{"id":7408,"date":"2023-09-11T09:00:59","date_gmt":"2023-09-11T17:00:59","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=7408"},"modified":"2025-04-24T17:14:19","modified_gmt":"2025-04-24T17:14:19","slug":"an-end-to-end-guide-on-using-comet-mls-model-versioning-feature-part-1","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/an-end-to-end-guide-on-using-comet-mls-model-versioning-feature-part-1\/","title":{"rendered":"An End-to-End Guide on Using Comet ML\u2019s Model Versioning Feature: Part 1"},"content":{"rendered":"\n
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Photo by Isaac Smith<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n

The world of machine learning and data science is awash with technicalities. With each passing day, it becomes ever more evident that a practitioner in this field needs to keep track of a lot of things lest they fall into the deluge of complexity.<\/p>\n

Fortunately, there are many workarounds to deal with many of the problems that normally arise. One problem that is particularly prevalent is model tracking. Machine learning problems could grow to such an extent that you constantly lose track of what you are doing. The direct effect of this is that it is possible to start getting deteriorating performance in models.<\/p>\n

The fix around this is model tracking. This is keeping track and recording the changes and the performance of a given model. A model that is constantly evolving could see sharp rises in performance or declines depending on the work that is going into it. For this reason, you need to know what works best for you.<\/p>\n

Comet ML has an intricate web of tools that combine simplicity and safety and allows one to not only track changes in their model but also deploy them as desired or shared in teams.<\/p>\n

Workflow Overview<\/h1>\n

The typical iterative ML workflow involves preprocessing a dataset and then developing the model further. This could involve tuning hyperparameters and combining different algorithms in order to leverage their strengths and come up with a better-performing model.<\/p>\n

On top of this, it will be critical to export and track a model after each improvement or change in order to not get lost in the complexity of this activity. There will be a few requirements for this entire workflow. They are:<\/p>\n

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  1. A Comet ML account<\/a><\/li>\n
  2. A suitable IDE, e.g., VSCode or Jupyter Notebook which can also run in VSCode<\/li>\n
  3. The latest versions of Scikit-learn, CometML, Pandas, NumPy, joblib, and XGboost libraries<\/li>\n
  4. A python 3.9+ install<\/li>\n
  5. A curious spirit<\/li>\n<\/ol>\n

    To install the above libraries, you can run the following in your terminal:<\/p>\n

    pip3 install comet-ml xgboost scikit-learn numpy pandas joblib<\/span><\/pre>\n
    Once the above libraries are installed, then we can begin our model versioning on Comet ML\u2019s platform.<\/p>\n
    Understanding Comet\u2019s Model Registry<\/h1>\n
    Using Comet for the first time to perform an activity like this may seem daunting at first, but in all honesty, all it requires is a slight understanding of the platform and adding a few lines of code to your workflow in order to succeed.<\/p>\n
    There are a few things you have to keep in mind when you\u2019re trying to keep track of your models. There are three tabs on the Comet homepage; you should be keen on two. These two tabs are \u201cProjects\u201d and \u201cModel Registry.\u201d<\/p>\n
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    \"Screenshot<\/figure>
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    Screenshot by author<\/figcaption>\n<\/figure>\n
    The reason you should be keen on this is that you will first log your experiments under the \u201cProjects\u201d tab in a single project. After doing so, you will need the directory of that experiment within the project in order to register and upload a model to the \u201cModel Registry\u201d tab.<\/p>\n
    All the above may seem convoluted but it will become much clearer once we see the full end-to-end project.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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    Big teams rely on big ideas. Learn how experts at Uber, WorkFusion, and The RealReal use Comet to scale out their ML models and ensure visibility and collaboration company-wide<\/a>.<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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    Project<\/h1>\n
    For this example, I had a simple project in mind that would demonstrate everything important. I decided it would be best to use the iris dataset because it is fairly simple and would easily allow us to see different iterations of code and track whether models are getting any better on some baseline data.<\/p>\n
    The first step is to load the dataset from the Scikit-learn library. It comes in the form of a NumPy array, so I convert it to a Pandas DataFrame to make it more comfortable to use. The first cell will look like this:<\/p>\n
    import<\/span> pandas as<\/span> pd\nfrom<\/span> sklearn import<\/span> datasets\nimport<\/span> numpy as<\/span> np\n\n#Taking it in as an np array<\/span>\ndf = datasets.load_iris()\n\n#converting it to a pandas dataframe<\/span>\ndf = pd.DataFrame(data=np.c_[df['data'<\/span>], df['target'<\/span>]],\n                  columns= df['feature_names'<\/span>] + ['target'<\/span>])\n\ndf<\/span><\/pre>\n
    The result is a Pandas DataFrame that looks like this:<\/p>\n
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    Screenshot by author<\/figcaption>\n<\/figure>\n
    I will skip the preprocessing steps to purely focus on the model tracking and get straight to testing the performance of different algorithms.<\/p>\n
    The next step is to define the features and labels, i.e., X<\/code> and y<\/code>, for fitting and prediction purposes.<\/p>\n
    #defining X and y<\/span>\nX = df.drop(['target'<\/span>], axis=1<\/span>)\ny = df['target'<\/span>]<\/span><\/pre>\n
    Now, we can move to testing and fitting an algorithm, then exporting the model and registering it to the Model Registry.<\/p>\n
    Model Extraction and Registration<\/h1>\n
    For the first version, I want to fit a KNeighborsClassifier<\/code> to fit the data. Additionally, I will use StratifiedKFold<\/code> cross-validation to perform multiple train-test splits.<\/p>\n
    After fitting our model, we will extract it with the Joblib<\/code> library and finally get it registered in the Model Registry.<\/p>\n
    from<\/span> sklearn.model_selection import<\/span> cross_val_score\nfrom<\/span> sklearn.neighbors import<\/span> KNeighborsClassifier\nfrom<\/span> sklearn.model_selection import<\/span> StratifiedKFold\nfrom<\/span> numpy import<\/span> mean\nfrom<\/span> joblib import<\/span> dump\nfrom<\/span> comet_ml import<\/span> Experiment\nimport<\/span> comet_ml\n\n#initializes a project named \"model_tracking\"<\/span>\ncomet_ml.login(project_name=\"model_tracking\"<\/span>)\n\n#Algorithm of choice<\/span>\nmodel  = KNeighborsClassifier(n_neighbors=3<\/span>)\n\n#Training using Stratified K-fold cross validation<\/span>\ndef<\/span> cross_val_eval<\/span>(model, X, y<\/span>):\n    cv = StratifiedKFold(n_splits=5<\/span>, shuffle=True<\/span>, random_state=5<\/span>)\n    cv_scores = cross_val_score(model, X, y, cv = cv, scoring='accuracy'<\/span>, n_jobs=-1<\/span>, error_score='raise'<\/span>)\n    return<\/span> cv_scores\n\ncv_scores = cross_val_eval(model, X, y)\nprint<\/span>(mean(cv_scores))\n\n#fitting and then extracting model<\/span>\nmodel.fit(X, y)\ndump(model, 'model.joblib'<\/span>)\n\n#Logs model experiment to the project<\/span>\nexperiment = Experiment()\nexperiment.log_model(\"model1\"<\/span>, \"model directory within the computer\"<\/span>)\n\nexperiment.end()<\/span><\/pre>\n
    The above model gives us an accuracy of 95.3%.<\/p>\n
    Note that the first line of code (comet_ml.login(project_name = \u201cmodel_tracking\u201d)<\/code>) in our Jupyter Notebook will cause the following to appear on your Project page on our browser:<\/p>\n
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    Screenshot by author<\/figcaption>\n<\/figure>\n
    Our project is on the right side in the image above. We can also see that it states there is one experiment in there. The next step is to take said experiment\u2019s directory after opening the project by clicking \u201cView Project\u201d.<\/p>\n
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    Screenshot by author<\/figcaption>\n<\/figure>\n
    After clicking the project and cycling to the \u201cExperiments\u201d tab, we can then see the name that it has been assigned and we can click on it.<\/p>\n
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    \"\"<\/figure>
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    Screenshot by author<\/figcaption>\n<\/figure>\n
    After selecting our experiment, intact_silo_3082<\/code>, we can now copy the path to this experiment (see the top section that is immediately below Comet\u2019s logo in the image above).<\/p>\n
    It reads mwanikinjagi\/model_tracking\/intact_silo_3082<\/code> on my page and this is what I will use to register a model into the model registry. To do this, I will use the code below:<\/p>\n
    from<\/span> comet_ml import<\/span> API\n\napi= API()\n#We feed the experiment in the get() method of the api <\/span>\nexperiment = api.get(\"mwanikinjagi\/model-tracking\/intact_silo_3082\"<\/span>)\n#finally registers with given name you have chosen<\/span>\nexperiment.register_model(\"model1\"<\/span>)<\/span><\/pre>\n
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    \"\"<\/figure>
    <\/picture><\/div>\n<\/div>\n
    Screenshot by author<\/figcaption>\n<\/figure>\n
    Now, we have our model1<\/code> (it could use a more intuitive name) and we have been provided with a default version number 1.0.0. You have now successfully registered the first version of your model and can find it in the Model Registry.<\/p>\n
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    \"\"<\/figure>
    <\/picture><\/div>\n<\/div>\n
    Screenshot by author<\/figcaption>\n<\/figure>\n
    When we select \u201cView model,\u201d it takes us to a page that allows us to do more with the model. For instance, if working with teams then one could download the different versions of the model from that central point.<\/p>\n
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    \"\"<\/figure>
    <\/picture><\/div>\n<\/div>\n
    Screenshot by author<\/figcaption>\n<\/figure>\n
    Wrap up<\/h1>\n
    In this first part, we have been able to set up our model in the Model Registry. It may have seemed difficult initially but it\u2019s actually very straightforward.<\/p>\n
    In the next part, I will take you through dealing with different versions of models with a simple example project like the one we have taken up there.<\/p>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"
    Photo by Isaac Smith on Unsplash The world of machine learning and data science is awash with technicalities. With each passing day, it becomes ever more evident that a practitioner in this field needs to keep track of a lot of things lest they fall into the deluge of complexity. 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