ikanez

C:\Users\UserName>activate tensorflow-gpu

(tensorflow-gpu) C:\Users\UserName>jupyter
usage: jupyter-script.py [-h] [--version] [--config-dir] [--data-dir]
                         [--runtime-dir] [--paths] [--json]
                         [subcommand]
jupyter-script.py: error: one of the arguments --version subcommand --config-dir --data-dir --runtime-dir --paths is required

(tensorflow-gpu) C:\Users\UserName>jupyter notebook
[I 01:02:35.990 NotebookApp] [nb_conda_kernels] enabled, 4 kernels found

ikanez

conda create --name tensorflow-gpu python=3.5
activate tensorflow-gpu
conda install jupyter
conda install scipy
pip install tensorflow-gpu

ikanez

conda create --name tensorflow python=3.5
activate tensorflow
conda install jupyter (this might fail due to :PaddingError: Placeholder of length '30' too short in package qt-5.6.2-vc14_0.% The package must be rebuilt with conda-build > 2.0. Try running 'conda update --all' in root env, and running it again)
conda install scipy
pip install tensorflow

ikanez

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-37-16682764c468> in <module>()
----> 1 fit(m, md, 1, lo.opt, F.binary_cross_entropy)
      2 # use F.binary_cross_entropy for multi-label problems

~\Dropbox\3.SelfStudy\fastai_pytorch\fastai\courses\dl1\fastai\model.py in fit(model, data, epochs, opt, crit, metrics, callbacks, **kwargs)
    104             i += 1
    105 
--> 106         vals = validate(stepper, data.val_dl, metrics)

ikanez

for param in parameters_list:
    # start mlflow run
    with mlflow.start_run(run_name='arima_param'):
    
        # log parameters
        mlflow.log_param('param-qs', param[0])
        mlflow.log_param('param-ps', param[1])

        try:
            model = SARIMAX(btc_month.close_box, order=(param[0], d, param[1])).fit(disp=-1)

ikanez

import os
import mlflow
import mlflow.sklearn
# Set the experiment name to an experiment 
mlflow.set_experiment("/Shared/experiments/cryptocurrency/analysis-forecasting-1")

ikanez

from datetime import timedelta
num_window = 10

def last_day_of_month(any_day):
    next_month = any_day.replace(day=28) + timedelta(days=4)  # this will never fail
    return next_month - timedelta(days=next_month.day)


with mlflow.start_run(run_name='sarima_backtest'):
    t1 = pd.to_datetime('2017-01-31')

ikanez

def call_openapi(question):
    response = openai.Completion.create(
      engine="davinci",
      prompt="""
        This is a banking expert.

        Q: What is interest rate?
        A: The interest rate is the amount a lender charges for the use of assets expressed as a percentage of the principal.

        Q: What is PD?

ikanez

def call_openapi(title, desc):
    response = openai.Completion.create(
      engine="davinci",
      prompt="""This is a job specialization classifier
      
                Job title: Account Executive
                Job description: Handle full set of accounts.\n
                                Familiar with Income Tax filing.\n
                                Maintain daily cash flow and reporting.\n
                                Prepare monthly and annual financial reports.\n

ikanez

resume_1 = """
  QUALIFICATIONS
  Highly motivated, customer focused professional with extensive experience in key client development and
  retention. Skilled in creating and growing solid customer relationships, needs analysis, and account activity
  tracking.
  EXPERIENCE
  EXPERIAN CORP 1998-2007
  Account Manager – Costa Mesa / Sacramento, CA 2002-2007
  Primary customer contact for the nation's largest collector and provider of real estate focused public record data.
  Industries serviced: Lending, Title, Investor and Government. Territory – AL, LA, MS, OK, TX