Asynchronous inference with a keras model and prefetch_to_device
I have build and trained a model with the Keras API. Now I need to create an efficient framework to run inference for a large number of input samples. The tricky part is that not all of those samples are available from the beginning on, but are selected during inference based on the results of previous samples.
I can create a basic pipeline for that, with one process selecting samples to push into a queue and a second process to retrieve and preprocess them. Those samples then get feed to a model on GPU (which only got initialized once in the beginning) with Keras' model.predict_on_batch(batch).
However, this would be quite slow. I'd rather have a small queue on GPU so that there are no loading times when the next batch is transferred from RAM to GPU.
This seems to be possible with the Tensorflow Dataset API and prefetch_to_device [1]. But it also seems to be not straight forward to use the Dataset API with a Keras model for inference:
- Inference with
tf.data.Datasethas been asked multiple times: [2], [3] but the answers aren't really explanatory beside the given code snippets. Also I'm not sure how to utilize the suggestions for my Keras model checkpoint .hdf5-file. - How to asynchronously feed the Dataset without rebuilding or reloading the graph each time the first process selects new samples? [4], [5]
I'm not to familiar with plain tensorflow code without the keras abstractions so I might have overseen something obvious in the references. I'd be very grateful for detailed explanation or pointers to more sources.
asynchronous keras gpu tensorflow-datasets inference
asked Nov 16 '18 at 8:18
Johnny TGunJohnny TGun
114
add a comment |
I have build and trained a model with the Keras API. Now I need to create an efficient framework to run inference for a large number of input samples. The tricky part is that not all of those samples are available from the beginning on, but are selected during inference based on the results of previous samples.
I can create a basic pipeline for that, with one process selecting samples to push into a queue and a second process to retrieve and preprocess them. Those samples then get feed to a model on GPU (which only got initialized once in the beginning) with Keras' model.predict_on_batch(batch).
However, this would be quite slow. I'd rather have a small queue on GPU so that there are no loading times when the next batch is transferred from RAM to GPU.
This seems to be possible with the Tensorflow Dataset API and prefetch_to_device [1]. But it also seems to be not straight forward to use the Dataset API with a Keras model for inference:
- Inference with
tf.data.Datasethas been asked multiple times: [2], [3] but the answers aren't really explanatory beside the given code snippets. Also I'm not sure how to utilize the suggestions for my Keras model checkpoint .hdf5-file. - How to asynchronously feed the Dataset without rebuilding or reloading the graph each time the first process selects new samples? [4], [5]
I'm not to familiar with plain tensorflow code without the keras abstractions so I might have overseen something obvious in the references. I'd be very grateful for detailed explanation or pointers to more sources.
asynchronous keras gpu tensorflow-datasets inference
asked Nov 16 '18 at 8:18
Johnny TGunJohnny TGun
114
add a comment |
I have build and trained a model with the Keras API. Now I need to create an efficient framework to run inference for a large number of input samples. The tricky part is that not all of those samples are available from the beginning on, but are selected during inference based on the results of previous samples.
I can create a basic pipeline for that, with one process selecting samples to push into a queue and a second process to retrieve and preprocess them. Those samples then get feed to a model on GPU (which only got initialized once in the beginning) with Keras' model.predict_on_batch(batch).
However, this would be quite slow. I'd rather have a small queue on GPU so that there are no loading times when the next batch is transferred from RAM to GPU.
This seems to be possible with the Tensorflow Dataset API and prefetch_to_device [1]. But it also seems to be not straight forward to use the Dataset API with a Keras model for inference:
- Inference with
tf.data.Datasethas been asked multiple times: [2], [3] but the answers aren't really explanatory beside the given code snippets. Also I'm not sure how to utilize the suggestions for my Keras model checkpoint .hdf5-file. - How to asynchronously feed the Dataset without rebuilding or reloading the graph each time the first process selects new samples? [4], [5]
I'm not to familiar with plain tensorflow code without the keras abstractions so I might have overseen something obvious in the references. I'd be very grateful for detailed explanation or pointers to more sources.
asynchronous keras gpu tensorflow-datasets inference
asked Nov 16 '18 at 8:18
Johnny TGunJohnny TGun
114
I have build and trained a model with the Keras API. Now I need to create an efficient framework to run inference for a large number of input samples. The tricky part is that not all of those samples are available from the beginning on, but are selected during inference based on the results of previous samples.
I can create a basic pipeline for that, with one process selecting samples to push into a queue and a second process to retrieve and preprocess them. Those samples then get feed to a model on GPU (which only got initialized once in the beginning) with Keras' model.predict_on_batch(batch).
However, this would be quite slow. I'd rather have a small queue on GPU so that there are no loading times when the next batch is transferred from RAM to GPU.
This seems to be possible with the Tensorflow Dataset API and prefetch_to_device [1]. But it also seems to be not straight forward to use the Dataset API with a Keras model for inference:
- Inference with
tf.data.Datasethas been asked multiple times: [2], [3] but the answers aren't really explanatory beside the given code snippets. Also I'm not sure how to utilize the suggestions for my Keras model checkpoint .hdf5-file. - How to asynchronously feed the Dataset without rebuilding or reloading the graph each time the first process selects new samples? [4], [5]
I'm not to familiar with plain tensorflow code without the keras abstractions so I might have overseen something obvious in the references. I'd be very grateful for detailed explanation or pointers to more sources.
asynchronous keras gpu tensorflow-datasets inference
asynchronous keras gpu tensorflow-datasets inference
asked Nov 16 '18 at 8:18
Johnny TGunJohnny TGun
114
asked Nov 16 '18 at 8:18
Johnny TGunJohnny TGun
114
edited Nov 16 '18 at 9:12
Johnny TGun
asked Nov 16 '18 at 8:18
Johnny TGunJohnny TGun
114
asked Nov 16 '18 at 8:18
Johnny TGunJohnny TGun
114
asked Nov 16 '18 at 8:18
Johnny TGunJohnny TGun
114
114
add a comment |
add a comment |
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