HPCwiki - User contributions [en]

JupyterHub with GPU

2023-10-25T11:34:59Z

Brumm025: /* Create conda environment that we can use for a jupyter kernel */

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Link lustre path to home directory ==

When working from Jupyterhub the default working directory is the home folder. However, it is recommended to put your data and code on the lustre pathings. To make this easier, we can create a link to lustre from our home directory:

<nowiki>
ln -s /lustre/[path to your lustre folder] [reference name, for example lustre_folders]</nowiki>

To remove a link:

<nowiki>
rm [reference name, for example lustre_folders]</nowiki>

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3.10 ipykernel
conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

NOTE: You can specific the python version for you conda environment with python=3.10 Please take care what python version is compatible with you required packages.

== Install required packages ==

For pytorch you can find information [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl here] and select:

* Select a location for your server: on the cluster (default option)
* 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)

= Using multiple GPUs =

* Select multiple GPUs in when starting jupyterhub in the extra options menu: --gres=gpu:x where x is amount of requested GPUs
* There should be multiple GPUs available to the jupyterhub notebook. Check this by using GPU tests in the following section.

= Test GPU availability =

== Pytorch ==

<nowiki>
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)

try:
check_cuda_ops()
except Exception as e:
print('check_cuda_ops() failed, exception message below:')
print(e)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-25T11:34:43Z

Brumm025: /* Create conda environment that we can use for a jupyter kernel */

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Link lustre path to home directory ==

When working from Jupyterhub the default working directory is the home folder. However, it is recommended to put your data and code on the lustre pathings. To make this easier, we can create a link to lustre from our home directory:

<nowiki>
ln -s /lustre/[path to your lustre folder] [reference name, for example lustre_folders]</nowiki>

To remove a link:

<nowiki>
rm [reference name, for example lustre_folders]</nowiki>

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3.10 ipykernel
conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl here] and select:

* Select a location for your server: on the cluster (default option)
* 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)

= Using multiple GPUs =

* Select multiple GPUs in when starting jupyterhub in the extra options menu: --gres=gpu:x where x is amount of requested GPUs
* There should be multiple GPUs available to the jupyterhub notebook. Check this by using GPU tests in the following section.

= Test GPU availability =

== Pytorch ==

<nowiki>
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)

try:
check_cuda_ops()
except Exception as e:
print('check_cuda_ops() failed, exception message below:')
print(e)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-25T11:31:57Z

Brumm025:

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Link lustre path to home directory ==

When working from Jupyterhub the default working directory is the home folder. However, it is recommended to put your data and code on the lustre pathings. To make this easier, we can create a link to lustre from our home directory:

<nowiki>
ln -s /lustre/[path to your lustre folder] [reference name, for example lustre_folders]</nowiki>

To remove a link:

<nowiki>
rm [reference name, for example lustre_folders]</nowiki>

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl here] and select:

* Select a location for your server: on the cluster (default option)
* 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)

= Using multiple GPUs =

* Select multiple GPUs in when starting jupyterhub in the extra options menu: --gres=gpu:x where x is amount of requested GPUs
* There should be multiple GPUs available to the jupyterhub notebook. Check this by using GPU tests in the following section.

= Test GPU availability =

== Pytorch ==

<nowiki>
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)

try:
check_cuda_ops()
except Exception as e:
print('check_cuda_ops() failed, exception message below:')
print(e)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-18T08:47:17Z

Brumm025:

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Link lustre path to home directory ==

When working from Jupyterhub the default working directory is the home folder. However, it is recommended to put your data and code on the lustre pathings. To make this easier, we can create a link to lustre from our home directory:

<nowiki>
ln -s /lustre/[path to your lustre folder] [reference name, for example lustre_folders]</nowiki>

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl here] and select:

* Select a location for your server: on the cluster (default option)
* 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)

= Using multiple GPUs =

* Select multiple GPUs in when starting jupyterhub in the extra options menu: --gres=gpu:x where x is amount of requested GPUs
* There should be multiple GPUs available to the jupyterhub notebook. Check this by using GPU tests in the following section.

= Test GPU availability =

== Pytorch ==

<nowiki>
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)

try:
check_cuda_ops()
except Exception as e:
print('check_cuda_ops() failed, exception message below:')
print(e)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-06T11:36:38Z

Brumm025: /* Start jupyter notebook with GPU */

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl here] and select:

* Select a location for your server: on the cluster (default option)
* 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 ==

<nowiki>
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)

try:
check_cuda_ops()
except Exception as e:
print('check_cuda_ops() failed, exception message below:')
print(e)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-06T11:36:27Z

Brumm025: /* Start jupyter notebook with GPU */

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl 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 ==

<nowiki>
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)

try:
check_cuda_ops()
except Exception as e:
print('check_cuda_ops() failed, exception message below:')
print(e)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-06T11:34:29Z

Brumm025: /* Tensorflow */

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl 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 ==

<nowiki>
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)

try:
check_cuda_ops()
except Exception as e:
print('check_cuda_ops() failed, exception message below:')
print(e)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-06T11:34:09Z

Brumm025: /* Pytorch */

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl 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 ==

<nowiki>
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)

try:
check_cuda_ops()
except Exception as e:
print('check_cuda_ops() failed, exception message below:')
print(e)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-06T11:30:14Z

Brumm025:

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl 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 ==

<nowiki>
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)</nowiki>

== Tensorflow ==

<nowiki>
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)</nowiki>

JupyterHub with GPU

2023-10-06T11:29:23Z

Brumm025: /* Install required packages */

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl 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 ==

<nowiki>
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)
</nowiki>

== Tensorflow ==

<nowiki>
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)
</nowiki>

JupyterHub with GPU

2023-10-06T11:28:54Z

Brumm025: How to create a jupyterhub instance with GPU enabled

Create a jupyterhub instance with GPU support enabled.

= setup =

* [https://wiki.anunna.wur.nl/index.php/Log_in_to_Anunna Connect to login node of Anunna]
* [https://wiki.anunna.wur.nl/index.php/Running_Snakemake_pipelines#Installation Install miniconda]

== Create conda environment that we can use for a jupyter kernel ==

<nowiki>
conda create -y -n kernel_test python=3 ipykernel && conda activate kernel_test
python -m ipykernel install --user --name kernel_test</nowiki>

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 [https://pytorch.org/get-started/locally/ here] and for TensorFlow [https://www.tensorflow.org/install/pip here].

As an example I use the following pytorch installation:

<nowiki>
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118</nowiki>

= Start jupyter notebook with GPU =

Go [https://notebook.anunna.wur.nl 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 ==

<nowiki>
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)
</nowiki>

== Tensorflow ==

<nowiki>
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)
</nowiki>