HERE ARE THE STORAGE REQUIREMENTS FOR DEEP LEARNING
Deep learning workloads are a special kind of beast: all DL data is considered hot data, which raises the dilemma of not being able to employ any sort of tiered storage management solution. This is because normal SSDs usually used for hot data under conventional conditions simply won’t move the data required for millions, billions, or even trillions of metadata transfers for an ML training model to classify an unknown something out of only a limited amount of examples.
Below are a few examples of a few storage requirements needed to avoid the dreaded curse of dimensionality.
COST EFFICIENCY
Enormous AI data sets become an even bigger burden if they don’t fall within the budget set aside for storage. Anyone who has been in charge of managing enterprise data for any amount of time knows well that highly-scalable systems have always been more high-priced on a capacity versus cost basis. The ultimate deep learning storage system must be both affordable and scalable to make sense.
PARALLEL ARCHITECTURE
In order to avoid those dreaded choke points that stunt a deep learning machine’s ability to learn, it’s essential for data sets t to have parallel-access architecture.
DATA LOCALITY
While it might be possible that many organizations may opt to keep some of their data on the cloud, most of it should remain on-site in a data center. There are at least three reasons for this: regulatory compliance, cost efficiency, and performance. For this reason, on-site storage must rival the cost of keeping it on the cloud.
HYBRID ARCHITECTURE
As touched on above, different types of data have unique performance requirements. Thus, storage solutions should offer the perfect mixture of storage technologies instead of an asymmetrical strategy that will eventually fail. It’s all about simultaneously meeting ML storage performance and scalability.
SOFTWARE-DEFINED STORAGE
Not all huge data sets are the same—especially in terms of DL and ML. While some of them can get by with the simplicity of pre-configured machines, others need hyper-scale data centers featuring purpose-built servers architectures that are previously set in place. This is what makes software-defined storage solutions the best option.
Our X-AI Accelerated is an any–scale DL and ML solution that offers unmatched versatility for any organization’s needs. X-AI Accelerated was engineered from the ground up and optimized for “ingest, training, data transformations, replication, metadata, and small data transfers.” Not only that but RAID Inc. offers all the aforementioned requirements such as all-flash NVMe X2-AI/X4-AI or the X5-AI, which are hybrid flash and hard drive storage platforms.
Both the NVMe X2-AI/X4-AI and the X5-AI support parallel access to flash and deeply expandable HDD storage as well. Furthermore, the X-AI Accelerated storage platform permits one to scale out from only a few TBs to tens of PBs.
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Storage requirements are depending on several factors.
For example, the language you will use, the data you will receive, do you use a RAID, etc.
Hi @Evgeny Belenky ,
HERE ARE THE STORAGE REQUIREMENTS FOR DEEP LEARNING
Deep learning workloads are a special kind of beast: all DL data is considered hot data, which raises the dilemma of not being able to employ any sort of tiered storage management solution. This is because normal SSDs usually used for hot data under conventional conditions simply won’t move the data required for millions, billions, or even trillions of metadata transfers for an ML training model to classify an unknown something out of only a limited amount of examples.
Below are a few examples of a few storage requirements needed to avoid the dreaded curse of dimensionality.
COST EFFICIENCY
Enormous AI data sets become an even bigger burden if they don’t fall within the budget set aside for storage. Anyone who has been in charge of managing enterprise data for any amount of time knows well that highly-scalable systems have always been more high-priced on a capacity versus cost basis. The ultimate deep learning storage system must be both affordable and scalable to make sense.
PARALLEL ARCHITECTURE
In order to avoid those dreaded choke points that stunt a deep learning machine’s ability to learn, it’s essential for data sets t to have parallel-access architecture.
DATA LOCALITY
While it might be possible that many organizations may opt to keep some of their data on the cloud, most of it should remain on-site in a data center. There are at least three reasons for this: regulatory compliance, cost efficiency, and performance. For this reason, on-site storage must rival the cost of keeping it on the cloud.
HYBRID ARCHITECTURE
As touched on above, different types of data have unique performance requirements. Thus, storage solutions should offer the perfect mixture of storage technologies instead of an asymmetrical strategy that will eventually fail. It’s all about simultaneously meeting ML storage performance and scalability.
SOFTWARE-DEFINED STORAGE
Not all huge data sets are the same—especially in terms of DL and ML. While some of them can get by with the simplicity of pre-configured machines, others need hyper-scale data centers featuring purpose-built servers architectures that are previously set in place. This is what makes software-defined storage solutions the best option.
Our X-AI Accelerated is an any–scale DL and ML solution that offers unmatched versatility for any organization’s needs. X-AI Accelerated was engineered from the ground up and optimized for “ingest, training, data transformations, replication, metadata, and small data transfers.” Not only that but RAID Inc. offers all the aforementioned requirements such as all-flash NVMe X2-AI/X4-AI or the X5-AI, which are hybrid flash and hard drive storage platforms.
Both the NVMe X2-AI/X4-AI and the X5-AI support parallel access to flash and deeply expandable HDD storage as well. Furthermore, the X-AI Accelerated storage platform permits one to scale out from only a few TBs to tens of PBs.
@Ariful Mondal ,
Thanks for your response.