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

# Initial approximation of parameters
Qs = range(0, 2)
qs = range(0, 3)
Ps = range(0, 3)
ps = range(0, 3)
D=1
d=1
parameters = product(ps, qs, Ps, Qs)
parameters_list = list(parameters)
len(parameters_list)

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