Python print_memory_timeline Examples

Example #1

File: linearize_test.py Project: yaroslavvb/stuff

def test_linearize():
  nodes = make_caterpillar_graph(5)
  linearize.linearize()

  sess = create_session()

  import memory_util
  memory_util.vlog(1)
  with memory_util.capture_stderr() as stderr:
    sess.run(nodes[-1].op)
  memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)

Example #2

File: linearize_test.py Project: yuzhile/stuff-1

def test_linearize():
    nodes = make_caterpillar_graph(5)
    linearize.linearize()

    sess = create_session()

    import memory_util
    memory_util.vlog(1)
    with memory_util.capture_stderr() as stderr:
        sess.run(nodes[-1].op)
    memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)

Example #3

File: scratch.py Project: nanddalal/memory_util

def run_and_analyze(in_shape):
    with memory_util.capture_stderr() as stderr:
        res = sess.run(y, feed_dict={x: np.random.randn(*in_shape)})

    print res.shape

    expected_mem = reduce(lambda i, j: i * j, in_shape)  # inputs
    expected_mem += (kernel_size**2) + in_channels * out_channels  # weights
    expected_mem += reduce(lambda i, j: i * j, res.shape)  # outputs
    expected_mem *= 4  # 4 bytes per float

    peak_mem = memory_util.peak_memory(stderr)

    print 'expected mem usage (MB): ', expected_mem / BYTES_PER_MB
    print 'peak     mem usage (MB): ', peak_mem / BYTES_PER_MB
    print 'peak:expected mem ratio: ', peak_mem / float(expected_mem)
    print memory_util.print_memory_timeline(stderr)
    memory_util.plot_memory_timeline(plt, stderr)

    import ipdb
    ipdb.set_trace()

Example #4

File: linearize_test.py Project: yaroslavvb/stuff

def test_linearize():
  nodes = make_caterpillar_graph(5)
  linearize.linearize()

  sess = create_session()

  import memory_util
  memory_util.vlog(1)
  with memory_util.capture_stderr() as stderr:
    sess.run(nodes[-1].op)
  memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)

if __name__=='__main__':
  setup_env()
  import memory_util
  memory_util.vlog(1)
  
  #  sess = create_session()
  #nodes = make_caterpillar_graph()
  #  test_print()
  #  linearize.print_tf_graph(linearize.get_graph())
  #  print(tf.get_default_graph().as_graph_def())
  #  test_toposort()
  test_linearize()
  sys.exit()
  #  with memory_util.capture_stderr() as stderr:
  #    print(sess.run(nodes[-1][0,0]))
  print(len(stderr.getvalue()))
  memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)

Example #5

    config = tf.ConfigProto(
        log_device_placement=False,
        graph_options=tf.GraphOptions(optimizer_options=tf.OptimizerOptions(
            opt_level=tf.OptimizerOptions.L0)))
    return tf.InteractiveSession(config=config)


node_mbs = 1
length = 5

dtype = np.float32
n = node_mbs * 250000
a0_ = tf.ones((n, ), dtype=dtype)
a0 = tf.Variable(a0_, name="a0")
a = a0
for i in range(1, length):
    name = "a" + str(i)
    a = tf.tanh(a, name=name)

grad = tf.gradients([a], [a0])[0]
sess = create_session()

sess.run(tf.global_variables_initializer())

with memory_util.capture_stderr() as stderr:
    sess.run(grad.op)

peak_memory = memory_util.peak_memory(stderr)
memory_util.print_memory_timeline(stderr)
print("Peak memory: %d" % (peak_memory, ))

Example #6

PARAMS["learning_rate"] = 0.00001
PARAMS["momentum"] = 0.95
PARAMS["max_steps"] = 101
PARAMS["batch_size"] =  400   # 40 the max min-batch size it can go without memory error for training
PARAMS["test_freq"] = 2
PARAMS["tf_prec"] = "tf.float64"
PARAMS["GradScaler"] = 1.0
PARAMS["DipoleScaler"]=1.0
PARAMS["NeuronType"] = "relu"
PARAMS["HiddenLayers"] = [1000, 1000, 1000]
PARAMS["EECutoff"] = 15.0
PARAMS["EECutoffOn"] = 7.0
PARAMS["Erf_Width"] = 0.4
#PARAMS["AN1_r_Rc"] = 8.0
#PARAMS["AN1_num_r_Rs"] = 64
PARAMS["EECutoffOff"] = 15.0
PARAMS["learning_rate_dipole"] = 0.0001
PARAMS["learning_rate_energy"] = 0.00001
PARAMS["SwitchEpoch"] = 10
d = MolDigester(TreatedAtoms, name_="ANI1_Sym_Direct", OType_="EnergyAndDipole")  # Initialize a digester that apply descriptor for the fragme
tset = TensorMolData_BP_Direct_EE_WithEle(a, d, order_=1, num_indis_=1, type_="mol",  WithGrad_ = True)
#tset = TensorMolData_BP_Direct_EE(a, d, order_=1, num_indis_=1, type_="mol",  WithGrad_ = True) # Initialize TensorMolData that contain the training data fo
#tset = TensorMolData_BP_Multipole_2_Direct(a, d, order_=1, num_indis_=1, type_="mol",  WithGrad_ = False)
#manager=TFMolManage("",tset,False,"fc_sqdiff_BP_Direct_EE_ChargeEncode") # Initialzie a manager than manage the training of neural network.
#manager=TFMolManage("",tset,False,"Dipole_BP_2_Direct")
manager=TFMolManage("",tset,False,"fc_sqdiff_BP_Direct_EE_Update")
PARAMS['Profiling']=1
with memory_util.capture_stderr() as stderr:
	manager.Train(1)
memory_util.print_memory_timeline(stderr, ignore_less_than_bytes=1000)