With Graphics Processing Units (GPUs), Keras can improve the development and training of Deep Learning models. In Deep Learning workloads, GPUs have become popular for their ability to dramatically speed up training times.
Using GPUs for Deep Learning, however, can be challenging.
In this post, I’ll show you Keras’ use on three different kinds of GPU setups: single GPUs, multi-GPUs, and TPUs. This will include step-by-step instructions, code examples, and tips and tricks for optimizing Deep Learning performance.
No matter where you are on your Deep Learning journey, this post will provide you with valuable insights into how to use Keras with GPUs.
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Table of Contents
Setting up Keras on a Single GPU
Setting up Keras on a single GPU is a complex process, you need to do it right to configure your system for Deep Learning tasks.
In this guide, I will cover the requirements, installation steps, and common issues you might face when setting up Keras with a single GPU.
Requirement for Keras Installation on a Single GPU
To install Keras on a single GPU, you will need the following requirements:
- NVIDIA GPU with CUDA Compute Capability 3.0 or higher
- NVIDIA CUDA Toolkit (version 7.5 or higher)
- cuDNN library (version 5.1 or higher)
- Python (version 3.5 or higher)
- pip (Python package manager)
- Keras library (latest version)
- TensorFlow or Theano backend library (latest version)
Your NVIDIA GPU and operating system should meet the requirements specified by NVIDIA CUDA Toolkit and cuDNN library. Run the following code in Python to check your GPU’s compute capability:
Python
import tensorflow as tf
device_name = tf.test.gpu_device_name()
if device_name != ‘/device:GPU:0’:
raise SystemError(‘GPU device not found’)
print(‘Found GPU at: {}’.format(device_name))
You can proceed with the installation process for Keras on a single GPU after these requirements are met.
How to Set up Keras to Use a Single GPU
To set up Keras to use a single GPU, follow these steps:
- Install the required software and drivers as per the requirements mentioned above.
- Install Keras and the backend library of your choice (TensorFlow or Theano) using pip. For example, to install Keras with the TensorFlow backend.
bash
pip install keras tensorflow-gpu
- Set the environment variable ‘CUDA_VISIBLE_DEVICES’ to the index of the GPU you want to use. For example, to use the first GPU, set it to ‘0’:
bash
export CUDA_VISIBLE_DEVICES=0
- Verify that Keras is using the correct backend by creating a Keras configuration file (‘~/.keras/keras.json’) with the following commands:
json
{
“backend”: “tensorflow”,
“image_data_format”: “channels_last”,
“floatx”: “float32”,
“epsilon”: 1e-7
}
- Test Keras by running a sample script that uses the GPU:
python
import keras
from keras import backend as K
K.tensorflow_backend._get_available_gpus()
This code will output the name of your GPU device, if Keras is using your GPU.
Congrats! Now you’re ready to use Keras on your single GPU setup for Deep Learning tasks.
Example of Training Deep Learning Models on a Single GPU
Use the following code as an example to train Deep Learning models on a single GPU:
python
import tensorflow as tf
from tensorflow import keras
# Define the model architecture
model = keras.Sequential([
keras.layers.Dense(64, activation=’relu’),
keras.layers.Dense(10, activation=’softmax’)
])
# Compile the model with necessary settings
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.CategoricalCrossentropy(),
metrics=[‘accuracy’])
# Load the dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# Preprocess the data
x_train = x_train.reshape((60000, 28 * 28))
x_train = x_train.astype(‘float32’) / 255
# Train the model on the GPU
with tf.device(‘/gpu:0’):
model.fit(x_train, y_train, epochs=10, batch_size=128)
This code defines a simple neural network architecture using the Keras Sequential API, compiles the model with necessary settings, loads the MNIST dataset, preprocesses the data, and trains the model on a single GPU using the ‘tf.device()’ context manager.
Also Read: TensorFlow GPU – Basic Operations And Multi-GPU Setup
Scaling Up to Multiple GPUs
Scaling up to multiple GPUs can enhance the speed and efficiency of your deep learning model training.
However, setting up and configuring multiple GPUs to work together can be challenging.
Worry not, I’ve got your back…
I’ll help you learn how to scale up to multiple GPUs and take advantage of their power for deep learning.
Let’s see…
How to Setup Keras in Multiple GPUs
Multi-GPU Keras optimization is an effective way to improve the speed and efficiency of deep learning models.
Here’s a step-by-step process on how to set up Keras to run on multiple GPUs:
- To run Keras on GPUs you need to install NVIDIA CUDA and cuDNN on your system.
- Make sure that you have multiple GPUs available on your system. Use the following code to check it:
python
import tensorflow as tf
print(“Num GPUs Available: “, len(tf.config.experimental.list_physical_devices(‘GPU’)))
- Next, you need to configure Keras to use multiple GPUs. You can do this by setting the ‘CUDA_VISIBLE_DEVICES‘ environment variable.
Here’s an example:
python
import os
os.environ[“CUDA_VISIBLE_DEVICES”] = “0,1,2,3” # Replace with the IDs of your available GPUs
Once you’ve set the ‘CUDA_VISIBLE_DEVICES‘ environment variable, you can create a Keras model and train it on multiple GPUs using the ‘fit()‘ method and the ‘multi_gpu_model‘ function.
Here’s an example:
python
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import multi_gpu_model
num_gpus = 4 # Replace with the number of available GPUs
# Define your Keras model as usual
model = Sequential()
model.add(Dense(64, input_dim=1000))
model.add(Dense(10, activation=’softmax’))
# Use the multi_gpu_model() function to parallelize your model across multiple GPUs
parallel_model = multi_gpu_model(model, gpus=num_gpus)
# Compile your parallel model as usual
parallel_model.compile(loss=’categorical_crossentropy’,
optimizer=’rmsprop’)
# Train your parallel model on your data
parallel_model.fit(x_train, y_train,
epochs=20,
batch_size=128 * num_gpus,
validation_data=(x_val, y_val))
The above-mentioned steps and example codes will help you set up Keras to run on multiple GPUs.
Common Multi-GPU Training Issues and their Solutions
I understand that while multi-GPU training can increase the training process for deep learning models, there are also some challenges that need to be aware of. Here are some common multi-GPU training issues and their solutions:
Synchronization Between GPUs
One of the main challenges of multi-GPU training is ensuring that the GPUs are synchronized during the training process. To solve this, use a data parallelism approach, where each GPU processes a different subset of the data and then the gradients are averaged across all GPUs before being applied to the model weights.
Here’s an example code snippet showing how to implement data parallelism in Keras:
scss
from keras.utils import multi_gpu_model
model = Sequential()
model.add(Dense(64, input_dim=1000))
model.add(Dense(10, activation=’softmax’))
parallel_model = multi_gpu_model(model, gpus=4)
parallel_model.compile(loss=’categorical_crossentropy’,
optimizer=’rmsprop’)
parallel_model.fit(x_train, y_train, epochs=10, batch_size=128*4)
Memory Constraints
Another challenge of multi-GPU training is the limited amount of memory available on each GPU. To overcome this, use a model parallelism approach – where different parts of the model are allocated to different GPUs.
Here’s an example code snippet showing how to implement model parallelism in Keras:
python
from keras.layers import Input, Dense
from keras.models import Model
from keras.utils import multi_gpu_model
input_tensor = Input(shape=(1000,))
output_tensor = Dense(10, activation=’softmax’)(input_tensor)
model = Model(inputs=input_tensor, outputs=output_tensor)
parallel_model = multi_gpu_model(model, gpus=4)
parallel_model.compile(loss=’categorical_crossentropy’,
optimizer=’rmsprop’)
parallel_model.fit(x_train, y_train, epochs=10, batch_size=128*4)
Load Balancing
Load balancing is another challenge of multi-GPU training, which ensures that each GPU is assigned a similar amount of work during the training process. You can use dynamic load balancing to avoid load balancing problems, where the workload will evenly distribute across the GPUs based on the availability of resources.
Here’s an example code showing how to implement dynamic load balancing in Keras:
scss
from keras.utils import multi_gpu_model
model = Sequential()
model.add(Dense(64, input_dim=1000))
model.add(Dense(10, activation=’softmax’))
parallel_model = multi_gpu_model(model, gpus=4)
parallel_model.compile(loss=’categorical_crossentropy’,
optimizer=’rmsprop’)
parallel_model.fit_generator(generator=training_data_generator(),
steps_per_epoch=1000,
epochs=10,
workers=4,
use_multiprocessing=True)
Out of Memory (OOM) Errors
Running out of memory is the most common issue with multi-GPU training. This occurs when the model or the batch size is too large for the available memory on the GPUs. To solve this issue, you can either reduce the batch size or implement model parallelism to divide the model across multiple GPUs.
Here’s an example code showing how to reduce the batch size:
scss
model.fit(x_train, y_train, batch_size=32 * num_gpus)
And how to implement model parallelism:
scss
from keras.utils import multi_gpu_model
with tf.device(‘/cpu:0’):
model = build_model()
parallel_model = multi_gpu_model(model, gpus=num_gpus)
parallel_model.compile(loss=’categorical_crossentropy’,
optimizer=optimizers.SGD(lr=learning_rate, momentum=momentum))
parallel_model.fit(x_train, y_train, batch_size=batch_size * num_gpus, epochs=epochs)
Poor GPU Utilization
Poor GPU utilization is also a critical issue with multi-GPU training, which occurs when the workload is not evenly distributed across the GPUs. To avoid this issue, you can implement data parallelism to divide the workload across the GPUs and ensure that each GPU is utilized equally.
Here’s an example code showing how to implement data parallelism:
scss
from keras.utils import multi_gpu_model
with tf.device(‘/cpu:0’):
model = build_model()
parallel_model = multi_gpu_model(model, gpus=num_gpus)
parallel_model.compile(loss=’categorical_crossentropy’,
optimizer=optimizers.SGD(lr=learning_rate, momentum=momentum))
parallel_model.fit(x_train, y_train, batch_size=batch_size * num_gpus, epochs=epochs)
Slow Training Speed
Finally, multi-GPU training can also suffer from slow training speed, which can occur when the communication between the GPUs is slow or when the data cannot be loaded into the GPUs fast enough.
To solve this issue, you can increase the batch size or implement asynchronous data loading to improve communication between the GPUs.
Here’s how to implement asynchronous data loading:
scss
import threading
import queue
def data_generator():
while True:
batch = next_batch()
q.put(batch)
q = queue.Queue(maxsize=100)
t = threading.Thread(target=data_generator)
t.start()
for i in range(num_epochs):
while not q.empty():
batch = q.get()
model.train_on_batch(batch)
If you take care of the above multi-GPU training issues you can optimize your Deep Learning models for speed and efficiency and can get faster and more accurate results.
How to Train Deep Learning Models on Multiple GPUs
Training deep learning models on multiple GPUs can speed up the training process and improve the performance of your models.
Here is how to train deep learning models on multiple GPUs.
Data Parallelism
Data parallelism is the best way to train deep learning models on multiple GPUs. In data parallelism, each GPU processes a portion of the input data and computes the gradients independently. These gradients are then aggregated, and the model weights are updated based on the combined gradients.
This technique is particularly useful for models that have a large number of parameters or require processing a large amount of data.
Here’s how to implement data parallelism in Keras:
scss
from keras.utils import multi_gpu_model
with tf.device(‘/cpu:0’):
model = build_model()
parallel_model = multi_gpu_model(model, gpus=num_gpus)
parallel_model.compile(loss=’categorical_crossentropy’,
optimizer=optimizers.SGD(lr=learning_rate, momentum=momentum))
parallel_model.fit(x_train, y_train, batch_size=batch_size * num_gpus, epochs=epochs)
In this code, ‘build_model()’ is a function that returns a Keras model. ‘num_gpus’ is the number of GPUs available for training, and ‘batch_size’ is the size of each batch. The ‘multi_gpu_model’ function creates a parallel model that distributes the workload across the available GPUs.
Model Parallelism
Another way for training deep learning models on multiple GPUs is model parallelism. In model parallelism, the model is divided into multiple parts, and each part is assigned to a different GPU.
This technique is useful for models that have a large number of layers or require a large amount of memory. Here’s how to implement model parallelism in TensorFlow:
css
import tensorflow as tf
strategy = tf.distribute.MirroredStrategy(devices=[“/gpu:0”, “/gpu:1”])
with strategy.scope():
model = build_model()
model.compile(loss=’categorical_crossentropy’,
optimizer=optimizers.SGD(lr=learning_rate, momentum=momentum))
model.fit(train_dataset, epochs=num_epochs)
In the above code, ‘build_model()’ is a function that returns a TensorFlow model. ‘strategy’ is a ‘MirroredStrategy’ object that specifies the GPUs to use. The ‘with strategy.scope()’ block creates the model within the scope of the strategy object, which ensures that the model is distributed across the specified GPUs.
By implementing data parallelism or model parallelism, you can distribute the workload across multiple GPUs and optimize your models for speed and efficiency.
Training on TPUs
TPUs, or Tensor Processing Units, are hardware accelerators developed by Google that are specifically designed for Deep Learning. TPUs are different from GPUs in their optimized performance for matrix operations, high memory bandwidth, and use of TensorFlow programming model.
TPUs are available in different configurations, making it important to choose the appropriate one for your models and data size.
TPUs offer significant performance benefits over GPUs, allowing for faster training of more complex models and processing of larger amounts of data.
Benefits of Using TPUs for Deep Learning
Here are some of the benefits of using TPUs for deep learning:
- Faster training times: TPUs can perform matrix operations with higher efficiency and speed than GPUs, resulting in faster training times for deep learning models.
- Increased scalability: TPUs are designed to work with large-scale distributed systems, making it possible to train models on massive amounts of data.
- Higher throughput: TPUs have a higher memory bandwidth than GPUs, which allows for higher throughput and faster processing of data.
- Reduced costs: Because TPUs are optimized for deep learning workloads, they can provide more efficient processing than traditional CPU or GPU instances, reducing the overall cost of training deep learning models.
- Simplified programming: TPUs can be programmed using the TensorFlow framework, which provides a high-level API for training models on TPUs. This makes it easier for developers to take advantage of the performance benefits of TPUs without needing to write low-level code.
- Increased accuracy: Because TPUs can process data more quickly and efficiently, it is possible to train more complex models and process larger amounts of data, leading to increased accuracy in deep learning models.
TPUs offer several benefits for training deep learning models, including faster training times, increased scalability, higher throughput, reduced costs, simplified programming, and increased accuracy.
Developers can create more accurate and efficient deep learning models for a variety of applications, if they use TPUs right.
How to Setup Keras to Use TPUs
Here’s a step-by-step process to set up Keras to use TPUs:
- Create a Google Cloud Platform (GCP) project and enable billing: TPUs are a GCP service, so you’ll need to create a project and enable billing to use them.
- Install the latest version of TensorFlow and Keras: Make sure to install the latest versions of TensorFlow and Keras to ensure compatibility with TPUs.
- Connect to your TPU: You’ll need to connect to your TPU instance before you can start using it.
Use following code to connect your TPU:
python
import os
import tensorflow as tf
# Set the name of your TPU
TPU_NAME = ‘my-tpu-instance’
# Connect to the TPU
tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=TPU_NAME)
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
- Configure your Keras model to use TPUs: Once you’re connected to your TPU, you can configure your Keras model to use TPUs by adding the following code:
python
# Define your Keras model
model = tf.keras.Sequential([…])
# Configure the distribution strategy to use TPUs
tpu_strategy = tf.distribute.TPUStrategy(tpu)
with tpu_strategy.scope():
# Compile your model as usual
model.compile([…])
- Train your Keras model on TPUs: With your Keras model now configured to use TPUs, you can start training it by using the ‘fit()’ method as you would normally:
python
model.fit([…])
By following these steps, you’ll be able to set up Keras to use TPUs for training your deep learning models. With TPU you can train more complex models and process larger amounts of data in less time than with traditional hardware accelerators.
Also Read: How to Find Best GPU for Deep Learning
How to Use TPUs for Training Deep Learning Models
Using TPUs for training deep learning models can provide significant performance and scalability advantages over traditional hardware accelerators.
Here’s a detailed explanation of how to use TPUs for training deep learning models:
- Set up your GCP project and enable billing: TPUs are a GCP service, so you’ll need to create a project and enable billing to use them.
- Choose a TPU instance type: TPUs are available in various sizes and configurations, so choose one that meets your requirements based on the amount of data you need to process and the complexity of your model.
- Connect to your TPU instance: You’ll need to connect to your TPU instance before you can start using it. Use the following code to do that:
python
import os
import tensorflow as tf
# Set the name of your TPU
TPU_NAME = ‘my-tpu-instance’
# Connect to the TPU
tpu = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=TPU_NAME)
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
- Load and preprocess your data: Load your training data and preprocess it for use with TPUs. This typically involves creating TensorFlow datasets and applying any necessary transformations.
python
# Load your data using TensorFlow datasets
train_dataset, test_dataset = tfds.load(‘dataset_name’, split=[‘train’, ‘test’])
# Preprocess your data
def preprocess_data(features):
# Apply any necessary transformations
return features
train_dataset = train_dataset.map(preprocess_data)
test_dataset = test_dataset.map(preprocess_data)
- Configure your Keras model to use TPUs: Once you’re connected to your TPU, you can configure your Keras model to use TPUs by adding the following code:
python
# Define your Keras model
model = tf.keras.Sequential([…])
# Configure the distribution strategy to use TPUs
tpu_strategy = tf.distribute.TPUStrategy(tpu)
with tpu_strategy.scope():
# Compile your model as usual
model.compile([…])
- Train your Keras model on TPUs: With your Keras model now configured to use TPUs, you can start training it by using the ‘fit()’ method as you would normally:
python
model.fit(train_dataset, epochs=10, validation_data=test_dataset)
- Evaluate your model: After your model is trained, you can evaluate its performance on a test dataset using the ‘evaluate()’ method:
python
loss, accuracy = model.evaluate(test_dataset)
By following these steps, you’ll be able to use TPUs for training deep learning models. With their ability to process large amounts of data and complex models quickly and efficiently, TPUs can accelerate your deep learning workflows and help you achieve better results in less time.
Examples Codes of Training Deep Learning Models on TPUs
Here are some examples codes for training deep learning models on TPUs:
1) Loading and preprocessing data for TPUs
python
import tensorflow_datasets as tfds
import tensorflow as tf
# Load the CIFAR-10 dataset
(ds_train, ds_test), info = tfds.load(
‘cifar10’,
split=[‘train’, ‘test’],
shuffle_files=True,
as_supervised=True,
with_info=True,
)
# Define the input shape of the model
input_shape = (32, 32, 3)
# Preprocess the data for use with TPUs
def preprocess(features, labels):
features = tf.cast(features, tf.float32)
features /= 255.0
return features, labels
ds_train = ds_train.map(preprocess).batch(1024)
ds_test = ds_test.map(preprocess).batch(1024)
2) Defining and compiling a Keras model for use with TPUs
python
from tensorflow.keras import layers
# Define a simple CNN model
def create_model(input_shape):
model = tf.keras.Sequential([
layers.Conv2D(32, 3, activation=’relu’, input_shape=input_shape),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(10, activation=’softmax’)
])
return model
# Create the model and compile it for use with TPUs
with tf.device(‘/TPU:0’):
model = create_model(input_shape)
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=’sparse_categorical_crossentropy’,
metrics=[‘accuracy’]
)
3) Training the Keras model on TPUs
python
# Train the model on TPUs
with tf.device(‘/TPU:0’):
history = model.fit(
ds_train,
epochs=10,
validation_data=ds_test
)
# Print the training history
print(history.history)
You can use these examples train deep learning models on TPUs using Keras and TensorFlow.
Performance Tuning
Improving performance on GPUs and TPUs is an important goal for deep learning practitioners.
Here are some tips to achieve this:
- Data parallelism: This technique involves splitting the data across multiple GPUs or TPUs, allowing each device to process a portion of the data in parallel. This can significantly reduce the time required to train a model.
- Model parallelism: This process split the model across multiple GPUs or TPUs, allowing each device to process a portion of the model in parallel. This can be useful for very large models that cannot fit into the memory of a single device.
- Mixed precision training: This technique involves using lower precision data types (such as float16) for some of the computations during training, which can reduce the memory requirements and increase the speed of the training process. However, this technique can also introduce numerical instability and require careful tuning.
- Gradient accumulation: This technique involves accumulating the gradients computed during multiple mini-batch iterations before updating the model parameters. This can help reduce the memory requirements of the training process, especially when using large batch sizes.
- Tensor cores: This is a specialized hardware feature available on some NVIDIA GPUs that can accelerate certain matrix multiplication operations commonly used in deep learning.
- XLA (Accelerated Linear Algebra): This is a domain-specific compiler developed by Google that can optimize TensorFlow computations for execution on TPUs. XLA can help improve the performance of models running on TPUs by reducing the overhead of communication between the CPU and the TPU.
Deep learning practitioners can use these methods to improve the performance of their models running on GPUs and TPUs.
Examples Codes for Training Deep Learning Models on TPUs
Here are some examples codes for training deep learning models on TPUs using the Keras framework:
1) Importing the necessary libraries and setting up the TPU strategy
python
import tensorflow as tf
import os
# Set up the TPU strategy
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=’grpc://’ + os.environ[‘COLAB_TPU_ADDR’])
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
2) Loading and preprocessing the data
scss
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
# Load the MNIST dataset
(x_train, y_train), (x_test, y_test) = mnist.load_data()
# Preprocess the data
x_train = x_train.reshape((60000, 28, 28, 1))
x_test = x_test.reshape((10000, 28, 28, 1))
x_train, x_test = x_train / 255.0, x_test / 255.0
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
3) Defining the deep learning model
scss
with strategy.scope():
model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(32, (3, 3), activation=’relu’, input_shape=(28, 28, 1)),
tf.keras.layers.MaxPooling2D((2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(10, activation=’softmax’)
])
model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’])
Tips to Optimize Your Deep Learning Models for GPUs and TPUs
To get maximum results of deep learning models for optimal performance on GPUs and TPUs requires a combination of domain expertise, experimentation, and focus.
Here are some tips and tricks that can help you improve the performance of your deep learning models:
- Choose the right hardware: When selecting hardware for training deep learning models, it’s important to consider the trade-offs between performance, cost, and convenience. GPUs are generally more cost-effective for small to medium-sized models, while TPUs can provide significant performance gains for large-scale models. It’s also important to consider the hardware compatibility with your deep learning framework of choice.
- Optimize your input pipeline: Efficient data loading and pre-processing are critical for maximizing training speed on GPUs and TPUs. Strategies such as using the tf.data API for data loading and preprocessing, shuffling and batching data, and caching data in memory can improve the efficiency of the input pipeline.
- Use appropriate activation functions: Choosing appropriate activation functions for your deep learning model can have a significant impact on its performance. ReLU activation functions are commonly used for hidden layers, while softmax activation functions are often used for classification tasks.
- Experiment with different optimization algorithms: The choice of optimization algorithm can also have a significant impact on the performance of your deep learning model. Experiment with different optimization algorithms such as Adam, RMSprop, and SGD to find the best one for your model and dataset.
- Use regularization techniques: Regularization techniques such as dropout, weight decay, and early stopping can help prevent overfitting and improve the generalization performance of your deep learning model.
- Perform hyperparameter tuning: Fine-tuning hyperparameters such as the learning rate, batch size, and regularization strength can significantly improve the performance of your deep learning model on GPUs and TPUs. Use techniques such as random search or grid search to find the optimal set of hyperparameters for your model and dataset.
By following these tips and tricks, you can fine-tune your Deep Learning models for optimal performance on GPUs and TPUs, and get state-of-the-art results on a wide range of Deep Learning tasks.
Keras GPU Virtualization with Ace Cloud
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In fact, with our expertise and resources, you’ll be able to achieve optimal performance and take your Deep Learning projects to new heights.
So why wait?
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