test run

Dockerfile.tmpl

@@ -43,11 +43,12 @@ RUN uv pip install \
     "nvidia-nvjitlink-cu12==12.5.82"
 RUN uv pip install --system --force-reinstall "pynvjitlink-cu12==0.5.2"
 
-# b/385145217 Latest Colab lacks mkl numpy, install it.
-RUN uv pip install --system --force-reinstall -i https://pypi.anaconda.org/intel/simple numpy
-
 # newer daal4py requires tbb>=2022, but libpysal is downgrading it for some reason
-RUN uv pip install --system "tbb>=2022" "libpysal==4.9.2"
+# b/315753846: Unpin translate package.
+RUN uv pip install --system "tbb>=2022" "libpysal==4.9.2" "google-cloud-translate==3.12.1"
+
+# b/385145217 Latest Colab lacks mkl numpy, install it.
+RUN uv pip install --system --force-reinstall -i  https://software.repos.intel.com/python/pypi "numpy==1.26.4"
 
 # b/404590350: Ray and torchtune have conflicting tune cli, we will prioritize torchtune.
 # b/415358158: Gensim removed from Colab image to upgrade scipy
@@ -58,8 +59,8 @@ ADD clean-layer.sh  /tmp/clean-layer.sh
 ADD patches/nbconvert-extensions.tpl /opt/kaggle/nbconvert-extensions.tpl
 ADD patches/template_conf.json /opt/kaggle/conf.json
 
-# /opt/conda/lib/python3.11/site-packages
-ARG PACKAGE_PATH=/usr/local/lib/python3.11/dist-packages
+# /opt/conda/lib/python3.12/site-packages
+ARG PACKAGE_PATH=/usr/local/lib/python3.12/dist-packages
 
 # Install GPU-specific non-pip packages.
 {{ if eq .Accelerator "gpu" }}
@@ -86,10 +87,14 @@ ADD patches/keras_internal.py \
 RUN apt-get install -y libfreetype6-dev && \
     apt-get install -y libglib2.0-0 libxext6 libsm6 libxrender1 libfontconfig1 --fix-missing
 
-# NLTK Project datasets
-# b/408298750: We currently reinstall the package, because we get the following error:
-# `AttributeError: module 'inspect' has no attribute 'formatargspec'. Did you mean: 'formatargvalues'?`
-RUN uv pip install --system --force-reinstall "nltk>=3.9.1"
+RUN apt-get install -y git-lfs && \
+    # vtk dependencies
+    apt-get install -y libgl1-mesa-glx && \
+    # xvfbwrapper dependencies
+    apt-get install -y xvfb && \
+    /tmp/clean-layer.sh
+
+RUN uv pip install --system --force-reinstall "nltk==3.9.1"
 RUN mkdir -p /usr/share/nltk_data && \
     # NLTK Downloader no longer continues smoothly after an error, so we explicitly list
     # the corpuses that work
@@ -108,13 +113,6 @@ RUN mkdir -p /usr/share/nltk_data && \
     twitter_samples udhr2 udhr unicode_samples universal_tagset universal_treebanks_v20 \
     vader_lexicon verbnet webtext word2vec_sample wordnet wordnet_ic words ycoe
 
-RUN apt-get install -y git-lfs && \
-    # vtk dependencies
-    apt-get install -y libgl1-mesa-glx && \
-    # xvfbwrapper dependencies
-    apt-get install -y xvfb && \
-    /tmp/clean-layer.sh
-
 # Download base easyocr models.
 # https://github.com/JaidedAI/EasyOCR#usage
 RUN mkdir -p /root/.EasyOCR/model && \
@@ -180,7 +178,7 @@ ADD patches/kaggle_gcp.py \
 
 # Figure out why this is in a different place?
 # Found by doing a export PYTHONVERBOSE=1 and then running python and checking for where it looked for it.
-ADD patches/sitecustomize.py /usr/lib/python3.11/sitecustomize.py
+ADD patches/sitecustomize.py /usr/lib/python3.12/sitecustomize.py
 
 ARG GIT_COMMIT=unknown \
     BUILD_DATE=unknown

config.txt

@@ -1,4 +1,4 @@
 BASE_IMAGE=us-docker.pkg.dev/colab-images/public/runtime
-BASE_IMAGE_TAG=release-colab_20250626-060053_RC00
+BASE_IMAGE_TAG=release-colab_20250916-060051_RC00
 CUDA_MAJOR_VERSION=12
 CUDA_MINOR_VERSION=5

kaggle_requirements.txt

@@ -9,11 +9,9 @@ PyArabic
 PyUpSet
 Pympler
 Rtree
-shapely<2
 SimpleITK
 # b/302136621: Fix eli5 import for learntools, newer version require scikit-learn > 1.3
 TPOT==0.12.1
-Theano
 Wand
 annoy
 arrow
@@ -49,11 +47,10 @@ geojson
 geopandas==v0.14.4
 gensim
 google-cloud-aiplatform
-# b/315753846: Unpin translate package.
-google-cloud-translate==3.12.1
 google-cloud-videointelligence
 google-cloud-vision
 google-genai
+google-adk
 gpxpy
 h2o
 haversine
@@ -74,7 +71,7 @@ jupyterlab-lsp
 kaggle>=1.7.4.2
 kaggle-environments
 keras-cv
-keras-nlp
+keras-hub
 keras-tuner
 kornia
 langid
@@ -109,12 +106,10 @@ preprocessing
 pudb
 pyLDAvis
 pycryptodome
-pydegensac
 pydicom
 pydub
 pyemd
 pyexcel-ods
-pymc3
 pymongo
 pypdf
 pytesseract
@@ -140,16 +135,15 @@ git+https://github.com/facebookresearch/segment-anything.git
 # b/329869023: shap 0.45.0 breaks learntools
 shap==0.44.1
 squarify
-tensorflow-cloud
-tensorflow-io
-tensorflow-text
-tensorflow_decision_forests
 timm
 torchao
 torchinfo
 torchmetrics
 torchtune
 transformers>=4.51.0
+tensorflow-cloud
+tensorflow-io
+tensorflow-text
 triton
 tsfresh
 vtk

tests/test_keras_nlp.py

@@ -1,6 +1,6 @@
 import unittest
 
-import keras_nlp
+import keras_hub
 import keras
 import numpy as np
 
@@ -9,7 +9,7 @@
 class TestKerasNLP(unittest.TestCase):
     def test_fit(self):
         with create_test_kagglehub_server():
-            classifier = keras_nlp.models.BertClassifier.from_preset(
+            classifier = keras_hub.models.BertClassifier.from_preset(
                 'bert_tiny_en_uncased',
                 load_weights=False, # load randomly initialized model from preset architecture with weights
                 num_classes=2,

tests/test_numpy.py

@@ -19,13 +19,8 @@ def test_array(self):
     # Numpy must be linked to the MKL. (Occasionally, a third-party package will muck up the installation
     # and numpy will be reinstalled with an OpenBLAS backing.)
     def test_mkl(self):
-        try:
-            from numpy.distutils.system_info import get_info
-            # This will throw an exception if the MKL is not linked correctly or return an empty dict.
-            self.assertTrue(get_info("blas_mkl"))
-        except:
-            # Fallback to check if mkl is present via show_config()
-            config_out = io.StringIO()
-            with redirect_stdout(config_out):
-                np.show_config()
-            self.assertIn("mkl_rt", config_out.getvalue())
+        # Fallback to check if mkl is present via show_config()
+        config_out = io.StringIO()
+        with redirect_stdout(config_out):
+            np.show_config()
+            self.assertIn("mkl-dynamic", config_out.getvalue())

tests/test_tensorflow_decision_forests.py

@@ -1,18 +1,18 @@
-import unittest
+# import unittest
 
-import numpy as np
-import pandas as pd
-import tensorflow_decision_forests as tfdf
+# import numpy as np
+# import pandas as pd
+# import tensorflow_decision_forests as tfdf
 
-class TestTensorflowDecisionForest(unittest.TestCase):
-    def test_fit(self):
-        train_df = pd.read_csv("/input/tests/data/train.csv")
+# class TestTensorflowDecisionForest(unittest.TestCase):
+#     def test_fit(self):
+#         train_df = pd.read_csv("/input/tests/data/train.csv")
 
-        # Convert the dataset into a TensorFlow dataset.
-        train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="label")
+#         # Convert the dataset into a TensorFlow dataset.
+#         train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(train_df, label="label")
 
-        # Train the model
-        model = tfdf.keras.RandomForestModel(num_trees=1)
-        model.fit(train_ds)
+#         # Train the model
+#         model = tfdf.keras.RandomForestModel(num_trees=1)
+#         model.fit(train_ds)
 
-        self.assertEqual(1, model.count_params())
+#         self.assertEqual(1, model.count_params())