MemoryError when running python script on google cloud

Question

I am trying to use the Google cloud to run a script that makes predictions for every line of a test.csv file. I use the cloud because it looks like Google Colab is going to take some time. However, when I run it there is a memory error:

(pre_env) mikempc3@instance-1:~$ python predictSales.py 
Traceback (most recent call last):
  File "predictSales.py", line 7, in <module>
    sales = pd.read_csv("sales_train.csv")
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/io/parsers.py", line 685, in parser_f
    return _read(filepath_or_buffer, kwds)
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/io/parsers.py", line 463, in _read
    data = parser.read(nrows)
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/io/parsers.py", line 1169, in read
    df = DataFrame(col_dict, columns=columns, index=index)
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/core/frame.py", line 411, in __init__
    mgr = init_dict(data, index, columns, dtype=dtype)
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/core/internals/construction.py", line 257, in init_dict
    return arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/core/internals/construction.py", line 87, in arrays_to_mgr
    return create_block_manager_from_arrays(arrays, arr_names, axes)
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/core/internals/managers.py", line 1694, in create_block_manager_from_arrays
    blocks = form_blocks(arrays, names, axes)
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/core/internals/managers.py", line 1764, in form_blocks
    int_blocks = _multi_blockify(items_dict["IntBlock"])
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/core/internals/managers.py", line 1846, in _multi_blockify
    values, placement = _stack_arrays(list(tup_block), dtype)
  File "/home/mikempc3/pre_env/lib/python3.5/site-packages/pandas/core/internals/managers.py", line 1874, in _stack_arrays
    stacked = np.empty(shape, dtype=dtype)
MemoryError: Unable to allocate 67.2 MiB for an array with shape (3, 2935849) and data type int64

Here is my script:

import statsmodels.tsa.arima.model as smt
import pandas as pd
import datetime
import numpy as np


sales = pd.read_csv("sales_train.csv")
test = pd.read_csv("test.csv")

sales.date = sales.date.apply(lambda x: datetime.datetime.strptime(x, "%d.%m.%Y"))

sales_monthly = sales.groupby(
    ["date_block_num", "shop_id", "item_id"])["date", "item_price",
                                              "item_cnt_day"].agg({
    "date": ["min", "max"],
    "item_price": "mean",
    "item_cnt_day": "sum"})

array = []

for i, row in test.iterrows():
    print("row['shop_id']: ", row['shop_id'], " row['item_id']: ", row['item_id'])
    print(statsmodels.__version__)
    ts = pd.DataFrame(sales_monthly.loc[pd.IndexSlice[:, [row['shop_id']], [row['item_id']]], :]['item_price'].values *
                      sales_monthly.loc[pd.IndexSlice[:, [row['shop_id']], [row['item_id']]], :][
                          'item_cnt_day'].values).T.iloc[0]
    print(ts.values)
    if ts.values != [] and len(ts.values) > 2:
        best_aic = np.inf
        best_order = None
        best_model = None

        ranges = range(1, 5)
        for difference in ranges:
            # try:
            tmp_model = smt.ARIMA(ts.values, order=(0, 1, 0), trend='t').fit()
            tmp_aic = tmp_model.aic
            if tmp_aic < best_aic:
                best_aic = tmp_aic
                best_difference = difference
                best_model = tmp_model
                # except Exception as e:
                #     print(e)
                #     continue
        if best_model is not None:
            y_hat = best_model.forecast()[0]
            if y_hat < 0:
                y_hat = 0
        else:
            y_hat = 0
    else:
        y_hat = 0
    print("predicted:", y_hat)
    d = {'id': row['ID'], 'item_cnt_month': y_hat}
    array.append(d)
    print("-------------------")

df = pd.DataFrame(array)
df.to_csv("submission.csv")

Answer 1

You can use the Fil memory profiler ( https://pythonspeed.com/fil ) to figure out which lines of code are responsible for peak memory use. It will also handle out-of-memory conditions and dump a report when you run out.

Only caveat is (1) it require Python 3.6 or later and (2) will only run on Linux or macOS. We're up to 3.9 so probably time to upgrade regardless.