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| Create a jupyterhub instance with GPU support enabled.
| | #REDIRECT [[Jupyter#GPU notebooks on JupyterHub]] |
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| = setup =
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| * [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
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| * [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]
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| == Create conda environment that we can use for a jupyter kernel ==
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| <nowiki>
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| conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
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| python -m ipykernel install --user --name kernel_test</nowiki>
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| NOTE: You can specific the python version for you conda environment with python=3 Please take care what python version is compatible with you required packages.
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| == Install required packages ==
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| For pytorch you can find information [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].
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|
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| As an example I use the following pytorch installation:
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| <nowiki>
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| pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>
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| = Start jupyter notebook with GPU =
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| Go [https://notebook.anunna.wur.nl here] and select:
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| * Select a location for your server: on the cluster (default option)
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| * Partition to use: gpu
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| * Partition to use: gpu
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| * Memory (in MB): desired memory
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| * Number of CPUs: desired CPU count
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| * Maximum execution time (hours:minutes:seconds): maximum amount of time the notebook is available
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| * Extra options: --gres=gpu:1 (default when selecting GPU, gpu:x for x amount of GPUs)
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| = Test GPU availability =
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| == Pytorch ==
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| <nowiki>
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| def check_all_cuda_devices():
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| device_count = torch.cuda.device_count()
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| for i in range(device_count):
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| print('>>>> torch.cuda.device({})'.format(i))
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| result = torch.cuda.device(i)
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| print(result, '\n')
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| print('>>>> torch.cuda.get_device_name({})'.format(i))
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| result = torch.cuda.get_device_name(i)
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| print(result, '\n')
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| def check_cuda():
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| print('>>>> torch.cuda.is_available()')
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| result = torch.cuda.is_available()
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| print(result, '\n')
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| print('>>>> torch.cuda.device_count()')
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| result = torch.cuda.device_count()
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| print(result, '\n')
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| print('>>>> torch.cuda.current_device()')
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| result = torch.cuda.current_device()
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| print(result, '\n')
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| print('>>>> torch.cuda.device(0)')
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| result = torch.cuda.device(0)
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| print(result, '\n')
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| print('>>>> torch.cuda.get_device_name(0)')
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| result = torch.cuda.get_device_name(0)
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| print(result, '\n')
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| check_all_cuda_devices()
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| def check_cuda_ops():
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| print('>>>> torch.zeros(2, 3)')
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| zeros = torch.zeros(2, 3)
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| print(zeros, '\n')
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| print('>>>> torch.zeros(2, 3).cuda()')
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| cuda_zero = torch.zeros(2, 3).cuda()
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| print(cuda_zero, '\n')
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| print('>>>> torch.tensor([[1, 2, 3], [4, 5, 6]])')
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| tensor_a = torch.tensor([[1, 2, 3], [4, 5, 6]]).cuda()
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| print(tensor_a, '\n')
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| print('>>>> tensor_a + cuda_zero')
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| sum = tensor_a + cuda_zero
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| print(sum, '\n')
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| print('>>>> tensor_a * cuda_twos')
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| tensor_a = tensor_a.to(torch.float)
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| cuda_zero = cuda_zero.to(torch.float)
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| cuda_twos = (cuda_zero + 1.0) * 2.0
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| product = tensor_a * cuda_twos
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| print(product, '\n')
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| print('>>>> torch.matmul(tensor_a, cuda_twos.T)')
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| mat_mul = torch.matmul(tensor_a, cuda_twos.T)
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| print(mat_mul, '\n')
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| try:
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| get_version()
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| except Exception as e:
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| print('get_version() failed, exception message below:')
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| print(e)
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| try:
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| check_cuda()
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| except Exception as e:
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| print('check_cuda() failed, exception message below:')
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| print(e)
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| try:
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| check_cuda_ops()
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| except Exception as e:
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| print('check_cuda_ops() failed, exception message below:')
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| print(e)</nowiki>
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| == Tensorflow ==
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| <nowiki>
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| import tensorflow as tf
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| hasGPUSupport = tf.test.is_built_with_cuda()
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| gpuList = tf.config.list_physical_devices('GPU')
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| print("Tensorflow Compiled with CUDA/GPU Support:", hasGPUSupport)
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| print("Tensorflow can access", len(gpuList), "GPU")
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| print("Accessible GPUs are:")
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| print(gpuList)
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| tf.debugging.set_log_device_placement(True)
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| # Place tensors on the GPU
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| with tf.device('device:GPU:0'):
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| a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
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| b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]])
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| # Run on the GPU
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| c = tf.matmul(a, b)
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| print(c)</nowiki>
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