operatorai · nlathia · Feb 22, 2026 · Feb 21, 2026 · Feb 21, 2026 · Feb 21, 2026
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,5 +1,20 @@
 # Change log
 
+## modelstore 0.0.82 ([February 2026](https://github.com/operatorai/modelstore/pull/293))
+
+**🆕  New functionality**
+
+* Added support for [statsmodels](https://www.statsmodels.org/) [#293](https://github.com/operatorai/modelstore/pull/293)
+* Added support for [Backblaze B2](https://www.backblaze.com/cloud-storage) storage backend via `ModelStore.from_backblaze()` [#289](https://github.com/operatorai/modelstore/pull/289), (thanks [jeronimodeleon](https://github.com/jeronimodeleon))
+
+**🐛  Bug fixes & general updates**
+
+* Updated AWS and Backblaze storage backends [#292](https://github.com/operatorai/modelstore/pull/292)
+* Removed deprecated `pkg_resources` from the project dependencies [#288](https://github.com/operatorai/modelstore/pull/288), (thanks [divineod](https://github.com/divineod))
+* Fixed `pkg_resources` migration issues: `PackageNotFoundError` import and platform-specific `np.float96` removal [#291](https://github.com/operatorai/modelstore/pull/291)
+* Migrated from `pyenv` to `uv` for Python environment management [#290](https://github.com/operatorai/modelstore/pull/290)
+* Stopped supporting Python versions that are past their EOL
+
 ## modelstore 0.0.81 ([May 2024](https://github.com/operatorai/modelstore/pull/270))
 
 **🆕  New functionality**

diff --git a/bin/_build_library b/bin/_build_library
@@ -1,13 +1,11 @@
 #!/bin/bash
 set -e
 
-VIRTUALENV_NAME=$(pyenv local)
+echo -e "\n ⏱  Building library..."
 
-echo "\n ⏱  Building library: $VIRTUALENV_NAME"
+rm -rf dist build modelstore.egg-info
+uv pip install --upgrade pip setuptools wheel
 
-rm -rf dist build modelstore.egg_info
-pip install --upgrade pip setuptools wheel
+uv run python setup.py sdist bdist_wheel
 
-python setup.py sdist bdist_wheel
-
-echo "\n ✅  Done: results are in the dist/ directory."
+echo -e "\n ✅  Done: results are in the dist/ directory."
diff --git a/bin/_cleanup b/bin/_cleanup
@@ -7,4 +7,7 @@ rm -rf *.egg-info
 rm -rf build
 rm -rf dist
 
+echo -e "\n 🧼  Removing docker things"
+docker system prune --all 
+
 echo -e "\n 🎉  Done."
diff --git a/bin/_release_prod b/bin/_release_prod
@@ -1,15 +1,15 @@
 #!/bin/bash
 set -e
 
-echo "\n ⏱  Uploading library to pypi..."
+echo -e "\n ⏱  Uploading library to pypi..."
 
-pip install --upgrade twine
+uv pip install --upgrade twine
 
-twine check dist/*
+uv run twine check dist/*
 
-twine upload \
+uv run twine upload \
 	--username $TWINE_PROD_USERNAME \
 	--password $TWINE_PROD_PWD \
 	dist/*
 
-echo "\n 🎉  Done."
+echo -e "\n 🎉  Done."
diff --git a/bin/_release_test b/bin/_release_test
@@ -1,15 +1,15 @@
 #!/bin/bash
 set -e
 
-echo "\n ⏱  Uploading library to testpypi..."
+echo -e "\n ⏱  Uploading library to testpypi..."
 
-pip install --upgrade twine
+uv pip install --upgrade twine
 
-twine check dist/*
+uv run twine check dist/*
 
-twine upload \
+uv run twine upload \
 	--username $TWINE_TEST_USERNAME \
 	--password $TWINE_TEST_PWD \
-	--repository testpypi dist/*
+	--repository testpypi dist/* --verbose
 
-echo "\n 🚢  Done."
+echo -e "\n 🚢  Done."
diff --git a/examples/Makefile-example b/examples/Makefile-example
@@ -1,37 +1,33 @@
-VIRTUALENV_NAME=modelstore.$(shell pwd | rev | cut -d '/' -f 1 | rev)
 REPO_ROOT=$(shell cd ../../ && pwd)
 
-.PHONY: name pyenv pyenv-local pyenv-prod pyenv-test pyenv-uninstall refresh gcloud
+.PHONY: uninstall install install-local install-test install-prod refresh gcloud
 
-name:
-	@echo $(VIRTUALENV_NAME)
-
-pyenv-uninstall:
-	@$(REPO_ROOT)/bin/_pyenv_uninstall $(VIRTUALENV_NAME)
+uninstall:
+	@if [ -d ".venv" ]; then rm -rf .venv; echo " ✅  Removed .venv"; else echo " ✅  Nothing to do."; fi
 
 gcloud:
 	@gcloud components update
 	@gcloud auth application-default login
 
-pyenv: pyenv-uninstall
-	# @$(REPO_ROOT)/bin/_setup_brew
-	@$(REPO_ROOT)/bin/_pyenv_install $(VIRTUALENV_NAME)
-	pip install -r https://raw.githubusercontent.com/ultralytics/yolov5/master/requirements.txt
+install: uninstall
+	@uv venv
+	@uv pip install --upgrade pip setuptools wheel
+	@uv pip install -r requirements.txt
 
-pyenv-local: pyenv
-	pip uninstall -y modelstore
-	pip install -e $(REPO_ROOT)
+install-local: install
+	@uv pip uninstall modelstore
+	@uv pip install -e $(REPO_ROOT)
 
-pyenv-test: pyenv
-	pip uninstall -y modelstore
-	pip install --no-cache-dir -i https://test.pypi.org/simple/ modelstore
+install-test: install
+	@uv pip uninstall modelstore
+	@uv pip install --no-cache-dir --extra-index-url https://test.pypi.org/simple/ --index-strategy unsafe-best-match modelstore==0.0.82
 
-pyenv-prod: pyenv
-	pip uninstall -y modelstore
-	pip install --no-cache-dir --upgrade modelstore
+install-prod: install
+	@uv pip uninstall modelstore
+	@uv pip install --no-cache-dir --upgrade modelstore
 
 refresh:
 	@echo "\n 🔵  Refreshing installation of modelstore"
-	pip install --upgrade pip setuptools wheel	
-	pip uninstall -y modelstore
-	pip install --no-cache-dir -e $(REPO_ROOT)
+	@uv pip install --upgrade pip setuptools wheel
+	@uv pip uninstall modelstore
+	@uv pip install --no-cache-dir -e $(REPO_ROOT)
diff --git a/examples/README.md b/examples/README.md
@@ -2,13 +2,13 @@
 
 This directory contains examples of training models and storing them into a model store over different types of storage.
 
-The Python script in `examples-by-ml-model` iterates over all of the supported ML frameworks and all of the supported storage types. For each pair, it trains a model, uploads it to storage, and then downloads/loads it back. 
+The Python script in `examples-by-ml-model` iterates over all of the supported ML frameworks and all of the supported storage types. For each pair, it trains a model, uploads it to storage, and then downloads/loads it back.
 
 The bash script `cli-examples` has exaples of how to run `python -m modelstore` commands.
 
 ## Pre-requisites
 
-As with the main library, these scripts have been developed using [pyenv](https://github.com/pyenv/pyenv) and [pyenv-virtualenv](https://github.com/pyenv/pyenv-virtualenv).
+As with the main library, these scripts have been developed using [uv](https://github.com/astral-sh/uv).
 
 ## Set up - examples by ML model
 
@@ -24,7 +24,7 @@ And then you can use this `Makefile` command that creates a new virtual environm
 and installs all of the requirements:
 
 ```bash
-❯ make pyenv
+❯ make install
 ```
 
 ## Running all of the examples

diff --git a/examples/cli-examples/model.py b/examples/cli-examples/model.py
@@ -33,7 +33,7 @@ def train_and_save():
         "max_depth": 4,
         "min_samples_split": 5,
         "learning_rate": 0.01,
-        "loss": "ls",
+        "loss": "squared_error",
     }
     model = GradientBoostingRegressor(**params)
     model.fit(X_train, y_train)

diff --git a/examples/cli-examples/run-all.sh b/examples/cli-examples/run-all.sh
@@ -5,14 +5,14 @@ TARGET_DIR="downloaded_model"
 FILE_NAME="model.joblib"
 
 echo "\n🔵  Training a model...\n"
-python model.py
+uv run python model.py
 
 echo "\n🔵  Uploading the model via the CLI...\n"
-MODEL_ID=$(python -m modelstore upload "$DOMAIN_NAME" "$FILE_NAME")
+MODEL_ID=$(uv run python -m modelstore upload "$DOMAIN_NAME" "$FILE_NAME")
 
 echo "\n🔵  Downloading model=$MODEL_ID via the CLI...\n"
 mkdir -p "$TARGET_DIR"
-python -m modelstore download "$DOMAIN_NAME" "$MODEL_ID" "$TARGET_DIR"
+uv run python -m modelstore download "$DOMAIN_NAME" "$MODEL_ID" "$TARGET_DIR"
 
 echo "\n✅  Done! Cleaning up..."
 

diff --git a/examples/examples-by-ml-library/Dockerfile b/examples/examples-by-ml-library/Dockerfile
@@ -0,0 +1,26 @@
+FROM python:3.11-slim
+WORKDIR /usr/src/app
+
+ARG DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && \
+    apt-get install -y build-essential git ninja-build ccache libopenblas-dev libopencv-dev cmake && \
+    apt-get install -y gcc mono-mcs g++ && \
+    apt-get install -y default-jdk && \
+    apt-get install -y libhdf5-dev && \
+    rm -rf /var/lib/apt/lists/*
+
+# Install modelstore library from test.pypi.org
+RUN pip install --upgrade pip setuptools wheel && \
+    pip install --index-url https://test.pypi.org/simple/ \
+                --extra-index-url https://pypi.org/simple/ \
+                modelstore
+
+# Install example dependencies
+COPY requirements.txt ./requirements.txt
+RUN pip install -r requirements.txt
+
+# Copy example source
+COPY . ./examples
+WORKDIR /usr/src/app/examples
+
+ENTRYPOINT ["python", "main.py"]
diff --git a/examples/examples-by-ml-library/Makefile b/examples/examples-by-ml-library/Makefile
@@ -1,6 +1,20 @@
 include ../Makefile-example
 
-.PHONY: run
+DOCKER_IMAGE ?= modelstore-examples
+MODELSTORE_IN ?= filesystem
+ML_FRAMEWORK ?= sklearn
+
+.PHONY: run docker-build docker-run
+
 run:
 	@./run-all.sh
-
+
+docker-build:
+	@docker build -f Dockerfile -t $(DOCKER_IMAGE) .
+
+docker-run:
+	@docker run --rm \
+		-e MODEL_STORE_ROOT_PREFIX=/tmp/modelstore \
+		$(DOCKER_IMAGE) \
+		--modelstore-in $(MODELSTORE_IN) \
+		--ml-framework $(ML_FRAMEWORK)
diff --git a/examples/examples-by-ml-library/libraries/skorch_example.py b/examples/examples-by-ml-library/libraries/skorch_example.py
@@ -62,7 +62,13 @@ def train_and_upload(modelstore: ModelStore) -> dict:
 def load_and_test(modelstore: ModelStore, model_domain: str, model_id: str):
     # Load the model back into memory!
     print(f'⤵️  Loading the skorch "{model_domain}" domain model={model_id}')
-    model = modelstore.load(model_domain, model_id)
+    loaded = modelstore.load(model_domain, model_id)
+
+    # When multiple files are saved, modelstore returns a dict
+    if isinstance(loaded, dict):
+        model = loaded["skorch"]
+    else:
+        model = loaded
 
     # Run some example predictions
     _, X_test, _, y_test = load_regression_dataset(as_numpy=True)

diff --git a/examples/examples-by-ml-library/main.py b/examples/examples-by-ml-library/main.py
@@ -11,72 +11,41 @@
 #    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #    See the License for the specific language governing permissions and
 #    limitations under the License.
+import importlib
 import sys
 
 import click
-from libraries import (
-    annoy_example,
-    catboost_example,
-    causalml_example,
-    fastai_example,
-    gensim_example,
-    keras_example,
-    lightgbm_example,
-    onnx_lightgbm_example,
-    onnx_sklearn_example,
-    prophet_example,
-    pyspark_example,
-    pytorch_example,
-    pytorch_lightning_example,
-    raw_file_example,
-    shap_example,
-    sklearn_example,
-    sklearn_with_explainer_example,
-    sklearn_with_extras_example,
-    skorch_example,
-    tensorflow_example,
-    xgboost_booster_example,
-    xgboost_example,
-    yolo_example
-)
-from libraries.huggingface import (
-    distilbert,
-    dpt,
-    gpt2_pytorch,
-    gpt2_tensorflow,
-    sam
-)
 from modelstores import MODELSTORES, create_model_store
 
 EXAMPLES = {
-    "annoy": annoy_example,
-    "catboost": catboost_example,
-    "causalml": causalml_example,
-    "dpt": dpt,
-    "fastai": fastai_example,
-    "file": raw_file_example,
-    "gensim": gensim_example,
-    "hf-distilbert": distilbert,
-    "hf-gpt2-pt": gpt2_pytorch,
-    "hf-gpt2-tf": gpt2_tensorflow,
-    "keras": keras_example,
-    "lightgbm": lightgbm_example,
-    "onnx-sklearn": onnx_sklearn_example,
-    "onnx-lightgbm": onnx_lightgbm_example,
-    "prophet": prophet_example,
-    "pyspark": pyspark_example,
-    "pytorch": pytorch_example,
-    "pytorch-lightning": pytorch_lightning_example,
-    "segment-anything": sam,
-    "shap": shap_example,
-    "sklearn": sklearn_example,
-    "sklearn-with-explainer": sklearn_with_explainer_example,
-    "sklearn-with-extras": sklearn_with_extras_example,
-    "skorch": skorch_example,
-    "tensorflow": tensorflow_example,
-    "xgboost": xgboost_example,
-    "xgboost-booster": xgboost_booster_example,
-    "yolov5": yolo_example,
+    "annoy": "libraries.annoy_example",
+    "catboost": "libraries.catboost_example",
+    "causalml": "libraries.causalml_example",
+    "dpt": "libraries.huggingface.dpt",
+    "fastai": "libraries.fastai_example",
+    "file": "libraries.raw_file_example",
+    "gensim": "libraries.gensim_example",
+    "hf-distilbert": "libraries.huggingface.distilbert",
+    "hf-gpt2-pt": "libraries.huggingface.gpt2_pytorch",
+    "hf-gpt2-tf": "libraries.huggingface.gpt2_tensorflow",
+    "keras": "libraries.keras_example",
+    "lightgbm": "libraries.lightgbm_example",
+    "onnx-sklearn": "libraries.onnx_sklearn_example",
+    "onnx-lightgbm": "libraries.onnx_lightgbm_example",
+    "prophet": "libraries.prophet_example",
+    "pyspark": "libraries.pyspark_example",
+    "pytorch": "libraries.pytorch_example",
+    "pytorch-lightning": "libraries.pytorch_lightning_example",
+    "segment-anything": "libraries.huggingface.sam",
+    "shap": "libraries.shap_example",
+    "sklearn": "libraries.sklearn_example",
+    "sklearn-with-explainer": "libraries.sklearn_with_explainer_example",
+    "sklearn-with-extras": "libraries.sklearn_with_extras_example",
+    "skorch": "libraries.skorch_example",
+    "tensorflow": "libraries.tensorflow_example",
+    "xgboost": "libraries.xgboost_example",
+    "xgboost-booster": "libraries.xgboost_booster_example",
+    "yolov5": "libraries.yolo_example",
 }
 
 
@@ -109,7 +78,7 @@ def main(modelstore_in, ml_framework):
 
     # Create a model store instance
     modelstore = create_model_store(modelstore_in)
-    example = EXAMPLES[ml_framework]
+    example = importlib.import_module(EXAMPLES[ml_framework])
 
     # Demo how we train and upload a model
     meta_data = example.train_and_upload(modelstore)