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100 lines
2.5 KiB
Python
100 lines
2.5 KiB
Python
import bisect
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import os
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import random as python_random
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import subprocess
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from toxicity_ml_pipeline.settings.default_settings_tox import LOCAL_DIR
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import numpy as np
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from sklearn.metrics import precision_recall_curve
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try:
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import tensorflow as tf
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except ModuleNotFoundError:
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pass
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def upload_model(full_gcs_model_path):
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folder_name = full_gcs_model_path
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if folder_name[:5] != "gs://":
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folder_name = "gs://" + folder_name
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dirname = os.path.dirname(folder_name)
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epoch = os.path.basename(folder_name)
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model_dir = os.path.join(LOCAL_DIR, "models")
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cmd = f"mkdir {model_dir}"
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try:
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execute_command(cmd)
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except subprocess.CalledProcessError:
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pass
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model_dir = os.path.join(model_dir, os.path.basename(dirname))
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cmd = f"mkdir {model_dir}"
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try:
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execute_command(cmd)
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except subprocess.CalledProcessError:
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pass
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try:
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_ = int(epoch)
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except ValueError:
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cmd = f"gsutil rsync -r '{folder_name}' {model_dir}"
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weights_dir = model_dir
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else:
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cmd = f"gsutil cp '{dirname}/checkpoint' {model_dir}/"
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execute_command(cmd)
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cmd = f"gsutil cp '{os.path.join(dirname, epoch)}*' {model_dir}/"
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weights_dir = f"{model_dir}/{epoch}"
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execute_command(cmd)
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return weights_dir
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def compute_precision_fixed_recall(labels, preds, fixed_recall):
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precision_values, recall_values, thresholds = precision_recall_curve(y_true=labels, probas_pred=preds)
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index_recall = bisect.bisect_left(-recall_values, -1 * fixed_recall)
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result = precision_values[index_recall - 1]
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print(f"Precision at {recall_values[index_recall-1]} recall: {result}")
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return result, thresholds[index_recall - 1]
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def load_inference_func(model_folder):
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model = tf.saved_model.load(model_folder, ["serve"])
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inference_func = model.signatures["serving_default"]
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return inference_func
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def execute_query(client, query):
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job = client.query(query)
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df = job.result().to_dataframe()
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return df
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def execute_command(cmd, print_=True):
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s = subprocess.run(cmd, shell=True, capture_output=print_, check=True)
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if print_:
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print(s.stderr.decode("utf-8"))
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print(s.stdout.decode("utf-8"))
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def check_gpu():
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try:
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execute_command("nvidia-smi")
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except subprocess.CalledProcessError:
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print("There is no GPU when there should be one.")
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raise AttributeError
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l = tf.config.list_physical_devices("GPU")
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if len(l) == 0:
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raise ModuleNotFoundError("Tensorflow has not found the GPU. Check your installation")
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print(l)
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def set_seeds(seed):
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np.random.seed(seed)
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python_random.seed(seed)
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tf.random.set_seed(seed)
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