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# Data file 
# 1 10
# 2 15
# 3 50
# 4 ...
# ....

# Can replace x2ticlabel with xticlabel as well
# When column(1) modulo 8 is zero, use "2^{....}" else ""

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# line styles for ColorBrewer Set3
# for use with qualitative/categorical data
# provides 8 colors that are more saturated than the Pastels but less so than Set2
# compatible with gnuplot >=4.2
# author: Anna Schneider
# Modified by Aditya

# line styles
set style line 1 lc rgb  '#a6cee3'
set style line 2 lc rgb  '#1f78b4'

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# Show all rows
pd.set_option('display.max_rows', None)

# Reset index after an operation
df.sort_values(['country', 'capital']).reset_index(drop=True)

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Pybind11 Doc

• Create binding for a simple function

#include<pybind11/pybind11.h>

int add(int i, int j) {
    return i + j;
}

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List of TF ops

A graph def has nodes. Every node has

• Op: E.g. AddV2, Placeholder, Const.
• Name (namescope): This is also the name of the output tensor(s). To refer to a specific index, you can say "name:0".
• Inputs (optional): E.g. Const and Placeholder ops do not have any inputs.

Placeholder ops are fed from the user input? Const ops are fed from the variables folder?

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Documentation.

import tensorflow as tf

raw_dataset = tf.data.TFRecordDataset("foo.tfrecord")

for raw_record in raw_dataset.take(1):
    example = tf.train.Example()
 example.ParseFromString(raw_record.numpy())

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The json.tool module provides a simple command line interface to validate and pretty-print JSON objects. link

python -m json.tool foo.json

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#!/usr/bin/env python3

import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2_grpc
import numpy as np
import grpc

channel = grpc.insecure_channel("localhost:8500")
stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)

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Frozen graph is deprecated in TF2. Using Saved model is the new approach. Instead of creating a configuration file and using bazel in steps 1 and 2 described here, simply use the saved_model_cli. The final compilation step of creating a binary can be performed using a makefile instead of bazel.

1. Create a TF saved_model using tf.saved_model.save.
2. Use the saved_model_cli to perform XLA AOT compilation.

saved_model_cli aot_compile_cpu \
    --dir resnet/1 \
    --tag_set serve \
 --signature_def_key serving_default \

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tf.function compiles a function into a callable TensorFlow graph. Documentation

@tf.function
def f(x):
  return x + 1

To obtain a graph / graphdef

Use the get_concrete_function method of the callable created by tf.function. It returns a tf.Graph object.