You must specify the region where your S3 bucket is located. You can find the right region in the list of buckets on the Amazon S3 console. The user you use must have read access to the specified S3 bucket. For more information about securely granting access to S3 buckets to a specific user, see Writing IAM Policies: How to Grant Access to an Amazon S3 Bucket. You should see something similar to the following screenshot. If you prefer to have an instance of TensorBoard permanently running and accessible to your whole team, you can deploy it as an independent application in the cloud. One of the easiest ways to do this without managing servers is AWS Fargate, a serverless compute engine for containers. The following diagram illustrates this architecture.

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation. Building on two basic abstractions, it offers flexible building blocks for probabilistic computation. Distributions provide fast, numerically stable methods for generating samples and computing statistics, e.g., log density. Bijectors provide composable volume-tracking transformations with automatic caching. Together these enable modular construction of high dimensional distributions and transformations not possible with previous libraries (e.g., pixelCNNs, autoregressive flows, and reversible residual networks). They are the workhorse behind deep probabilistic programming systems like Edward and empower fast black-box inference in probabilistic models built on deep-network components. TensorFlow Distributions has proven an important part of the TensorFlow toolkit within Google and in the broader deep learning community.