161–170 of 201 posts
Is anyone here using Gorgonia? I’m working in a Go code base and I’m thinking of using it instead of creating a separate service in Python. https://gorgonia.org/
I definitely prefer using it to deploy services than PyTorch, MXNet or TF
Whatever happened to Julia? Wasn't it supposed to incorporate all these incredible abstractions at the language level and run quickly on GPUs and everything in-between? Is it just lack of adoption or is has it something else?
[0] https://trends.google.com/trends/explore?date=2017-01-11%202...
Whatever happened to Julia? Wasn't it supposed to incorporate all these incredible abstractions at the language level and run quickly on GPUs and everything in-between? Is it just lack of adoption or is has it something else?
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RHEL is popular for solutions like running a datacenter mostly because it has a nice enterprise support story. It's what the E in that acronym is for, after all. Ubuntu, meanwhile, is quite popular among us mere mortals who have to fix our own boxen. Debian is popular for Docker images exactly because many of the people trying Docker were already familiar with Ubuntu. Those users quickly ended up wanting smaller imag…
But it was so much fun running Slackware servers and compiling our own minimal kernels make menuconfig
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how did you strip out weighs for other classes? what does that even mean?
So this will depend a bit on the architecture (I was working with CenterNet). In my situation, the final feature maps for each class are all obtained by a series of network "heads" that perform a set of convolutions on the same set of slightly deeper set of featuremaps. Each convolutional "head" is responsible for object-detections for a single class. So in the case of COCO, if you have 80 classes, you have 80 such h…
I work at a small company as an engineer and recently was asked to do a project that would require some neural net magic. I had some experience with keras/tensorflow so that was my first choice. Despite the absolute nightmare of getting it installed and running on a gpu, I managed it and had a fantastic model. It was doing so well that the company wanted to expand the project and build out a multi-gpu rig as part of…
> Great API. Most researchers prefer PyTorch’s API to TensorFlow’s API. This is partially because PyTorch is better designed and partially because TensorFlow has handicapped itself by switching APIs so many times (e.g. ‘layers’ -> ‘slim’ -> ‘estimators’ -> ‘tf.keras’).
Arguably, one of the biggest issues Google had with Angular was the switch from 1.x to 2.x. You'd have thought they learned about how not to make major changes on OSS projects.
Facebook on React for instance do an amazing job here, they use prefixes to anything they don't want to support like "UNSTABLE_" and show warnings forever when they actually plan to make something small obsolete.
I tried to learn from both, so in some of my bigger personal OSS projects (amount of work involved) like npm's "server". I purposefully made some APIs a bit more limited than I could to have more flexibility later on if I didn't like the direction. Of course at a different level, I am a single dev doing OSS on my free time after all.
But I understand in a project of the size of e.g. Tensorflow it's not an individual dev learning, it's more about the company learning how to do things better.
I'm using Keras from last 3 years. Most of the time where I have to deal with core TF code is when I have to write some custom layers. I totally agree on a part where hacking together TF code seems nightmare (well, initially.. but not once you know what you're doing), where PyTorch more looks like blissful experience (I have not tried PyT yet, just speaking from reading all these comments). I'm genuinely curious abou…