Update, can use TensorFlow ops to query allocator:
# maximum across all sessions and .run calls so far
sess.run(tf.contrib.memory_stats.MaxBytesInUse())
# current usage
sess.run(tf.contrib.memory_stats.BytesInUse())
Also you can get detailed information about session.run
call including all memory being allocations during run
call by looking at RunMetadata
. IE something like this
run_metadata = tf.RunMetadata()
sess.run(c, options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE, output_partition_graphs=True), run_metadata=run_metadata)
Here's an end-to-end example -- take column vector, row vector and add them to get a matrix of additions:
import tensorflow as tf
no_opt = tf.OptimizerOptions(opt_level=tf.OptimizerOptions.L0,
do_common_subexpression_elimination=False,
do_function_inlining=False,
do_constant_folding=False)
config = tf.ConfigProto(graph_options=tf.GraphOptions(optimizer_options=no_opt),
log_device_placement=True, allow_soft_placement=False,
device_count={"CPU": 3},
inter_op_parallelism_threads=3,
intra_op_parallelism_threads=1)
sess = tf.Session(config=config)
with tf.device("cpu:0"):
a = tf.ones((13, 1))
with tf.device("cpu:1"):
b = tf.ones((1, 13))
with tf.device("cpu:2"):
c = a+b
sess = tf.Session(config=config)
run_metadata = tf.RunMetadata()
sess.run(c, options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE, output_partition_graphs=True), run_metadata=run_metadata)
with open("/tmp/run2.txt", "w") as out:
out.write(str(run_metadata))
If you open run.txt
you'll see messages like this:
node_name: "ones"
allocation_description {
requested_bytes: 52
allocator_name: "cpu"
ptr: 4322108320
}
....
node_name: "ones_1"
allocation_description {
requested_bytes: 52
allocator_name: "cpu"
ptr: 4322092992
}
...
node_name: "add"
allocation_description {
requested_bytes: 676
allocator_name: "cpu"
ptr: 4492163840
So here you can see that a
and b
allocated 52 bytes each (13*4), and the result allocated 676 bytes.
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