Is multi-node Sagemaker training batched per-node or shared? Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern) Data science time! April 2019 and salary with experience The Ask Question Wizard is Live!how to read batches in one hdf5 data file for training?Feed Slim Training Loop with Data Batches from Keras ImageDataGeneratorMulti GPU Training in Tensorflow (Data Parallelism) when Using feed_dictBatch Training Accuracy is always multiple of 10%Tensorflow Train on incomplete batchPredict batch of images with a SageMaker modelIs there a way to partially load weights into memory in Tensorflow or Pytorch, in cases where only a fraction of weights are trained per step?SageMaker fails when using Multi-GPU with keras.utils.multi_gpu_modelAWS Sagemaker | Why multiple instances training taking time multiplied to instance numbertf: Save a batch-trained model to a servable with no input batching?
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Is multi-node Sagemaker training batched per-node or shared?
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 23, 2019 at 00:00UTC (8:00pm US/Eastern)
Data science time! April 2019 and salary with experience
The Ask Question Wizard is Live!how to read batches in one hdf5 data file for training?Feed Slim Training Loop with Data Batches from Keras ImageDataGeneratorMulti GPU Training in Tensorflow (Data Parallelism) when Using feed_dictBatch Training Accuracy is always multiple of 10%Tensorflow Train on incomplete batchPredict batch of images with a SageMaker modelIs there a way to partially load weights into memory in Tensorflow or Pytorch, in cases where only a fraction of weights are trained per step?SageMaker fails when using Multi-GPU with keras.utils.multi_gpu_modelAWS Sagemaker | Why multiple instances training taking time multiplied to instance numbertf: Save a batch-trained model to a servable with no input batching?
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I am using Tensorflow, and am noticing that individual steps are slower with multiple nodes than with one, so I am a bit confused as to what constitutes a step on multiple training nodes on Sagemaker.
If my batch size is 10 and I have 5 training nodes, is a "step" 2 from each node or 10 from each node?
What if I have a batch size of 1 and 5 nodes?
Note - a 'node' here is an individual training instance, count created from train_instance_count=5
amazon-web-services tensorflow machine-learning amazon-sagemaker
asked Mar 8 at 20:14
King DededeKing Dedede
347618
add a comment |
I am using Tensorflow, and am noticing that individual steps are slower with multiple nodes than with one, so I am a bit confused as to what constitutes a step on multiple training nodes on Sagemaker.
If my batch size is 10 and I have 5 training nodes, is a "step" 2 from each node or 10 from each node?
What if I have a batch size of 1 and 5 nodes?
Note - a 'node' here is an individual training instance, count created from train_instance_count=5
amazon-web-services tensorflow machine-learning amazon-sagemaker
asked Mar 8 at 20:14
King DededeKing Dedede
347618
add a comment |
I am using Tensorflow, and am noticing that individual steps are slower with multiple nodes than with one, so I am a bit confused as to what constitutes a step on multiple training nodes on Sagemaker.
If my batch size is 10 and I have 5 training nodes, is a "step" 2 from each node or 10 from each node?
What if I have a batch size of 1 and 5 nodes?
Note - a 'node' here is an individual training instance, count created from train_instance_count=5
amazon-web-services tensorflow machine-learning amazon-sagemaker
asked Mar 8 at 20:14
King DededeKing Dedede
347618
I am using Tensorflow, and am noticing that individual steps are slower with multiple nodes than with one, so I am a bit confused as to what constitutes a step on multiple training nodes on Sagemaker.
If my batch size is 10 and I have 5 training nodes, is a "step" 2 from each node or 10 from each node?
What if I have a batch size of 1 and 5 nodes?
Note - a 'node' here is an individual training instance, count created from train_instance_count=5
amazon-web-services tensorflow machine-learning amazon-sagemaker
amazon-web-services tensorflow machine-learning amazon-sagemaker
asked Mar 8 at 20:14
King DededeKing Dedede
347618
asked Mar 8 at 20:14
King DededeKing Dedede
347618
asked Mar 8 at 20:14
King DededeKing Dedede
347618
asked Mar 8 at 20:14
King DededeKing Dedede
347618
asked Mar 8 at 20:14
King DededeKing Dedede
347618
347618
add a comment |
add a comment |
1 Answer
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Please look at this notebook for an example of distributed training with TF: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb
"Each instance will predict a batch of the dataset, calculate loss and minimize the optimizer. One entire loop of this process is called training step.
A global step is a global variable shared between the instances. It's necessary for distributed training, so the optimizer will keep track of the number of training steps between runs:
train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())
That is the only required change for distributed training!"
answered Mar 11 at 15:54
Julien SimonJulien Simon
53928
add a comment |
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1 Answer
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Please look at this notebook for an example of distributed training with TF: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb
"Each instance will predict a batch of the dataset, calculate loss and minimize the optimizer. One entire loop of this process is called training step.
A global step is a global variable shared between the instances. It's necessary for distributed training, so the optimizer will keep track of the number of training steps between runs:
train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())
That is the only required change for distributed training!"
answered Mar 11 at 15:54
Julien SimonJulien Simon
53928
add a comment |
Please look at this notebook for an example of distributed training with TF: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb
"Each instance will predict a batch of the dataset, calculate loss and minimize the optimizer. One entire loop of this process is called training step.
A global step is a global variable shared between the instances. It's necessary for distributed training, so the optimizer will keep track of the number of training steps between runs:
train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())
That is the only required change for distributed training!"
answered Mar 11 at 15:54
Julien SimonJulien Simon
53928
add a comment |
Please look at this notebook for an example of distributed training with TF: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb
"Each instance will predict a batch of the dataset, calculate loss and minimize the optimizer. One entire loop of this process is called training step.
A global step is a global variable shared between the instances. It's necessary for distributed training, so the optimizer will keep track of the number of training steps between runs:
train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())
That is the only required change for distributed training!"
answered Mar 11 at 15:54
Julien SimonJulien Simon
53928
Please look at this notebook for an example of distributed training with TF: https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-sdk/tensorflow_distributed_mnist/tensorflow_distributed_mnist.ipynb
"Each instance will predict a batch of the dataset, calculate loss and minimize the optimizer. One entire loop of this process is called training step.
A global step is a global variable shared between the instances. It's necessary for distributed training, so the optimizer will keep track of the number of training steps between runs:
train_op = optimizer.minimize(loss, tf.train.get_or_create_global_step())
That is the only required change for distributed training!"
answered Mar 11 at 15:54
Julien SimonJulien Simon
53928
answered Mar 11 at 15:54
Julien SimonJulien Simon
53928
answered Mar 11 at 15:54
Julien SimonJulien Simon
53928
answered Mar 11 at 15:54
Julien SimonJulien Simon
53928
53928
add a comment |
add a comment |
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