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FAQs
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Is a framework agreement a contract?
A framework is an agreement with suppliers to establish terms governing contracts that may be awarded during the life of the agreement. In other words, it is a general term for agreements that set out terms and conditions for making specific purchases (call-offs). -
What is a framework agreement construction?
Definition. A framework is an agreement with suppliers to establish terms governing contracts that may be awarded during the life of the agreement. In other words, it is a general term for agreements that set out terms and conditions for making specific purchases (call-offs). -
Are framework agreements legally binding?
If no consideration is paid, the framework agreement is not legally binding on the contractor(s). However, framework agreements are subject to Regulation 20 of the DSPCR whether they are legally binding or not. -
What is a purchasing framework?
A purchasing framework is an 'umbrella agreement' that sets out the terms (particularly relating to price and quality) under which individual purchases (call-offs) can be made throughout the period of the agreement. -
What is a framework agreement procurement?
In the context of procurement, a framework agreement is an agreement between one or more businesses or organisations, "the purpose of which is to establish the terms governing contracts to be awarded during a given period, in particular with regard to price and, where appropriate, the quantity envisaged". -
What is a government procurement framework?
A procurement framework is an agreement put in place with a provider or range of providers that enables buyers to place orders for services without running lengthy full tendering exercises. ... The National LGPS Frameworks are multi-provider, allowing several qualified providers to be on the framework. -
Can a framework agreement be extended?
Call-off contracts based on framework agreements may be longer than four years, and may extend beyond the expiry date of the framework (Recital 62 Public Procurement Directive). For single provider framework agreements, call-offs are placed according to the terms and conditions laid out in the framework agreement. -
Who can use a framework agreement?
Who can use them? Any organisation subject to EU public procurement regulations can publish a framework agreement. Many are published either on behalf of multiple buyers or left open for use by some or all public sector organisations. How do I secure a place on one? -
What is a framework agreement?
Definition. A framework is an agreement with suppliers to establish terms governing contracts that may be awarded during the life of the agreement. In other words, it is a general term for agreements that set out terms and conditions for making specific purchases (call-offs). -
What is a framework procurement?
A procurement framework is an agreement put in place with a provider or range of providers that enables buyers to place orders for services without running lengthy full tendering exercises. Frameworks are based on large volume buying.
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Autograph framework agreement
[MUSIC PLAYING] ALEXANDRE PASSOS: I'm Alex, and I'm here to tell you about how you're going to build graphs in TensorFlow 2.0. And this might make you a little uncomfortable, because we already spent quite some time earlier today telling you that in TensorFlow 2.0, we use eager execution by default. So why am I taking that away from you? And I'm not. You still have your eager execution by default, but graphs are useful for quite a few things. The two ones that I care the most about personally are that some hardware, like our TPUs, really benefit from the kind of full program optimization that we can get if we have graphs. And if you have graphs, you can take your model and deploy it on servers and deploy it on mobile devices and deploy it on whatever thing you want, make it available to as many people as you can think of. So at this point, you're probably, eh, I remember TensorFlow 1.0. I remember the kind of code I had to write to use graphs, and I wasn't proud of it. Is he just going to tell me that I have to keep doing that? And no. One of the biggest changes we're doing with TensorFlow 2.0 is we're fundamentally changing the programming model with which you build graphs in TensorFlow. We're removing that model where you first add a bunch of nodes to a graph and then rely on session.run to prune things out of the graph, to figure out the precise things you want to run in the correct order and replacing it with a much simpler model based on this notion of a function. We're calling it tf.function, because that's the main API entry point for you to use it. And I'm here to tell you that with tf.function, many things that you're used to are going to go away. And I dearly hope you're not going to miss them. The first one that goes away that I really think no one in this room is going to miss is that you'll never have to use session.run anymore. [APPLAUSE] So if you've TensorFlow with eager execution, you know how it works. You have your tensors and you have your operations, and you pass your tensors to your operations, and the operations execute. And this is all very simple and straightforward. And tf.function is just like an operation except one that you get to define using a composition of the other operations in TensorFlow however you wish. Once you have your tf.function, you can call it. You can call it inside another function. You can take its gradient. You can run it on the GPU, on the TPU, on the CPU, on distributed things, just like how you would do with any other TensorFlow operation. So really, the way you should think about tf.function is we're letting you define your own operations in Python and making it as easy as possible for you to do this and trying to preserve as many of the semantics of the Python programming language that you already know and love and when you execute these functions in a graph. So obviously, the first thing you would think is that is it actually faster? And if you look at models that are large convolutions or big matrix multiplications, large reductions, it's not actually any faster, because you get executions plenty fast. But as your models get small, and as the operations in them get small, you can actually measure the difference in performance. And here, I show that for this tiny lstm_cell with 10 units, there is actually a tenfold speed up if we used tf.function versus if you don't use tf.function to execute it. And as I was saying, we really try to preserve as much of the Python semantics as we can to make this code easy to use. So if you've seen TensorFlow graphs, you know that they are very much not polymorphic. If you built a graph for float64, you cannot use it for float32 or, God forbid, float16. But tf.function-- but Python code tends to be very free into the types of things it accepts. With tf.function, we do the same. So under the hood, when you call a tf.function, we look at the tensors you're passing as inputs and then try to see, have we already made a function graph that is compatible with those inputs? If not, we make a new one. And we hide this from you so that you can just use your tf.function as you would use normal TensorFlow operation. And eventually, you'll get all the graphs you need built up, and your code will run blazingly fast. And this is not completely hidden. If you want to have access to the graphs that we're generating, you can get them. We expose them to you. So if you need to manipulate these graphs somehow or do weird things to them that I do not approve, you can still do it. But really, the main reason why we changed this model is not to replace session.run with tf.function, it's that by changing the promise for what we do to your code, we can do so much more for you than we could do before. With the model where you add a bunch of notes to a graph and then prune them, it's very hard for the TensorFlow runtime to know what order do you want those operations to be executed in. Almost every TensorFlow operation is stateless so that doesn't matter. But for the few ones where it does matter, you probably had to use control dependencies and other complicated things to make it work. So again, I'm here to tell you that you will never have to use control dependencies again if you're using tf.function. And how can I make this claim happen? So the premise behind tf.function is that you write code that you'd like to run eagerly, we take it and we make it fast. So as we trace your Python code to generate a graph, we look at the operations you run, and every time we see a stateful operation, we add the minimum necessary set of control dependencies to ensure that all the resources accessed by those stateful operations are accessed in the order you want them to be. So if you have two variables and you're updating them, we'll do that in parallel. When you have one variable and you're updating it many times, we'll order those updates so that you're not surprised by them happening out of order or something like that. So there's really no crazy surprises and weird, undefined behavior. And really, you should never need to explicitly add control dependencies to your code. But you'll still get the ability of knowing what order things execute. And if you want something to execute before somebody else, just put that line of code above that other line of code. You know, how you do in a normal program. Another thing that we can dramatically simplify in tf.function is how you use variables in TensorFlow. And I'm sure you've all used variables before. And you know that while they're very useful-- they allow you to share state across devices, they let you persist, checkpoint, do all those things, it can be a little finicky. Things like initializing them is very hard, especially if you're using your variables of any kind of non-trivial initialization. So another thing that we're removing from TensorFlow is the need to manually initialize variables yourself. And the story for variables is a little complicated, though, because as you try to make code compatible with both eager execution and graph semantics, you very quickly find examples where it's unclear what we should do. My favorite one is this one-- if you run this code in TensorFlow 1.x, and you session.run repeatedly, the result, you're going to get a series of numbers that goes up. But if you run this code eagerly, every time you run it, you're going to get the same number back, because we're creating a new variable, updating it, and then destroying it. So if I wanted to turn this code-- wrap this code with tf.function-- which one should it do? Should it follow the 1.x behavior or the eager behavior? And I think if I took a poll, I would probably find that you don't agree with each other. I don't agree with myself, so this is an error. Nonambiguous at creating variables, though, is perfectly OK. So as you've seen in an earlier slide, you can create the variable and capture it by closure in a function. That's a way a lot of TensorFlow code gets written. This just works. Another way you can do is like write your function such that it only creates variables the first time it's called. This is incidentally what most libraries in TensorFlow do under the hood. This is how Keras layers are implemented, how the TF 1.x layer is implemented, Sonnet, and all sorts of other libraries that use TensorFlow variables. They try to take care to not create variables every time they're called, otherwise you're creating way too many variables, and you're not actually training anything. So code that behaves well just gets turned into function, and it's fine. And if you've seen this, I didn't actually need to call the initializer for this variable that I'm creating, and it's even better. I can make the initializer depend on the value of the arguments to the function or the value of other variables in arbitrarily complicated ways. And because we control-- we add the necessary control dependencies to ensure that the state updates happen in the way you want them to happen. There is no need for you to worry about this. You can just create your variables, like how you would use in a normal programming language. And things will behave the way you want them to behave. Another thing that I'm really happy about tf.function is our autograph integration. And if anyone here has used Control Flow in TensorFlow, you probably know that it can be awkward. And I'm really happy to tell you that with Autograph, we're finally breaking up with tf.cond and tf.while_loop. And now, you can just write code that looks like this-- so if you see here, I have a while loop, where the predicate depends on the value of a tf.reduce_sum on a tensor. This is probably the worst way to make a tensor sum to 1 that I could think of. But it fits in a slide. So yay. If you put this in a tf.function, we'll create a graph and we'll execute it. And this is nice. This is great. But how does it work? Under the hood, things like tf.cond and tf with our while loop are still there, but we wrote this Python compiler called Autograph that rewrites Control Flow expressions into something that looks like this-- yes. Something that looks like this, which is not like how you would want to write code. And this then can be taken by TensorFlow and turned into fast dynamic graph code. So how does this work? To explain that, I like to take a step back and think about how does anything in TensorFlow work? So you can have a tensor, and you can do tensor plus tensor times other tensor, et cetera. Just use a tensor as you'd use a normal Python integer or floating point number. And how do we do that? I'm sure you all know this, but Python has a thing called operator overloading that lets us change the behavior of standard Python operators when applied on our custom data types, like tensors. So we can override the __add, __sub, et cetera, and change how TensorFlow does addition, subtraction of tensors. This is all fine and dandy, but Python does not let us override __if. Indeed, that's not an operator in Python. It makes me very sad. But if you think about it for a few seconds, you can probably come up with rewrite rules that would let us, like, lower to byte code that would have __if overwritable. So for example, if code looks like this, if condition a else b, you could conceptually write this as condition.if a and b. You would need to do some fiddling with the scopes, because I'm sure you know that Python's lexical scoping is not really as lexical as you would think, and names can leak out of scopes. And it's kind of a little messy, but that's also a mechanical transformation. So if this is potentially a mechanical transformation, let's do this mechanical transformation. So we wrote this Python to TensorFlow compiler called Autograph that does this-- it takes your Python code, and it rewrites it in a form that lets us call __if, __while, et cetera on tensors. This is all it does, but this just unlocks a lot of the power of native Python Control Flow into your TensorFlow graphs. And you got to choose. So for example, on this function here, I have two loops. One, it's a static Python loop, because I write for i in range. I is an integer, because a range returns integers. Autograph sees this, leaves it untouched. So you've still got to use Python Control Flow to choose how many layers a network's going to have and constructing dynamically or iterate over a sequential, et cetera. But when your Control Flow does depend on the properties of tensors, like in the second loop for i in tf.range, then Autograph sees it and turns it into a dynamic tf.while loop. This means that you can implement something like a dynamic or an n in TensorFlow in 10 lines of code, just like how you would use in a normal language, which is pretty nice. And anything really that you can do in a TensorFlow graph, you can make happen dynamically. So you can make your prints and assertions happen dynamically if you want to debug. But just use in tf.print and tf.Assert. And notice here that I don't need to add control dependencies to ensure that they happen in the right order, because of the thing that we were talking earlier. We already do this, like, we've tried these control dependencies automatically for you to try to really make your code look and behave the same as Python code would look like. But all that we're doing here is converting Control Flow. We're not actually compiling Python to TensorFlow graph, because the TensorFlow runtime right now is not really powerful enough to support everything that Python can do. So for example, if you're manipulating lists of tensors at runtime, you should still use a tensor array. It's a perfectly fine data structure. It works very well. It compiles down to very efficient TensorFlow code and CPUs, GPUs, TPUs. But you no longer need to write a lot of the boilerplate associated with it. So this is how you stack a bunch of tensors together in a loop. So wrapping up, I think we've changed a lot in TF 2.0, how we build graphs, how we use those graphs. And I think you'll all agree that these changes are very big. But I hope you'll agree with me that those changes are worth it. And I'll just quickly walk you through a diff of what your code is going to look like before and after this. So session.run goes away. Control dependencies go away. Variable initialization goes away. Combed and while loop go away, and you just use functions, like how you would use in a normal programming language. So thank you, and welcome to TF 2.0. [APPLAUSE] All the examples on these slides, they run. If you go on tensorflow.org/alpha and you dig it a little, you'll find a colab notebook that has these and a lot more, which will play around with tf.function and Autograph. [MUSIC PLAYING]
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