Post by account_disabled on Mar 14, 2024 4:03:15 GMT -5
Most likely the picture will be like this. The data scientist took the entire resource for himself but used the GPU say four hours a day. The rest of the time he trained the model on the CPU drank coffee or solved other problems. To ensure that paid resources do not disappear during this time they should be given to other specialists. And here we gradually approach the next point. Youll have to take into account the nuances of GPU sharing GPU sharing is a great move when you need to simultaneously solve several problems none of which requires all the available resources.
We take the card divide it into several isolated pieces which have their own memory cores cache etc. and Buy Email List give one to each of the data scientists for their task. But of course not everything is so simple. Firstly each sharing method has its pros and cons. Secondly the same technology for example MIG can divide the A into a maximum of seven logical blocks and the A into a maximum of four. Thirdly even with sharing some resources may remain idle. Lets say we take a GB A card and use MIG to divide it into the maximum possible seven partitions by the way the minimum latency and maximum throughput.
In this case we only use GB of memory out of the available. Yes it wont always be this way because you can divide the card into GB but this is just one of the options and not a panacea. To summarize if you are not ready for radical measures for example operating several dozen A or H connected to the motherboard via NVSwitch the issue of choosing hardware for ML tasks will become very difficult. At the very least the technical characteristics of the hardware alone are not enough to make a decision since it is important to pay attention to the additional capabilities of the GPU in terms of sharing and monitoring operation.
We take the card divide it into several isolated pieces which have their own memory cores cache etc. and Buy Email List give one to each of the data scientists for their task. But of course not everything is so simple. Firstly each sharing method has its pros and cons. Secondly the same technology for example MIG can divide the A into a maximum of seven logical blocks and the A into a maximum of four. Thirdly even with sharing some resources may remain idle. Lets say we take a GB A card and use MIG to divide it into the maximum possible seven partitions by the way the minimum latency and maximum throughput.
In this case we only use GB of memory out of the available. Yes it wont always be this way because you can divide the card into GB but this is just one of the options and not a panacea. To summarize if you are not ready for radical measures for example operating several dozen A or H connected to the motherboard via NVSwitch the issue of choosing hardware for ML tasks will become very difficult. At the very least the technical characteristics of the hardware alone are not enough to make a decision since it is important to pay attention to the additional capabilities of the GPU in terms of sharing and monitoring operation.