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https://github.com/ultravioletrs/cocos.git
synced 2026-08-07 07:14:50 +00:00
Sammy Kerata Oina
c274521faf
Changed the internal representation of algorithms and datasets within the service from byte slices to file paths, writing received data directly to temp files. This modification allows for handling potentially large data sets without the need to load them entirely into memory, improving the memory efficiency and scalability of the service. Additionally, it aligns the call signature of external algorithms with the new approach, updating documentation and examples accordingly. Updated the linear regression example for consistency with the new data handling process. Resolves issues with memory bloat when processing large datasets. Signed-off-by: SammyOina <sammyoina@gmail.com>
48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
import sys, io
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import joblib
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import socket
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.linear_model import LogisticRegression
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csv_file_path = sys.argv[2]
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iris = pd.read_csv(csv_file_path)
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# Droping the Species since we only need the measurements
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X = iris.drop(['Species'], axis=1)
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# converting into numpy array and assigning petal length and petal width
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X = X.to_numpy()[:, (3,4)]
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y = iris['Species']
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# Splitting into train and test
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X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.5, random_state=42)
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log_reg = LogisticRegression()
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log_reg.fit(X_train,y_train)
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# Serialize the trained model to a byte buffer
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model_buffer = io.BytesIO()
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joblib.dump(log_reg, model_buffer)
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# Get the serialized model as a bytes object
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model_bytes = model_buffer.getvalue()
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# Define the path for the Unix domain socket
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socket_path = sys.argv[1]
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# Create a Unix domain socket client
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client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
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try:
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# Connect to the server
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client.connect(socket_path)
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# Send the serialized model over the socket
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client.send(model_bytes)
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finally:
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# Close the socket
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client.close()
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