Image Generation

Please review.
Thank you!

@ayiyoh Congratulations on your first contribution with an image generation model!

Hi @ayiyoh. Thank you for your pull request.
I am running into problems running the style-gan task. When I executed mlflow run . in tasks/computer-vision/image-generation/style-gan, I get the following exception:
Traceback (most recent call last):
  File "generate.py", line 268, in <module>
    imgs = generator(latents, resolution_log2, alpha)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/style_gan_net.py", line 186, in forward
    dlatents = self.g_mapping(latents)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/style_gan_net.py", line 48, in forward
    w = super().forward(x)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
    input = module(input)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/custom_layers.py", line 177, in forward
    x = self.module(x)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 92, in forward
    return F.linear(input, self.weight, self.bias)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/functional.py", line 1408, in linear
    output = input.matmul(weight.t())
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1556653183467/work/aten/src/THC/THCBlas.cu:259

Full Trace
2019/07/22 04:17:33 INFO mlflow.projects: === Creating conda environment mlflow-289a9e0465548407194d4668d858beea1ece7139 ===
Warning: you have pip-installed dependencies in your environment file, but you do not list pip itself as one of your conda dependencies.  Conda may not use the correct pip to install your packages, and they may end up in the wrong place.  Please add an explicit pip dependency.  I'm adding one for you, but still nagging you.
Collecting package metadata: done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
  current version: 4.6.14
  latest version: 4.7.10

Please update conda by running

    $ conda update -n base -c defaults conda

Downloading and Extracting Packages
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Preparing transaction: done
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Executing transaction: done
Ran pip subprocess with arguments:
['/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/bin/python', '-m', 'pip', 'install', '-U', '-r', '/content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt']
Pip subprocess output:
Collecting mlflow>=1.0 (from -r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/01/ec/8c9448968d4662e8354b9c3a62e635f8929ed507a45af3d9fdb84be51270/mlflow-1.0.0-py3-none-any.whl
Requirement already satisfied, skipping upgrade: entrypoints in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (0.3)
Collecting cloudpickle (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
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Requirement already satisfied, skipping upgrade: protobuf>=3.6.0 in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (3.8.0)
Collecting alembic (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
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Collecting requests>=2.17.3 (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
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Collecting databricks-cli>=0.8.0 (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/5f/38/f83bc71c5e7351a03e8d44aaf04647d076bbf8f097e3f93b921704b7a74c/databricks_cli-0.8.7-py3-none-any.whl
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Successfully installed Flask-1.1.1 Mako-1.0.14 alembic-1.0.11 chardet-3.0.4 click-7.0 cloudpickle-1.2.1 configparser-3.7.4 databricks-cli-0.8.7 ddt-1.2.1 docker-4.0.2 gitdb-0.6.4 gitdb2-2.0.5 gitpython-2.1.12 gunicorn-19.9.0 idna-2.8 itsdangerous-1.1.0 mlflow-1.0.0 pandas-0.25.0 python-editor-1.0.4 pyyaml-5.1.1 querystring-parser-1.2.3 requests-2.22.0 simplejson-3.16.0 smmap-0.9.0 smmap2-2.0.5 sqlalchemy-1.3.6 sqlparse-0.3.0 tabulate-0.8.3 urllib3-1.25.3 websocket-client-0.56.0

#
# To activate this environment, use:
# > conda activate mlflow-289a9e0465548407194d4668d858beea1ece7139
#
# To deactivate an active environment, use:
# > conda deactivate
#

2019/07/22 04:30:24 INFO mlflow.projects: === Created directory /tmp/tmpkgpic3fz for downloading remote URIs passed to arguments of type 'path' ===
2019/07/22 04:30:24 INFO mlflow.projects: === Running command 'source /root/miniconda3/bin/activate mlflow-289a9e0465548407194d4668d858beea1ece7139 && python generate.py \
  --dataset ffhq \
  --convert True \
  --use-official-checkpoints True \
  --random-seed 77 \
  --nrow 2 \
  --ncol 2 \
  --g-checkpoint ./checkpoints/generator.32x32.1.000000.3000000.135.pt \
  --target-resolution 128
' in run with ID '166f6f77d47e4b61b5e1cbe98b445902' ===
start conversion
Downloading https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ .... done
WARNING:tensorflow:From /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-07-22 04:30:48.178953: W tensorflow/core/framework/allocator.cc:124] Allocation of 16777216 exceeds 10% of system memory.
ours only g_synthesis.blocks.8x8.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.16x16.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.32x32.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.64x64.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.128x128.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.256x256.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.512x512.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.1024x1024.blur.kernel torch.Size([1, 1, 3, 3])
finished conversion
Traceback (most recent call last):
  File "generate.py", line 268, in <module>
    imgs = generator(latents, resolution_log2, alpha)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/style_gan_net.py", line 186, in forward
    dlatents = self.g_mapping(latents)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/style_gan_net.py", line 48, in forward
    w = super().forward(x)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
    input = module(input)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/custom_layers.py", line 177, in forward
    x = self.module(x)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 92, in forward
    return F.linear(input, self.weight, self.bias)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/functional.py", line 1408, in linear
    output = input.matmul(weight.t())
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1556653183467/work/aten/src/THC/THCBlas.cu:259
2019/07/22 04:31:27 ERROR mlflow.cli: === Run (ID '166f6f77d47e4b61b5e1cbe98b445902') failed ===

Hi @ayiyoh. Thank you for your pull request.
I am running into problems running the style-gan task. When I executed mlflow run . in tasks/computer-vision/image-generation/style-gan, I get the following exception:
Traceback (most recent call last):
  File "generate.py", line 268, in <module>
    imgs = generator(latents, resolution_log2, alpha)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/style_gan_net.py", line 186, in forward
    dlatents = self.g_mapping(latents)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/style_gan_net.py", line 48, in forward
    w = super().forward(x)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
    input = module(input)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/custom_layers.py", line 177, in forward
    x = self.module(x)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 92, in forward
    return F.linear(input, self.weight, self.bias)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/functional.py", line 1408, in linear
    output = input.matmul(weight.t())
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1556653183467/work/aten/src/THC/THCBlas.cu:259

I am unsure whether this is a problem in the code or in my CUDA setup. The following references 1, 2 suggest that it could be a problem with the dataset.
Full Trace
2019/07/22 04:17:33 INFO mlflow.projects: === Creating conda environment mlflow-289a9e0465548407194d4668d858beea1ece7139 ===
Warning: you have pip-installed dependencies in your environment file, but you do not list pip itself as one of your conda dependencies.  Conda may not use the correct pip to install your packages, and they may end up in the wrong place.  Please add an explicit pip dependency.  I'm adding one for you, but still nagging you.
Collecting package metadata: done
Solving environment: done

==> WARNING: A newer version of conda exists. <==
  current version: 4.6.14
  latest version: 4.7.10

Please update conda by running

    $ conda update -n base -c defaults conda

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Preparing transaction: done
Verifying transaction: done
Executing transaction: done
Ran pip subprocess with arguments:
['/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/bin/python', '-m', 'pip', 'install', '-U', '-r', '/content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt']
Pip subprocess output:
Collecting mlflow>=1.0 (from -r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/01/ec/8c9448968d4662e8354b9c3a62e635f8929ed507a45af3d9fdb84be51270/mlflow-1.0.0-py3-none-any.whl
Requirement already satisfied, skipping upgrade: entrypoints in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (0.3)
Collecting cloudpickle (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/09/f4/4a080c349c1680a2086196fcf0286a65931708156f39568ed7051e42ff6a/cloudpickle-1.2.1-py2.py3-none-any.whl
Requirement already satisfied, skipping upgrade: protobuf>=3.6.0 in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (3.8.0)
Collecting alembic (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
Collecting pyyaml (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/a3/65/837fefac7475963d1eccf4aa684c23b95aa6c1d033a2c5965ccb11e22623/PyYAML-5.1.1.tar.gz
Collecting sqlparse (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/ef/53/900f7d2a54557c6a37886585a91336520e5539e3ae2423ff1102daf4f3a7/sqlparse-0.3.0-py2.py3-none-any.whl
Collecting gitpython>=2.1.0 (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/1a/44/fc1ce6e0692f09d1bcdbbb5d2bd008acd824505a12dd85a627ef74f44844/GitPython-2.1.12-py2.py3-none-any.whl
Collecting requests>=2.17.3 (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Downloading https://files.pythonhosted.org/packages/51/bd/23c926cd341ea6b7dd0b2a00aba99ae0f828be89d72b2190f27c11d4b7fb/requests-2.22.0-py2.py3-none-any.whl (57kB)
Collecting gunicorn (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/8c/da/b8dd8deb741bff556db53902d4706774c8e1e67265f69528c14c003644e6/gunicorn-19.9.0-py2.py3-none-any.whl
Collecting databricks-cli>=0.8.0 (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/5f/38/f83bc71c5e7351a03e8d44aaf04647d076bbf8f097e3f93b921704b7a74c/databricks_cli-0.8.7-py3-none-any.whl
Collecting sqlalchemy (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/55/98/56b7155bab287cd0c78dee26258835db36e91f2efef41f125ed6f6f1f334/SQLAlchemy-1.3.6.tar.gz
Collecting Flask (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/9b/93/628509b8d5dc749656a9641f4caf13540e2cdec85276964ff8f43bbb1d3b/Flask-1.1.1-py2.py3-none-any.whl
Collecting click>=7.0 (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/fa/37/45185cb5abbc30d7257104c434fe0b07e5a195a6847506c074527aa599ec/Click-7.0-py2.py3-none-any.whl
Requirement already satisfied, skipping upgrade: six>=1.10.0 in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (1.12.0)
Requirement already satisfied, skipping upgrade: numpy in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (1.16.4)
Collecting docker>=3.6.0 (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/95/47/5560c9cf0c92b50da24216f0e7733250fbed5a497f69e3c70e1be62143fe/docker-4.0.2-py2.py3-none-any.whl
Collecting simplejson (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
Requirement already satisfied, skipping upgrade: python-dateutil in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (2.8.0)
Collecting querystring-parser (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
Collecting pandas (from mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Downloading https://files.pythonhosted.org/packages/1d/9a/7eb9952f4b4d73fbd75ad1d5d6112f407e695957444cb695cbb3cdab918a/pandas-0.25.0-cp36-cp36m-manylinux1_x86_64.whl (10.5MB)
Requirement already satisfied, skipping upgrade: setuptools in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from protobuf>=3.6.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (41.0.1)
Collecting Mako (from alembic->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
Collecting python-editor>=0.3 (from alembic->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/c6/d3/201fc3abe391bbae6606e6f1d598c15d367033332bd54352b12f35513717/python_editor-1.0.4-py3-none-any.whl
Collecting gitdb2>=2.0.0 (from gitpython>=2.1.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/da/30/a407568aa8d8f25db817cf50121a958722f3fc5f87e3a6fba1f40c0633e3/gitdb2-2.0.5-py2.py3-none-any.whl
Collecting gitdb>=0.6.4 (from gitpython>=2.1.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
Collecting ddt>=1.1.1 (from gitpython>=2.1.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/cf/f5/f83dea32dc3fb3be1e5afab8438dce73ed587740a2a061ae2ea56e04a36d/ddt-1.2.1-py2.py3-none-any.whl
Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from requests>=2.17.3->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (2019.6.16)
Collecting chardet<3.1.0,>=3.0.2 (from requests>=2.17.3->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Downloading https://files.pythonhosted.org/packages/bc/a9/01ffebfb562e4274b6487b4bb1ddec7ca55ec7510b22e4c51f14098443b8/chardet-3.0.4-py2.py3-none-any.whl (133kB)
Collecting idna<2.9,>=2.5 (from requests>=2.17.3->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Downloading https://files.pythonhosted.org/packages/14/2c/cd551d81dbe15200be1cf41cd03869a46fe7226e7450af7a6545bfc474c9/idna-2.8-py2.py3-none-any.whl (58kB)
Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 (from requests>=2.17.3->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Downloading https://files.pythonhosted.org/packages/e6/60/247f23a7121ae632d62811ba7f273d0e58972d75e58a94d329d51550a47d/urllib3-1.25.3-py2.py3-none-any.whl (150kB)
Collecting configparser>=0.3.5 (from databricks-cli>=0.8.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/ba/05/6c96328e92e625fc31445d24d75a2c92ef9ba34fc5b037fe69693c362a0d/configparser-3.7.4-py2.py3-none-any.whl
Collecting tabulate>=0.7.7 (from databricks-cli>=0.8.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/c2/fd/202954b3f0eb896c53b7b6f07390851b1fd2ca84aa95880d7ae4f434c4ac/tabulate-0.8.3.tar.gz
Requirement already satisfied, skipping upgrade: Werkzeug>=0.15 in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from Flask->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (0.15.4)
Collecting itsdangerous>=0.24 (from Flask->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/76/ae/44b03b253d6fade317f32c24d100b3b35c2239807046a4c953c7b89fa49e/itsdangerous-1.1.0-py2.py3-none-any.whl
Requirement already satisfied, skipping upgrade: Jinja2>=2.10.1 in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from Flask->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (2.10.1)
Collecting websocket-client>=0.32.0 (from docker>=3.6.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/29/19/44753eab1fdb50770ac69605527e8859468f3c0fd7dc5a76dd9c4dbd7906/websocket_client-0.56.0-py2.py3-none-any.whl
Requirement already satisfied, skipping upgrade: pytz>=2017.2 in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from pandas->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (2019.1)
Requirement already satisfied, skipping upgrade: MarkupSafe>=0.9.2 in /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages (from Mako->alembic->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1)) (1.1.1)
Collecting smmap2>=2.0.0 (from gitdb2>=2.0.0->gitpython>=2.1.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
  Using cached https://files.pythonhosted.org/packages/55/d2/866d45e3a121ee15a1dc013824d58072fd5c7799c9c34d01378eb262ca8f/smmap2-2.0.5-py2.py3-none-any.whl
Collecting smmap>=0.8.5 (from gitdb>=0.6.4->gitpython>=2.1.0->mlflow>=1.0->-r /content/ai-platform/tasks/computer-vision/image-generation/style-gan/condaenv.g5w7bd2u.requirements.txt (line 1))
Building wheels for collected packages: pyyaml, sqlalchemy, tabulate
  Building wheel for pyyaml (setup.py): started
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Successfully built pyyaml sqlalchemy tabulate
Installing collected packages: cloudpickle, Mako, python-editor, sqlalchemy, alembic, pyyaml, sqlparse, smmap2, gitdb2, smmap, gitdb, ddt, gitpython, chardet, idna, urllib3, requests, gunicorn, configparser, click, tabulate, databricks-cli, itsdangerous, Flask, websocket-client, docker, simplejson, querystring-parser, pandas, mlflow
Successfully installed Flask-1.1.1 Mako-1.0.14 alembic-1.0.11 chardet-3.0.4 click-7.0 cloudpickle-1.2.1 configparser-3.7.4 databricks-cli-0.8.7 ddt-1.2.1 docker-4.0.2 gitdb-0.6.4 gitdb2-2.0.5 gitpython-2.1.12 gunicorn-19.9.0 idna-2.8 itsdangerous-1.1.0 mlflow-1.0.0 pandas-0.25.0 python-editor-1.0.4 pyyaml-5.1.1 querystring-parser-1.2.3 requests-2.22.0 simplejson-3.16.0 smmap-0.9.0 smmap2-2.0.5 sqlalchemy-1.3.6 sqlparse-0.3.0 tabulate-0.8.3 urllib3-1.25.3 websocket-client-0.56.0

#
# To activate this environment, use:
# > conda activate mlflow-289a9e0465548407194d4668d858beea1ece7139
#
# To deactivate an active environment, use:
# > conda deactivate
#

2019/07/22 04:30:24 INFO mlflow.projects: === Created directory /tmp/tmpkgpic3fz for downloading remote URIs passed to arguments of type 'path' ===
2019/07/22 04:30:24 INFO mlflow.projects: === Running command 'source /root/miniconda3/bin/activate mlflow-289a9e0465548407194d4668d858beea1ece7139 && python generate.py \
  --dataset ffhq \
  --convert True \
  --use-official-checkpoints True \
  --random-seed 77 \
  --nrow 2 \
  --ncol 2 \
  --g-checkpoint ./checkpoints/generator.32x32.1.000000.3000000.135.pt \
  --target-resolution 128
' in run with ID '166f6f77d47e4b61b5e1cbe98b445902' ===
start conversion
Downloading https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ .... done
WARNING:tensorflow:From /root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
2019-07-22 04:30:48.178953: W tensorflow/core/framework/allocator.cc:124] Allocation of 16777216 exceeds 10% of system memory.
ours only g_synthesis.blocks.8x8.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.16x16.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.32x32.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.64x64.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.128x128.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.256x256.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.512x512.blur.kernel torch.Size([1, 1, 3, 3])
ours only g_synthesis.blocks.1024x1024.blur.kernel torch.Size([1, 1, 3, 3])
finished conversion
Traceback (most recent call last):
  File "generate.py", line 268, in <module>
    imgs = generator(latents, resolution_log2, alpha)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/style_gan_net.py", line 186, in forward
    dlatents = self.g_mapping(latents)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/style_gan_net.py", line 48, in forward
    w = super().forward(x)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward
    input = module(input)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/content/ai-platform/tasks/computer-vision/image-generation/style-gan/networks/custom_layers.py", line 177, in forward
    x = self.module(x)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__
    result = self.forward(*input, **kwargs)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 92, in forward
    return F.linear(input, self.weight, self.bias)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/functional.py", line 1408, in linear
    output = input.matmul(weight.t())
RuntimeError: cublas runtime error : the GPU program failed to execute at /opt/conda/conda-bld/pytorch_1556653183467/work/aten/src/THC/THCBlas.cu:259
2019/07/22 04:31:27 ERROR mlflow.cli: === Run (ID '166f6f77d47e4b61b5e1cbe98b445902') failed ===

Which CUDA version do you have in your system? The mlflow run . command only do the image generation using the official checkpoint (converted to mine) and no dataset is fed into the model.

Is the problem resolved? I couldn't replicate it on my side. @mlaradji

Hi @ayiyoh. Thank you for your pull request.
I am running into problems running the style-gan task. When I executed mlflow run . in tasks/computer-vision/image-generation/style-gan, I get the following exception:
Traceback (most recent call last):
  File "generate.py", line 268, in <module>
    imgs = generator(latents, resolution_log2, alpha)
  File "/root/miniconda3/envs/mlflow-289a9e0465548407194d4668d858beea1ece7139/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__

Which CUDA version do you have in your system? Mine is 9.0 that's why the dependency is set to cudatoolkit=9.0. Also the mlflow run . command only do the image generation using the official checkpoint (converted to mine) and no dataset is fed into the model.

Hi @ayiyoh. I apologize for the late reply.
Running the mlflow task does install cudatoolkit=9.0 through conda. However, the external cuda version on the system is 10.0 as nvidia-smi indicates:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 418.67       Driver Version: 410.79       CUDA Version: 10.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |
| N/A   51C    P8    15W /  70W |      0MiB / 15079MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID   Type   Process name                             Usage      |
|=============================================================================|
|  No running processes found                                                 |
+-----------------------------------------------------------------------------+

It is possible that there are some conflict issues between the two different versions of CUDA. I am also running it on a jupyter notebook on Google Colab, which might be part of the problem.
I am currently investigating whether CUDA is the source of my problem.

Comments
Image generation is running quite smoothly for me. I have a few comments:

I am unsure of where the image generated by mlflow run . is stored. Since generate.py uses plt.show(), I presume that the image is meant to be displayed in IPython. It might be preferable to instead save the image in either the mlruns directory, or in a path that can be passed: e.g. mlflow run . -o gen_images_dir=generated_images.
There are some code typos in tasks/computer-vision/image-generation/style-gan/README.md:
a. The mlflow commands in the README.md do not run on my setup. I had to change mlflow . to mlflow run ..
b. There is no longer a generate endpoint in MLproject.
c. I think that random-seed=777 in mlflow -e generate -P dataset=cats -P random-seed=777 -P nrow=2 -P ncol=5 needs to be changed to  random_seed=777.

I am currently training on the celeba dataset using mlflow run . -e train -P resume=False. I will post an update here once the results are ready.

Hi @ayiyoh. I would like to clarify that your pull request will be ready to be merged after the following two changes:

Point 2 of my previous comment needs to be addressed: there are code typos in README.md. Points 1 and 3 do not need to be addressed.
I have created a pull request in https://github.com/ayiyoh/ai-platorm to prepare your task for merging into AI-Platform. Please accept the pull request if it looks alright to you.

Great work on implementing Style-GAN for mlflow :+1:.


Make known path patterns relative to config file containing the setting

Fixes #729
To solve this, easiest would be to walk the path up looking for __init__.py files until not found anymore and that way find the source root, but since this is python 2 and 3, init files may not be present.
This pull request uses the directory of the config file, setting the known paths as the root dir.
Example:
Lets say we have a project in directory /tmp/myapp with a src dir and a config file:
tmp/
 - myapp/
   - setup.cfg
   - src/
      - foo/
        - __init__.py
        - bar.py

# setup.cfg
[isort]
known_first_party = src/

Executing isort bar.py from /tmp/myapp/src/foo will make the path in known_first_party absolute while reading the config file setup.cfg.
>>> settings['known_first_party]
['/tmp/myapp/src/']

Any thoughts on this PR @timothycrosley or ready for merge?

This looks great, sorry for the long review delay, merged


Added History Tab and Fixed "Add Favourite Tab" for 0.4.5

I done the History Tab yesterday but forgot to do a pull request. I went to play KissMP today and realised KissMP breaks when pressing Add under the Favourites Tab but it's fixed now.

Branch is full of conflicts, please resolve

No conflicts were resolved, closing


add story_gold to pt-BR translation

Add the story_gold according to #1199

Translations should go through the translation site: https://translate.opendota.com/
I don't think this new string is there yet, but it should show up when the translations PR is merged


Release<EMAIL_ADDRESS>

0.2.1 - 2022-04-30
Changed

Updated Container.get to create instance of newable unbinded types.
Updated ContainerModuleMetadata to allow class metadata
Updated ContainerModuleMetadata.imports to be optional.
Updated ContainerModuleMetadata.injects to be optional.

Codecov Report

Merging #92 (5a80de9) into master (a7a2325) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master      #92   +/-   ##
=======================================
  Coverage   67.38%   67.38%
=======================================
  Files         110      110
  Lines        1941     1941
  Branches      206      206
=======================================
  Hits         1308     1308
  Misses        626      626
  Partials        7        7

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Coverage Δ

cuaktask
92.72% <ø> (ø)

iocuak
56.22% <ø> (ø)

porygon
97.88% <ø> (ø)

porygon-typeorm
98.90% <ø> (ø)

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[Bug] Get-DbaDbView

Environmental information
Win
```
PowerShell version:
Name                           Value

PSVersion                      5.1.18362.145
PSEdition                      Desktop
PSCompatibleVersions           {1.0, 2.0, 3.0, 4.0...}
BuildVersion                   10.0.18362.145
CLRVersion                     4.0.30319.42000
WSManStackVersion              3.0
PSRemotingProtocolVersion      2.3
SerializationVersion           <IP_ADDRESS>
dbatools Module version:
Name    : dbatools
Path    : C:\Program Files\WindowsPowerShell\Modules\dbatools\1.0.51\dbatools.psd1
Version : 1.0.51

#### SQL Server:
Microsoft SQL Azure (RTM) - 12.0.2000.8   Oct 12 2019 22:46:48   Copyright (C) 2019 Microsoft Corporation

### Report

#### Host used
- [x] powershell.exe
- [x] ISE
- [x] VS Code
- [ ] Other (please specify)

If anything other than powershell.exe was used, please confirm that you can duplicate the issue with powershell.exe

- [x] Still buggy in powershell.exe

#### Errors Received
```
writeErrorStream      : True
PSMessageDetails      :
Exception             : System.Management.Automation.ParameterBindingException: The input object cannot be bound to any
                        parameters for the command either because the command does not take pipeline input or the input
                        and its properties do not match any of the parameters that take pipeline input.
TargetObject          : [ReportUse02]
CategoryInfo          : InvalidArgument: ([ReportUse02]:PSObject) [Get-DbaDbView], ParameterBindingException
FullyQualifiedErrorId : InputObjectNotBound,Get-DbaDbView
ErrorDetails          :
InvocationInfo        : System.Management.Automation.InvocationInfo
ScriptStackTrace      : at Select-DefaultView<Process>, C:\Program
                        Files\WindowsPowerShell\Modules\dbatools\1.0.51\allcommands.ps1: line 78928
                        at Get-DbaDatabase<Process>, C:\Program
                        Files\WindowsPowerShell\Modules\dbatools\1.0.51\allcommands.ps1: line 21251
                        at <ScriptBlock>, <No file>: line 1
PipelineIterationInfo : {0, 1, 0}

Steps to Reproduce
Get-DbaDatabase -SqlInstance $server -SqlCredential $cred -ExcludeSystem | Get-DbaDbView
Expected Behavior
I expected behavior similar to Get-DbaDbTable:
Get-DbaDatabase -SqlInstance $server -SqlCredential $cred -ExcludeSystem | Get-DbaDbTable
Actual Behavior
error above

Also noticed this behavior for Get-DbaDbStoredProcedure


Allow watchdog to listen to bedrock port if using geyser

This should fix https://github.com/doctorray117/minecraft-ondemand/issues/58, where the watchdog closes the server if it's a Java server using Geyser plugin and the online players are connecting using the Bedrock edition.
I've published a watchdog container to test. You can replace doctorray/minecraft-ecsfargate-watchdog on minecraft-stack.ts with yurisasc/minecraft-ecsfargate-watchdog.
I'm currently still testing the container itself. Any help will be appreciated.

I keep getting this error whenever I try to use the updated watchdog. I don't seem to have it when running the watchdog container locally.
exec ./watchdog.sh: exec format error

I can confirm it was due to me building the docker image using arm64. The latest image should be working.

@yurisasc I think I'm good to merge as is, however after reviewing I've got some thoughts.  First, maybe the watchdog script shouldn't care what edition is in use and perform auto-detection based on listening ports, and decide how to check them at that point.  This might be the next step.
Second, do you think the geyser plugin should be integrated to be run by default here?  It sure seems like a good benefit but I wonder if it can integrate easily for vanilla minecraft or if it requires one of the popular mod platforms (spigot, paper, whatever).    I had always thought giving these as options up front would be a great addition but a potential maintenance nightmare.  LMK what you think.

To your first point, it didn't cross my mind because I thought the method to check connections for java and bedrock edition are essentially different. While implementing this feature, I treated them as a black box because I didn't know much about how they work. I think it would be good if the watchdog could receive ports as the input and use them to check player connections without knowing which editions (or even whether they use Geyser or not).
For your second point, Geyser requires a mod loader such as Paper, Fabric, etc. People looking to host vanilla servers won't need to care about it. I think a good approach would be to treat Geyser as a possible extension for those who want to host mod loader servers. In terms of maintenance, we can avoid it by only giving ways for people to use Geyser, instead of making it a default behaviour.
To add Geyser, people will need to:

Choose the java edition
Open a new UDP port, preferably 19132
Add additional configurations in the Minecraft container environment variables, which is to choose a mod loader and download Geyser

The CDK already supports the first and the third steps, and this PR resolves the issue with the watchdog when using Geyser. I think just adding the second step won't require much maintenance. What I can think about is that we will only need to add docs.

Note that the current docker image is very advanced and you can just add agruments like TYPE=SPIGOT like the EULA=TRUE.


Improve constructors of outbound basic auth and oauth2 providers

Purpose
$subject
Check List

[x] Read the Contributing Guide
[ ] Required Balo version update
[ ] Updated Change Log
[ ] Checked Tooling Support (#)
[ ] Added necessary tests

[ ] Unit Tests
[ ] Spec Conformance Tests
[ ] Integration Tests
[ ] Ballerina By Example Tests

[ ] Increased Test Coverage
[ ] Added necessary documentation

[ ] API documentation
[ ] Module documentation in Module.md files
[ ] Ballerina By Examples

Codecov Report

Merging #17289 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master   #17289   +/-   ##
=======================================
  Coverage   68.85%   68.85%
=======================================
  Files           5        5
  Lines         122      122
  Branches       21       21
=======================================
  Hits           84       84
  Misses         38       38

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ContextFinder falls back to context classloader resulting in class cast errors

When we call JAXBContext.newInstance(Class<?>...) or JAXBContext.newInstance(Class<?>[], Map<String,?>) to create a new JAXBContext we can't pass in a classloader. This then in return means that ContextFinder.newInstance gets called with the context classloader.
Given that the context classloader can be "higher" in the chain of classloaders than the current class's classloader and that, since the jaxb-api is no longer part of java core, this might mean that the JAXBContext class has already been loaded (for instance, in the current class's classloader) in a way that makes it inaccessible to the current thread's context classloader.
This means that if we end up at line 362 and 363 of ContextFinder.find(Class<?>[], Map<String, ?>) and then eventually at line 230  where we will load the factory class using the context classloader
spi = ServiceLoaderUtil.safeLoadClass(className, ModuleUtil.DEFAULT_FACTORY_CLASS, getContextClassLoader());
we might end up with a JAXBContextImpl whose class is loaded on the context clasloader and which implements JAXBContext whose class is loaded on the context classloader and which therefore is not an instance of the JAXBContext whose class got loaded on the current class's classloader!
This in return means that the instanceof check on line 252 of ContextFinder would fail since the returned JAXBContextImpl is not an instance of the JAXBContext the ContextFinder knows.
I ran into this issue while using javax.xml.bind:jaxb-api:2.3.1 on JDK 11 (specifically, while trying to read a persistence.xml using the hibernate jpa metamodel generator through the maven processor plugin). Some more detail on the hibernate jira HHH-13794 and in my blog post.
I ended up with a stacktrace like:

Caused by: javax.xml.bind.JAXBException: ClassCastException: attempting to cast jar:file:/home/haster/.m2/repository/javax/xml/bind/jaxb-api/2.3.1/jaxb-api-2.3.1.jar!/javax/xml/bind/JAXBContext.class to jar:file:/home/haster/.m2/repository/javax/xml/bind/jaxb-api/2.3.1/jaxb-api-2.3.1.jar!/javax/xml/bind/JAXBContext.class.  Please make sure that you are specifying the proper ClassLoader.
at javax.xml.bind.ContextFinder.handleClassCastException(ContextFinder.java:157)
at javax.xml.bind.ContextFinder.newInstance(ContextFinder.java:300)
at javax.xml.bind.ContextFinder.newInstance(ContextFinder.java:286)
at javax.xml.bind.ContextFinder.find(ContextFinder.java:409)
at javax.xml.bind.JAXBContext.newInstance(JAXBContext.java:721)
at javax.xml.bind.JAXBContext.newInstance(JAXBContext.java:662)
at org.hibernate.jpamodelgen.util.xml.XmlParserHelper.getJaxbRoot(XmlParserHelper.java:122)
... 39 more

I see no reason why this wouldn't fail with the current version of jaxb-api but I haven't yet been able to test it. I think this will fail any time where the JAXBContext as used in ContextFinder gets loaded on a classloader that is not the current thread's context classloader.
Constructing a testcase is somewhat less trivial since you have to make sure that the classloaders are configured correctly. I'm fairly certain that the testcase I provided for HHH-13794](https://hibernate.atlassian.net/browse/HHH-13794) would work for (or be easily adapted to) the current jakarta jaxb-api.
Update: I tested the testcase in HHH-13794 against jakarta.xml.bind:jakarta.xml.bind-api:2.3.2 and the error was there as well.
This issue is similar to, though definitely not the same as, #99.
I propose two solutions to mitigate this issue:

Add an API method to construct a JAXBContext with a passed set of classes to be bound while also passing in a ClassLoader to be used to load the context. Something like JAXBContext.newInstance(Class<?>[], Map<String,?>, ClassLoader).

Use a different "default classloader" instead of the current thread's context classloader for all such situations . Maybe just getClass().getClassloader() or JAXBContext.class.getClassLoader() . That last one would at the very least ensure that the instanceof check against JAXBContext succeeds.

Any solution here?
I'm now starting migration from "javax." to the "jakarta." namespace and I'm getting:
Caused by: jakarta.xml.bind.JAXBException: ClassCastException: attempting to cast jar:file:/home/developer/.m2/repository/jakarta/xml/bind/jakarta.xml.bind-api/3.0.0/jakarta.xml.bind-api-3.0.0.jar!/jakarta/xml/bind/JAXBContext.class to jar:file:/home/developer/.m2/repository/jakarta/xml/bind/jakarta.xml.bind-api/3.0.0/jakarta.xml.bind-api-3.0.0.jar!/jakarta/xml/bind/JAXBContext.class.  Please make sure that you are specifying the proper ClassLoader.
	at jakarta.xml.bind.ContextFinder.handleClassCastException(ContextFinder.java:111)
	at jakarta.xml.bind.ContextFinder.newInstance(ContextFinder.java:254)
	at jakarta.xml.bind.ContextFinder.newInstance(ContextFinder.java:240)
	at jakarta.xml.bind.ContextFinder.find(ContextFinder.java:375)
	at jakarta.xml.bind.JAXBContext.newInstance(JAXBContext.java:691)
	at jakarta.xml.bind.JAXBContext.newInstance(JAXBContext.java:632)

re proposed solutions:
I do not think adding new method is really required. I think that current definition of the discovery of the implementation is good enough as it states that the lookup should be done by using the associated classloader — this is the owner class loader for a Class argument, and for a package the specified ClassLoader. The problem here is/was that for JAXBContext.newInstance(Class...) methods, the lookup needs to be done from the ClassLoader of the passed in Class (this is, IMHO, the associated classloader ) instead of (wrong) thread context classloader.

btw @haster Thanks a lot for the test case

That sounds like a good solution, nice! 👍


new idle worker function blocks late game scouting

I think it sends the scouting drone back to mining, this has to be fixed, or maybe this isn't even the problem

Yeah, it was a problem with drone.py a flag that didn't need to exist, Ill fix it in #97

fixed on #97


[Lexical][Gallery] add option to add gallery content in Meta Intern only

WHAT

add option to add gallery content in Meta Intern only

WHY

Building a prototype for UI Gallery in Meta intern 1st before starting in Open Source.

Test Plan

Meta Build

Lexical Build remains Not impacted

https://lexical-git-lexicalgallerywip-fbopensource.vercel.app/

Failures are related to flaky test ,upvoting the discussion thread for failure Can type two paragraphs of text and correctly undo and redo


Fix for #236: Relocation of REST API interfaces

Relocated the service interfaces of components from 'core' to 'api' module

See #236


Updated to caddy 0.9

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.Killian Brackey seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.

Thanks for getting this done so fast :)

Yeah absolutely! Thanks for keeping us updated :)


Improve inconsistency of Dataset.map interface for load_from_cache_file

Feature request

Replace the load_from_cache_file default value to True.
Remove or alter checks from is_caching_enabled logic.

Motivation
I stumbled over an inconsistency in the Dataset.map interface. The documentation (and source) states for the parameter load_from_cache_file:
load_from_cache_file (`bool`, defaults to `True` if caching is enabled):
    If a cache file storing the current computation from `function`
    can be identified, use it instead of recomputing.

load_from_cache_file default value is None, while being annotated as bool
It is inconsistent with other method signatures like filter, that have the default value True
The logic is inconsistent, as the map method checks if caching is enabled through is_caching_enabled. This logic is not used for other similar methods.

Your contribution
I am not fully aware of the logic behind caching checks. If this is just a inconsistency that historically grew, I would suggest to remove the is_caching_enabled logic as the "default" logic. Maybe someone can give insights, if environment variables have a higher priority than local variables or vice versa.
If this is clarified, I could adjust the source  according to the "Feature request" section of this issue.

Hi, thanks for noticing this! We can't just remove the cache control as this allows us to control where the arrow files generated by the ops are written (cached on disk if enabled or a temporary directory if disabled). The right way to address this inconsistency would be by having load_from_cache_file=None by default everywhere.

Hi! Yes, this seems more plausible. I can implement that. One last thing is the type annotation load_from_cache_file: bool = None. Which I then would change to load_from_cache_file: Optional[bool] = None.

PR #5515

Yes, Optional[bool] is the correct type annotation and thanks for the PR.


Ad support for an "application config"

The config specifies everything needed to spin up the server: listening ports, outgoing clients, etc.
It also can specify paths to N "application" Python files that are mapped by canonical name, these application threads will be added as server daemons (i.e. app setup and app loop) and then the whole thing will be started.

A possible solution for this is more detailed in #13.

This should be implemented by creating message types for all of the necessary information (metadata) to decode telemetry.

Messages should carry a hash separate from the identifier so clients can check to see if they already have that exact message's contents

next action is to begin work on serialization of messages, as well as building out life-cycle management features for them that are suitable for use by both producers (i.e. telemetry emitters) and consumers (i.e. telemetry clients)

we can add a stream channel under channel and a message frame under frame

should we add message and stream sub-directories where we add a framer.py to each? maybe

In building a message cache, canonical / expected message contents can be committed as package data so that when a client runs and the cache doesn't exist, just load the default messages
servers should still send the messages in case the client doesn't have the se version
this can be used for registries?

next step here is to implement a message cache / message-fragment aggregator

probably they should just be organized/indexed by crc per message type, where you can look at messages fully decoded (i.e. not in a partial state)

Not that we have a message cache, we should add a callback interface so the upstream owner of the cache can wire desired behaviors and message triggers to other functions.

the "message dispatcher" could be a class derived from the cache that adds the callback interfaces (or a new class entirely)

we could still refactor the message dispatcher into another class, but it's implemented. now we need to do any refactors we want and then implement messages for client use

ready to implement a TcpClient based on SocketClient

Once we have the IO clients, we need to make a generic client that ties everything together (figure out App IDs / channel domains, multiple clients of various IO types, etc.)

We also need to spin up the TCP service on the server and have services enabled or disabled (not just always enabled)

Added the "services" definition, next is to implement the TcpClient

Clients are implemented, time to continue work on messages!

All clients are implemented at this point, it's time to continue work towards "messages" as a primary path forward for a server sharing registry (type, channel, etc.) information with clients so data frames can be interpreted.

We pretty much need to finish #24 before coming back to this.

Working through #25 now.

Next we need to clean up the notion of "registries", we need to make sure UserEnum now correctly plugs in to an enum registry and registries in general should be Serializable sub-classes.


meaning of scenario in tests

what does Scenario 53667 in test cases mean ?
"Scenario 53666" should "" in
"Scenario 53667" should "" in
....

Dear Jeado,
I just generated that scenarios by the dump of MQTT TestKit requests. Names are meaningless here


Suggestion on the design of the web app

Some Improvements/Suggestion on making the web app more organize and improve UX.
🐞 Problem

The list of project is a bit too wide. Can consider making the width smaller and maybe put a container around them.
The social media should be after the header and before the list of projects. So that, it's more easily accessible.
The filter project can be on top of the project list, with a search feature(maybe?).
Try to standard all project card size. Eg. Some of the image is longer while some shorter, the card description should have the same size even though some is longer/shorter.

P.S. All of this is just a suggestion and of course, not everyone has the same preference.
🎯 Goal
Improve the user experience on the web app.
💡 Possible solutions
I'll provide a brief pic of my suggestion to each issues.
📋  Steps to solve the problem

Comment below about what you've started working on.
Add, commit, push your changes
Submit a pull request and add this in comments - Addresses #<put issue number here>
Ask for a review in comments section of pull request
Celebrate your contribution to this project 🎉

I thought of something like this..

But of course the wording, phrases are subject to change.

Hey @YernYee,

The list of project is a bit too wide. Can consider making the width smaller and maybe put a container around them.

I tried increasing the padding of project list container. I liked it. It was rendering 4 cards in a row. We decided on the padding taking in to account how project list should be displayed in multiple screen sizes. Lemme talk to my designer friends and get back to you on this one.
About the container, If you're suggesting a border, I don't feel so strongly about that. It just provides an extra framing visually. I think it's not needed.

The social media should be after the header and before the list of projects. So that, it's more easily accessible.

I beg to differ here. User flow we have in mind is like following
User comes to GitHub repo and makes first pull request > Shares their first contribution > Repeatedly comes back to web app for finding projects.
So, share is only used once. Project list should be given more importance than sharing. I'm also thinking if header should be different for people coming to app later (for finding projects). Also we're following a pattern most of our users will be familiar with (Header, then main content, then social share and such links)

The filter project can be on top of the project list, with a search feature(maybe?).

We do have filter and search in our roadmap. Not exactly sure of the implementation yet. You can find we have 2 project boards for this repo. Take a look at the web app project board. I'm welcoming all suggestions for this.

Try to standard all project card size. Eg. Some of the image is longer while some shorter, the card description should have the same size even though some is longer/shorter.

Images will be easier to fix. Let's address this. I'm not sure of description though. Like how do we standardise this and come up with description that follows that standard? I was just using description from those GitHub repos.
One solution might be to make height of cards static

How about we ask our community about how they feel about these changes? We could do voting in here or slack

Hi @Roshanjossey,

I tried increasing the padding of project list container. I liked it. It was rendering 4 cards in a row. We decided on the padding taking in to account how project list should be displayed in multiple screen sizes. Lemme talk to my designer friends and get back to you on this one.
About the container, If you're suggesting a border, I don't feel so strongly about that. It just provides an extra framing visually. I think it's not needed.

This one I'm ok with it too. It's just personal opinion and I realized when I'm viewing the web app on 100%, it's rendering 5 cards in a row and it become kinda tight on both end. But it looks good on 110% rendering 4 cards. =)

I beg to differ here. User flow we have in mind is like following
User comes to GitHub repo and makes first pull request > Shares their first contribution > Repeatedly comes back to web app for finding projects.
So, share is only used once. Project list should be given more importance than sharing. I'm also thinking if header should be different for people coming to app later (for finding projects). Also we're following a pattern most of our users will be familiar with (Header, then main content, then social share and such links)

Oh. Now I understand your user flow. Then, I agree that project list should be given more importance than sharing since the sharing is only used once after the user finish the tutorial.
The pattern (Header, main content, social share and links) is good but currently, our header is bringing user to 'get started', then main content is like 'other project you can contribute', but the social share is more likely to be used when user finished the 'get started tutorial' provided. So, that's why I suggest it to be (Header - get started, Intermediate - Social Share when finish get started, Main Content - More Projects list to contribute)

I'm also thinking if header should be different for people coming to app later (for finding projects)

This is a great idea and I like it. =) Don't know how to implement this though.

We do have filter and search in our roadmap. Not exactly sure of the implementation yet. You can find we have 2 project boards for this repo. Take a look at the web app project board. I'm welcoming all suggestions for this.

Yes. I saw it there in web app project board and there's even an issue open for it too, to filter the project by language, framework, domain. This is just a comment/suggestion or idea for the implementation.

Images will be easier to fix. Let's address this. I'm not sure of description though. Like how do we standardise this and come up with description that follows that standard? I was just using description from those GitHub repos.
One solution might be to make height of cards static

For this, we can make height of cards static by making images size static and set a number of line for description (maybe 4 since that's the max number of line I see there).
Or maybe we can implement like Facebook, fade a bit and limit the words.
Vuejs

A progressive, incrementally-
adoptable JavaScript
framework for building UI on...

I'm just giving my opinions and comments and I'm truly sorry if I did offend anyone.

@YernYee, don't worry about offending anyone by sharing your opinion. You have the right to express it.
We're planning to add tags to cards. So there is gonna be a change in cards. Shall we pick up filtering and standardising card height after that?

@Roshanjossey, sounds like a great plan to me but what should be the tags to use besides language?

@YernYee, we could use tags provided in those repos.
Like for react, it could be frontend declarative ui library (Did that just form a sentence?)
for webpack, moduel-bundler module-builder build tool compiler
for funretro, retro angularjs agile
Like we can have domins, frameworks, use case etc

@Roshanjossey, so are we going to make a search bar for users soon to search tag? And do you think it's applicable to put the tags on the project cards as well? I think we can put language tag on the project card as to briefly tell the user what language you'll need for contributing to this project.

@YernYee, I was thinking that search could be more than tag search. We'll have filters for tags and search could handle something more generic. I'm not sure about that now.
I was thinking about putting 3 to 4 tags on each card. Like language, framework, type  ( is it a framework, library, web app etc) and something else that's special about the project

@YernYee, added issue for tags
https://github.com/Roshanjossey/first-contributions/issues/1066


AUR: ERROR: tenv-bin is not available for the 'x86_64' architecture.

Describe the bug
https://github.com/tofuutils/tenv/pull/173 introduced a new PKGBUILD on AUR. This new PKGBUILD supports only:
arch=('aarch64' 'i686')

Expected behavior
AUR tenv-bin package is available for all supported archs again, like x86_64.
Screenshots
Environment (please complete the following information):
Additional context

@muchgitsuchwow thank you for reporting. I'm working on this issue, hope that it will be fixed today

Trying to find what's wrong with goreleaser;
Based on that I see that we have to had x86_64, 386 packages: https://github.com/goreleaser/goreleaser/blob/8ad6b3479bc9999f0567cd84c33a247b5d17d98e/internal/pipe/aur/aur.go#L267

Dunno why goreleaser decided to use i686 instead of pure 386. @clee231 @muchgitsuchwow guys, do you think that it's a problem for 386 users?
	case "386":
		return "i686"

@kvendingoldo
I think it is fine to use i686. It's a newer revision of the x86 platform and should be backwards compatible with i386. My understanding is that each newer revision supports additional instruction sets. They don't remove usually remove opcodes after introduction.
At least for ArchLinux, the latest release of the 32-bit variant only mentions support for i686, so that may be why goreleaser is making that switch.
I took some time to also take a look at the executables being generated in the latest v2.1.5 release and they are all built for the i386 platform, so even if someone tried to force install on an older platform, it should work still.

@clee231 @muchgitsuchwow looks like it works now, please check it

I was able to test it on my machine. I was able to install v2.1.6 with no issue now on x86_64 using the latest PKGBUILD.
Thanks for addressing this so quickly, @kvendingoldo !

@clee231 I've added you to AUR as contributor/maintainer.
Will wait for @muchgitsuchwow response and if everything ok, will close the ticket.


How to Run

I have just started looking into leaflet, TypeScript, and Angular 2, and I am used to being able to run an application by typing "npm start" in the terminal. This does not work for me.
I have already typed in "npm install" and "webpack" but am unsure if that is correct and what to do from there.

What you need to do after running webpack is to go a folder called public, open the index.html file in the browser. If you make updates, you'll have to run webpack again to recompile.
A better way is to use webpack-dev-serve in order to serve the project on http://localhost:8080

@Bilal-io Thank you for your answer!
@tdchen5 More specifically, the compiled files are at public folder, which is automatically created by the command webpack. Please open the index.html there, not the one in the public_src folder.

Thank you for the prompt answer!


[dartdevc] Mark root library in ddc debug metadata

Currently debugger is trying to take a guess (and sometimes fails) at the root library of the app:
https://github.com/dart-lang/sdk/issues/44760
Can DDC infer what library contains the main and mark that library as the rootlib in the metadata?

DDC could probably track if a library contains a top level method named main but no way to know for sure if that is the actual entry point for the application being loaded.

Closing this one as impossible for ddc - we probably should store it in some app-level metadata that can be generated by the builds.