Full-stack observability with actually useful AI | Grafana Cloud
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Gemini 3 and 200+ AI Models on One Platform
Access Google's best plus Claude, Llama, and Gemma. Fine-tune and deploy from one console.
Build generative AI apps with Vertex AI. Switch between models without switching platforms.
...Model explainability for Diffusers. Get explanations for your generated images. Install directly from PyPI. It is possible to visualize pixel attributions of the input image as a saliency map. diffusers-interpret also computes these token/pixel attributions for generating a particular part of the image. To analyze how a token in the input prompt influenced the generation, you can study the token attribution scores. You can also check all the images that the diffusion process generated at the end of each step. Gradient checkpointing also reduces GPU usage, but makes computations a bit slower.
A Neural Net Training Interface on TensorFlow, with focus on speed
Tensorpack is a neural network training interface based on TensorFlow v1. Uses TensorFlow in the efficient way with no extra overhead. On common CNNs, it runs training 1.2~5x faster than the equivalent Keras code. Your training can probably gets faster if written with Tensorpack. Scalable data-parallel multi-GPU / distributed training strategy is off-the-shelf to use. Squeeze the best data loading performance of Python with tensorpack.dataflow. Symbolic programming (e.g. tf.data) does not...
A collection of Matlab functions and scripts for computing the saliency map for an image, for determining the extent of a proto-object, and for serially scanning the image with the focus of attention.