DataBloomHave people been using deep learning to do regression? I noticed
that fitting polynomials using least squares leads to much better
accuracy! Is there any rule of thumb to get arbitrary accuracy with
deep regression?
submitted by /u/matibilkis
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[comments]
Hello All,
I am new to TensorFlow and I have a problem wherein I need to
count boats on a lake using Keras. I have seen this done in two
separate papers now one counting
whales and another
counting ships in the ocean. however, both are using python.
While I am not apposed to learning another language, I was curious
if there are any tutorials out there about using Keras to count
objects coding in R. does anyone know of anything like this that I
could read over? atm I am stuck with either trying to muddle my way
through building a CNN from scratch without any guidance or
learning a new language, neither of which is something I am
particularly excited about tackling.
any help would be greatly appreciated.
submitted by /u/mthompson2100
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CUDA 11.2 includes improved user experience and application performance through a combination of driver/toolkit compatibility enhancements, new memory suballocator feature, and compiler upgrades.
CUDA Toolkit is a complete, fully-featured software development platform for building GPU-accelerated applications, providing all the components needed to develop apps targeting every NVIDIA GPU platform.
CUDA 11 announced support for the new NVIDIA A100 based on the NVIDIA Ampere architecture, and CUDA 11.1 delivered support for NVIDIA GeForce RTX 30 Series and Quadro RTX Series GPU platforms.
Today, CUDA 11.2 is introducing improved user experience and application performance through a combination of driver/toolkit compatibility enhancements, new memory suballocator feature, and compiler enhancements including an LLVM upgrade.
This new 11.2 release also delivers programming model updates to CUDA Graphs and Cooperative Groups, as well as expanding support for latest generation operating systems and compilers.
We describe some of these innovative feature introductions with more detail in a new blog Enhancing Memory Allocation with New NVIDIA CUDA 11.2 Features, and we will publish additional blogs on compiler enhancements shortly. Follow all CUDA Developer Blogs here.
Download CUDA 11.2 Toolkit today.
Watch [GTC Fall Session] CUDA New Features and Beyond: Ampere Programming for Developers
The 2020.3 release of NVIDIA Nsight Compute included in CUDA Toolkit 11.2 introduces several new features that simplify the process of CUDA kernel profiling and optimization.
The 2020.3 release of NVIDIA Nsight Compute included in CUDA Toolkit 11.2 introduces several new features that simplify the process of CUDA kernel profiling and optimization.
The new Profile Series feature allows developers to configure ranges for multiple kernel parameters. Nsight Compute will automatically iterate through the ranges and profile each combination to help you find the best configuration. These parameters include the number of registers per thread, shared memory sizes, and the shared memory configuration. This automates a process that previously would need manual support, and can provide optimized performance configurations with minimal changes to source code.
The Profile Series configuration is available in the UI’s Interactive Profiling activity.
This highly requested feature enables users to archive source files within their Nsight Compute results. It allows any user with access to the results to resolve performance data to lines in the source code, even if they don’t have access to the original source files. Sharing results with teammates and archiving them for future analysis are just a couple of uses for this new feature. Users can import source files with the (–import-source) command-line option or via the UI when configuring the profile.
Source Files can also be imported later via the Profile Menu.
Additionally, there are several other new capabilities available in this release. These include Memory Allocation Tracking, support for derived metrics, and additional configurations and advice for the recently released Application Replay feature.
For complete details, check out the Nsight Compute Release Notes.
Download Nsight Compute 2020.3 and check out featured spotlight video demonstrations on Roofline Analysis and Application Replay!
libcu++, the NVIDIA C++ Standard Library, provides a C++ Standard Library for your entire system which can be used in and between CPU and GPU codes.
libcu++, the NVIDIA C++ Standard Library, provides a C++ Standard Library for your entire system which can be used in and between CPU and GPU codes. The NVIDIA C++ Standard Library is an open source project.
Version 1.4.0 of libcu++ is a major release providing several feature enhancements and bug fixes. It adds support for the following: , NVCC + MSVC support for , and backports of C++20 and C++17 features to C++14.
Other enhancements include improved and reorganized documentation, atomics decoupled from host Standard Library in MSVC, and revamped examples and benchmarks.
Additional information, examples and documentation can be found below.
libcu++ is available on GitHub and is included in the NVIDIA HPC SDK and the CUDA Toolkit.
Learn more:
We are happy to announce that the version 0.2.0 of torch just
landed on CRAN.
This release includes many bug fixes and some nice new features
that we will present in this blog post. You can see the full
changelog in the NEWS.md
file.
The features that we will discuss in detail are:
Multi-worker dataloaders
dataloaders now respond to the num_workers argument and will run
the pre-processing in parallel workers.
For example, say we have the following dummy dataset that does a
long computation:
library(torch) dat <- dataset( "mydataset", initialize = function(time, len = 10) { self$time <- time self$len <- len }, .getitem = function(i) { Sys.sleep(self$time) torch_randn(1) }, .length = function() { self$len } ) ds <- dat(1) system.time(ds[1])
user system elapsed 0.029 0.005 1.027
We will now create two dataloaders, one that executes
sequentially and another executing in parallel.
seq_dl <- dataloader(ds, batch_size = 5) par_dl <- dataloader(ds, batch_size = 5, num_workers = 2)
We can now compare the time it takes to process two batches
sequentially to the time it takes in parallel:
seq_it <- dataloader_make_iter(seq_dl) par_it <- dataloader_make_iter(par_dl) two_batches <- function(it) { dataloader_next(it) dataloader_next(it) "ok" } system.time(two_batches(seq_it)) system.time(two_batches(par_it))
user system elapsed 0.098 0.032 10.086 user system elapsed 0.065 0.008 5.134
Note that it is batches that are obtained in parallel, not
individual observations. Like that, we will be able to support
datasets with variable batch sizes in the future.
Using multiple workers is not necessarily
faster than serial execution because there’s a considerable
overhead when passing tensors from a worker to the main session as
well as when initializing the workers.
This feature is enabled by the powerful callr package and works in all
operating systems supported by torch. callr let’s us create
persistent R sessions, and thus, we only pay once the overhead of
transferring potentially large dataset objects to workers.
In the process of implementing this feature we have made
dataloaders behave like coro iterators. This means that
you can now use coro’s syntax for looping
through the dataloaders:
coro::loop(for(batch in par_dl) { print(batch$shape) })
[1] 5 1 [1] 5 1
This is the first torch release including the multi-worker
dataloaders feature, and you might run into edge cases when using
it. Do let us know if you find any problems.
Initial JIT support
Programs that make use of the torch package are inevitably R
programs and thus, they always need an R installation in order to
execute.
As of version 0.2.0, torch allows users to JIT trace torch R
functions into TorchScript. JIT (Just in time) tracing will invoke
an R function with example inputs, record all operations that
occured when the function was run and return a script_function
object containing the TorchScript representation.
The nice thing about this is that TorchScript programs are
easily serializable, optimizable, and they can be loaded by another
program written in PyTorch or LibTorch without requiring any R
dependency.
Suppose you have the following R function that takes a tensor,
and does a matrix multiplication with a fixed weight matrix and
then adds a bias term:
w <- torch_randn(10, 1) b <- torch_randn(1) fn <- function(x) { a <- torch_mm(x, w) a + b }
This function can be JIT-traced into TorchScript with jit_trace
by passing the function and example inputs:
x <- torch_ones(2, 10) tr_fn <- jit_trace(fn, x) tr_fn(x)
torch_tensor -0.6880 -0.6880 [ CPUFloatType{2,1} ]
Now all torch operations that happened when computing the result
of this function were traced and transformed into a graph:
tr_fn$graph
graph(%0 : Float(2:10, 10:1, requires_grad=0, device=cpu)): %1 : Float(10:1, 1:1, requires_grad=0, device=cpu) = prim::Constant[value=-0.3532 0.6490 -0.9255 0.9452 -1.2844 0.3011 0.4590 -0.2026 -1.2983 1.5800 [ CPUFloatType{10,1} ]]() %2 : Float(2:1, 1:1, requires_grad=0, device=cpu) = aten::mm(%0, %1) %3 : Float(1:1, requires_grad=0, device=cpu) = prim::Constant[value={-0.558343}]() %4 : int = prim::Constant[value=1]() %5 : Float(2:1, 1:1, requires_grad=0, device=cpu) = aten::add(%2, %3, %4) return (%5)
The traced function can be serialized with jit_save:
jit_save(tr_fn, "linear.pt")
It can be reloaded in R with jit_load, but it can also be
reloaded in Python with torch.jit.load:
import torch fn = torch.jit.load("linear.pt") fn(torch.ones(2, 10))
tensor([[-0.6880], [-0.6880]])
How cool is that?!
This is just the initial support for JIT in R. We will continue
developing this. Specifically, in the next version of torch we plan
to support tracing nn_modules directly. Currently, you need to
detach all parameters before tracing them; see an example
here. This will allow you also to take benefit of TorchScript
to make your models run faster!
Also note that tracing has some limitations, especially when
your code has loops or control flow statements that depend on
tensor data. See ?jit_trace to learn more.
New print method for nn_modules
In this release we have also improved the nn_module printing
methods in order to make it easier to understand what’s
inside.
For example, if you create an instance of an nn_linear module
you will see:
nn_linear(10, 1)
An `nn_module` containing 11 parameters. ── Parameters ────────────────────────────────────────────────────────────────── ● weight: Float [1:1, 1:10] ● bias: Float [1:1]
You immediately see the total number of parameters in the module
as well as their names and shapes.
This also works for custom modules (possibly including
sub-modules). For example:
my_module <- nn_module( initialize = function() { self$linear <- nn_linear(10, 1) self$param <- nn_parameter(torch_randn(5,1)) self$buff <- nn_buffer(torch_randn(5)) } ) my_module()
An `nn_module` containing 16 parameters. ── Modules ───────────────────────────────────────────────────────────────────── ● linear: <nn_linear> #11 parameters ── Parameters ────────────────────────────────────────────────────────────────── ● param: Float [1:5, 1:1] ── Buffers ───────────────────────────────────────────────────────────────────── ● buff: Float [1:5]
We hope this makes it easier to understand nn_module objects. We
have also improved autocomplete support for nn_modules and we will
now show all sub-modules, parameters and buffers while you
type.
torchaudio
torchaudio
is an extension for torch developed by Athos Damiani (@athospd), providing audio
loading, transformations, common architectures for signal
processing, pre-trained weights and access to commonly used
datasets. An almost literal translation from PyTorch’s Torchaudio
library to R.
torchaudio is not yet on CRAN, but you can already try the
development version available here.
You can also visit the pkgdown website for examples
and reference documentation.
Other features and bug fixes
Thanks to community contributions we have found and fixed many
bugs in torch. We have also added new features including:
You can see the full list of changes in the NEWS.md
file.
Thanks very much for reading this blog post, and feel free to
reach out on GitHub for help or discussions!
The photo used in this post preview is by
Oleg Illarionov on
Unsplash
We are happy to announce that the version 0.2.0 of torch just
landed on CRAN.
This release includes many bug fixes and some nice new features
that we will present in this blog post. You can see the full
changelog in the NEWS.md
file.
The features that we will discuss in detail are:
Multi-worker dataloaders
dataloaders now respond to the num_workers argument and will run
the pre-processing in parallel workers.
For example, say we have the following dummy dataset that does a
long computation:
library(torch) dat <- dataset( "mydataset", initialize = function(time, len = 10) { self$time <- time self$len <- len }, .getitem = function(i) { Sys.sleep(self$time) torch_randn(1) }, .length = function() { self$len } ) ds <- dat(1) system.time(ds[1])
user system elapsed 0.029 0.005 1.027
We will now create two dataloaders, one that executes
sequentially and another executing in parallel.
seq_dl <- dataloader(ds, batch_size = 5) par_dl <- dataloader(ds, batch_size = 5, num_workers = 2)
We can now compare the time it takes to process two batches
sequentially to the time it takes in parallel:
seq_it <- dataloader_make_iter(seq_dl) par_it <- dataloader_make_iter(par_dl) two_batches <- function(it) { dataloader_next(it) dataloader_next(it) "ok" } system.time(two_batches(seq_it)) system.time(two_batches(par_it))
user system elapsed 0.098 0.032 10.086 user system elapsed 0.065 0.008 5.134
Note that it is batches that are obtained in parallel, not
individual observations. Like that, we will be able to support
datasets with variable batch sizes in the future.
Using multiple workers is not necessarily
faster than serial execution because there’s a considerable
overhead when passing tensors from a worker to the main session as
well as when initializing the workers.
This feature is enabled by the powerful callr package and works in all
operating systems supported by torch. callr let’s us create
persistent R sessions, and thus, we only pay once the overhead of
transferring potentially large dataset objects to workers.
In the process of implementing this feature we have made
dataloaders behave like coro iterators. This means that
you can now use coro’s syntax for looping
through the dataloaders:
coro::loop(for(batch in par_dl) { print(batch$shape) })
[1] 5 1 [1] 5 1
This is the first torch release including the multi-worker
dataloaders feature, and you might run into edge cases when using
it. Do let us know if you find any problems.
Initial JIT support
Programs that make use of the torch package are inevitably R
programs and thus, they always need an R installation in order to
execute.
As of version 0.2.0, torch allows users to JIT trace torch R
functions into TorchScript. JIT (Just in time) tracing will invoke
an R function with example inputs, record all operations that
occured when the function was run and return a script_function
object containing the TorchScript representation.
The nice thing about this is that TorchScript programs are
easily serializable, optimizable, and they can be loaded by another
program written in PyTorch or LibTorch without requiring any R
dependency.
Suppose you have the following R function that takes a tensor,
and does a matrix multiplication with a fixed weight matrix and
then adds a bias term:
w <- torch_randn(10, 1) b <- torch_randn(1) fn <- function(x) { a <- torch_mm(x, w) a + b }
This function can be JIT-traced into TorchScript with jit_trace
by passing the function and example inputs:
x <- torch_ones(2, 10) tr_fn <- jit_trace(fn, x) tr_fn(x)
torch_tensor -0.6880 -0.6880 [ CPUFloatType{2,1} ]
Now all torch operations that happened when computing the result
of this function were traced and transformed into a graph:
tr_fn$graph
graph(%0 : Float(2:10, 10:1, requires_grad=0, device=cpu)): %1 : Float(10:1, 1:1, requires_grad=0, device=cpu) = prim::Constant[value=-0.3532 0.6490 -0.9255 0.9452 -1.2844 0.3011 0.4590 -0.2026 -1.2983 1.5800 [ CPUFloatType{10,1} ]]() %2 : Float(2:1, 1:1, requires_grad=0, device=cpu) = aten::mm(%0, %1) %3 : Float(1:1, requires_grad=0, device=cpu) = prim::Constant[value={-0.558343}]() %4 : int = prim::Constant[value=1]() %5 : Float(2:1, 1:1, requires_grad=0, device=cpu) = aten::add(%2, %3, %4) return (%5)
The traced function can be serialized with jit_save:
jit_save(tr_fn, "linear.pt")
It can be reloaded in R with jit_load, but it can also be
reloaded in Python with torch.jit.load:
import torch fn = torch.jit.load("linear.pt") fn(torch.ones(2, 10))
tensor([[-0.6880], [-0.6880]])
How cool is that?!
This is just the initial support for JIT in R. We will continue
developing this. Specifically, in the next version of torch we plan
to support tracing nn_modules directly. Currently, you need to
detach all parameters before tracing them; see an example
here. This will allow you also to take benefit of TorchScript
to make your models run faster!
Also note that tracing has some limitations, especially when
your code has loops or control flow statements that depend on
tensor data. See ?jit_trace to learn more.
New print method for nn_modules
In this release we have also improved the nn_module printing
methods in order to make it easier to understand what’s
inside.
For example, if you create an instance of an nn_linear module
you will see:
nn_linear(10, 1)
An `nn_module` containing 11 parameters. ── Parameters ────────────────────────────────────────────────────────────────── ● weight: Float [1:1, 1:10] ● bias: Float [1:1]
You immediately see the total number of parameters in the module
as well as their names and shapes.
This also works for custom modules (possibly including
sub-modules). For example:
my_module <- nn_module( initialize = function() { self$linear <- nn_linear(10, 1) self$param <- nn_parameter(torch_randn(5,1)) self$buff <- nn_buffer(torch_randn(5)) } ) my_module()
An `nn_module` containing 16 parameters. ── Modules ───────────────────────────────────────────────────────────────────── ● linear: <nn_linear> #11 parameters ── Parameters ────────────────────────────────────────────────────────────────── ● param: Float [1:5, 1:1] ── Buffers ───────────────────────────────────────────────────────────────────── ● buff: Float [1:5]
We hope this makes it easier to understand nn_module objects. We
have also improved autocomplete support for nn_modules and we will
now show all sub-modules, parameters and buffers while you
type.
torchaudio
torchaudio
is an extension for torch developed by Athos Damiani (@athospd), providing audio
loading, transformations, common architectures for signal
processing, pre-trained weights and access to commonly used
datasets. An almost literal translation from PyTorch’s Torchaudio
library to R.
torchaudio is not yet on CRAN, but you can already try the
development version available here.
You can also visit the pkgdown website for examples
and reference documentation.
Other features and bug fixes
Thanks to community contributions we have found and fixed many
bugs in torch. We have also added new features including:
You can see the full list of changes in the NEWS.md
file.
Thanks very much for reading this blog post, and feel free to
reach out on GitHub for help or discussions!
The photo used in this post preview is by
Oleg Illarionov on
Unsplash
We are happy to announce that the version 0.2.0 of torch just
landed on CRAN.
This release includes many bug fixes and some nice new features
that we will present in this blog post. You can see the full
changelog in the NEWS.md
file.
The features that we will discuss in detail are:
Multi-worker dataloaders
dataloaders now respond to the num_workers argument and will run
the pre-processing in parallel workers.
For example, say we have the following dummy dataset that does a
long computation:
library(torch) dat <- dataset( "mydataset", initialize = function(time, len = 10) { self$time <- time self$len <- len }, .getitem = function(i) { Sys.sleep(self$time) torch_randn(1) }, .length = function() { self$len } ) ds <- dat(1) system.time(ds[1])
user system elapsed 0.029 0.005 1.027
We will now create two dataloaders, one that executes
sequentially and another executing in parallel.
seq_dl <- dataloader(ds, batch_size = 5) par_dl <- dataloader(ds, batch_size = 5, num_workers = 2)
We can now compare the time it takes to process two batches
sequentially to the time it takes in parallel:
seq_it <- dataloader_make_iter(seq_dl) par_it <- dataloader_make_iter(par_dl) two_batches <- function(it) { dataloader_next(it) dataloader_next(it) "ok" } system.time(two_batches(seq_it)) system.time(two_batches(par_it))
user system elapsed 0.098 0.032 10.086 user system elapsed 0.065 0.008 5.134
Note that it is batches that are obtained in parallel, not
individual observations. Like that, we will be able to support
datasets with variable batch sizes in the future.
Using multiple workers is not necessarily
faster than serial execution because there’s a considerable
overhead when passing tensors from a worker to the main session as
well as when initializing the workers.
This feature is enabled by the powerful callr package and works in all
operating systems supported by torch. callr let’s us create
persistent R sessions, and thus, we only pay once the overhead of
transferring potentially large dataset objects to workers.
In the process of implementing this feature we have made
dataloaders behave like coro iterators. This means that
you can now use coro’s syntax for looping
through the dataloaders:
coro::loop(for(batch in par_dl) { print(batch$shape) })
[1] 5 1 [1] 5 1
This is the first torch release including the multi-worker
dataloaders feature, and you might run into edge cases when using
it. Do let us know if you find any problems.
Initial JIT support
Programs that make use of the torch package are inevitably R
programs and thus, they always need an R installation in order to
execute.
As of version 0.2.0, torch allows users to JIT trace torch R
functions into TorchScript. JIT (Just in time) tracing will invoke
an R function with example inputs, record all operations that
occured when the function was run and return a script_function
object containing the TorchScript representation.
The nice thing about this is that TorchScript programs are
easily serializable, optimizable, and they can be loaded by another
program written in PyTorch or LibTorch without requiring any R
dependency.
Suppose you have the following R function that takes a tensor,
and does a matrix multiplication with a fixed weight matrix and
then adds a bias term:
w <- torch_randn(10, 1) b <- torch_randn(1) fn <- function(x) { a <- torch_mm(x, w) a + b }
This function can be JIT-traced into TorchScript with jit_trace
by passing the function and example inputs:
x <- torch_ones(2, 10) tr_fn <- jit_trace(fn, x) tr_fn(x)
torch_tensor -0.6880 -0.6880 [ CPUFloatType{2,1} ]
Now all torch operations that happened when computing the result
of this function were traced and transformed into a graph:
tr_fn$graph
graph(%0 : Float(2:10, 10:1, requires_grad=0, device=cpu)): %1 : Float(10:1, 1:1, requires_grad=0, device=cpu) = prim::Constant[value=-0.3532 0.6490 -0.9255 0.9452 -1.2844 0.3011 0.4590 -0.2026 -1.2983 1.5800 [ CPUFloatType{10,1} ]]() %2 : Float(2:1, 1:1, requires_grad=0, device=cpu) = aten::mm(%0, %1) %3 : Float(1:1, requires_grad=0, device=cpu) = prim::Constant[value={-0.558343}]() %4 : int = prim::Constant[value=1]() %5 : Float(2:1, 1:1, requires_grad=0, device=cpu) = aten::add(%2, %3, %4) return (%5)
The traced function can be serialized with jit_save:
jit_save(tr_fn, "linear.pt")
It can be reloaded in R with jit_load, but it can also be
reloaded in Python with torch.jit.load:
import torch fn = torch.jit.load("linear.pt") fn(torch.ones(2, 10))
tensor([[-0.6880], [-0.6880]])
How cool is that?!
This is just the initial support for JIT in R. We will continue
developing this. Specifically, in the next version of torch we plan
to support tracing nn_modules directly. Currently, you need to
detach all parameters before tracing them; see an example
here. This will allow you also to take benefit of TorchScript
to make your models run faster!
Also note that tracing has some limitations, especially when
your code has loops or control flow statements that depend on
tensor data. See ?jit_trace to learn more.
New print method for nn_modules
In this release we have also improved the nn_module printing
methods in order to make it easier to understand what’s
inside.
For example, if you create an instance of an nn_linear module
you will see:
nn_linear(10, 1)
An `nn_module` containing 11 parameters. ── Parameters ────────────────────────────────────────────────────────────────── ● weight: Float [1:1, 1:10] ● bias: Float [1:1]
You immediately see the total number of parameters in the module
as well as their names and shapes.
This also works for custom modules (possibly including
sub-modules). For example:
my_module <- nn_module( initialize = function() { self$linear <- nn_linear(10, 1) self$param <- nn_parameter(torch_randn(5,1)) self$buff <- nn_buffer(torch_randn(5)) } ) my_module()
An `nn_module` containing 16 parameters. ── Modules ───────────────────────────────────────────────────────────────────── ● linear: <nn_linear> #11 parameters ── Parameters ────────────────────────────────────────────────────────────────── ● param: Float [1:5, 1:1] ── Buffers ───────────────────────────────────────────────────────────────────── ● buff: Float [1:5]
We hope this makes it easier to understand nn_module objects. We
have also improved autocomplete support for nn_modules and we will
now show all sub-modules, parameters and buffers while you
type.
torchaudio
torchaudio
is an extension for torch developed by Athos Damiani (@athospd), providing audio
loading, transformations, common architectures for signal
processing, pre-trained weights and access to commonly used
datasets. An almost literal translation from PyTorch’s Torchaudio
library to R.
torchaudio is not yet on CRAN, but you can already try the
development version available here.
You can also visit the pkgdown website for examples
and reference documentation.
Other features and bug fixes
Thanks to community contributions we have found and fixed many
bugs in torch. We have also added new features including:
You can see the full list of changes in the NEWS.md
file.
Thanks very much for reading this blog post, and feel free to
reach out on GitHub for help or discussions!
The photo used in this post preview is by
Oleg Illarionov on
Unsplash
Each morning millions of bleary-eyed people pour milk into their bowls of cereal or cups of coffee without a second thought as to where that beverage came from. Few will consider the processes in place to maintain the health of the animals involved in milk production and to ensure that the final product is fit Read article >
The post Scotland’s Rural College Makes Moo-ves Against Bovine Tuberculosis with AI appeared first on The Official NVIDIA Blog.
Hi there! I am brand new to unbuntu and I have just build a
computer running it. I am currently trying to download TensorFlow
for my computer and I am trying to run the comamnd:
$. ./venv/bin/activate.fish # fish
and it’s returning this:
bash: ./venv/bin/activate.fish: line 4: syntax error near
unexpected token `-d’
bash: ./venv/bin/activate.fish: line 4: `function deactivate -d
“Exit virtualenv and return to normal shell environment”‘
Any thoughts?
submitted by /u/Elrekl
[visit reddit]
[comments]