sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: pytorch
Issue: Torch not compiled with CUDA enabled
Labels: awaiting response (this tag is deprecated),module: binaries,module: cuda,triaged

**Hi, I'm trying to run this code from the following repository (https://github.com/CorentinJ/Real-Time-Voice-Cloning), I already installed all requirements, however I'm getting this error when I run the demo_cli.py:**

AssertionError: Torch not compiled with CUDA enabled


**This is the full text I get when I run it:**

runfile('C:/Users/isabel/Desktop/VOICE CLONER/Real-Time-Voice-Cloning-master/demo_cli.py', wdir='C:/Users/isabel/Desktop/VOICE CLONER/Real-Time-Voice-Cloning-master')
Arguments:
    enc_model_fpath:   encoder\saved_models\pretrained.pt
    syn_model_dir:     synthesizer\saved_models\logs-pretrained
    voc_model_fpath:   vocoder\saved_models\pretrained\pretrained.pt
    low_mem:           False
    no_sound:          False

Running a test of your configuration...

Your PyTorch installation is not configured to use CUDA. If you have a GPU ready for deep learning, ensure that the drivers are properly installed, and that your CUDA version matches your PyTorch installation. CPU-only inference is currently not supported.
Traceback (most recent call last):

  File "<ipython-input-6-e2d49148a56d>", line 1, in <module>
    runfile('C:/Users/isabel/Desktop/VOICE CLONER/Real-Time-Voice-Cloning-master/demo_cli.py', wdir='C:/Users/isabel/Desktop/VOICE CLONER/Real-Time-Voice-Cloning-master')

  File "C:\Users\isabel\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile

  File "C:\Users\isabel\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
    # =============================================================================

  File "C:/Users/isabel/Desktop/VOICE CLONER/Real-Time-Voice-Cloning-master/demo_cli.py", line 47, in <module>
    device_id = torch.cuda.current_device()

  File "C:\Users\isabel\Anaconda3\lib\site-packages\torch\cuda\__init__.py", line 377, in current_device
    _lazy_init()

  File "C:\Users\isabel\Anaconda3\lib\site-packages\torch\cuda\__init__.py", line 196, in _lazy_init
    _check_driver()

  File "C:\Users\isabel\Anaconda3\lib\site-packages\torch\cuda\__init__.py", line 94, in _check_driver
    raise AssertionError("Torch not compiled with CUDA enabled")

AssertionError: Torch not compiled with CUDA enabled


**CUDA 10.0 is installed. I already tried uninstalling Pytorch and installing it again with the command "conda install pytorch torchvision cudatoolkit=10.0 -c pytorch", but it didn't work. Can anyone help with this issue?? Thank you so much!**

cc @ezyang @ngimel
Repository: svelte
Issue: SSR Actions
Labels: feature request

**Is your feature request related to a problem? Please describe.**
It would be nice if actions could output attributes in SSR context.

**Describe the solution you'd like**
```html
<script>
  function myAction(node, params) {
    // the normal action stuff for client side
  }
  myAction.SSR = function (params) {
    return {
      'data-some-key': params.someKey,
    };
  }

  export let someKey = 'someValue';
</script>

<div use:myAction={{ someKey }}>
<!-- would render <div data-some-key="someValue"></div> -->
```

**How important is this feature to you?**
Somewhat. It's just an idea i had, that could play well with something like [svelte-css-vars](https://github.com/kaisermann/svelte-css-vars). Imagine that svelte-css-vars could return `{ style: '--color: red' }` when rendered on the server.
Repository: vue
Issue: Slot wrapped in `v-if` disappears after re-render
Labels: bug,has workaround

### Version
2.6.11

### Reproduction link
[https://jsfiddle.net/y9kmcbg6/](https://jsfiddle.net/y9kmcbg6/)







### Steps to reproduce
1. JSFiddle will render both elements.
2. After 3s, it will toggle off
3. After 3s, it will toggle on
4. To see it happen again, rerun the jsfiddle

### What is expected?
At step 3, the bottom slot content should re-appear when ToggleComponent toggles back

### What is actually happening?
The bottom slot never re-appears

---
Seems to be related to the `v-if` on line 21. I'm doing this b/c it's useful to have an optional slot, but I don't want an extra div to appear when the slot isn't used (for layout reasons). I'm fairly new to vue, so I don't know if there's a different recommended way to do this.

Not documented in the example, but if ParentComponent updates, the slot content will re-appear

example code included here for perusal:

```vue
const ToggleComponent = {
  data() {
    return {
      toggleEl: true,
    };
  },

  created() {
    setTimeout(() => this.toggleEl = false, 3000);
    setTimeout(() => this.toggleEl = true, 6000);
  },
  render(h) {
    return this.toggleEl ? h('div', this.$slots.default) : null
  }
};

const SlotComponent = {
  template: `
    <div>
      <slot></slot>
      <div v-if="$slots.other">
        <slot name="other"></slot>
      </div>
    </div>
  `
}

const ParentComponent = {
  template: `
    <ToggleComponent>
      <SlotComponent>
        <template v-slot:default>
          I should have an element below me
        </template>
        <template v-slot:other>
          I am the element below you
        </template>
      </SlotComponent>
    </ToggleComponent>
  `,

  components: {
    ToggleComponent,
    SlotComponent
  },
}

var demo = new Vue({
  el: '#demo',
  template: `
    <ParentComponent/>
  `,

  components: {
    ParentComponent
  },
});

```

<!-- generated by vue-issues. DO NOT REMOVE -->
Repository: pytorch
Issue: Scripting fails to preserve attribute aliasing
Labels: oncall: jit,triaged

Script fails to preserve aliasing between attributes. As shown in example below:

    def test_module_with_aliased_attr(self):
        class M(torch.nn.Module):
            def __init__(self):
                super(M, self).__init__()
                self.a = [1, 2, 3, 4, 5, 6]
                self.b = self.a

            def forward(self, x):
                self.b[1] += 10
                return self.a

        m = M()
        m_s = torch.jit.script(m)
        m_s.eval()
        inp = torch.tensor([5])
        out = m_s.forward(inp)
        expected = m.forward(inp)
        print("expected: ", expected, "out ", out)
        self.assertEqual(out, expected)

Prints:  expected:  [1, 12, 3, 4, 5, 6] out  [1, 2, 3, 4, 5, 6]


cc @suo
Repository: pytorch
Issue: Support batch linear transformation
Labels: module: nn,triaged,module: batching,function request

## 🚀 Feature
https://github.com/pytorch/pytorch/blob/1b746b95fb6f3df45bd2883045de9fbd82b72100/torch/nn/functional.py#L1362
expects the weight and bias tensors to have shapes `(out_features, in_features)`  and `(out_features)`, respectively. We would like for this function to optionally support `(N, out_features, in_features)` and `(N, out_features)`, as well.

This request extends to other `torch.nn.functional` functions, but this is the painful one to us right now.

## Motivation

It's currently a bit of a pain to use `torch.nn.functional` with batch networks.

## Pitch

This request is effectively asking to replace 
https://github.com/pytorch/pytorch/blob/1b746b95fb6f3df45bd2883045de9fbd82b72100/torch/nn/functional.py#L1379 with
```py
output = input.matmul(weight.transpose(-1, -2))
```

## Alternatives

Writing our own `torch.nn.functional.linear`

## Additional context

In writing this request, github helpfully pointed out https://github.com/pytorch/pytorch/issues/7500 as being similar. I suppose this is a request to take that beyond algebra operators and into nn operators.

cc @albanD @mruberry
Repository: go
Issue: x/crypto/ssh: Semantics of Conn.Wait and Conn.Close unclear in docs
Labels: Documentation,NeedsInvestigation

The semantics of Conn.Wait and Conn.Close are unclear as of the docs currently.

https://pkg.go.dev/golang.org/x/crypto/ssh?tab=doc

```
	// Close closes the underlying network connection
	Close() error

	// Wait blocks until the connection has shut down, and returns the
	// error causing the shutdown.
	Wait() error
```

Is it necessary to call both, and in which order, or is just Close enough?
Repository: rust
Issue: Poor error message when indexing result of dbg!()
Labels: C-enhancement,A-diagnostics,T-compiler

Consider:
```rust
fn main() {
  dbg!(1, 3)[1];
}
```

On nightly (2020-02-05) this yields the error:
```
error[E0608]: cannot index into a value of type `({integer}, {integer})`
 --> src/main.rs:2:3
  |
2 |   dbg!(1, 3)[1];
  |   ^^^^^^^^^^^^^ help: to access tuple elements, use: `($ ($ crate :: dbg ! ($ val)), +,).1`
```

Maybe the `help: to access tuple elements, use` part should either avoid printing the left-hand side of the dot operator, or use the span of the source file like `help: to access tuple elements, use: 'dbg!(1, 3).1'`
Repository: godot
Issue: Control focus inside viewport will get stuck after mouse wheel scroll (e.g. in a scroll container)
Labels: bug,confirmed,topic:gui

**Godot version:**
3.2-stable

**OS/device including version:**
Distributor ID:	Ubuntu
Description:	Pop!_OS 19.10
Release:	19.10
Codename:	eoan

(Happens on windows as well)

**Issue description:**
When the mouse wheel is used to scroll a scroll container inside a viewport, the currently focused control will steal focus and block any further mouse movement events from reaching controls inside the viewport. (gui.mouse_focus gets set and never released) 

**Steps to reproduce:**
Create a viewport scene (like the '2d in 3d demo' and add a scroll container with overflowing content such as a vboxcontainer full of buttons. Use the mouse wheel and notice that you can't click on any other buttons until after the mouse is moved out of the window or the viewport's mouse focus is reset some other way. 

*NOTE* I've discovered that it only happens when the viewport has `handle_input_locally=false`
This may mean that this as 'not a bug' but I'll still leave this report for future generations ;)
  
**Minimal reproduction project:**
[gui_in_3d_mfocus_scrollwheel_repro.zip](https://github.com/godotengine/godot/files/4163347/gui_in_3d_mfocus_scrollwheel_repro.zip)
Repository: go
Issue: go.dev: How to get listed in "In-person training"?
Labels: NeedsInvestigation,website

There are 4 companies mentioned in the "In-person training" section at https://learn.go.dev/.

It's not clear why these companies where selected and now to get listed there. IMO there should be clear guidelines and instructions on how to get listed there.

Also it might be a good idea to split these companies by country.

Disclaimer: I want my company to be listed there as well.
Repository: vscode
Issue: Add a task instancePolicy to task runOptions.
Labels: help wanted,feature-request,tasks

When a task has reached the max number of instances it can have (default is 1, can be changed with `instanceLimit`) the `instancePolicy` determines what happens when another instance is started.

History: https://github.com/microsoft/vscode/issues/32264
PR for `insanceLimit` which is relevant to anyone interested in implementing `instancePolicy`: https://github.com/microsoft/vscode/pull/89872
Repository: tensorflow
Issue: Loading Tensorflow models from disk is instant while loading from gcs is very slow
Labels: stat:awaiting tensorflower,comp:apis,type:performance,TF 2.4

**System information**
- OS Platform and Distribution: Both MacOS and Ubuntu 18 (Tensorflow/serving)
- TensorFlow version (use command below): 2.0.0
- Python version: 3.6

**Describe the current behavior**

I want to use Tensorflow serving to load multiple Models. If I mount a directory containing the model, loading everything is done in an instant, while loading them from a `gs://` path takes around 10 seconds per model. 

While researching the issue I discovered this is probably a Tensorflow issue and not a Tensorflow Serving issue as loading them in Tensorflow is a huge difference as well:
```
    [ins] In [22]: %timeit tf.saved_model.load('test/1')
    3.88 s ± 719 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

    [ins] In [23]: %timeit tf.saved_model.load('gs://path/to/test/1')
    30.6 s ± 2.66 s per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Then it could be that downloading the model (which is very small) is slow, but I tested this as well:
```
    def test_load():
        bucket_name = 'path'
        folder='test'
        delimiter='/'
        file = 'to/test/1'
     
        bucket=storage.Client().get_bucket(bucket_name)
        blobs=bucket.list_blobs(prefix=file, delimiter=delimiter)
        for blob in blobs:
            print(blob.name)
            destination_uri = '{}/{}'.format(folder, blob.name)
            blob.download_to_filename(destination_uri)

    [ins] In [31]: %timeit test_load()
    541 ms ± 54.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```
Any idea what is happening here?

**Describe the expected behavior**
Around the same load time for external vs local models. The first load from gs can be slow if the auth needs to happen, but even authenticating is still way faster than the models loads.
Repository: create-react-app
Issue: Provide stacktrace for react-scripts/build.js `build` function
Labels: issue: proposal,needs triage

### Is your proposal related to a problem?

<!--
  Provide a clear and concise description of what the problem is.
  For example, "I'm always frustrated when..."
-->

I've encountered a problem that I think could've been diagnosed more easily if `build` script were more verbose and provided a stack trace aside of error message.

My scenario was pretty simple - I've used CRA, eject, then played a bit with `entry` record:
https://github.com/facebook/create-react-app/blob/687c4ebf211ad30238f2d59e063b8171e015bfc7/packages/react-scripts/config/webpack.config.js#L151
by replacing an array configuration with an object configuration ([which is valid webpack option](https://webpack.js.org/configuration/entry-context/#entry))

After that I couldn't build my app (which is fine) and I coundn't easily find a problem, see attached image:  
![original](https://user-images.githubusercontent.com/13132449/73928305-f09fc500-48e3-11ea-81e1-b08900f4cc2e.PNG)
As you can see, the error message didn't tell me where the problem occured or what the context was, so if I've played a lot around my configuration then I have to either revert my changes line by line to find which exactly broke the build or debug build script itself.


### Describe the solution you'd like

<!--
  Provide a clear and concise description of what you want to happen.
-->

Error message was formed here: 
https://github.com/facebook/create-react-app/blob/687c4ebf211ad30238f2d59e063b8171e015bfc7/packages/react-scripts/scripts/build.js#L196

If there would've been an additional stacktrace added to that array:
```js
if (err) {
    messages.errors.push(err.stack);
}
return reject(new Error(messages.errors.join('\n\n'), ));
```
then I would have a more precise description of a problem that shows me that 'filter of undefined' situation occured exactly at 583 line of my webpack config which I should further work with:
![suggestion](https://user-images.githubusercontent.com/13132449/73929061-390bb280-48e5-11ea-9e2e-370f015b1207.PNG)

### Describe alternatives you've considered

<!--
  Let us know about other solutions you've tried or researched.
-->

There is an obvious concern that stacktraces might not have a place in defined policy of user-friendly error messages, but they probably could be enabled by additional `--verbose` flag.
Repository: material-ui
Issue: [material-ui][Button] Text not aligned vertically with icon
Labels: component: button,package: material-ui,ready to take

When button has an icon, text is not aligned vertically.
You can see it at [example page](https://material-ui.com/components/buttons/#buttons-with-icons-and-label).
![image](https://user-images.githubusercontent.com/19357901/73930230-40cc5680-48e7-11ea-9782-d037e7e22443.png)
I made an screenshot and check at graphics designer - button "delete" has 12px from top to text and 14px from text to bottom. While icon has exactly 10px from top and from bottom.
Repository: material-ui
Issue: [Select] Pass rendered content of MenuItem to renderValue function
Labels: new feature,component: select,ready to take

<!-- Provide a general summary of the feature in the Title above -->
<!--
  Thank you very much for contributing to Material-UI by creating an issue! ❤️
  To avoid duplicate issues we ask you to check off the following list.
-->

<!-- Checked checkbox should look like this: [x] -->

- [x] I have searched the [issues](https://github.com/mui-org/material-ui/issues) of this repository and believe that this is not a duplicate.

## Summary 💡
In the current situation it's possible to customize the rendered content of a Select component using the renderValue prop. This works really well I just have one gripe. I would like the rendered content of the selected MenuItem to be passed as an argument as well.

As a solution that wont break existing implementations I propose it would be passed as a second function argument, e.g.:

`function(value: any, content: ReactNode) => ReactNode`

When using the multiple prop the content argument would be an array of ReactNodes.

<!-- Describe how it should work. -->

<!--
  Provide a link to the Material design specification, other implementations,
  or screenshots of the expected behavior.
-->

## Motivation 🔦
Often I'm using customized content inside the MenuItems in my Select, like icons and avatars. When not using the renderValue prop this content is automatically rendered inside the Select when a selecting a value. However when using the renderValue prop you only get passed the value as function parameter, you would have to generate the content again inside the renderValue function.

<!--
  What are you trying to accomplish? How has the lack of this feature affected you?
  Providing context helps us come up with a solution that is most useful in the real world.
-->
Repository: pytorch
Issue: [debatable] Better infer dtype in torch.as_tensor
Labels: triaged,module: tensor creation

Supporting generator inputs in `torch.tensor` / `torch.as_tensor` would save some keystrokes to the user. Converting the input to list internally would do the trick. On the contrary note: numpy does not support this.

```
>>> torch.as_tensor(list(False for i in range(3)))
tensor([False, False, False])

>>> torch.as_tensor([False for i in range(3)])
tensor([False, False, False])

>>> torch.as_tensor(False for i in range(3))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
RuntimeError: Could not infer dtype of generator
```

The relevant line is in https://github.com/pytorch/pytorch/blob/62b06b9fae9b151b20d25c8e54ae9ae1081f23ad/torch/csrc/utils/tensor_new.cpp#L200
Repository: pytorch
Issue: bytearray(tensor) behaves very differently from bytearray(tensor.numpy())
Labels: module: printing,triaged,module: numpy

```python
import torch

a = torch.ShortTensor([10000])
print(bytearray(a.numpy()))
# bytearray(b"\x10\'")

print(bytearray(a))
# bytearray(b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00 ............')
```

I thought this is the Python prescribed interface of getting byte representation of some object, but this fails with PyTorch arrays. My goal is to pass the obtained byte array or byte string to webrtcvad method working with audio byte frames.

Related: https://discuss.pytorch.org/t/getting-tensor-raw-data/18961/2

cc @mruberry @rgommers @heitorschueroff
Repository: scrcpy
Issue: Fire Stick 4K Low FPS
Labels: performance

I am trying to copy my Amazon Fire Stick 4K display on my Laptop using the scrcpy. Everything seems to be working fine, but when I run the scrcpy.exe, the screen I see is super low FPS. I mean it is around 1FPS.

And When I try scrcpy.exe -m640 

it is almost 60fps, but it is unusable for me at that resolution. Any resolution above that has very low FPS.

It only happens with this new Fire Stick 4K. My old Fire Stick and my phone seems to be working fine.
Repository: node
Issue: Improve performance of accessing globals in scripts running in `vm` modules
Labels: vm,v8 engine,performance

**Is your feature request related to a problem? Please describe.**

Accessing globals from within a `vm.Script` is guarded by interceptors which makes accessing them slow, (from my understanding, @fhinkel has an excellent comment explaining some of this here: https://github.com/facebook/jest/issues/5163#issuecomment-355509597). The suggested workaround is to evaluate and cache the global you want access to (`Math` in my example case below) and inject that into the `vm.Script`. This is an acceptable workaround for `vm.Script` and `vm.compileFunction` but it cannot be done when using ESM `vm.SourceTextModule` as there is no wrapping function call.

I've put together this script you can run to see the numbers:

```js
// test.js
const {
  compileFunction,
  createContext,
  runInContext,
  SourceTextModule
} = require("vm");
const context = createContext({ console });

const sourceCode = `
  const runTimes = 10000000;
  let sum = 0;
  console.time();
  for (let i = 0; i < runTimes; i++) {
    sum += Math.abs(0);
  }
  console.timeEnd();
`;

(async () => {
  const compiledFunction = compileFunction(sourceCode, [], {
    parsingContext: context
  });

  const vmModule = new SourceTextModule(sourceCode, { context });

  await vmModule.link(() => {
    throw new Error("should never be called");
  });

  // These are both super slow (1.6 seconds on my machine)
  compiledFunction();
  await vmModule.evaluate();

  // however, this is quick (less than 10 milliseconds on my machine)
  const compiledWithMathFunction = compileFunction(sourceCode, ["Math"], {
    parsingContext: context
  });
  compiledWithMathFunction(runInContext("Math", context));
})().catch(error => {
  console.error(error);
  process.exitCode = 1;
});
```

Running this gives the following results on my machine:

```sh-session
$ node --experimental-vm-modules test.js
(node:19958) ExperimentalWarning: VM Modules is an experimental feature. This feature could change at any time
default: 1.698s
default: 1.620s
default: 7.306ms
```
So ~1600ms if not using the workaround, which reduces it to ~7ms.

**Describe the solution you'd like**

I'd like for accessing globals to be as fast, or as close to as possible, as fast as accessing globals outside of `vm`. Would it be possible for Node's `vm` implementation to cache the global property lookups, so that the price is only paid once instead of on every single access?

**Describe alternatives you've considered**

As mentioned, caching and injecting the global manually works when there is a function wrapper, but this doesn't work with ESM. I've tried assigning the `Math` global to the `context`, and while that halves the time spent (~800ms), it's still 2 orders of magnitude slower than injecting a reference.