![]() All guests will enjoy a one-hour cocktail reception starting at 7pm sponsored by Umberto Cesari iOVE wine, Barista Coffee Liqueur, and Lost Craft beer prior to the one-hour panel discussion starting at 8pm, followed by an after-party for all the guests to enjoy. VIP guests will enjoy an extended two-hour cocktail reception starting at 6pm to meet with the esteemed panelists, moderator, and UforChange alum. ![]() The event is set to take place at Artscape Daniels Launchpad at 130 Queens Quay East in Toronto. ![]() ![]() Scott Paterson moderated by philanthropist and Mantella Corporation’s Chief Marketing Officer Sylvia Mantella and emceed by Dani Kagan of Stratus Events. To see some examples of this, take a look at Acme, a library purpose built to use Launchpad for distributed reinforcement learning agents.On Wednesday, November 21, 2018, five of the country’s most brilliant and innovative minds will come together in support of UforChange with “The Talk” – a live panel discussion with producer/director and advertising executive Barry Avrich, ELLE Canada Editor-in-Chief Vanessa Craft, fashion designer Mikhael Kale, and technology entrepreneur G. Finally, as mentioned above the reason for Launchpad’s existence is to make complicated distributed systems simple to write and read. Obviously we have just scratched the surface! For more information about Launchpad take a look at our github repository for all of the code as well as some helpful examples and documentation. This node type bridges the gap between local and distributed computation, since from the perspective of the launched class they both look one and the same. This is a particular node implementation which, given a constructor to a python class, will create an instance of that class and expose its public methods to clients as a gRPC service. To make this process easier, Launchpad also provides CourierNode (named for our internal gRPC wrapper). However, Launchpad does have a little more to say about this communication mechanism, namely that the communication edges are exposed at runtime as calls to a gRPC service. However, by modifying or building upon launch we are able to (and do!) use this same mechanism for launching on multiple machines at once.Įdges in the Launchpad program denote that communication occurs between the two nodes, but not necessarily how it happens-that’s ultimately up to the nodes themselves. Right now we are releasing a version of this mechanism that runs this computation within different processes on a single machine. By implementing different launchers we are also able to target different backends. Launching the program is then as simple as passing it to a given launching mechanism, e.g. Passing a handle to another node at its creation time forms an edge in the communication graph. As nodes are added the system returns a handle to that node which can be used as a reference for that service. Interacting with Launchpad is a simple matter of creating an empty graph (a program) and incrementally adding nodes to it. on a single machine, a cloud provider, or on a self-hosted cluster). Further, by clearly separating the program definition, given by its graph data-structure, from the launching mechanism Launchpad can also be used to launch the same distributed system on different platforms (e.g. ![]() By making the graph representation explicit, Launchpad makes it easy to both design and later modify a program's topology in a single script-something that would not be possible were the system defined in a more decentralised or implicit fashion. The fundamental concept of Launchpad is that of a program, which represents computation as a directed graph of service nodes with edges in this graph denoting communication between nodes. To address this we have built Launchpad, a programming model that simplifies the process of defining and launching instances of distributed computation. This is due in large part to the fact that distributed communication is often implicit, which can obscure program flow in a way that makes such algorithms hard to reason about. Modern numerical frameworks-such as TensorFlow, PyTorch, and JAX-have significantly contributed to recent advances in machine learning, however building complicated distributed systems with these tools can prove difficult. A programming model for distributed machine learning research ![]()
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