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Learn to use GPT-3.5 to do the heavy lifting for knowledge acquisition, preprocessing, mannequin coaching, and deployment
A variety of repetitive boilerplate code exists within the mannequin improvement part of any machine studying utility. Common libraries akin to PyTorch Lightning have been created to standardize the operations carried out when coaching/evaluating neural networks, resulting in a lot cleaner code. Nevertheless, boilerplate extends far past coaching loops. Even the information acquisition part of machine studying initiatives is stuffed with steps which might be vital however time consuming. One technique to cope with this problem can be to create a library just like PyTorch Lightning for the whole mannequin improvement course of. It must be normal sufficient to work with a wide range of mannequin sorts past neural networks, and able to integrating a wide range of knowledge sources.
Code examples for extracting knowledge, preprocessing, mannequin coaching, and deployment is available on the web, although gathering it, and integrating it right into a challenge takes time. Since such code is on the web, chances are high it has been skilled on by a big language mannequin (LLM) and could be rearranged in a wide range of helpful methods via pure language instructions. The purpose of this submit is to indicate how simple it’s to automate lots of the steps frequent to ML initiatives by utilizing the GPT-3.5 API from OpenAI. I’ll present some failure circumstances alongside the way in which, and how you can tune prompts to repair bugs when potential. Ranging from scratch, with out even a lot as a dataset, we’ll find yourself with a mannequin that is able to be deployed on AWS SageMaker. In case you’re following alongside, ensure to setup the OpenAI API as follows:
import openai
openai.api_key = "YOUR KEY HERE"
Additionally, the next utility operate is useful for calling the GPT-3.5 API:
def get_api_result(immediate):
request = openai.ChatCompletion.create(
mannequin="gpt-3.5-turbo-0301",
messages=[{"role": "user", "content": prompt}]
)consequence = request['choices'][0]['message']['content']
print(consequence)
This part is simplified because it solely considers a single knowledge supply, however can in precept be prolonged so conditions the place knowledge comes from a number of sources (csv recordsdata, databases, photographs, and so on.). Step one is to extract some knowledge. For the sake of simplicity, I’ll use the Income Prediction¹ dataset the place the purpose is to foretell if a person earns extra/lower than $50k per yr based mostly on their schooling, job place, business, and so on. The operate beneath will probably be used to generate the code that downloads our knowledge. Be aware how the immediate template is designed to bias the API to generate python code:
def extract(immediate):
prompt_template = """You're a ChatGPT language mannequin that may generate Python code. Please present a pure language enter textual content, and I'll generate the corresponding Python code.nInput: {}nPython code:""".format(immediate)get_api_result(prompt_template)
Right here we ask GPT-3.5 to:
Retrieve the grownup revenue prediction dataset from openml utilizing the sklearn fetch_openml operate. Ensure to retrieve the information as a single dataframe which incorporates the goal in a column named “goal”. Identify the ensuing dataframe “df”.
which supplies us the output:
from sklearn.datasets import fetch_openmladult_income = fetch_openml(identify='grownup', model=2)
df = pd.DataFrame(adult_income.knowledge, columns=adult_income.feature_names)
df['target'] = adult_income.goal
This code is freed from bugs and offers us precisely what we wish. Had I used a less complicated immediate by eradicating mentions of openml and the operate to make use of for retrieval, we’d get:
import pandas as pddf = pd.read_csv("adult_income_prediction.csv")
df.columns = ["age", "workclass", "fnlwgt", "education", "education-num",
"marital-status", "occupation", "relationship", "race",
"sex", "capital-gain", "capital-loss", "hours-per-week",
"native-country", "target"]
This assumes that the information is regionally out there. What’s fascinating about this result’s that it has the right column names as an inventory, though we didn’t embody them within the API name! These names nonetheless are all around the internet, significantly on this Medium submit, apart from the goal column which is added by GPT. The following step is to remodel the information right into a format that’s usable by machine studying fashions. We’ll have to make use of a extra superior immediate template right here since GPT must be made conscious of the column names and their sorts:
def remodel(columns, column_types, immediate):
prompt_template = """You're a ChatGPT language mannequin that may generate Python code. Please present a pure language enter textual content, and I'll generate the corresponding Python code utilizing the Pandas to preprocess the DataFrame. The DataFrame columns are {} and their corresponding dtypes are {}.nInput: {}nPython code:""".format(columns, column_types, immediate)get_api_result(prompt_template)
I then make the next request:
Preprocess the dataframe by changing all categorical columns to their one-hot encoded equivalents, and normalizing numerical columns. Drop rows which have an NA or NaN worth in any column. Drop rows which have numeric column outliers as decided by their z rating. A numeric column outlier is a worth that’s outdoors of the 1 to 99 inter-quantile vary. The numerical columns needs to be normalized utilizing StandardScaler from sklearn. The values within the goal colummn needs to be transformed to 0 or 1 and needs to be of kind int.
We now get:
import pandas as pd
from sklearn.preprocessing import StandardScaler# Load the DataFrame
df = pd.read_csv('knowledge.csv')
# Convert categorical columns to one-hot encoded equivalents
df = pd.get_dummies(df, columns=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country'])
# Normalize numerical columns
scaler = StandardScaler()
numerical_cols = ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week']
df[numerical_cols] = scaler.fit_transform(df[numerical_cols])
# Drop rows with NA or NaN values
df.dropna(inplace=True)
# Drop rows with numeric column outliers
z_scores = df[numerical_cols].apply(lambda x: (x - x.imply()) / x.std())
df = df[(z_scores > -3).all(axis=1) & (z_scores < 3).all(axis=1)]
# Convert goal column to 0 or 1 and alter kind to int
df['target'] = df['target'].apply(lambda x: 1 if x == '>50K' else 0).astype(int)
which is sort of excellent for our use if we exclude the half that tries to load within the knowledge from a csv file. It’s fairly tough to persuade GPT to exclude this, even when we explicitly inform it to imagine that df exists and shouldn’t be loaded. Lastly, we have to load the information into a neighborhood database. That is overkill for such a easy use case, however is an efficient behavior to develop.
def load(immediate):
prompt_template = """You're a ChatGPT language mannequin that may generate Python code. Please present a pure language enter textual content, and I'll generate the corresponding Python code.nInput: {}nPython code:""".format(immediate)get_api_result(prompt_template)
I exploit the next instruction:
Connect with an sqlite database named “knowledge”. Use pandas to insert knowledge from a DataFrame named “df” right into a desk named “revenue”. Don’t embody the index column. Commit the adjustments earlier than closing the connection.
to be able to get:
import sqlite3
import pandas as pd# Create a connection to the database
connection = sqlite3.join("knowledge.db")
# Outline the DataFrame
df = pd.DataFrame()
# Outline the desk identify
table_name = "revenue"
# Insert the information into the desk
df.to_sql(table_name, connection, if_exists="change", index=False)
# Commit the adjustments to the database
connection.commit()
# Shut the connection
connection.shut()
which is ideal, minus the pointless creation of df. With this processed knowledge in hand, we’re prepared to coach some fashions.
Sources allowing, it’s a good suggestion to check out a number of totally different mannequin sorts to establish the one with the best degree of complexity for the given process. Subsequently, we ask GPT-3.5 to check out a number of totally different fashions. First, let’s arrange the generic immediate template for mannequin coaching:
def practice(immediate):
prompt_template = """You're a ChatGPT language mannequin that may generate Python code. Deal with utilizing scikit-learn when relevant. Please present a pure language enter textual content, and I'll generate the corresponding Python code.nInput: {}nPython code:""".format(immediate)get_api_result(prompt_template)
and the immediate we’ll be utilizing is:
Practice a wide range of classification fashions to foretell the “goal” column utilizing all different columns. Accomplish that utilizing 5-fold cross validation to decide on the perfect mannequin and corresponding set of hyperparameters, and return the perfect general mannequin and corresponding hyperparameter settings. Select the perfect mannequin based mostly on accuracy. Assume a dataframe named “df” exists which is for use for coaching. Log the whole course of utilizing MLFlow. Begin logging with mlflow earlier than coaching any fashions so solely a single run is saved. Guarantee that the mannequin is logged utilizing the sklearn module of mlflow. Guarantee that solely the perfect general mannequin is logged, however log metrics for all mannequin sorts. The imply worth of the next metrics on all cross validation folds needs to be logged: accuracy, AUC, F1 rating
Let’s take a look on the generated output and this time go deeper into why a few of the particular directions needed to be offered.
import pandas as pd
from sklearn.model_selection import GridSearchCV, cross_validate
from sklearn.metrics import accuracy_score, roc_auc_score, f1_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
import mlflow
import mlflow.sklearn# load the dataframe
df = pd.read_csv('path/to/dataframe.csv')
# outline the options and goal columns
options = df.columns.drop('goal')
goal = 'goal'
# arrange mlflow
mlflow.set_tracking_uri('localhost:5000') # or arrange your individual server
mlflow.set_experiment("my_experiment")
# arrange cross-validation
cv = 5
# outline the fashions and corresponding hyperparameters to be tuned
fashions = [
{
'name': 'Logistic Regression',
'estimator': LogisticRegression(),
'hyperparameters': {
'penalty': ['l1', 'l2'],
'C': [0.1, 1, 10]
}
},
{
'identify': 'Choice Tree',
'estimator': DecisionTreeClassifier(),
'hyperparameters': {
'max_depth': [2, 4, 6, 8]
}
},
{
'identify': 'Random Forest',
'estimator': RandomForestClassifier(),
'hyperparameters': {
'n_estimators': [10, 25, 50, 100],
'max_depth': [None, 2, 4, 6, 8]
}
}
]
# iterate via the fashions and corresponding hyperparameters to coach and tune them
with mlflow.start_run():
best_model = None
best_metrics = {}
for mannequin in fashions:
print(f"Coaching {mannequin['name']} mannequin...")
# arrange grid seek for hyperparameter tuning
gs = GridSearchCV(mannequin['estimator'], mannequin['hyperparameters'], cv=cv, verbose=2, n_jobs=-1, scoring='accuracy')
gs.match(df[features], df[target])
# log metrics for all mannequin sorts
metrics = cross_validate(gs.best_estimator_, df[features], df[target], cv=cv, n_jobs=-1,
scoring=["accuracy", "roc_auc", "f1"])
for metric, values in metrics.gadgets():
mlflow.log_metric(f"{mannequin['name']}_{metric}", values.imply())
# verify if that is the perfect mannequin based mostly on accuracy
if best_model is None or gs.best_score_ > best_metrics['accuracy']:
best_model = gs.best_estimator_
best_metrics = {'accuracy': gs.best_score_,
'AUC': roc_auc_score(df[target], best_model.predict_proba(df[features])[:, 1]),
'F1': f1_score(df[target], best_model.predict(df[features]))
}
# register the perfect mannequin in mlflow
mlflow.sklearn.log_model(sk_model=best_model, artifact_path="best_model")
# log the perfect mannequin's metrics
for metric, worth in best_metrics.gadgets():
mlflow.log_metric(f"best_model_{metric}", worth)
If we take away the loading of df and the part # arrange mlflow , we find yourself with precisely what’s desired. Particularly, a loop over a 3 totally different mannequin sorts, performing a grid search utilizing 5-fold cross validation to establish the perfect hyperparmeters for the given mannequin kind, whereas holding monitor of metrics. With out specifying “select the perfect mannequin based mostly on accuracy”, the generated code will use scoring=[“accuracy”, “roc_auc", “f1”] for the grid search which won’t work since there may be ambiguity as to how you can choose the perfect mannequin in line with a number of metrics. With out “ensure that the mannequin is logged utilizing the sklearn module of mlflow”, we typically find yourself with mlflow.log_model() which is incorrect. Additionally, “ensure that solely the perfect general mannequin is logged” is important to keep away from storing all fashions. General, this output is appropriate, however it’s unstable, and working it a number of instances is prone to introduce totally different bugs. In an effort to have every thing prepared for the serving step, it’s helpful so as to add the mannequin signature when saving the mannequin. This signature is mainly a set of characteristic names and their corresponding sorts. It’s a ache to get GPT-3.5 so as to add this, so some handbook labor must be finished by first including the import:
from mlflow.fashions.signature import infer_signature
after which modifying the road of code which logs the mannequin by way of:
mlflow.sklearn.log_model(sk_model=best_model, artifact_path="best_model", signature=infer_signature(df[features], best_model.predict(df[features])))
Since we used MLflow to log the perfect mannequin, we’ve a few choices to serve the mannequin. The best choice is to host the mannequin regionally. Let’s first design the overall immediate template for mannequin serving:
def serve_model(model_path, immediate):
prompt_template = """You're a ChatGPT language mannequin that may generate shell code for deploying fashions utilizing MLFlow. Please present a pure language enter textual content, and I'll generate the corresponding command to deploy the mannequin. The mannequin is positioned within the file {}.nInput: {}nShell command:""".format(model_path, immediate)get_api_result(prompt_template)
and the immediate will probably be:
Serve the mannequin utilizing port quantity 1111, and use the native surroundings supervisor
By calling serve_model("<mannequin path right here>", query) we get:
mlflow fashions serve -m <mannequin path right here> -p 1111 --no-conda
As soon as we run this command within the shell, we’re able to make predictions by sending knowledge encoded as JSON to the mannequin. We’ll first generate the command to ship knowledge to the mannequin, after which create the JSON payload to be inserted into the command.
def send_request(immediate):
prompt_template = """You're a ChatGPT language mannequin that may generate code for sending knowledge to deployed MLFlow fashions. Please present a pure language enter textual content, and I'll generate the corresponding command. nInput: {}nCommand:""".format(immediate)get_api_result(prompt_template)
The next request will probably be inserted into the immediate template in send_request():
Use the “curl” command to ship knowledge “<knowledge right here>” to an mlflow mannequin hosted at port 1111 on localhost. Guarantee that the content material kind is “utility/json”.
The output generated by GPT-3.5 is:
curl -X POST -H "Content material-Kind: utility/json" -d '<knowledge right here>' http://localhost:1111/invocations
It’s preferable to have the URL instantly after curl as a substitute of being on the very finish of the command, i.e.
curl http://localhost:1111/invocations -X POST -H "Content material-Kind: utility/json" -d '<knowledge right here>'
Getting GPT-3.5 to do that isn’t simple. Each of the next requests fail to take action:
Use the “curl” command to ship knowledge “<knowledge right here>” to an mlflow mannequin hosted at port 1111 on localhost. Place the URL instantly after “curl”. Guarantee that the content material kind is “utility/json”.
Use the “curl” command, with the URL positioned earlier than any argument, to ship knowledge “<knowledge right here>” to an mlflow mannequin hosted at port 1111 on localhost. Guarantee that the content material kind is “utility/json”.
Perhaps it’s potential to get the specified output if we’ve GPT-3.5 modify an current command fairly than generate one from scratch. Right here is the generic template for modifying instructions:
def modify_request(immediate):
prompt_template = """You're a ChatGPT language mannequin that may modify instructions for sending knowledge utilizing "curl". Please present a pure language instruction, corresponding command, and I'll generate the modified command. nInput: {}nCommand:""".format(immediate)get_api_result(prompt_template)
We’ll name this operate as follows:
code = """curl -X POST -H "Content material-Kind: utility/json" -d '<knowledge right here>' http://localhost:1111/invocations"""
immediate = """Please modify the next by putting the url earlier than the "-X POST" argument:n{}""".format(code)
modify_request(immediate)
which lastly provides us:
curl http://localhost:1111/invocations -X POST -H "Content material-Kind: utility/json" -d '<knowledge right here>'
Now time to create the payload:
def create_payload(immediate):
prompt_template = """You're a ChatGPT language mannequin that may generate code for sending knowledge to deployed MLFlow fashions. Please present a pure language enter textual content, and I'll generate the corresponding command. nInput: {}nPython code:""".format(immediate)get_api_result(prompt_template)
The immediate for this half wanted fairly a little bit of tuning to get the specified output format:
Convert the DataFrame “df” to json format that may be acquired by a deployed MLFlow mannequin. Wrap the ensuing json in an object known as “dataframe_split”. The ensuing string shouldn’t have newlines, and it shouldn’t escape quotes. Additionally, “dataframe_split” needs to be surrounded by doubles quotes as a substitute of single quotes. Don’t embody the “goal” column. Use the cut up “orient” argument
With out the express instruction to keep away from newlines and escaping quotes, a name to json.dumps() is made which isn’t the format that the MLflow endpoint expects. The generated command is:
json_data = df.drop("goal", axis=1).to_json(orient="cut up", double_precision=15)
wrapped_data = f'{{"dataframe_split":{json_data}}}'
Earlier than changing <knowledge right here> within the curl request with the worth of wrapped_data, we most likely wish to ship just a few rows of information for prediction, in any other case the ensuing payload is simply too massive. So we modify the above to be:
json_data = df[:5].drop("goal", axis=1).to_json(orient="cut up", double_precision=15)
wrapped_data = f'{{"dataframe_split":{json_data}}}'
Invoking the mannequin provides:
{"predictions": [0, 0, 0, 1, 0]}
whereas the precise targets are [0, 0, 1, 1, 0].
There we’ve it. At the start of this submit, we didn’t even have entry to a dataset, but we’ve managed to finish up with a deployed mannequin that was chosen to be the perfect via cross-validation. Importantly, GPT-3.5 did all of the heavy lifting, and solely required minimal help alongside the way in which. I did nonetheless must specify specific libraries to make use of and strategies to name, however this was primarily required to resolve ambiguities. Had I specified “Log the whole course of” as a substitute of “Log the whole course of utilizing MLFlow”, GPT-3.5 would have too many libraries to select from, and the ensuing mannequin format may not have been helpful for serving with MLflow. Thus, some information of the instruments used to carry out the assorted steps within the ML pipeline is required to have success utilizing GPT-3.5, however it’s minimal in comparison with the information required to code from scratch.
Another choice for serving the mannequin is to host it as a SageMaker endpoint on AWS. Regardless of how simple this may occasionally look on the MLflow website, I guarantee you that as with many examples on the internet involving AWS, issues will go incorrect. Initially, Docker have to be put in to be able to generate the Docker Imager utilizing the command:
mlflow sagemaker build-and-push-container
Second, the Ptyhon library boto3 used to speak with AWS additionally requires set up. Past this, permissions have to be correctly setup such that SageMaker, ECR, and S3 providers can talk with one another on behalf of your account. Listed here are the instructions I ended up having to make use of:
mlflow deployments run-local -t sagemaker -m <mannequin path> --name income_classifier
mlflow deployments create -t sagemaker --name income_classifier -m mannequin/ --config image_url=<docker picture url> --config bucket=mlflow-serving --config region_name=us-east-1
together with some handbook tinkering behind the scenes to get the S3 bucket to be within the right area.
With the assistance of GPT-3.5 we went via the ML pipeline in a (largely) painless manner, although the final mile was a bit trickier. Be aware how I didn’t use GPT-3.5 to generate the instructions for serving the mannequin on AWS. It really works poorly for this use case, and creates made up argument names. I can solely speculate that switching to the GPT-4.0 API would assist resolve a few of the above bugs, and result in a fair simpler mannequin improvement expertise.
Whereas the ML pipeline could be totally automated utilizing LLMs, it isn’t but protected to have a non-expert be chargeable for the method. The bugs within the above code had been simply recognized as a result of the Python interpreter would throw errors, however there are extra refined bugs that may be dangerous. For instance, the elimination of outlier values within the preprocessing code may very well be incorrect resulting in extra or inadequate samples being discarded. Within the worst case, it might inadvertently drop total subgroups of individuals, exacerbating potential equity points.
Moreover, the grid search over hyperparameters might have been finished over a poorly chosen vary, resulting in overfitting or underfitting relying on the vary. This may be fairly tough to establish for somebody with little ML expertise because the code in any other case appears right, however an understanding of how regularization works in these fashions is required. Thus, it isn’t but applicable to have an unspecialized software program engineer stand in for an ML engineer, however that point is quick approaching.
[1] Dua, D. and Graff, C. (2019). UCI Machine Studying Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: College of California, Faculty of Data and Pc Science. (CC BY 4.0)
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