AI News, Deep Learning with Apache Spark and TensorFlow

Deep Learning with Apache Spark and TensorFlow

To answer this question, we walk through two use cases and explain how you can use Spark and a cluster of machines to improve deep learning pipelines with TensorFlow: An example of a deep learning machine learning (ML) technique is artificial neural networks.

In practice, machine learning practitioners rerun the same model multiple times with different hyperparameters in order to find the best set.

In this case, we can use Spark to broadcast the common elements such as data and model description, and then schedule the individual repetitive computations across a cluster of machines in a fault-tolerant manner.

Distributing the computations scaled linearly with the number of nodes added to the cluster: using a 13-node cluster, we were able to train 13 models in parallel, which translates into a 7x speedup compared to training the models one at a time on one machine.

This shows a typical tradeoff curve for neural networks: By using a sparse sample of parameters, we can zero in on the most promising sets of parameters.

The following notebooks below show how to install TensorFlow and let users rerun the experiments of this blog post: TensorFlow models can directly be embedded within pipelines to perform complex recognition tasks on datasets.

The model is first distributed to the workers of the clusters, using Spark’s built-in broadcasting mechanism: Then this model is loaded on each node and applied to images.

And here is the interpretation of this image according to the neural network, whichis pretty accurate: We have shown how to combine Spark and TensorFlow to train and deploy neural networks on handwritten digit recognition and image labeling.

Even though the neural network framework we used itself only works in a single-node, we can use Spark to distribute the hyperparameter tuning process and model deployment.

Deep Learning with Apache Spark and TensorFlow

To answer this question, we walk through two use cases and explain how you can use Spark and a cluster of machines to improve deep learning pipelines with TensorFlow: An example of a deep learning machine learning (ML) technique is artificial neural networks.

In practice, machine learning practitioners rerun the same model multiple times with different hyperparameters in order to find the best set.

In this case, we can use Spark to broadcast the common elements such as data and model description, and then schedule the individual repetitive computations across a cluster of machines in a fault-tolerant manner.

Distributing the computations scaled linearly with the number of nodes added to the cluster: using a 13-node cluster, we were able to train 13 models in parallel, which translates into a 7x speedup compared to training the models one at a time on one machine.

This shows a typical tradeoff curve for neural networks: By using a sparse sample of parameters, we can zero in on the most promising sets of parameters.

The following notebooks below show how to install TensorFlow and let users rerun the experiments of this blog post: TensorFlow models can directly be embedded within pipelines to perform complex recognition tasks on datasets.

The model is first distributed to the workers of the clusters, using Spark’s built-in broadcasting mechanism: Then this model is loaded on each node and applied to images.

And here is the interpretation of this image according to the neural network, whichis pretty accurate: We have shown how to combine Spark and TensorFlow to train and deploy neural networks on handwritten digit recognition and image labeling.

Even though the neural network framework we used itself only works in a single-node, we can use Spark to distribute the hyperparameter tuning process and model deployment.

O'Reilly

On that day, Facebook released a paper showing the methods they used to reduce the training time for a convolutional neural network (RESNET-50 on ImageNet) from two weeks to one hour, using 256 GPUs spread over 32 servers.

In software, they introduced a technique to train convolutional neural networks (ConvNets) with very large mini-batch sizes: make the learning rate proportional to the mini-batch size.

In this tutorial, we will explore two different distributed methods for using TensorFlow: We will provide code examples of methods (1) and (2) in this post, but, first, we need to clarify the type of distributed deep learning we will be covering.

In 'data parallelism' (or “between-graph replication” in the TensorFlow documentation), you use the same model for every device, but train the model in each device using different training samples.

Each device will independently compute the errors between its predictions for its training samples and the labeled outputs (correct values for those training samples).

We also want to reduce the total number of iterations required to train a model because each iteration requires the updated model to be broadcast to all nodes.

The rule states that “when the minibatch size is multiplied by k, multiply the learning rate by k,” with the proviso that the learning rate should be increased slowly over a few epochs before it reaches the target learning rate.

In the peer architecture, each device runs a loop that reads data, computes the gradients, sends them (directly or indirectly) to all devices, and updates the model to the latest version.

The parameter servers aggregate all the gradients from the workers and wait until all workers have completed before they calculate the new model for the next iteration, which is then broadcast to all workers.

Instead, in a training iteration, each worker reads its own split for a mini-batch, calculates its gradients, sends its gradients to its successor neighbor on the ring, and receives gradients from its predecessor neighbor on the ring.

For a ring with N workers, all workers will have received the gradients necessary to calculate the updated model after N-1 gradient messages are sent and received by each worker.

Ring-allreduce is bandwidth optimal, as it ensures that the available upload and download network bandwidth at each host is fully utilized (in contrast to the parameter server model).

Ring-allreduce can also overlap the computation of gradients at lower layers in a deep neural network with the transmission of gradients at higher layers, further reducing training time.

Below, we define a launch function that takes as parameters (1) the Spark session object, (2) a map_fun that names the TensorFlow function to be executed at each Spark executor, and (3) an args_dict dictionary containing the hyperparameters.

In this example, each executor will calculate the hyperparameters it should use from the args_dict using its executor_num to index into the correct param_val, and then run the supplied training function with those hyperparameters.

Note that we only call launch once, but for each hyperparameter combination, a task is executed on a different executor (four in total): We will briefly cover three frameworks for distributed training on TensorFlow: native Distributed TensorFlow, TensorFlowOnSpark, and Horovod.

One of the workers, the chief worker, coordinates model training, initializes the model, counts the number of training steps completed, monitors the session, saves logs for TensorBoard, and saves and restores model checkpoints to recover from failures.

Hopefully, you will never have to write code like this, defining a ClusterSpec manually: It is error-prone and impractical to create a ClusterSpec using host endpoints (IP address and port number).

TENSORFLOW input mode is generally preferred, as data can be read using a more efficient multi-threaded input queue from a distributed filesystem, such as HDFS.

If a parameter server dies, the chief worker can recover from the last checkpoint after a new parameter server joins the system.

If the chief worker itself fails, training fails, and a new training job has to be started, but it can recover training from the latest complete checkpoint.

Having seen many of the distributed training architectures for TensorFlow and large mini-batch stochastic gradient descent (SGD), we can now define the following hierarchy of scale.

The top of the pyramid is currently the most scalable approach on TensorFlow, the allreduce family of algorithms (including ring-allreduce), and the bottom is the least scalable (and hence the slowest way to train networks).

Although parallel experiments are complementary to distributed training, they are, as we have shown, trivially parallelized (with weak scaling), and thus are found lower on the pyramid.

Create a convolutional neural network based deep learning model using TensorFlow

• What we'll doThis Deep Learning introduction session will cover the foundations of neural networks, its infrastructure requirements, different neural network architectures, its areas of applications and commonly used frameworks to train a model.

Shashank V VagaraliHe is a strong technical professional with 9 years of experience in building complex distributed systems on the cloud.He is leading teams that is working on building features into complex cloud services like IBM Watson Machine Learning, IBM dashDB and DB2.He specialises in Machine Learning, Cloud computing , Data Warehousing systems , Analytics , Agile and Continuous Integration.• What to bringLaptops• Important to know

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