kaknikhil commented on a change in pull request #425: DL: Add training for
multiple models
URL: https://github.com/apache/madlib/pull/425#discussion_r310255606
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File path: src/ports/postgres/modules/deep_learning/madlib_keras.py_in
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@@ -589,31 +637,49 @@ def get_loss_metric_from_keras_eval(schema_madlib,
table, compile_params,
def internal_keras_eval_transition(state, dependent_var, independent_var,
model_architecture, serialized_weights,
compile_params,
current_seg_id, seg_ids, images_per_seg,
- gpus_per_host, segments_per_host, **kwargs):
+ gpus_per_host, segments_per_host,
+ is_final, **kwargs):
SD = kwargs['SD']
device_name = get_device_name_and_set_cuda_env(gpus_per_host,
current_seg_id)
agg_loss, agg_metric, agg_image_count = state
- if not agg_image_count:
- set_keras_session(device_name, gpus_per_host, segments_per_host)
- model = model_from_json(model_architecture)
- compile_and_set_weights(model, compile_params, device_name,
- serialized_weights)
+ # User called evaluate will always set is_final to true.
+ # If is_final is false, that means the fit already created a session and a
graph
+ # Otherwise, we may (last iteration of fit) or may not (user evaluate call)
+ # have a session.
+ if is_final and 'sess' not in SD:
+ sess = get_keras_session(device_name, gpus_per_host, segments_per_host)
+ SD['sess'] = sess
+ K.set_session(sess)
Review comment:
again call set_keras_session() instead of setting it here directly
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