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alias gssh='gcloud compute ssh'
alias gci='gcloud compute instances'

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shuf -zn10 -e *.txt | xargs -0 cp -vt ../targetdir/

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# Install python
RUN apt-get update \
  && apt-get install -y python3-pip python3-dev git \
  && cd /usr/local/bin \
  && ln -s /usr/bin/python3 python \
  && pip3 install --upgrade pip

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find . -type f -name "*.bak" -delete
or
find . -name "FILE-TO-FIND" -exec rm -rf {} \;

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# Default nohup.out
nohup myprogram &

# Custom output file
nohup myprogram > myprogram.out &

# Redirect stderr to stdout
nohup myprogram > myprogram.out 2>&1 &

# Multiple commands

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device_name = tf.test.gpu_device_name()
if device_name != '/device:GPU:0':
  raise SystemError('GPU device not found')
print('Found GPU at: {}'.format(device_name))
print("GPU Available: ", tf.test.is_gpu_available())

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import concurrent.futures

def do(params):
    return "hello"

with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:
    future = executor.submit(do, params)
    
    for future in concurrent.futures.as_completed(future_to_url):
        url = future_to_url[future]

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def predict_score(model_pickle_path, target_filepath, n_meta_columns, n_feature_columns):
    """
    model_pickle_path : 
    target_filepath : target gene expression examples with meta
    n_meta_columns : number of meta columns
    n_feature_columns : number of feature(gene)s
    return score DataFrame
    """
    df0 = pd.read_csv(target_filepath, header=-1)
    x_header = df0.iloc[:, 0:n_meta_columns]

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def cosine_distances(X, Y):
    """
    X : Target example score vector DataFrame with inst_id as the first column
    Y : All example score vector DataFrame with inst_id as the first column
    return pair-wise cosine distance DataFrame
    """
    from sklearn.metrics import pairwise
    x_header = X.iloc[:, 0].values
    X = X.iloc[:, 1:]
    y_header = Y.iloc[:, 0].values

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for idx in range(3,351):
    tablename = "table_{0}".format(idx)
    print(tablename)
    !bq rm -f -t $tablename