cluster:184
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cluster:184 [2019/09/27 12:45] hmeij07 [AWS deploys T4] |
cluster:184 [2020/01/03 13:22] (current) hmeij07 |
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| **[[cluster: | **[[cluster: | ||
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| + | ==== Turing/ | ||
| + | |||
| + | * https:// | ||
| ==== AWS deploys T4 ==== | ==== AWS deploys T4 ==== | ||
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| * https:// | * https:// | ||
| - | Look at this, the smallest Elastic Cloud Compute Instances are **g4dn.xlarge** yielding access to 4 vCPUs and 1x T4 GPU. The largest is **g4dn.16xlarge** yielding access to 64 vCPUs and 1x T4 GPUs. Now the smallest is priced at $0.526/hr, and running that card 24/7 for a year is a cost of $4,607.76 ... meaning ... option #7 below with 26 GPUs would cost you a whopping $119,802. Annually! That's the low tide water mark. | + | Look at this, the smallest Elastic Cloud Compute Instances are **g4dn.xlarge** yielding access to 4 vCPUs, 16GiB memory |
| The high tide water mark? The largest instance is priced at $4.352 and would cost you near one million dollars to run per year if you matched option #7. | The high tide water mark? The largest instance is priced at $4.352 and would cost you near one million dollars to run per year if you matched option #7. | ||
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| ==== 2019 GPU Expansion ==== | ==== 2019 GPU Expansion ==== | ||
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| + | More focus... | ||
| + | |||
| + | * Vendor A: | ||
| + | * Option 1: 48 gpus, 12 nodes, 24U, each: two 4214 12-core cpus (silver), 96 gb ram, 1tb SSD, four NVIDIA RTX 2080 SUPER 8GB GPU, centos7 yes, cuda yes, 3 yr, 2x gbe nics, 17.2w 31.5d 3.46h" | ||
| + | | ||
| + | With the Deep Learning Ready docker containers...[[cluster: | ||
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| + | The SUPER model quote above is what we selected\\ | ||
| + | --- // | ||
| + | |||
| + | |||
| + | Focus on RTX2080 model... | ||
| + | |||
| + | * Vendor A: | ||
| + | * Option 1: 48 gpus, 12 nodes, 24U, each: two 4116 12-core cpus (silver), 96 gb ram, 1tb SSD, four rtx2080 gpus (8gb), | ||
| + | * Option 2: 40 gpus, 10 nodes, 20U, each: two 4116 12-core cpus (silver), 96 gb ram, 1tb SSD, four rtx2080ti gpus (11gb), | ||
| + | * A1+A2 installed, configured and tested: NGC Docker containers Deep Learning Software Stack: NVIDIA DIGITS, TensorFlow, Caffe, NVIDIA CUDA, PyTorch, RapidsAI, Portainer ... NGC Catalog can be found at | ||
| + | https:// | ||
| + | | ||
| + | * Vendor B: | ||
| + | * Option 1: 36 gpus, 9 nodes, 18U, each: two 4214 12-core cpus (silver), 96 gb ram, 2x960gb SATA, four rtx2080tifsta gpus (11gb), | ||
| + | | ||
| + | * Vendor C: | ||
| + | * Option 1: 40 gpus, 10 nodes, 40U, each: two 4214 12-core cpus (silver), 96 gb ram, 240 gb SSD, four rtx2080ti gpus (11gb), | ||
| + | * Option 2: 48 gpus, 12 nodes, 48U, each: two 4214 12-core cpus (silver), 96 gb ram, 240 gb SSD, four rtx2080s gpus (8gb), | ||
| + | | ||
| + | * Vendor D: | ||
| + | * Option 1: 48 gpus, 12 nodes, 12U, each: two 4214 12-core cpus (silver), 64 gb ram, 2x480gb SATA, four rtx2080s gpus (8gb), | ||
| Ok, we try this year. Here are some informational pages. | Ok, we try this year. Here are some informational pages. | ||
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| | Gpus | 48 | 16 | 36 | 28 | 20 | 34 | 26 | 16 | 28 | 60 | total| | | Gpus | 48 | 16 | 36 | 28 | 20 | 34 | 26 | 16 | 28 | 60 | total| | ||
| | Cores | 209 | 74 | 157 | 72 | 92 | 75 | 67 | 74 | 72 | 138 | cuda K| | | Cores | 209 | 74 | 157 | 72 | 92 | 75 | 67 | 74 | 72 | 138 | cuda K| | ||
| - | | Cores | 26 | 9 | 20 | | + | | Cores | 26 | 9 | 20 | |
| | Tflops | | Tflops | ||
| | Tflops | | Tflops | ||
cluster/184.1569588307.txt.gz · Last modified: 2019/09/27 12:45 by hmeij07
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