maciejskorski

maciejskorski

print_lcp_config_options
	 struct netdissect_options * TypeKind.POINTER arg declared in ./tcpdump/print-ppp.c:L403,C39-L403,C59 netdissect_options declared in ./tcpdump/netdissect.h:L161
	 const unsigned char TypeKind.ELABORATED arg declared in ./tcpdump/print-ppp.c:L403,C61-L403,C73 u_char declared in /Library/Developer/CommandLineTools/SDKs/MacOSX13.sdk/usr/include/sys/_types/_u_char.h:L30
	 const unsigned int TypeKind.ELABORATED arg declared in ./tcpdump/print-ppp.c:L403,C75-L403,C86 u_int declared in /Library/Developer/CommandLineTools/SDKs/MacOSX13.sdk/usr/include/sys/_types/_u_int.h:L30
print_ipcp_config_options
	 struct netdissect_options * TypeKind.POINTER arg declared in ./tcpdump/print-ppp.c:L404,C40-L404,C60 netdissect_options declared in ./tcpdump/netdissect.h:L161
	 const unsigned char * TypeKind.POINTER arg declared in ./tcpdump/print-ppp.c:L404,C62-L404,C77 u_char declared in /Library/Developer/CommandLineTools/SDKs/MacOSX13.sdk/usr/include/sys/_types/_u_char.h:L30
	 unsigned int TypeKind.ELABO

maciejskorski

# gcr.io/kaggle-images/python:latest
# checked on April 21 2023
# root@9735f873cb2d:/# pip list show
Package                                Version        Editable project location
-------------------------------------- -------------- -------------------------
absl-py                                1.4.0
accelerate                             0.12.0
access                                 1.1.9
affine                                 2.4.0
aiobotocore                            2.5.0

maciejskorski

# Read more about SSH config files: https://linux.die.net/man/5/ssh_config
Host snarto_kaggle_cpu
    # update the instance IP under console.cloud.google.com/compute/instances -> more (...) -> network details
    HostName 34.170.120.99
    # add the public part under console.cloud.google.com/compute/metadata
    IdentityFile ~/.ssh/id_rsa
    # match the name under the SSH key 
    User maciej.skorski
    Port 22

maciejskorski

# wrap the TF code to be profiled with <start> and <stop> methods as below

tf.profiler.experimental.start('logs/profile')
for (x,y) in data:
  train_step(x,y) 
tf.profiler.experimental.stop()

maciejskorski

## train a logistic regression (images 28x28 and 10 classes)

w = tf.Variable(tf.random.normal(shape=(28*28,10),stddev=0.1),trainable=True)
optimizer = tf.optimizers.SGD(0.01)

@tf.function
def train_step(x, y):
  with tf.GradientTape() as tape:
    all_logits = tf.matmul(x,w) # (n_batch,n_class)
    y_logits = tf.gather(all_logits,y,batch_dims=1) # (n_batch,)

maciejskorski

def SparseCategoricalCrossentropy(labels,logits):
  ''' labels: shape [n_batch] contains true classes as numbers from 0 to n_classes-1
      logits: shape [n_batch,n_classes], predicted log probabilities '''
  Z = tf.reduce_logsumexp(logits,axis=-1)
  lookup_labels = tf.stack([tf.range(tf.shape(labels)[0]),tf.cast(labels,tf.int32)],1)
  true_logits = tf.gather_nd(logits,lookup_labels,batch_dims=0)
  return -true_logits + Z

maciejskorski

from itertools import islice,chain
from collections import deque

def gen_skipgrams(itr,window=1,symmetric=False,Q=None):
  itr = iter(itr)
  if not Q:
    Q = deque(islice(itr,window-1),maxlen=window)
  append = Q.append
  for i in itr:
    for j in Q: