Comments (8)
python train.py ae_configs/cvpr/low pc_configs/cvpr/res_shallow --restore
"/public/home/xqqstu/fab/code/ckpts/0515_1103 cvpr@low cvpr@res_shallow/ckpts"
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maybe an issue with GPU? do you have one in the system?
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I checked it again and found that I do have one! We can see it in the log: 2020-06-12 13:40:33.870168: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1030] Found device 0 with properties
name: Tesla V100-PCIE-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.38
pciBusID: 0000:3b:00.0
totalMemory: 31.75GiB freeMemory: 31.03GiB
And the picture above is the code stop place.
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ok. what’s the training data?
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and are you running this in your local machine or in some cloud / cluster
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Thank you for your reply. Well, the dataset is the ImageNet, and I am running code on the CentOS7 of the cluster system. And this morning I‘ve tried running the code on the Windows system of my laptop, unexpectedly it succeeded at last. So I am very confused why it did't work in the cluster. Maybe some configurations on the cluster were wrong.
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hm one issue could be that you don’t have enough RAM on the cluster. did you check this? you want probably at least 40GB
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Following your advice, I searched on the google and found some suggestions such as reduce the batch-size, I changed the batch_size of your model form 30 to 16, and reuse the training command, it works finally!! Thank you very much!
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Related Issues (20)
- The size of your trained model HOT 2
- compressed images are large HOT 11
- why only mask the last channel? HOT 1
- plot HOT 6
- Testing monochromatic images on your code HOT 6
- about soft_quantize sigma parameter
- bpp comparison HOT 3
- test error HOT 2
- inference error HOT 4
- log_dir_root HOT 4
- training
- CodecDistance HOT 4
- python version
- inference using real_bpp HOT 2
- about soft and hard quantization HOT 2
- How to optimize with mse? HOT 1
- Question about 'distortion_to_minimize' HOT 3
- Download link/ pretrained models HOT 2
- which time we can get torch ?
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