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Isonet support NVIDIA A100 #26
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Hi, We only tested tensorflow2.5. and python3.6 on A100 GPUs. IsoNet works fine with A100. Could you run the command with parameter: "--log_level debug" and see what is the error message? |
Dear procyontao, |
I sloved the problem. |
Hi all, I used TF2.6.0 Cuda11.6 IsoNet0.1 on the three tutorial tomograms System Time/step speedup Cheers |
Hi proteincommandr, Thank you for providing the GPU benchmarks. We do not even afford that many types of GPU for a speed test. One question, did you consider the differences in batch_size (i.e. number of subtomograms processed in one step)? If you do not specify the batch size in your command, your list should be: |
Dear Author,
First thanks a lot for this powerful software !
I have a question: Is IsoNet support CUDA 11.2 and NVIDIA A100 with tensorflow-gpu_2.7?
I install isonet with conda python3.9 and tensorflow-gpu_2.7+cuda11.2+NVIDIA A100 get this error:
(py39) [root@Isonet]$ isonet.py refine subtomo.star --gpuID 0,1,2,3 --iterations 30 --noise_start_iter 10,15,20,25 --noise_level 0.05,0.1,0.15,0.2
04-16 22:14:09, INFO
######Isonet starts refining######
04-16 22:14:27, INFO Note: detected 128 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
04-16 22:14:27, INFO Note: NumExpr detected 128 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 8.
04-16 22:14:27, INFO NumExpr defaulting to 8 threads.
04-16 22:14:30, WARNING The results folder already exists before the 1st iteration
The old results folder will be renamed (to results~)
04-16 22:14:50, INFO Done preperation for the first iteration!
04-16 22:14:50, INFO Start Iteration1!
/data1/apps/miniconda3/envs/py39/lib/python3.9/site-packages/keras/optimizer_v2/adam.py:105: UserWarning: The
lr
argument is deprecated, uselearning_rate
instead.super(Adam, self).init(name, **kwargs)
/data1/apps/miniconda3/envs/py39/lib/python3.9/site-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.
layer_config = serialize_layer_fn(layer)
04-16 22:14:54, INFO Noise Level:0.0
2022-04-16 22:15:06.826516: F tensorflow/stream_executor/cuda/cuda_driver.cc:153] Failed setting context: CUDA_ERROR_NOT_INITIALIZED: initialization error
Thanks a lot!
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