Forward-merge branch-25.02 into branch-25.06

CHANGELOG.md

@@ -15,6 +15,19 @@ See the License for the specific language governing permissions and
 limitations under the License.
 -->
 
+# Morpheus 25.02.01 (28 Feb 2025)
+
+## 🐛 Bug Fixes
+
+- Perform apt upgrade during docker build of the models container ([#2174](https://github.com/nv-morpheus/Morpheus/pull/2174)) [@dagardner-nv](https://github.com/dagardner-nv)
+- Remove pe_count option from MonitorStage ([#2178](https://github.com/nv-morpheus/Morpheus/pull/2178)) [@yczhang-nv](https://github.com/yczhang-nv)
+- Pick up changes from nv-morpheus/morpheus-visualizations#50 ([#2186](https://github.com/nv-morpheus/Morpheus/pull/2186)) [@dagardner-nv](https://github.com/dagardner-nv)
+
+## 📖 Documentation
+
+- Remove out of date documentation ([#2173](https://github.com/nv-morpheus/Morpheus/pull/2173)) [@dagardner-nv](https://github.com/dagardner-nv)
+- Include a Third Party OSS notice in the entrypoint banner ([#2176](https://github.com/nv-morpheus/Morpheus/pull/2176)) [@dagardner-nv](https://github.com/dagardner-nv)
+
 # Morpheus 25.02.00 (04 Feb 2025)
 
 ## 🐛 Bug Fixes

ci/scripts/github/conda_libs.sh

@@ -31,10 +31,12 @@ fetch_base_branch
 # Print the info just to be sure base is active
 conda info
 
+rapids-logger "Installing Boa and Git LFS"
+conda install --solver=libmamba -c conda-forge boa git-lfs
+
 # Pull down data needed for running the per-library unit tests
 rapids-logger "Pulling LFS assets"
 
-conda install git-lfs
 git lfs install
 ${MORPHEUS_ROOT}/scripts/fetch_data.py fetch tests validation
 

docs/source/developer_guide/guides/2_real_world_phishing.md

@@ -413,7 +413,7 @@ else:
     pipeline.add_stage(RecipientFeaturesStage(config))
 ```
 
-To tokenize the input data we will use Morpheus' `PreprocessNLPStage`. This stage uses the [cuDF subword tokenizer](https://docs.rapids.ai/api/cudf/legacy/user_guide/api_docs/subword_tokenize/#subwordtokenizer) to transform strings into a tensor of numbers to be fed into the neural network model. Rather than split the string by characters or whitespaces, we split them into meaningful subwords based upon the occurrence of the subwords in a large training corpus. You can find more details here: [https://arxiv.org/abs/1810.04805v2](https://arxiv.org/abs/1810.04805v2). All we need to know for now is that the text will be converted to subword token ids based on the vocabulary file that we provide (`vocab_hash_file=vocab file`).
+To tokenize the input data we will use Morpheus' `PreprocessNLPStage`. This stage uses the [cuDF subword tokenizer](https://docs.rapids.ai/api/cudf/legacy/user_guide/api_docs/pylibcudf/nvtext/subword_tokenize/#module-pylibcudf.nvtext.subword_tokenize) to transform strings into a tensor of numbers to be fed into the neural network model. Rather than split the string by characters or whitespaces, we split them into meaningful subwords based upon the occurrence of the subwords in a large training corpus. You can find more details here: [https://arxiv.org/abs/1810.04805v2](https://arxiv.org/abs/1810.04805v2). All we need to know for now is that the text will be converted to subword token ids based on the vocabulary file that we provide (`vocab_hash_file=vocab file`).
 
 Let's go ahead and instantiate our `PreprocessNLPStage` and add it to the pipeline: