$ sudo neo4j status`
Neo4j is not running
`$ sudo ps -aux | grep org.neo4j.server.CommunityEntryPoint | head -n1 | awk -F ' ' '{print $2}' | sudo tee /var/run/neo4j/neo4j.pid`
24831
$ sudo neo4j status
Neo4j is running at pid 24831
$ sudo neo4j stop
Stopping Neo4j.. stopped
$ sudo neo4j start
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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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