Update workflow to run example tests

.github/workflows/test_openvino_examples.yml

@@ -7,11 +7,11 @@ on:
   push:
     paths:
     - '.github/workflows/test_openvino_examples.yml'
-    - 'examples/openvino/*'
+    - 'examples/openvino/**'
   pull_request:
     paths:
     - '.github/workflows/test_openvino_examples.yml'
-    - 'examples/openvino/*'
+    - 'examples/openvino/**'
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
@@ -22,9 +22,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.10"]
+        python-version: ["3.8", "3.11"]
 
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
 
     steps:
     - uses: actions/checkout@v2
@@ -35,12 +35,12 @@ jobs:
 
     - name: Install dependencies
       run: |
-        pip install optimum[openvino] jstyleson nncf pytest
-        pip install -r examples/openvino/audio-classification/requirements.txt
-        pip install -r examples/openvino/image-classification/requirements.txt
-        pip install -r examples/openvino/question-answering/requirements.txt
-        pip install -r examples/openvino/text-classification/requirements.txt
+        pip install .[openvino] jstyleson pytest
+        pip install -r examples/openvino/audio-classification/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -r examples/openvino/image-classification/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -r examples/openvino/question-answering/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
+        pip install -r examples/openvino/text-classification/requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
 
     - name: Test examples
       run: |
-        python -m pytest examples/openvino/test_examples.py
+        python -m pytest examples/openvino/test_examples.py

.github/workflows/test_openvino_notebooks.yml

@@ -23,9 +23,9 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.10"]
+        python-version: ["3.8", "3.11"]
 
-    runs-on: ubuntu-20.04
+    runs-on: ubuntu-22.04
 
     steps:
     - uses: actions/checkout@v2

examples/openvino/question-answering/README.md

@@ -13,7 +13,7 @@ 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.
 -->
-# Question answering
+# Question nswering
 
 This folder contains [`run_qa.py`](https://github.com/huggingface/optimum/blob/main/examples/openvino/question-answering/run_qa.py), a script to fine-tune a 🤗 Transformers model on a question answering dataset while applying quantization aware training (QAT). QAT can be easily applied by replacing the Transformers [`Trainer`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#trainer) with the Optimum [`OVTrainer`].
 An `QuestionAnsweringOVTrainer` is defined in [`trainer_qa.py`](https://github.com/huggingface/optimum/blob/main/examples/openvino/question-answering/trainer_qa.py), which inherits from `OVTrainer` and is adapted to perform question answering tasks evaluation.
@@ -47,6 +47,7 @@ python run_qa.py \
 ```
 
 ### Joint Pruning, Quantization and Distillation (JPQD) for BERT on SQuAD1.0
+
 `OVTrainer` also provides an advanced optimization workflow through the NNCF when Transformer model can be structurally pruned along with 8-bit quantization and distillation. Below is an example which demonstrates how to jointly prune, quantize BERT-base for SQuAD 1.0 using NNCF config `--nncf_compression_config` and distill from BERT-large teacher. This example closely resembles the movement sparsification work of [Lagunas et al., 2021, Block Pruning For Faster Transformers](https://arxiv.org/pdf/2109.04838.pdf). This example takes about 12 hours with a single V100 GPU and ~40% of the weights of the Transformer blocks were pruned. For launching the script on multiple GPUs specify `--nproc-per-node=<number of GPU>`. Note, that different batch size and other hyperparameters qmight be required to achieve the same results as on a single GPU.
 
 More on how to configure movement sparsity, see NNCF documentation [here](https://github.com/openvinotoolkit/nncf/blob/develop/nncf/experimental/torch/sparsity/movement/MovementSparsity.md).

notebooks/openvino/requirements.txt

@@ -1,4 +1,4 @@
-optimum-intel[openvino, nncf]
+optimum-intel[openvino]
 datasets
 evaluate[evaluator]
 ipywidgets