JupyterHub with GPU
Create a jupyterhub instance with GPU support enabled.
setup
Create conda environment that we can use for a jupyter kernel
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test python -m ipykernel install --user --name kernel_test
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.
Install required packages
For pytorch you can find information here and for TensorFlow here.
As an example I use the following pytorch installation:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
Start jupyter notebook with GPU
Go here and select:
- Select a location for your server: on the cluster (default option)
- Partition to use: gpu
- Partition to use: gpu
- Memory (in MB): desired memory
- Number of CPUs: desired CPU count
- Maximum execution time (hours:minutes:seconds): maximum amount of time the notebook is available
- Extra options: --gres=gpu:1 (default when selecting GPU, gpu:x for x amount of GPUs)
Test GPU availability
Pytorch
def check_all_cuda_devices(): device_count = torch.cuda.device_count() for i in range(device_count): print('>>>> torch.cuda.device({})'.format(i)) result = torch.cuda.device(i) print(result, '\n') print('>>>> torch.cuda.get_device_name({})'.format(i)) result = torch.cuda.get_device_name(i) print(result, '\n') def check_cuda(): print('>>>> torch.cuda.is_available()') result = torch.cuda.is_available() print(result, '\n') print('>>>> torch.cuda.device_count()') result = torch.cuda.device_count() print(result, '\n') print('>>>> torch.cuda.current_device()') result = torch.cuda.current_device() print(result, '\n') print('>>>> torch.cuda.device(0)') result = torch.cuda.device(0) print(result, '\n') print('>>>> torch.cuda.get_device_name(0)') result = torch.cuda.get_device_name(0) print(result, '\n') check_all_cuda_devices() def check_cuda_ops(): print('>>>> torch.zeros(2, 3)') zeros = torch.zeros(2, 3) print(zeros, '\n') print('>>>> torch.zeros(2, 3).cuda()') cuda_zero = torch.zeros(2, 3).cuda() print(cuda_zero, '\n') print('>>>> torch.tensor([[1, 2, 3], [4, 5, 6]])') tensor_a = torch.tensor([[1, 2, 3], [4, 5, 6]]).cuda() print(tensor_a, '\n') print('>>>> tensor_a + cuda_zero') sum = tensor_a + cuda_zero print(sum, '\n') print('>>>> tensor_a * cuda_twos') tensor_a = tensor_a.to(torch.float) cuda_zero = cuda_zero.to(torch.float) cuda_twos = (cuda_zero + 1.0) * 2.0 product = tensor_a * cuda_twos print(product, '\n') print('>>>> torch.matmul(tensor_a, cuda_twos.T)') mat_mul = torch.matmul(tensor_a, cuda_twos.T) print(mat_mul, '\n') try: get_version() except Exception as e: print('get_version() failed, exception message below:') print(e) try: check_cuda() except Exception as e: print('check_cuda() failed, exception message below:') print(e) #print('>>>> time.sleep(20)') #time.sleep(20) try: check_cuda_ops() except Exception as e: print('check_cuda_ops() failed, exception message below:') print(e)
Tensorflow
import tensorflow as tf hasGPUSupport = tf.test.is_built_with_cuda() gpuList = tf.config.list_physical_devices('GPU') print("Tensorflow Compiled with CUDA/GPU Support:", hasGPUSupport) print("Tensorflow can access", len(gpuList), "GPU") print("Accessible GPUs are:") print(gpuList) tf.debugging.set_log_device_placement(True) # Place tensors on the GPU with tf.device('device:GPU:0'): a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) # Run on the GPU c = tf.matmul(a, b) print(c)