Okay, so today I messed around with this thing called “Continuum Gym”. Let me tell you, it was a bit of a learning curve, but kinda cool in the end.
Getting Started
First off, I had to get this whole thing set up. That meant installing a bunch of stuff. It’s not just one click and go, you know? I had to do:
- Checked my python installation and had to make sure I had the right version, because, you know, versioning issues and stuff.
- Installed the `continuum-gym` package using pip install, the command was `pip install continuum-gym`.
- Grabbed a few other packages, just to be on the safe side, like `torch` and `torchvision`
It felt like a ton of preliminary steps, but I guess it’s all part of the process. I kept muttering to myself, “Just get through the setup, just get through the setup…”
Diving into Tasks
Once I got past the installation hurdles, I started playing with some basic tasks. I wanted to see how this whole “continual learning” thing actually worked. I basically did this:
- Picked a dataset. I think I went with something simple, like MNIST, just to get a feel for things. Gotta start small, right?
- Created a task set. It’s kinda like organizing the data into different learning stages.
- Defined my model. I wasn’t about to build something super complex from scratch, so I picked a pre-existing one. I think it’s a basic convolutional.
Training Time
Now, the fun part – actually training the model! This is where I watched the magic (or the errors) happen. My steps:
- Created a training loop, you know, the usual forward pass, backward pass, optimize kind of deal.
- Fed the data, chunk by chunk, task by task, into the model.
- Watched the loss go down (hopefully!). There were some moments, let me tell you. Like watching paint dry, but with more anxiety.
It definitely took some time. I mean, it is training after all. Not instant gratification.
Seeing the Results
After all that waiting, I finally got to see if my model learned anything. I
- Evaluated the model on some test data. Always gotta have that test data, otherwise you’re just fooling yourself.
- Looked at the accuracy. Was it good? Was it terrible? Was it somewhere in the middle, like most things in life?
- Tried a few different things, tweaked some parameters, you know the usual tuning.
It wasn’t perfect, far from it, but it was working! That’s the main thing. I saw the accuracy improve over time as it learned each new task. That’s the whole point of this continual learning thing, I guess. Not bad for a day’s work.
So, that was my adventure with Continuum Gym. A bit of a setup hassle, some head-scratching moments during training, but ultimately a pretty neat experience. I think I’ll keep tinkering with it. Maybe try some more complex datasets and models next time. But for now, I’m calling it a day!