The docker-compose command is missing from recent versions of Docker, replaced by a plugin built into Docker: docker compose. To restore compatibility with scripts which use docker-compose, we can create a wrapper script which forwards its arguments to docker compose. Here’s the script:
# Switch to rootsudo su -
# Write script to filecat<<EOF > /usr/local/bin/docker-compose
#!/bin/bash
docker compose $@EOF
# Make the script executable so that we can invoke it directly from the shellchmod +x /usr/local/bin/docker-compose
Siamese network is an architecture which runs two networks with shared weights (effectively runs the same network twice) on two different inputs simultaneously. It is commonly trained with a contrastive loss such as triplet loss in order to draw together the representations of similar inputs and push apart the representations of contrasting inputs.
Use ${BASH_SOURCE[0]} to reference the filepath of a Bash script. Unlike $0, this works if the script is called via bash script.sh or source script.sh.
I fell into the habit of using any() to check if a list is empty. It’s nice because it works for any enumerable, including generators, even if len() is not defined. However, it has a pitfall where if the list is nonempty but contains only falsy values, any() returns False. For this reason, I advise to use len() to check if a list is empty.
You may know that you can use head -n $n to get the first N lines of a list. But you may not know that you can supply n=-0 to get all items in the list.
Recently I wanted to write program logs to a file, then copy it to the directory where I store my checkpoints. Because I want to log things before I create the checkpoint directory, I attached my logger to a file in my root directory.
PyTorch Lightning + TorchMetrics can log metrics per step and per epoch. It also has MetricCollection, which can be used to compute several metrics at once, getting rid of redundant code. Here is how I have it set up:
Embeddings are used to map high-dimensional data to low-dimensional floating-point representation. This may improve the performance because it improves the representation of the input given to the model.
