nchaimov

#include "rose.h"
class InheritedAttribute {
};

class visitorTraversal : public AstTopDownProcessing<InheritedAttribute>{
    public:       
    virtual InheritedAttribute evaluateInheritedAttribute(SgNode* n, InheritedAttribute inheritedAttribute);
};
InheritedAttribute visitorTraversal::evaluateInheritedAttribute(SgNode* n, InheritedAttribute inheritedAttribute) {    SgExprStatement * expr = isSgExprStatement(n);
    if(expr != NULL) {       

nchaimov

Keybase proof

I hereby claim:

• I am nchaimov on github.
• I am nchaimov (https://keybase.io/nchaimov) on keybase.
• I have a public key whose fingerprint is D64A E10A 7F24 BC7D 3E1A 7F8A 96F5 E0A5 BC6A 8B88

To claim this, I am signing this object:

nchaimov

#include <stdio.h>
#include <string.h>
#include <stdlib.h>
#include <netdb.h>
#include <sys/types.h>
#include <sys/socket.h>
#include <arpa/inet.h>

int lookup_host (const char *host, int use_canonname) {
  struct addrinfo hints, *res;

nchaimov

#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif

#include <stdlib.h>
#include <stdio.h>
#include <sys/types.h>
#include <sys/socket.h>
#include <arpa/inet.h>
#include <netdb.h>

nchaimov

#!/bin/env python
"""TAU trial data for TAU Profile.x.y.z format profiles

Parses a set of TAU profile files and yields multi-indexed Pandas dataframes for the
interval and atomic events.
"""
from __future__ import print_function
import csv
import glob
import mmap

nchaimov

#!/bin/env python
"""TAU trial data for TAU Profile.x.y.z format profiles

Parses a set of TAU profile files and yields multi-indexed Pandas dataframes for the
interval and atomic events.
"""
from __future__ import print_function
import csv
import glob
import mmap

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nchaimov

We couldn’t find that file to show.

nchaimov

"""
`Learn the Basics <intro.html>`_ ||
**Quickstart** ||
`Tensors <tensorqs_tutorial.html>`_ ||
`Datasets & DataLoaders <data_tutorial.html>`_ ||
`Transforms <transforms_tutorial.html>`_ ||
`Build Model <buildmodel_tutorial.html>`_ ||
`Autograd <autogradqs_tutorial.html>`_ ||
`Optimization <optimization_tutorial.html>`_ ||
`Save & Load Model <saveloadrun_tutorial.html>`_

nchaimov

#!/usr/bin/env python
# coding: utf-8

# # Microbenchmarking Neuron Devices (Trn1/Inf2)

# ## Introduction
# 
# This guide reviews the best practices for benchmarking performance of Neuron devices. It shows how to separate compilation and execution time, how to isolate the device time from the end-to-end execution time, how to warm-up the device, and covers few pitfalls one should be aware of. This guide provides an example code, in PyTorch, that can be used as a template for measuring performance.
# 
# This Jupyter notebook should be run on a Trn1/Inf2 instance (trn1.2xlarge/inf2.xlarge or larger).