates, and affinity
scheduling, MotusAI significantly reduces data caching cycles.
These enhancements greatly improve AI development and training
efficiency, resulting in 2-3 times boost in model efficiency during
data training.
Reliable, and Automatically Fault-Tolerant Platform
MotusAI supports performance monitoring and alerts for computing
resources, providing real-time status updates for core platform
services. It employs sandbox isolation mechanisms for data with
higher security levels. In case of resource failures or
abnormalities, MotusAI automatically initiates fault tolerance
processes to ensure the quickest possible recovery during
interrupted training tasks. This approach reduces fault handling
time by over 90%, on average.
Comprehensive Management of AI Model Development in One
Integrated Solution
MotusAI accelerates AI development and supports every stage of
large model development. From managing data samples and software
stacks to designing model architectures, debugging code, training
models, tuning parameters, and conducting evaluation testing,
MotusAI offers a complete platform. It integrates popular
development frameworks like PyTorch and TensorFlow, along with
distributed training frameworks such as Megatron and DeepSpeed.
Moreover, MotusAI enables comprehensive lifecycle management of
AI inferencing services, including offline and online testing, A/B
testing, rolling release, service orchestration, and service
decommissioning. These features collectively enhance the business
value of AI technology, fostering continuous business growth.
Additionally, MotusAI provides an integrated visual management
and operation interface that covers computing, networking, storage,
and application resources. Operational staff can comprehensively
manage, monitor, and evaluate the overall platform operation status
through a single interface.
Free Trial Available
MotusAI is now available worldwide for a trial period, offering
free remote access for one month, along with testing, training, and
support. Users can also opt for local deployment using their own
devices and environment, with local deployment testing support from
KAYTUS. For more information1 and to register2, please visit Link1,
Link2.
About KAYTUS
KAYTUS is a premier provider of IT infrastructure products and
solutions, delivering a suite of cutting-edge, open, and
environmentally friendly infrastructure solutions for cloud, AI,
edge computing, and other emerging technologies. With a
customer-centric approach, KAYTUS adapts flexibly to user needs
through its agile business model. Learn more at KAYTUS.com
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