Optimizing Computational Resources for Machine Learning and Data Science Projects: A Practical Approach
Every computation requires computing resources. In machine learning, powerful computing resources are necessary for feeding massive amounts of data to the model, performing calculations for each data point, and adjusting parameters to teach the model correct mappings. However, the amount of computational resources is always insufficient, leading to challenges in allocation and utilization.
At deepsense.ai, we specialize in addressing machine learning and data science challenges with custom solutions tailored to our client’s specific needs. Our in-house computational resources are utilized for developing and testing these solutions. While cloud computing is trendy, it may not always be practical due to cost, availability of on-demand GPUs, or data confidentiality concerns.
Adopting SLURM for Efficient Resource Management
We have established our cluster using SLURM (formerly known as Simple Linux Utility for Resource Management). SLURM supports all required resources, including CPU, RAM, and GPU, and is compatible with the Linux operating system, Python, and other AI tools and models we commonly use. It is a stable and widely used solution, allowing for efficient resource management and queuing of tasks.
Practical Experiences with Using SLURM
We gain access to the cluster via SSH through the login node, where we configure, prepare, and run computational tasks. Tasks can be run in interactive mode for immediate verification or in batch mode for background processing. The flexibility of SLURM allows for efficient resource allocation and utilization, enabling tasks to be queued and launched as resources become available.
If you want to evolve your company with AI, stay competitive, and optimize your computational resources for machine learning and data science projects, connect with us for professional consultation and assistance with SLURM configuration.
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