From 1b5fd058b69f0f595bb1359829ff79577ff9e8db Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Wed, 12 Nov 2025 18:11:30 -0800 Subject: [PATCH 01/17] adding venv and fix regex Signed-off-by: Sachin Pisal --- .github/workflows/docker_images.yml | 1 + docs/notebook_validation.py | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/docker_images.yml b/.github/workflows/docker_images.yml index f549db071b5..58e02204810 100644 --- a/.github/workflows/docker_images.yml +++ b/.github/workflows/docker_images.yml @@ -841,6 +841,7 @@ jobs: # In containers without GPU support, UCX does not work properly since it is configured to work with GPU-support. # Hence, don't enforce UCX when running these tests. docker exec cuda-quantum bash -c "python3 -m pip install --break-system-packages pandas scipy seaborn h5py contfrac" + docker exec cuda-quantum bash -c "apt install python3.12-venv" (docker exec cuda-quantum bash -c "unset OMPI_MCA_pml && set -o pipefail && bash validate_container.sh | tee /tmp/validation.out") && passed=true || passed=false docker cp cuda-quantum:"/tmp/validation.out" /tmp/validation.out docker stop cuda-quantum diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 9dcba09729a..ac6defc5c91 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -29,7 +29,7 @@ def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() for notebook_content in lines: - match = re.search('set_target[\\\s\(]+"(.+)\\\\"[)]', notebook_content) + match = re.search('set_target[\\s\\(]+"(.+)\\\\"[)]', notebook_content) if match and (match.group(1) not in available_backends): return False for notebook_content in lines: From e90d13649a08a38b4a5ca552ab253cd48dc5cf9d Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Wed, 12 Nov 2025 20:09:14 -0800 Subject: [PATCH 02/17] adding sudo Signed-off-by: Sachin Pisal --- .github/workflows/docker_images.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker_images.yml b/.github/workflows/docker_images.yml index 58e02204810..c4c022cb373 100644 --- a/.github/workflows/docker_images.yml +++ b/.github/workflows/docker_images.yml @@ -841,7 +841,7 @@ jobs: # In containers without GPU support, UCX does not work properly since it is configured to work with GPU-support. # Hence, don't enforce UCX when running these tests. docker exec cuda-quantum bash -c "python3 -m pip install --break-system-packages pandas scipy seaborn h5py contfrac" - docker exec cuda-quantum bash -c "apt install python3.12-venv" + docker exec cuda-quantum bash -c "sudo apt install -y python3.12-venv" (docker exec cuda-quantum bash -c "unset OMPI_MCA_pml && set -o pipefail && bash validate_container.sh | tee /tmp/validation.out") && passed=true || passed=false docker cp cuda-quantum:"/tmp/validation.out" /tmp/validation.out docker stop cuda-quantum From 2e8fdd102fd534e7cc0006988597d85df9b9069c Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Wed, 12 Nov 2025 21:42:20 -0800 Subject: [PATCH 03/17] reverting regex Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index ac6defc5c91..9dcba09729a 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -29,7 +29,7 @@ def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() for notebook_content in lines: - match = re.search('set_target[\\s\\(]+"(.+)\\\\"[)]', notebook_content) + match = re.search('set_target[\\\s\(]+"(.+)\\\\"[)]', notebook_content) if match and (match.group(1) not in available_backends): return False for notebook_content in lines: From 50427be95549a8fd7ff66cec8f82ce977e903f12 Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Wed, 12 Nov 2025 22:07:03 -0800 Subject: [PATCH 04/17] fixing the regex Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 9dcba09729a..dc1be591cb9 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -29,7 +29,7 @@ def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() for notebook_content in lines: - match = re.search('set_target[\\\s\(]+"(.+)\\\\"[)]', notebook_content) + match = re.search('set_target[\\s\\(]+"(.+)"[\\)]', notebook_content) if match and (match.group(1) not in available_backends): return False for notebook_content in lines: From db980f37b04d6b4944cf6cdaa135b6242293eed7 Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Wed, 12 Nov 2025 22:28:29 -0800 Subject: [PATCH 05/17] capturing single quotes in regex Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index dc1be591cb9..2e75d1271f2 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -29,7 +29,7 @@ def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() for notebook_content in lines: - match = re.search('set_target[\\s\\(]+"(.+)"[\\)]', notebook_content) + match = re.search('set_target[\\s\\(]+[\'|"](.+)[\'|"][\\)]', notebook_content) if match and (match.group(1) not in available_backends): return False for notebook_content in lines: From a629bdfaf8b3ce39c03a4fdcad83b795dd2bc8ba Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Wed, 12 Nov 2025 22:46:46 -0800 Subject: [PATCH 06/17] capture only the target Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 2e75d1271f2..53d887996af 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -29,7 +29,7 @@ def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() for notebook_content in lines: - match = re.search('set_target[\\s\\(]+[\'|"](.+)[\'|"][\\)]', notebook_content) + match = re.search('set_target\\(["|\']([^"=]+)["|\']', notebook_content) if match and (match.group(1) not in available_backends): return False for notebook_content in lines: From 2a84ee126de780e174126c9eb2202c648306b06e Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 08:51:08 -0800 Subject: [PATCH 07/17] fix regex Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 53d887996af..46bcbafe44c 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -29,7 +29,7 @@ def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() for notebook_content in lines: - match = re.search('set_target\\(["|\']([^"=]+)["|\']', notebook_content) + match = re.search(r'set_target\(\s*\\"([^"]+)\\"', notebook_content) if match and (match.group(1) not in available_backends): return False for notebook_content in lines: From 96960afe7bc7db331cce970d04eebf040cd3f524 Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 10:37:36 -0800 Subject: [PATCH 08/17] fixing the regex to combine the target and option as a valid backend Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 46bcbafe44c..1cee7da0e22 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -25,13 +25,24 @@ def read_available_backends(): return [backend.strip() for backend in available_backends] +pattern = r'set_target\(\s*\\"([^"]+)\\"(?:\s*,\s*option\s*=\s*\\"([^"]+)\\")?' + def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() for notebook_content in lines: - match = re.search(r'set_target\(\s*\\"([^"]+)\\"', notebook_content) - if match and (match.group(1) not in available_backends): - return False + if re.search(r'\s*#', notebook_content): + continue + + match = re.search(pattern, notebook_content) + if match: + target = match.group(1) + opt = match.group(2) + combined = f"{target}-{opt}" if opt else target + if combined not in available_backends: + return False + else: + return True for notebook_content in lines: match = re.search('--target ([^ ]+)', notebook_content) if match and (match.group(1) not in available_backends): From a053910e42eb8874b56df2469de186c1f0cfe1a2 Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 10:38:24 -0800 Subject: [PATCH 09/17] adding escape for # Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 1cee7da0e22..749bd437ecd 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -31,7 +31,7 @@ def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() for notebook_content in lines: - if re.search(r'\s*#', notebook_content): + if re.search(r'\s*\#', notebook_content): continue match = re.search(pattern, notebook_content) From aac894f0fdf05027a1fe96cda60bb4411e2b6945 Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 10:57:08 -0800 Subject: [PATCH 10/17] format Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 749bd437ecd..219b7789c34 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -27,6 +27,7 @@ def read_available_backends(): pattern = r'set_target\(\s*\\"([^"]+)\\"(?:\s*,\s*option\s*=\s*\\"([^"]+)\\")?' + def validate(notebook_filename, available_backends): with open(notebook_filename) as f: lines = f.readlines() From 15a2d96063021aef50c55fe185ef325aae10607d Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 11:12:58 -0800 Subject: [PATCH 11/17] simplifying the condition Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 219b7789c34..75d3ddd1f40 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -40,10 +40,7 @@ def validate(notebook_filename, available_backends): target = match.group(1) opt = match.group(2) combined = f"{target}-{opt}" if opt else target - if combined not in available_backends: - return False - else: - return True + return combined in available_backends for notebook_content in lines: match = re.search('--target ([^ ]+)', notebook_content) if match and (match.group(1) not in available_backends): From 8c7fe033825e34653482792248fcfb0ac11de0d6 Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 14:07:47 -0800 Subject: [PATCH 12/17] updating torch version Signed-off-by: Sachin Pisal --- .../hybrid_quantum_neural_networks.ipynb | 23 ++++++++++++++----- 1 file changed, 17 insertions(+), 6 deletions(-) diff --git a/docs/sphinx/applications/python/hybrid_quantum_neural_networks.ipynb b/docs/sphinx/applications/python/hybrid_quantum_neural_networks.ipynb index 4c08b3e0ae4..c11a0c7bde8 100644 --- a/docs/sphinx/applications/python/hybrid_quantum_neural_networks.ipynb +++ b/docs/sphinx/applications/python/hybrid_quantum_neural_networks.ipynb @@ -28,13 +28,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Install the relevant packages.\n", "\n", - "!pip install matplotlib==3.8.4 torch==2.9.0+cu126 torchvision==0.24.0+cu126 scikit-learn==1.4.2 -q --extra-index-url https://download.pytorch.org/whl/cu126" + "!pip install matplotlib==3.8.4 torch==2.9.1+cu126 torchvision==0.24.1+cu126 scikit-learn==1.4.2 -q --extra-index-url https://download.pytorch.org/whl/cu126" ] }, { @@ -159,7 +159,18 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9.91M/9.91M [00:04<00:00, 2.30MB/s]\n", + "100%|██████████| 28.9k/28.9k [00:00<00:00, 336kB/s]\n", + "100%|██████████| 1.65M/1.65M [00:00<00:00, 2.42MB/s]\n", + "100%|██████████| 4.54k/4.54k [00:00<00:00, 20.1MB/s]\n" + ] + } + ], "source": [ "x_train, x_test, y_train, y_test = prepare_data(target_digits, sample_count,\n", " test_size)" @@ -405,7 +416,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -444,7 +455,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "CUDA-Q Version proto-0.8.0-developer (https://github.com/NVIDIA/cuda-quantum 018ba9130a0e5800b93e86d0096a22daf7a132f8)\n" + "CUDA-Q Version proto-0.8.0 (https://github.com/NVIDIA/cuda-quantum f52f3f8e0830e0c78e05ed8d087ae7faf1e58c9f)\n" ] } ], @@ -469,7 +480,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.12.3" }, "orig_nbformat": 4 }, From 61318d92e8d19b444fc157f50574e4ba8b9165fa Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 16:28:03 -0800 Subject: [PATCH 13/17] Adding pattern matching examples and fetching only the target, ignoring option Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 75d3ddd1f40..66d94033415 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -25,7 +25,20 @@ def read_available_backends(): return [backend.strip() for backend in available_backends] -pattern = r'set_target\(\s*\\"([^"]+)\\"(?:\s*,\s*option\s*=\s*\\"([^"]+)\\")?' +# Following pattern matches +# set_target("abc") +# set_target( "abc") +# set_target("abc", option="xyz") +# set_target("abc", option = "xyz") +# set_target(\"abc\") +# set_target( \"abc\") +# set_target(\"abc\", option=\"xyz\") +# set_target(\"abc\", option = \"xyz\") +# set_target('abc') +# set_target( 'abc') +# set_target('abc', option='xyz') +# set_target('abc', option = 'xyz') +pattern = r"set_target\(\s*(\\?['\"])([^'\"]+)\1(?:\s*,\s*option\s*=\s*(\\?['\"])([^'\"]+)\3)?\)" def validate(notebook_filename, available_backends): @@ -37,10 +50,8 @@ def validate(notebook_filename, available_backends): match = re.search(pattern, notebook_content) if match: - target = match.group(1) - opt = match.group(2) - combined = f"{target}-{opt}" if opt else target - return combined in available_backends + target = match.group(2) + return target in available_backends for notebook_content in lines: match = re.search('--target ([^ ]+)', notebook_content) if match and (match.group(1) not in available_backends): From 895d674468dfc475e6f0891243f6746e8983cf3f Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 16:36:57 -0800 Subject: [PATCH 14/17] updating python3-venv Signed-off-by: Sachin Pisal --- .github/workflows/docker_images.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/docker_images.yml b/.github/workflows/docker_images.yml index c4c022cb373..e74d397f1d5 100644 --- a/.github/workflows/docker_images.yml +++ b/.github/workflows/docker_images.yml @@ -841,7 +841,7 @@ jobs: # In containers without GPU support, UCX does not work properly since it is configured to work with GPU-support. # Hence, don't enforce UCX when running these tests. docker exec cuda-quantum bash -c "python3 -m pip install --break-system-packages pandas scipy seaborn h5py contfrac" - docker exec cuda-quantum bash -c "sudo apt install -y python3.12-venv" + docker exec cuda-quantum bash -c "sudo apt install -y python3-venv" (docker exec cuda-quantum bash -c "unset OMPI_MCA_pml && set -o pipefail && bash validate_container.sh | tee /tmp/validation.out") && passed=true || passed=false docker cp cuda-quantum:"/tmp/validation.out" /tmp/validation.out docker stop cuda-quantum From c0db96742a5e3d545ba72e12545d240cd279ab1b Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Thu, 13 Nov 2025 16:54:08 -0800 Subject: [PATCH 15/17] making spellchecker happy Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 66d94033415..1e32097d0ea 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -26,18 +26,18 @@ def read_available_backends(): # Following pattern matches -# set_target("abc") -# set_target( "abc") -# set_target("abc", option="xyz") -# set_target("abc", option = "xyz") -# set_target(\"abc\") -# set_target( \"abc\") -# set_target(\"abc\", option=\"xyz\") -# set_target(\"abc\", option = \"xyz\") -# set_target('abc') -# set_target( 'abc') -# set_target('abc', option='xyz') -# set_target('abc', option = 'xyz') +# `set_target("abc")` +# `set_target( "abc")` +# `set_target("abc", option="xyz")` +# `set_target("abc", option = "xyz")` +# `set_target(\"abc\")` +# `set_target( \"abc\")` +# `set_target(\"abc\", option=\"xyz\")` +# `set_target(\"abc\", option = \"xyz\")` +# `set_target('abc')` +# `set_target( 'abc')` +# `set_target('abc', option='xyz')` +# `set_target('abc', option = 'xyz')` pattern = r"set_target\(\s*(\\?['\"])([^'\"]+)\1(?:\s*,\s*option\s*=\s*(\\?['\"])([^'\"]+)\3)?\)" From 5870944a6ce746035b1ed445f2eaf99fa1f30a6d Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Fri, 14 Nov 2025 08:29:37 -0800 Subject: [PATCH 16/17] skipping logical_aim_sqale and updating pip install for hqnn Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 2 +- .../hybrid_quantum_neural_networks.ipynb | 18 +++++------------- 2 files changed, 6 insertions(+), 14 deletions(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 1e32097d0ea..88aa852d3b2 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -142,7 +142,7 @@ def print_results(success, failed, skipped=[]): ## `quantum_transformer`: ## See: https://github.com/NVIDIA/cuda-quantum/issues/2689 - notebooks_skipped = ['quantum_transformer.ipynb'] + notebooks_skipped = ['quantum_transformer.ipynb', 'logical_aim_sqale.ipynb'] for notebook_filename in notebook_filenames: base_name = os.path.basename(notebook_filename) diff --git a/docs/sphinx/applications/python/hybrid_quantum_neural_networks.ipynb b/docs/sphinx/applications/python/hybrid_quantum_neural_networks.ipynb index c11a0c7bde8..bb8f28c0d28 100644 --- a/docs/sphinx/applications/python/hybrid_quantum_neural_networks.ipynb +++ b/docs/sphinx/applications/python/hybrid_quantum_neural_networks.ipynb @@ -34,7 +34,10 @@ "source": [ "# Install the relevant packages.\n", "\n", - "!pip install matplotlib==3.8.4 torch==2.9.1+cu126 torchvision==0.24.1+cu126 scikit-learn==1.4.2 -q --extra-index-url https://download.pytorch.org/whl/cu126" + "!pip install matplotlib==3.8.4 torch==2.9.1 torchvision==0.24.1 scikit-learn==1.4.2 -q\n", + "\n", + "# Use this line if using GPU, change CUDA version according to your system (e.g. cu126, cu128, or cu130)\n", + "# !pip install matplotlib==3.8.4 torch==2.9.1+cu126 torchvision==0.24.1+cu126 scikit-learn==1.4.2 -q --extra-index-url https://download.pytorch.org/whl/cu126" ] }, { @@ -159,18 +162,7 @@ "cell_type": "code", "execution_count": 6, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 9.91M/9.91M [00:04<00:00, 2.30MB/s]\n", - "100%|██████████| 28.9k/28.9k [00:00<00:00, 336kB/s]\n", - "100%|██████████| 1.65M/1.65M [00:00<00:00, 2.42MB/s]\n", - "100%|██████████| 4.54k/4.54k [00:00<00:00, 20.1MB/s]\n" - ] - } - ], + "outputs": [], "source": [ "x_train, x_test, y_train, y_test = prepare_data(target_digits, sample_count,\n", " test_size)" From 810926250b52ddb8da3d2c058c8d601b67c74e4f Mon Sep 17 00:00:00 2001 From: Sachin Pisal Date: Fri, 14 Nov 2025 08:30:41 -0800 Subject: [PATCH 17/17] format Signed-off-by: Sachin Pisal --- docs/notebook_validation.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/notebook_validation.py b/docs/notebook_validation.py index 88aa852d3b2..acd22e4fa09 100644 --- a/docs/notebook_validation.py +++ b/docs/notebook_validation.py @@ -142,7 +142,9 @@ def print_results(success, failed, skipped=[]): ## `quantum_transformer`: ## See: https://github.com/NVIDIA/cuda-quantum/issues/2689 - notebooks_skipped = ['quantum_transformer.ipynb', 'logical_aim_sqale.ipynb'] + notebooks_skipped = [ + 'quantum_transformer.ipynb', 'logical_aim_sqale.ipynb' + ] for notebook_filename in notebook_filenames: base_name = os.path.basename(notebook_filename)