SQL's implementation of database triggers.
This feature is really cool, as it allows you to keep caches up to date!
In particular, everything that happens in a trigger happens as if it were in a transaction. This way, you can do less explicit transactions when you use triggers. It is a bit like the advantages of SQL CASCADE.
DBMS:
TODO what is the standard compliant syntax?
PostgreSQL requires you to define a SQL stored procedure: stackoverflow.com/questions/28149494/is-it-possible-to-create-trigger-without-execute-procedure-in-postgresql Their syntax may be standard compliant, not sure about the
EXECUTE part. Their docs: www.postgresql.org/docs/current/sql-createtrigger.htmlSQLite does not support SQL stored procedures at all, so maybe that's why they can't be standard compliant here: stackoverflow.com/questions/3335162/creating-stored-procedure-in-sqlite
SQL:1999 11.38 covers "Trigger definition". The Abstract syntax tree starts with the
CREATE TRIGGER and ends in:<triggered SQL statement> ::=
<SQL procedure statement>This is defined at 13.5 "SQL procedure statement", but that is humongous and I'm not sure what it is at all.
Basic example tested on SQLite 3.40.1, Ubuntu 23.04:output:
sqlite3 :memory: 'select 1 union select 2'1
2Two columns two rows:output:
sqlite3 :memory: <<EOF
select * from (values (1, 2), (2, 3))
union
select * from (values (2, 3), (3, 4))
EOF1|2
2|3
3|4Note how duplicates are removed, to keep them we output:
UNION ALL instead:sqlite3 :memory: <<EOF
select * from (values (1, 2), (2, 3))
union all
select * from (values (2, 3), (3, 4))
EOF1|2
2|3
2|3
3|4Version of TensorFlow with a Cirq backend that can run in either quantum computers or classical computer simulations, with the goal of potentially speeding up deep learning applications on a quantum computer some day.
Bibliography:
E.g.: thecollegestore.co.uk/products/ladies-oxford-college-puffer-jacket?variant=40590030864549 Black with 5 rows, on left chest "colege name", logo, "Oxford", and right chest optional initials (or sometimes other identifiers/nicknames) to help distinguish from all the other people's identical clothes.
This has a whitelabel version: www.workweargiant.co.uk/product/result-urban-holkham-down-feel-jacket/, the name appears to be "Holkham Down Feel Jacket".
Circa 2020, these are likely given out by each college for free, and are widely used.
If you look 20 and wear one of those, it's almost an ID, you can get anywhere that does not require a key card, porters won't look at you twice!
- www.oxfordstudent.com/2019/03/25/in-opposition-to-stash/ In opposition to stash by Morgan Jones (2019), basically because university is your last chance to wear what you want on many professions.
University of Oxford student newspaper by 
Ciro Santilli 35 Updated 2024-12-23 +Created 1970-01-01
Ciro Santilli 35 Updated 2024-12-23 +Created 1970-01-01They actually have two The Oxford Student and Cherwell. As brilliantly highlighted in this first of April piece:
Related:
- www.thestudentroom.co.uk/showthread.php?t=1167619 "OxStu vs. Cherwell"
Sometimes it just feels like Ubuntu devs don't actually use Ubuntu as a desktop.
Some extremelly anoying problems are introduced and just never get fixed, even though they feel so obvious!
Would never happen on Mac...
Like the U.S.' spring term.
Like the U.S.' summer term.
E-learning system prior to Canvas: weblearn.ox.ac.uk/portal. Appears fully custom and closed source?
Closed in 2023 in favour of Canvas.
activatedgeek/LeNet-5 use ONNX for inference by Ciro Santilli 35 Updated 2024-12-23 +Created 1970-01-01
Ciro Santilli 35 Updated 2024-12-23 +Created 1970-01-01Note that the images must be drawn with white on black. If you use black on white, it the accuracy becomes terrible. This is a good very example of brittleness in AI systems!
Number 9 drawn with mouse on GIMP by Ciro Santilli (2023)
We can try the code adapted from thenewstack.io/tutorial-using-a-pre-trained-onnx-model-for-inferencing/ at python/onnx_cheat/infer_mnist.py:and it works pretty well! The protram outputs:as desired.
cd python/onnx_cheat
./infer_mnist.py lenet.onnx infer_mnist_9.png9We can also try with images directly from Extract MNIST images.and the accuracy is great as expected.
for f in /home/ciro/git/mnist_png/out/testing/1/*.png; do echo $f; infer.py $f ; doneBy default, the setup runs on CPU only, not GPU, as could be seen by running htop. But by the magic of PyTorch, modifying the program to run on the GPU is trivial:and leads to a faster runtime, with less
cat << EOF | patch
diff --git a/run.py b/run.py
index 104d363..20072d1 100644
--- a/run.py
+++ b/run.py
@@ -24,7 +24,8 @@ data_test = MNIST('./data/mnist',
data_train_loader = DataLoader(data_train, batch_size=256, shuffle=True, num_workers=8)
data_test_loader = DataLoader(data_test, batch_size=1024, num_workers=8)
-net = LeNet5()
+device = 'cuda'
+net = LeNet5().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=2e-3)
@@ -43,6 +44,8 @@ def train(epoch):
net.train()
loss_list, batch_list = [], []
for i, (images, labels) in enumerate(data_train_loader):
+ labels = labels.to(device)
+ images = images.to(device)
optimizer.zero_grad()
output = net(images)
@@ -71,6 +74,8 @@ def test():
total_correct = 0
avg_loss = 0.0
for i, (images, labels) in enumerate(data_test_loader):
+ labels = labels.to(device)
+ images = images.to(device)
output = net(images)
avg_loss += criterion(output, labels).sum()
pred = output.detach().max(1)[1]
@@ -84,7 +89,7 @@ def train_and_test(epoch):
train(epoch)
test()
- dummy_input = torch.randn(1, 1, 32, 32, requires_grad=True)
+ dummy_input = torch.randn(1, 1, 32, 32, requires_grad=True).to(device)
torch.onnx.export(net, dummy_input, "lenet.onnx")
onnx_model = onnx.load("lenet.onnx")
EOFuser as now we are spending more time on the GPU than CPU:real 1m27.829s
user 4m37.266s
sys 0m27.562s
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