text:
MarathonSchedulerActorTest has a flaky test

MarathonSchedulerActorTest:
- Kill tasks with scaling *** FAILED *** (5 seconds, 27 milliseconds)
  org.mockito.exceptions.verification.WantedButNotInvoked: Wanted but not invoked:
taskFailureRepository.store(
    /test-app,
    TaskFailure(/test-app,value: "test-app.e9bfaf7d-5255-11e6-98a4-4a78e4b13508"
,TASK_FAILED,,,1970-01-01T00:00:00.000Z,2016-07-25T10:52:42.874Z,Some(value: ""
))
);
-> at mesosphere.marathon.MarathonSchedulerActorTest$$anonfun$5$$anonfun$apply$mcV$sp$6.apply(MarathonSchedulerActorTest.scala:173)
Actually, there were zero interactions with this mock.
  at mesosphere.marathon.MarathonSchedulerActorTest$$anonfun$5$$anonfun$apply$mcV$sp$6.apply(MarathonSchedulerActorTest.scala:173)
  at mesosphere.marathon.MarathonSchedulerActorTest$$anonfun$5$$anonfun$apply$mcV$sp$6.apply(MarathonSchedulerActorTest.scala:173)
  at akka.testkit.TestKitBase$class.poll$2(TestKit.scala:266)
  at akka.testkit.TestKitBase$class.awaitAssert(TestKit.scala:277)
  at mesosphere.marathon.MarathonSchedulerActorTest.awaitAssert(MarathonSchedulerActorTest.scala:39)
  at mesosphere.marathon.MarathonSchedulerActorTest$$anonfun$5.apply$mcV$sp(MarathonSchedulerActorTest.scala:173)
  at mesosphere.marathon.MarathonSchedulerActorTest$$anonfun$5.apply(MarathonSchedulerActorTest.scala:131)
  at mesosphere.marathon.MarathonSchedulerActorTest$$anonfun$5.apply(MarathonSchedulerActorTest.scala:131)
  at org.scalatest.Transformer$$anonfun$apply$1.apply$mcV$sp(Transformer.scala:22)
  at org.scalatest.OutcomeOf$class.outcomeOf(OutcomeOf.scala:85)
  at org.scalatest.OutcomeOf$.outcomeOf(OutcomeOf.scala:104)
  at org.scalatest.Transformer.apply(Transformer.scala:22)
  at org.scalatest.Transformer.apply(Transformer.scala:20)
  at org.scalatest.FunSuiteLike$$anon$1.apply(FunSuiteLike.scala:166)
  at org.scalatest.Suite$class.withFixture(Suite.scala:1122)
  at mesosphere.marathon.MarathonSchedulerActorTest.withFixture(MarathonSchedulerActorTest.scala:39)
  at org.scalatest.FunSuiteLike$class.invokeWithFixture$1(FunSuiteLike.scala:163)
  at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
  at org.scalatest.FunSuiteLike$$anonfun$runTest$1.apply(FunSuiteLike.scala:175)
  at org.scalatest.SuperEngine.runTestImpl(Engine.scala:306)
  at org.scalatest.FunSuiteLike$class.runTest(FunSuiteLike.scala:175)
  at mesosphere.marathon.MarathonSchedulerActorTest.org$scalatest$BeforeAndAfter$$super$runTest(MarathonSchedulerActorTest.scala:39)
  at org.scalatest.BeforeAndAfter$class.runTest(BeforeAndAfter.scala:200)
  at mesosphere.marathon.MarathonSchedulerActorTest.runTest(MarathonSchedulerActorTest.scala:39)
  at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
  at org.scalatest.FunSuiteLike$$anonfun$runTests$1.apply(FunSuiteLike.scala:208)
  at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:413)
  at org.scalatest.SuperEngine$$anonfun$traverseSubNodes$1$1.apply(Engine.scala:401)
  at scala.collection.immutable.List.foreach(List.scala:381)
  at org.scalatest.SuperEngine.traverseSubNodes$1(Engine.scala:401)
  at org.scalatest.SuperEngine.org$scalatest$SuperEngine$$runTestsInBranch(Engine.scala:396)
  at org.scalatest.SuperEngine.runTestsImpl(Engine.scala:483)
  at org.scalatest.FunSuiteLike$class.runTests(FunSuiteLike.scala:208)
  at mesosphere.marathon.MarathonSchedulerActorTest.runTests(MarathonSchedulerActorTest.scala:39)
  at org.scalatest.Suite$class.run(Suite.scala:1424)
  at mesosphere.marathon.MarathonSchedulerActorTest.org$scalatest$BeforeAndAfterAll$$super$run(MarathonSchedulerActorTest.scala:39)
  at org.scalatest.BeforeAndAfterAll$class.liftedTree1$1(BeforeAndAfterAll.scala:257)
  at org.scalatest.BeforeAndAfterAll$class.run(BeforeAndAfterAll.scala:256)
  at mesosphere.marathon.MarathonSchedulerActorTest.org$scalatest$FunSuiteLike$$super$run(MarathonSchedulerActorTest.scala:39)
  at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
  at org.scalatest.FunSuiteLike$$anonfun$run$1.apply(FunSuiteLike.scala:212)
  at org.scalatest.SuperEngine.runImpl(Engine.scala:545)
  at org.scalatest.FunSuiteLike$class.run(FunSuiteLike.scala:212)
  at mesosphere.marathon.MarathonSchedulerActorTest.org$scalatest$BeforeAndAfter$$super$run(MarathonSchedulerActorTest.scala:39)
  at org.scalatest.BeforeAndAfter$class.run(BeforeAndAfter.scala:241)
  at mesosphere.marathon.MarathonSchedulerActorTest.run(MarathonSchedulerActorTest.scala:39)
  at org.scalatest.tools.Framework.org$scalatest$tools$Framework$$runSuite(Framework.scala:357)
  at org.scalatest.tools.Framework$ScalaTestTask.execute(Framework.scala:502)
  at sbt.ForkMain$Run$2.call(ForkMain.java:296)
  at sbt.ForkMain$Run$2.call(ForkMain.java:286)
  at java.util.concurrent.FutureTask.run(FutureTask.java:266)
  at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
  at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
  at java.lang.Thread.run(Thread.java:745)

TestCase "Kill tasks with scaling" is not marked as Unstable and runs green.

authors: aquamatthias gkleiman


text:
MarathonSchedulerActor: Remove some indirect dependencies

The MarathonSchedulerActor constructor did not only get dependencies for itself but also for other actors/SchedulerActions which were create in preStart.

A step in the right direction - I still see the spiders web....

source: gharchive/pull-request

authors: aquamatthias kolloch

repo: mesosphere/marathon

license_type: permissive

license_source: github-api


text:
Add troubleshooting step for secret too short

When trying to set up framework auth today, I had a persistent issue with starting Marathon because of:
ERROR Failed to take over leadership: java.io.IOException: Error reading authentication secret from file [file:///tmp/crd] (mesosphere.marathon.MarathonSchedulerService:263)
After a bit of kicking it around, I added more chars to the secret string. This had the unexpected effect of resolving the issue.

Thanks @jgarcia-mesosphere!

authors: aquamatthias jgarcia-mesosphere


text:
Release beta-tensorflow 0.1.1-1.3.0-beta (automated commit)

Release beta-tensorflow 0.1.1-1.3.0-beta (automated commit)
Description:
a13644bc68aa7070c511c301d226e3b7c789e2ff
Source URL: https://infinity-artifacts-ci.s3.amazonaws.com/autodelete7d/tensorflow/20171108-194808-B5jqfxLX6Myo9o0D/stub-universe-tensorflow.json
Changes since revision 0:
0 files added: []
0 files removed: []
3 files changed:
--- 0/marathon.json.mustache
+++ 1/marathon.json.mustache
@@ -47,7 +47,7 @@
     "USE_GCS_KEY": "{{service.use_gcs_key_secret}}",
     {{/service.use_gcs_key_secret}}
     {{#service.use_tensorboard}}
-    "USE_GCS_KEY": "{{service.use_tensorboard}}",
+    "USE_TENSORBOARD": "{{service.use_tensorboard}}",
     {{/service.use_tensorboard}}
     "WORKER_COUNT": "{{worker.count}}",
     "WORKER_PLACEMENT": "{{worker.placement_constraint}}",
--- 0/package.json
+++ 1/package.json
@@ -2,11 +2,13 @@
     "packagingVersion": "4.0",
     "minDcosReleaseVersion": "1.9",
     "upgradesFrom": [
+        "*"
     ],
     "downgradesTo": [
+        "*"
     ],
     "name": "beta-tensorflow",
-    "version": "0.1.0-1.3.0-beta",
+    "version": "0.1.1-1.3.0-beta",
     "maintainer": "https://dcos.io/community/",
     "description": "TensorFlow on DC/OS",
     "selected": false,
@@ -15,6 +17,6 @@
         "tensorflow"
     ],
     "preInstallNotes": "This is a community package. Community packages are unverified and unreviewed content from the community.",
-    "postInstallNotes": "The DC/OS TensorFlow service is being installed!\n\n\tDocumentation: https://mesosphere.github.io/dcos-tensorflow/\n\tIssues: https://chat.dcos.io\n\n\tIf you installed from the UI, enter `dcos package install beta-tensorflow --cli` from the DC/OS CLI to install the Beta TensorFlow CLI subcommands.",
+    "postInstallNotes": "The DC/OS TensorFlow service is being installed!\n\n\tDocumentation: https://mesosphere.github.io/dcos-tensorflow/\n\tIssues: https://chat.dcos.io",
     "postUninstallNotes": "The DC/OS TensorFlow service is being uninstalled.\n\nFor DC/OS versions from 1.10 no further action is required. For older DC/OS versions follow the instructions at https://mesosphere.github.io/dcos-tensorflow/uninstall to remove any persistent state if required."
 }
--- 0/resource.json
+++ 1/resource.json
@@ -4,9 +4,9 @@
       "bootstrap-zip": "https://downloads.mesosphere.com/dcos-commons/artifacts/0.30.2/bootstrap.zip",
       "jre-tar-gz": "https://downloads.mesosphere.com/java/jre-8u144-linux-x64.tar.gz",
       "libmesos-bundle-tar-gz": "https://downloads.mesosphere.io/libmesos-bundle/libmesos-bundle-1.10-1.4-63e0814.tar.gz",
-      "scheduler-zip": "https://downloads.mesosphere.com/tensorflow/assets/0.1.0-1.3.0-beta/tensorflow-scheduler.zip",
+      "scheduler-zip": "https://downloads.mesosphere.com/tensorflow/assets/0.1.1-1.3.0-beta/tensorflow-scheduler.zip",
       "executor-zip": "https://downloads.mesosphere.com/dcos-commons/artifacts/0.30.2/executor.zip",
-      "examples-zip": "https://downloads.mesosphere.com/tensorflow/assets/0.1.0-1.3.0-beta/tensorflow-examples.zip"
+      "examples-zip": "https://downloads.mesosphere.com/tensorflow/assets/0.1.1-1.3.0-beta/tensorflow-examples.zip"
     }
   },
   "images": {
@@ -25,7 +25,7 @@
             }
           ],
           "kind": "executable",
-          "url": "https://downloads.mesosphere.com/tensorflow/assets/0.1.0-1.3.0-beta/dcos-service-cli-darwin"
+          "url": "https://downloads.mesosphere.com/tensorflow/assets/0.1.1-1.3.0-beta/dcos-service-cli-darwin"
         }
       },
       "linux": {
@@ -37,7 +37,7 @@
             }
           ],
           "kind": "executable",
-          "url": "https://downloads.mesosphere.com/tensorflow/assets/0.1.0-1.3.0-beta/dcos-service-cli-linux"
+          "url": "https://downloads.mesosphere.com/tensorflow/assets/0.1.1-1.3.0-beta/dcos-service-cli-linux"
         }
       },
       "windows": {
@@ -49,7 +49,7 @@
             }
           ],
           "kind": "executable",
-          "url": "https://downloads.mesosphere.com/tensorflow/assets/0.1.0-1.3.0-beta/dcos-service-cli.exe"
+          "url": "https://downloads.mesosphere.com/tensorflow/assets/0.1.1-1.3.0-beta/dcos-service-cli.exe"
         }
       }
     }

The preliminary release notes here: https://github.com/mesosphere/dcos-tensorflow/releases/tag/untagged-90d803bc279e045a615f
The major change is support for HDFS (instead of requiring GCE).

Closing this PR as we need a new stub.

authors: elezar mesosphere-ci

repo: mesosphere/universe

license_source: bigquery


text:
pip install -e . or pip install -U git+https://github.com/meta-llama/llama-recipes leads to ResolutionTooDeep error

System Info
python3.12
2.4.1+cu121
Information

[X] The official example scripts
[ ] My own modified scripts

🐛 Describe the bug
When running pip install llama-recipes, everything installs correctly without issues. However, when I attempt to run either:

pip install -e .
pip install -U git+https://github.com/meta-llama/llama-recipes

The installation process takes a long time and eventually results in the following error:
File "path/to/resolvelib/resolvers.py", line 392, in resolve
    raise ResolutionTooDeep(max_rounds)
pip._vendor.resolvelib.resolvers.ResolutionTooDeep: 2000000

Steps taken:

I have tried both conda and virtual environments.
Tested across different Python versions: 3.9, 3.10, 3.11, and 3.12.
I also tried on a different computer and encountered the same issue.

Additional attempt:

I tried using the following workaround:
pip install git+https://github.com/notatallshaw/pip.git@prefer-conflicting-causes

This solved the issue during installation. However, after installing, I encountered another problem when trying to save the model:
[rank1]: AttributeError: 'Params4bit' object has no attribute 'absmax'

Any guidance on resolving this issue would be appreciated. Thank you!
Error logs
File "path/to/resolvelib/resolvers.py", line 392, in resolve
    raise ResolutionTooDeep(max_rounds)
pip._vendor.resolvelib.resolvers.ResolutionTooDeep: 2000000

Expected behavior
The installation should complete successfully, similar to the pip install llama-recipes command.

Running into the same installation issue with both conda and venv with Python 3.9.18
ERROR: Exception:
Traceback (most recent call last):
  File "/cm/local/apps/python39/lib/python3.9/site-packages/pip/_internal/cli/base_command.py", line 180, in exc_logging_wrapper
    status = run_func(*args)
  File "/cm/local/apps/python39/lib/python3.9/site-packages/pip/_internal/cli/req_command.py", line 248, in wrapper
    return func(self, options, args)
  File "/cm/local/apps/python39/lib/python3.9/site-packages/pip/_internal/commands/install.py", line 377, in run
    requirement_set = resolver.resolve(
  File "/cm/local/apps/python39/lib/python3.9/site-packages/pip/_internal/resolution/resolvelib/resolver.py", line 92, in resolve
    result = self._result = resolver.resolve(
  File "/cm/local/apps/python39/lib/python3.9/site-packages/pip/_vendor/resolvelib/resolvers.py", line 546, in resolve
    state = resolution.resolve(requirements, max_rounds=max_rounds)
  File "/cm/local/apps/python39/lib/python3.9/site-packages/pip/_vendor/resolvelib/resolvers.py", line 457, in resolve
    raise ResolutionTooDeep(max_rounds)
pip._vendor.resolvelib.resolvers.ResolutionTooDeep: 200000

Hi @lucas-ventura @aishwaryap I hope your installation troubles are solved after #676. Regarding the absmax, I have isolated the issue to this line where we configure cpu_offload=True. Not sure yet why but disabling this lets it run successful for me. Hope to have a real fix soon.

Yes! Everything works now, thank you!!

Is there any update on [rank1]: AttributeError: 'Params4bit' object has no attribute 'absmax' without disabling the cpu_offload flag?

It is working for me. How are you installing llama-recipes?

With:
git clone git@github.com:meta-llama/llama-recipes.git
cd llama-recipes
pip install -U pip setuptools
pip install -e .

source: gharchive/issue

authors: PanagiotisFytas aishwaryap lucas-ventura mreso

repo: meta-llama/llama-recipes

license: MIT


text:
Guardrail Loading Failed with Unexpected Large GPU Memory Requirement at Multi-GPU Server

System Info
Python version: 3.10.12
Pytorch version:
llama_models version: 0.0.42
llama_stack version: 0.0.42
llama_stack_client version: 0.0.41
Hardware: 4xA100 (40GB VRAM/GPU)
local-gpu-run.yaml file content is as following:
version: '2'
built_at: '2024-10-11T00:06:23.964162'
image_name: local-gpu
docker_image: local-gpu
conda_env: null
apis:
- safety
- memory
- inference
- models
- agents
- memory_banks
- shields
providers:
  inference:
  - provider_id: meta0
    provider_type: meta-reference
    config:
      model: Llama3.1-8B-Instruct
      quantization: null
      torch_seed: null
      max_seq_len: 4096
      max_batch_size: 1
  - provider_id: meta1
    provider_type: meta-reference
    config:
      model: Llama-Guard-3-1B
      quantization: null
      torch_seed: null
      max_seq_len: 4096
      max_batch_size: 1
  safety:
  - provider_id: meta-reference
    provider_type: meta-reference
    config:
      llama_guard_shield:
        model: Llama-Guard-3-1B
        excluded_categories: []
      enable_prompt_guard: true
  memory:
  - provider_id: meta-reference
    provider_type: meta-reference
    config: {}
  agents:
  - provider_id: meta-reference
    provider_type: meta-reference
    config:
      persistence_store:
        namespace: null
        type: sqlite
        db_path: /home/dell/.llama/runtime/kvstore.db
  telemetry:
  - provider_id: meta-reference
    provider_type: meta-reference
    config: {}

Information

[ ] The official example scripts
[ ] My own modified scripts

🐛 Describe the bug
Trying to load the model to initialize the host with the Command:
 docker run --gpus=all -it -p 5000:5000 -v ~/.llama/builds/docker/local-gpu-run.yaml:/app/config.yaml -v ~/.llama:/root/.llama llamastack/distribution-meta-reference-gpu python -m llama_stack.distribution.server.server --yaml_config /app/config.yaml --port 5000
Error logs
Log file shows the following:
Loading model `Llama3.1-8B-Instruct`
> initializing model parallel with size 1
> initializing ddp with size 1
> initializing pipeline with size 1
/opt/conda/lib/python3.10/site-packages/torch/__init__.py:696: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered
internally at /opt/conda/conda-bld/pytorch_1708025847130/work/torch/csrc/tensor/python_tensor.cpp:451.)
  _C._set_default_tensor_type(t)
Loaded in 8.79 seconds
Loaded model...
Loading model `Llama-Guard-3-1B`
> initializing model parallel with size 1
> initializing ddp with size 1
> initializing pipeline with size 1
/opt/conda/lib/python3.10/site-packages/torch/__init__.py:696: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered
internally at /opt/conda/conda-bld/pytorch_1708025847130/work/torch/csrc/tensor/python_tensor.cpp:451.)
  _C._set_default_tensor_type(t)
Loaded in 4.46 seconds
Loaded model...
Some weights of LlamaForSequenceClassification were not initialized from the model checkpoint at /root/.llama/checkpoints/Prompt-Guard-86M and are newly initialized:  ...
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.                                                                                [39/1803]
Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/runpy.py", line 196, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/opt/conda/lib/python3.10/runpy.py", line 86, in _run_code
    exec(code, run_globals)
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/distribution/server/server.py", line 343, in <module>
    fire.Fire(main)
  File "/opt/conda/lib/python3.10/site-packages/fire/core.py", line 135, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/opt/conda/lib/python3.10/site-packages/fire/core.py", line 468, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "/opt/conda/lib/python3.10/site-packages/fire/core.py", line 684, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/distribution/server/server.py", line 279, in main
    impls = asyncio.run(resolve_impls(config))
  File "/opt/conda/lib/python3.10/asyncio/runners.py", line 44, in run
    return loop.run_until_complete(main)
  File "/opt/conda/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
    return future.result()
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/distribution/resolver.py", line 181, in resolve_impls
    impl = await instantiate_provider(
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/distribution/resolver.py", line 268, in instantiate_provider
    impl = await fn(*args)
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/safety/__init__.py", line 16, in get_provider_impl
    await impl.initialize()
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/safety/safety.py", line 40, in initialize
    _ = PromptGuardShield.instance(model_dir)
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/safety/prompt_guard.py", line 37, in instance
    PromptGuardShield._instances[key] = PromptGuardShield(
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/safety/prompt_guard.py", line 66, in __init__
    model = AutoModelForSequenceClassification.from_pretrained(
  File "/opt/conda/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 564, in from_pretrained
    return model_class.from_pretrained(
  File "/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py", line 4091, in from_pretrained
    dispatch_model(model, **device_map_kwargs)
  File "/opt/conda/lib/python3.10/site-packages/accelerate/big_modeling.py", line 494, in dispatch_model
    model.to(device)
  File "/opt/conda/lib/python3.10/site-packages/transformers/modeling_utils.py", line 2958, in to
    return super().to(*args, **kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1152, in to
    return self._apply(convert)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
    module._apply(fn)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
    module._apply(fn)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 802, in _apply
    module._apply(fn)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 825, in _apply
    param_applied = fn(param)
  File "/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1150, in convert
    return t.to(device, dtype if t.is_floating_point() or t.is_complex() else None, non_blocking)
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 172.00 MiB. GPU 0 has a total capacity of 39.38 GiB of which 144.25 MiB is free. Process 878221 has 16.76 GiB memory in use. Process 878311 has 3.98 GiB memory in use. Pr
ocess 878077 has 18.48 GiB memory in use. Of the allocated memory 18.08 GiB is allocated by PyTorch, and 1.27 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=exp
andable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

Expected behavior
Expect that the models will be successfully loaded into the GPU VRAM with right memory consumption. Note same configuration does not give errors with a 1xH100 machine.

Note same configuration does not give errors with a 1xH100 machine.

The error occurs because you ran out of memory on your GPU0, and our meta-reference provider can only load models onto 1 device.

You can chose to not load prompt guard via enable_prompt_guard: false.
Or use a remote inference provider (e.g. TGI) to run Llama3.1-8B-Instruct on another GPU. E.g. run TGI server on GPU=1 via

docker run --rm -it --network host -v $HOME/.cache/huggingface:/data -p 5009:5009 --gpus 1 ghcr.io/huggingface/text-generation-inference:latest --usage-stats on --sharded false --model-id meta-llama/Llama3.1-8B-Instruct --port 5009

Then set inference to point to the TGI server.
inference:
  - provider_id: tgi0
    provider_type: remote::tgi
    config:
      url: http://127.0.0.1:5009

Verified suggestion 1, it worked by disabling Prompt Guard. However, I don't understand why a 40GB GPU is not enough to hold an additional Prompt-Guard-86M model?
For suggestion 2, if we run remote tgi for Llama3.1-8B-Instruct, where should I run Prompt-Guard-86M and Llama-Guard-3-1B? Which image is recommended (distribution-meta-reference-gpu or 'llamastack-local-gpu' or 'llamastack-local-cpu')? Using distribution-meta-reference-gpu give me the following error:
  File "/opt/conda/lib/python3.10/asyncio/base_events.py", line 649, in run_until_complete
    return future.result()
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/distribution/resolver.py", line 181, in resolve_impls
    impl = await instantiate_provider(
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/distribution/resolver.py", line 268, in instantiate_provider
    impl = await fn(*args)
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/providers/adapters/inference/tgi/__init__.py", line 28, in get_adapter_impl
    await impl.initialize(config)
  File "/opt/conda/lib/python3.10/site-packages/llama_stack/providers/adapters/inference/tgi/tgi.py", line 171, in initialize
    endpoint_info = await self.client.get_endpoint_info()
  File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/inference/_generated/_async_client.py", line 3144, in get_endpoint_info
    async with self._get_client_session() as client:
  File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/inference/_generated/_async_client.py", line 2998, in _get_client_session
    aiohttp = _import_aiohttp()
  File "/opt/conda/lib/python3.10/site-packages/huggingface_hub/inference/_common.py", line 117, in _import_aiohttp
    raise ImportError("Please install aiohttp to use `AsyncInferenceClient` (`pip install aiohttp`).")
ImportError: Please install aiohttp to use `AsyncInferenceClient` (`pip install aiohttp`).

I did some further experiments on the suggestion 2 by adding the guardrail model along with a remote TGI server. It will trigger the fairscale module not found error even I installed it. The following is the run.yaml file:
version: '2'
built_at: '2024-10-08T17:40:45.325529'
image_name: local
docker_image: null
conda_env: local
apis:
- shields
- agents
- models
- memory
- memory_banks
- inference
- safety
providers:
  inference:
  - provider_id: tgi0
    provider_type: remote::tgi
    config:
      url: http://127.0.0.1:80
  - provider_id: meta0
    provider_type: meta-reference
    config:
      model: Llama-Guard-3-1B
      quantization: null
      torch_seed: null
      max_seq_len: 4906
      max_batch_size: 1
  safety:
  - provider_id: meta-reference
    provider_type: meta-reference
    config:
      llama_guard_shield:
        model: Llama-Guard-3-1B
        excluded_categories: []
        disable_input_check: false
        disable_output_check: false
      enable_prompt_guard: false
      prompt_guard_shield:
        model: Prompt-Guard-86M
  memory:
  - provider_id: meta0
    provider_type: meta-reference
    config: {}
  agents:
  - provider_id: meta0
    provider_type: meta-reference
    config:
      persistence_store:
        namespace: null
        type: sqlite
        db_path: ~/.llama/runtime/kvstore.db
  telemetry:
  - provider_id: meta0
    provider_type: meta-reference
    config: {}

Command to run: docker run --network host -it -p 5000:5000 -v ./run.yaml:/root/my-run.yaml --gpus=all llamastack/distribution-tgi --yaml_config /root/my-run.yaml
Error message:
─
  File "/usr/local/lib/python3.10/site-packages/llama_stack/distribution/resolver.py", line 278, in instantiate_provider                             [3/1934]
    impl = await fn(*args)
  File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/inference/__init__.py", line 16, in get_provider_impl
    from .inference import MetaReferenceInferenceImpl
  File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/inference/inference.py", line 18, in <module>
    from .generation import Llama
  File "/usr/local/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/inference/generation.py", line 20, in <module>
    from fairscale.nn.model_parallel.initialize import (
ModuleNotFoundError: No module named 'fairscale'

If I disable the following in the yaml file, it runs ok.
  - provider_id: meta0
    provider_type: meta-reference
    config:
      model: Llama-Guard-3-1B
      quantization: null
      torch_seed: null
      max_seq_len: 4906
      max_batch_size: 1

The version of the related packages are:
fairscale                                0.4.13
llama_models                             0.0.47
llama_stack                              0.0.47
llama_stack_client                       0.0.48

You are running the tgi distribution, which do not install the required dependency for fairscale.
docker run --network host -it -p 5000:5000 -v ./run.yaml:/root/my-run.yaml --gpus=all llamastack/distribution-tgi --yaml_config /root/my-run.yaml

I did have all the following packages installed:
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep accelerate
accelerate                               1.1.0
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep blobfile
blobfile                                 3.0.0
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep torch
torch                                    2.5.1
torchvision                              0.20.1
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep transformers
transformers                             4.46.2
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep zmq
pyzmq                                    26.2.0
zmq                                      0.0.0
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep lm-format-enforcer
lm-format-enforcer                       0.10.9
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep fairscale
fairscale                                0.4.13

I even tried different versions of the fairscale package. It always gave the same error ModuleNotFoundError: No module named 'fairscale'.

I did have all the following packages installed:
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep accelerate
accelerate                               1.1.0
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep blobfile
blobfile                                 3.0.0
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep torch
torch                                    2.5.1
torchvision                              0.20.1
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep transformers
transformers                             4.46.2
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep zmq
pyzmq                                    26.2.0
zmq                                      0.0.0
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep lm-format-enforcer
lm-format-enforcer                       0.10.9
(llamastk_delltgi_env) tao@r7625h100:~/demo_1104/llama-stack/distributions/dell-tgi$ pip list | grep fairscale
fairscale                                0.4.13

I even tried different versions of the fairscale package. It always gave the same error ModuleNotFoundError: No module named 'fairscale'.

The reason is

The fairscale package is installed outside the docker container.
However, docker run command is used to run the docker container, the docker image you are using is distribution-tgi which do not have fairscale package installed. Hence it will give a ModuleNotFoundError.

To be able to run both meta-reference and tgi inference. You could build your own distribution using the following build config.
llama stack build --config ./build.yaml

where .build.yaml contains the following config:
name: tgi-meta-reference
distribution_spec:
  description: Use code from `llama_stack` itself to serve all llama stack APIs
  docker_image: null
  providers:
    inference:
    - meta-reference
    - remote::tgi
    memory: meta-reference-faiss
    safety: meta-reference
    agentic_system: meta-reference
    telemetry: meta-reference
image_type: docker

Thanks. With the config, I was able to build the image with the name distribution-tgi-meta-reference and noticed that I have the fairscale has been installed during the image building process. However, I got model not found issue even I downloaded it into ~/.llama/checkpoints folder and export LLAMA_CHECKPOINT_DIR=~/.llama. From the error message below, it still looks for root folder.
AssertionError: Could not find checkpoints in: /root/.llama/checkpoints/Llama-Guard-3-1B. Please download model using llama download --model-id Llama-Guard-3-1B`.

repo: meta-llama/llama-stack


text:
Update Metafacture dependencies

Will resolve https://github.com/metafacture/metafacture-playground/issues/18.
Also adds all metafacture-core modules to support all Flux commands in the Playground.
Deployed to http://test.lobid.org/playground/

I checked the test deployment and realized that the fields aren't collapsable anymore.

authors: fsteeg katauber

repo: metafacture/metafacture-playground


text:
Kubernetes version upgrade fails both new and old nodes due to keepalived failure

What steps did you take and what happened:
In metal3-dev-env, set KUBERNETES_VERSION to "v1.18.0"
./scripts/v1alphaX/provision_cluster.sh
./scripts/v1alphaX/provision_controlplane.sh
Once provisioned, ssh into the node and run
kubeletcl get pods -A, if you see pods, proceed.
update k8s version to "v1.18.1" by edit the kcp resource.
kubectl edit kcp -n metal3
After a while the upgrade process should start.
However, both nodes are not usable for the following reason
The new node (upgraded)
ubuntu@node-0:~$ kubectl get pods
The connection to the server 192.168.111.249:6443 was refused - did you specify the right host or port?

The original node
ubuntu@node-3:~$ kubectl get pods
The connection to the server 192.168.111.249:6443 was refused - did you specify the right host or port?

What did you expect to happen:

A new nodes with kubernetes version v1.18.1 is provisioned.
The existing node is deprovisioned.

Anything else you would like to add:
Note: We should focus on why the original node has failed despite the new one not being successful.
Environment:

Cluster-api version: v1alpha3
CAPM3 version: v1alpha3
Minikube version: 1.9.2
environment" metal3-dev-env
Kubernetes version: v1.8.0 and v1.8.1

/kind bug

did you ssh on the nodes to check if one of them has the VIP assigned ? any keepalived logs ?

Original node
3: enp2s0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc fq_codel state UP group default qlen 1000
    link/ether 00:50:64:14:42:99 brd ff:ff:ff:ff:ff:ff
    inet 192.168.111.22/24 brd 192.168.111.255 scope global dynamic enp2s0
       valid_lft 2440sec preferred_lft 2440sec
    inet 192.168.111.249/32 scope global enp2s0
       valid_lft forever preferred_lft forever
    inet6 fe80::250:64ff:fe14:4299/64 scope link
       valid_lft forever preferred_lft forever

And,
ubuntu@node-2:~$ kubectl get pods
The connection to the server 192.168.111.249:6443 was refused - did you specify the right host or port?

new node
3: enp2s0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc fq_codel state UP group default qlen 1000
    link/ether 00:50:64:14:42:91 brd ff:ff:ff:ff:ff:ff
    inet 192.168.111.20/24 brd 192.168.111.255 scope global dynamic enp2s0
       valid_lft 2862sec preferred_lft 2862sec
    inet6 fe80::250:64ff:fe14:4291/64 scope link
       valid_lft forever preferred_lft forever

And,
ubuntu@node-0:~$ kubectl get pods
The connection to the server 192.168.111.249:6443 was refused - did you specify the right host or port?

ubuntu@upgrade-env-anwar:~/logs$ kubectl get bmh -A
NAMESPACE   NAME     STATUS   PROVISIONING STATUS   CONSUMER                   BMC                         HARDWARE PROFILE   ONLINE   ERROR
metal3      node-0   OK       provisioned           test1-controlplane-4mzg2   ipmi://192.168.111.1:6230   unknown            true
metal3      node-1   OK       ready                                            ipmi://192.168.111.1:6231   unknown            true
metal3      node-2   OK       provisioned           test1-controlplane-lfdmb   ipmi://192.168.111.1:6232   unknown            true
metal3      node-3   OK       ready                                            ipmi://192.168.111.1:6233   unknown            true

capi-controller-manager-6b584cb4b7-v7nwq.log

I0430 05:48:01.281650       1 machine_controller_noderef.go:53] controllers/Machine "msg"="Machine doesn't have a valid ProviderID yet" "cluster"="test1" "machine"="test1-controlplane-fzjhg" "namespace"="metal3"
E0430 05:48:01.281883       1 machine_controller.go:215] controllers/Machine "msg"="Reconciliation for Machine asked to requeue" "error"="Infrastructure provider for Machine \"test1-controlplane-fzjhg\" in namespace \"metal3\" is not ready, requeuing: requeue in 30s" "cluster"="test1" "machine"="test1-controlplane-fzjhg" "namespace"="metal3"
I0430 05:48:04.055253       1 machine_controller_noderef.go:53] controllers/Machine "msg"="Machine doesn't have a valid ProviderID yet" "cluster"="test1" "machine"="test1-controlplane-fzjhg" "namespace"="metal3"
....
E0430 05:48:41.909869       1 machine_controller.go:215] controllers/Machine "msg"="Reconciliation for Machine asked to requeue" "error"="cannot assign NodeRef to Machine \"test1-controlplane-fzjhg\" in namespace \"metal3\", no matching Node: requeue in 10s" "cluster"="test1" "machine"="test1-controlplane-fzjhg" "namespace"="metal3"
E0430 05:52:12.948409       1 machine_controller_noderef.go:74] controllers/Machine "msg"="Failed to assign NodeRef" "error"="etcdserver: request timed out" "cluster"="test1" "machine"="test1-controlplane-fzjhg" "namespace"="metal3"
E0430 05:52:12.950279       1 controller.go:258] controller-runtime/controller "msg"="Reconciler error" "error"="etcdserver: request timed out"  "controller"="machine" "request"={"Namespace":"metal3","Name":"test1-controlplane-fzjhg"}
I0430 05:52:13.077865       1 machine_controller_noderef.go:81] controllers/Machine "msg"="Set Machine's NodeRef" "cluster"="test1" "machine"="test1-controlplane-fzjhg" "namespace"="metal3" "noderef"="node-0"

capi-kubeadm-control-plane-controller-manager-57bcb55696-f9zk6.log
E0430 07:00:42.465143       1 controller.go:147] controllers/KubeadmControlPlane "msg"="Failed to update KubeadmControlPlane Status" "error"="failed to create remote cluster client: failed to create client for workload cluster metal3/test1: Get https://192.168.111.249:6443/api?timeout=30s: dial tcp 192.168.111.249:6443: connect: connection refused" "cluster"="test1" "kubeadmControlPlane"="test1-controlplane" "namespace"="metal3"
E0430 07:00:42.466509       1 controller.go:258] controller-runtime/controller "msg"="Reconciler error" "error"="[failed to create client for workload cluster metal3/test1: Get https://192.168.111.249:6443/api?timeout=30s: dial tcp 192.168.111.249:6443: connect: connection refused, failed to create remote cluster client: failed to create client for workload cluster metal3/test1: Get https://192.168.111.249:6443/api?timeout=30s: dial tcp 192.168.111.249:6443: connect: connection refused]"  "controller"="kubeadmcontrolplane" "request"={"Namespace":"metal3","Name":"test1-controlplane"}
I0430 07:01:13.237270       1 controller.go:179] controllers/KubeadmControlPlane "msg"="Reconcile KubeadmControlPlane" "cluster"="test1" "kubeadmControlPlane"="test1-controlplane" "namespace"="metal3"
I0430 07:01:13.238521       1 controller.go:225] controllers/KubeadmControlPlane "msg"="Upgrading Control Plane" "cluster"="test1" "kubeadmControlPlane"="test1-controlplane" "namespace"="metal3"
E0430 07:01:13.242710       1 upgrade.go:43]  "msg"="failed to get remote client for workload cluster" "error"="failed to create client for workload cluster metal3/test1: Get https://192.168.111.249:6443/api?timeout=30s: dial tcp 192.168.111.249:6443: connect: connection refused" "cluster-name"="test1" "name"="test1-controlplane" "namespace"="metal3" "cluster key"={"Namespace":"metal3","Name":"test1"}
E0430 07:01:13.246184       1 controller.go:147] controllers/KubeadmControlPlane "msg"="Failed to update KubeadmControlPlane Status" "error"="failed to create remote cluster client: failed to create client for workload cluster metal3/test1: Get https://192.168.111.249:6443/api?timeout=30s: dial tcp 192.168.111.249:6443: connect: connection refused" "cluster"="test1" "kubeadmControlPlane"="test1-controlplane" "namespace"="metal3"
E0430 07:01:13.247722       1 controller.go:258] controller-runtime/controller "msg"="Reconciler error" "error"="[failed to create client for workload cluster metal3/test1: Get https://192.168.111.249:6443/api?timeout=30s: dial tcp 192.168.111.249:6443: connect: connection refused, failed to create remote cluster client: failed to create client for workload cluster metal3/test1: Get https://192.168.111.249:6443/api?timeout=30s: dial tcp 192.168.111.249:6443: connect: connection refused]"  "controller"="kubeadmcontrolplane" "request"={"Namespace":"metal3","Name":"test1-controlplane"}

keepalived log, new node
Apr 30 05:52:14 node-0 Keepalived_vrrp[11007]: Opening file '/etc/keepalived/keepalived.conf'.
Apr 30 05:52:14 node-0 Keepalived_vrrp[11007]: Using LinkWatch kernel netlink reflector...
Apr 30 05:52:15 node-0 Keepalived_vrrp[11007]: VRRP_Instance(VI_2) Transition to MASTER STATE
Apr 30 05:52:15 node-0 Keepalived_vrrp[11007]: VRRP_Instance(VI_2) Received advert with higher priority 101, ours 101
Apr 30 05:52:15 node-0 Keepalived_vrrp[11007]: VRRP_Instance(VI_2) Entering BACKUP STATE

keepalived log, original node
Apr 30 05:39:57 node-2 Keepalived_vrrp[9132]: Opening file '/etc/keepalived/keepalived.conf'.
Apr 30 05:39:57 node-2 Keepalived_vrrp[9132]: Using LinkWatch kernel netlink reflector...
Apr 30 05:39:58 node-2 Keepalived_vrrp[9132]: VRRP_Instance(VI_2) Transition to MASTER STATE
Apr 30 05:39:59 node-2 Keepalived_vrrp[9132]: VRRP_Instance(VI_2) Entering MASTER STATE
Apr 30 05:52:15 node-2 Keepalived_vrrp[9132]: VRRP_Instance(VI_2) Received advert with lower priority 101, ours 101, forcing new election

did you ssh on the nodes to check if one of them has the VIP assigned ? any keepalived logs ?

Relevant logs added. Also, issue description updated to point the focus of investigation.

inet 192.168.111.249/32 scope global enp2s0

The VIP is present on the old node, so it would look like keepalived is doing its job. The problem is probably somewhere else, no ?

I agree, this may not even be keepalived. However, as indicated on the description, this issue is a split of #42.
I could be wrong, but I see two issues.

The VIP should migrate to the new node
Until the new one succeeds, the old one should function as usual.

It depends, with the current configuration it would change to the new node only if the ip address is higher on the new node.
that's the core of the issue here. Is api-server still running ? did it crash ? any logs of errors on the node anywhere ?

And I would say that this issue is a metal3-dev-env issue, not a CAPM3, as it depends on the Kubeadmconfig templates that we provide in metal3-dev-env

1. It depends, with the current configuration it would change to the new node only if the ip address is higher on the new node.

2. that's the core of the issue here. Is api-server still running ? did it crash ? any logs of errors on the node anywhere ?

Thanks. I will into both. And, move the issue to metal3-dev-env based on the outcome of the above two.

1. It depends, with the current configuration it would change to the new node only if the ip address is higher on the new node.

2. that's the core of the issue here. Is api-server still running ? did it crash ? any logs of errors on the node anywhere ?

The issue is caused by 1.
The VIP is kept in the old while there are not services running (etcd is one) on the old one.
This issue can be closed.

authors: Xenwar maelk


text:
:seedling: Fix package versions mismatches

As titled.

/test ?

/test metal3-centos-e2e-integration-test-main
/test metal3-ubuntu-e2e-integration-test-main

authors: mquhuy tuminoid


text:
Post demo cleanup - Annotation page component overhaul and formatting

General

All UI text has been placed in a uiText object in the data of their respective component
Fields from the store are given to annotation components and subcomponents via provide/inject mechanism. Other fields are passed as props.
All props now use the object notation with type and required. If a prop is given to a component but is not used, it is listed but not marked as required: true.
All entries in the script section of Vue template files have been arranged according to the Vue style guide: props, provide/inject, data, computed, Vue lifecycle hooks, methods. Within each group all fields, methods, etc. are alphabetized.
Functional programming solutions (e.g. Object.fromEntries(), entries(), map(), etc.) remain where useful. However, instances of multiple-levels-deep functional programming were reevaluated with two concerns in mind. 1) Readability and 2) efficiency - with the latter taking precedence over the former. Cases where both readability and computational efficiency could be improved were changed to an imperative approach.
Formatting consistency with four space indents and line spacing has been added. A few other bits of tidying - like capitalizing the beginning of comments and the addition of a p_ hungarian notation to distinguish parameters from local variables. Some of this is just for consistency's sake for the time being until we introduce a prettifier - which will be coming up very soon.
All instances of '==' and '!=' have been replaced with '===' and '!==' respectively.

index.js

Corresponding action/mutation functions for the new annotationDetails field have been added to the store. This field contains information for generating the individual annotation tabs. This field holds the same purpose as the former pages field in annotation.vue, but is now moved to the more central place of the store where it can be properly initialized via config (in the future). Each page object also contains a new options field for any specialized parameters needed for any of the tabs. The field is initialized in the nuxtServerInit action just like the categories.
Corresponding action/mutation functions to revert annotation changes if a column unlinked from its category post-annotation has been added to the store,revertColumnToOriginal. This is currently called when a remove button is clicked in the annot-columns component.
New getters have been added to the store to retrieve column and value descriptions from the data dictionary: columnDescription and valueDescription.
Function convertTsvLinesToDict in the store has been renamed to convertTsvLinesToTableData in order to better reflect its purpose. But more importantly, a bug in this function was fixed that was still allowing
blank lines in files to enter the data table.

category-*

category-*.vue files have all been removed. They have been replaced with the more general annot-tab component.

annot-tab

annot-tab contains the annot-explanation and annot-columns components of the previous category-* components but now includes a component representing the data type specialized components for each category on the annotation page. The information to generate these tabs is now found in the store in the annotationDetails field which is initialized in the nuxtServerInit action.
Three computed properties that were previously in the specialized annotation components have been moved up into the annot-tab component: filteredTable (now filteredDataTable), relevantColumns, and uniqueValues

annot-age-values, annot-discrete-choices, and annot-vocabulary

Save annotation buttons remain in the specialized components of the annotation tabs. This is because decoupling them without messy event handling or refs to child methods was not possible. The small redundancy of each component having its own save button is preferable.
Function names and fields not moved to the annot-tab component that remain in the specialized annotation components have been standardized  (i.e. saveButtonDisabled, saveButtonColor, applyAnnotation, transformedValue, initializeMapping, updateMapping, etc.)
applyTransformation has been renamed to applyAnnotation and a new helper function has been introduced for the sake of uniformity: transformedValue. This latter function is the place where an annotation component can go to get the new value given a column and raw value. The hope was that applyAnnotation could be raised up to the annot-tab level but given some slight differences, it made sense to keep it here. In the very least, applyAnnotation and transformedValue can give these components consistent function naming.
Similarly, 'mapping' function names have all been standardized - initializeMapping, updateMapping, and uploadMapping (future functionality withstanding).
As the checkAnnotationState functions just have the purpose of setting the disabled status of the save annotation button, that statement has been pulled out of the function. The disabled status is now more explicitly set via the new boolean return value of checkAnnotationState instead.

annot-columns

retrieveColumnDescription has been implemented to display the data dictionary description of the column next to its name in this component. (Can always be dropped if the screen appears to be too cluttered because of it.) This uses the new columnDescription retrieval method in the store
The v-for key warning has been removed by adding the key as columnName

annot-vocabulary

getDescription has been split into two functions and moved to the store since it contained general, reusable data dictionary functionality. These are the new columnDescription and valueDescription functions

@surchs Yup! I noticed. Pushed the eslint changes for the pages in a separate commit outside the PR right afterward.


text:
Spotting improvements

Description
Updated dot size calculation when axis option includes class or pathway info., removed NaN from filterValues return and changed front end to restrict possible axis combinations. #1153
How to test it
metaspace_url/spotting

please merge master in this branch before

authors: lmacielvieira sergii-mamedov


text:
Change per_atom to sample_kind, allowing other sample formats

This PR changes per_atom to sample_kind in the ModelOutput class, allowing, for example, per-pair targets, or potentially other formats for the samples.
Contributor (creator of pull-request) checklist

[ ] Tests updated (for new features and bugfixes)?
[ ] Documentation updated (for new features)?
[ ] Issue referenced (for PRs that solve an issue)?

Reviewer checklist

[ ] CHANGELOG updated with public API or any other important changes?

Should the default for sample_kind be [] or ["system"]? The second one would be more consistent with what we have now, where one can create a usable ModelOuput simply by calling ModelOutput()

repo: metatensor/metatensor


text:
F#221 Fix css errors and wording

Description

1 . 두 개 이상의 데이터 소스로 생성한 대시보드에서 차트 편집 시 데이터 소스를 선택하는 팝업 창의 검색어 문구가 이상합니다.

메타데이터 위도/경도 논리 타입이 잘 표시는 되는데 다른 논리 타입은 모두 대문자인데 위도/경도는 첫 글자만 대문자이고 소문자라서 통일성이 없습니다.

Related Issue :
#221
How Has This Been Tested?

Search Formula 가 아니라 Search로 수정

2 . Longitude -> LONGITUDE, Latitude -> LATITUDE 로 수정

3 . 텍스트 위젯을 만든 후 대시보드 화면에서 편집 버튼 호버 후 글씨가 보이는지 확인

바 차트 생성 시 measure 3개 와 dimension 3개를 올린 후 차트 생성 후 대시보드 화면에서 위젯 정보 아이콘 호버 후 설명이 길어졌을 때 박스 밖으로 나가지 않는지 확인

IE 에서 값이 나오는지 확인

Types of changes

[x] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

[ ] My code follows the code style of this project. it will be added soon
[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly.
[ ] I have read the CONTRIBUTING document. it will be added soon
[ ] I have added tests to cover my changes.
[x] All new and existing tests passed.

Additional Context

run build
deploy to 4

authors: paigechoi ufoscw

repo: metatron-app/metatron-discovery


text:
The methods are missing return type hints

Since version Version 3.5 Python support type hints for the return type of functions[1].
I'm unsure about their widespread use, but I would encourage using them none the less.
After approval of some people involved in the project, I might fork it and implement the obvious type hints.
Sincerely,
Aaron
[1]https://docs.python.org/3/library/typing.html

Good point. Let's do that!

authors: Weyaaron clampr

repo: meteostat/meteostat-python


text:
Copy_metadata

Ticket: https://metoffice.atlassian.net/browse/EPPT-1907
Acceptance_test_data: https://github.com/metoppv/improver_test_data/pull/61
Update copy_attributes plugin to copy_metadata to include the ability to copy auxilary coordinates from the provided template cube
Testing:

[x] Ran tests and they passed OK
[x] Added new tests for the new feature(s)

was external contributors notified of this? (BoM etc.)

repo: metoppv/improver


text:
Changed bats tests to use cmp -b rather than cmp -bl

Avoids printing loads of extra stuff when things go wrong.
Details of bug noted on IMPRO-452.
Testing:

[ ] Ran tests and they passed OK

Best way to test this is to point one of these tests to the wrong kgo and see the difference in the output from improver/tests cli nbhood --debug with cmp -bl and cmp -b. cmp -bl may not even complete in a reasonable time.


text:
Always use "xml" output, provide a nice UI for results.

Test to see how the XML gets output, maybe it can be used to provide better run pane (like the PHPUnit one.)
  --xml       <path>

              If provided, test results will be written as xml files to this path.

This ends up dumping an XML file in a directory for each test.

This plugin runs a shell script. We can't have a console viewer like PHPUnit because that is running each class itself. If there are improvements, I don't think this is a valid route.

repo: mglaman/intellij-drupal-run-tests

license: mit


text:
🛑 Merchant APP (Terms & Conditions API) is down

In 7b5cb35, Merchant APP (Terms & Conditions API) (https://gateway.wavemoney.io/v2/merchant-api/merchant-term-and-condition-content/get-latest) was down:

HTTP code: 403
Response time: 19 ms

Resolved: Merchant APP (Terms & Conditions API) is back up in 2800b4d.

repo: mgmgpyaesonewin/strapi-upptime


text:
Add support for commonjs on Restangular.

This PR relates to #1088.

Any news on this one? :)

Closed by the merge of #1088

authors: daviesgeek dbartholomae pgom

repo: mgonto/restangular


text:
Exclude the Visual Studio solution cache folder '.vs'

Exclude the .vs folder from the Project Explorer, because this is just a
cache folder like the .git or .svn folders are.

Thank you @claudiospizzi !


text:
🛑 UAT Barracuda v3 is down

In e8ab9d1, UAT Barracuda v3 (https://barracuda-uat.mhtr.be/v3/docs) was down:

HTTP code: 503
Response time: 203 ms

Resolved: UAT Barracuda v3 is back up in 3fb9d70 after 5 minutes.

repo: mh-data-science/upptime


text:
TypeError: undefined is not a function

Hi there,
I get this message when I try to run your example code:
(ng serve compiles without errors/warnings)
ERROR Error: Uncaught (in promise): TypeError: undefined is not a function TypeError: undefined is not a function     at Array.map (<anonymous>)     at webpackAsyncContext ($_lazy_route_resource lazy namespace object:15)     at SystemJsNgModuleLoader.push../node_modules/@angular/core/fesm5/core.js.SystemJsNgModuleLoader.loadAndCompile (core.js:4808)     at SystemJsNgModuleLoader.push../node_modules/@angular/core/fesm5/core.js.SystemJsNgModuleLoader.load (core.js:4800)     at RouterConfigLoader.push../node_modules/@angular/router/fesm5/router.js.RouterConfigLoader.loadModuleFactory (router.js:3230)     at RouterConfigLoader.push../node_modules/@angular/router/fesm5/router.js.RouterConfigLoader.load (router.js:3218)     at MergeMapSubscriber.project (router.js:1494)     at MergeMapSubscriber.push../node_modules/rxjs/_esm5/internal/operators/mergeMap.js.MergeMapSubscriber._tryNext (mergeMap.js:60)     at MergeMapSubscriber.push../node_modules/rxjs/_esm5/internal/operators/mergeMap.js.MergeMapSubscriber._next (mergeMap.js:50)     at MergeMapSubscriber.push../node_modules/rxjs/_esm5/internal/Subscriber.js.Subscriber.next (Subscriber.js:54)     at Array.map (<anonymous>)     at webpackAsyncContext ($_lazy_route_resource lazy namespace object:15)     at SystemJsNgModuleLoader.push../node_modules/@angular/core/fesm5/core.js.SystemJsNgModuleLoader.loadAndCompile (core.js:4808)     at SystemJsNgModuleLoader.push../node_modules/@angular/core/fesm5/core.js.SystemJsNgModuleLoader.load (core.js:4800)     at RouterConfigLoader.push../node_modules/@angular/router/fesm5/router.js.RouterConfigLoader.loadModuleFactory (router.js:3230)     at RouterConfigLoader.push../node_modules/@angular/router/fesm5/router.js.RouterConfigLoader.load (router.js:3218)     at MergeMapSubscriber.project (router.js:1494)     at MergeMapSubscriber.push../node_modules/rxjs/_esm5/internal/operators/mergeMap.js.MergeMapSubscriber._tryNext (mergeMap.js:60)     at MergeMapSubscriber.push../node_modules/rxjs/_esm5/internal/operators/mergeMap.js.MergeMapSubscriber._next (mergeMap.js:50)     at MergeMapSubscriber.push../node_modules/rxjs/_esm5/internal/Subscriber.js.Subscriber.next (Subscriber.js:54)     at resolvePromise (zone.js:814)     at resolvePromise (zone.js:771)     at zone.js:873     at ZoneDelegate.push../node_modules/zone.js/dist/zone.js.ZoneDelegate.invokeTask (zone.js:421)     at Object.onInvokeTask (core.js:3645)     at ZoneDelegate.push../node_modules/zone.js/dist/zone.js.ZoneDelegate.invokeTask (zone.js:420)     at Zone.push../node_modules/zone.js/dist/zone.js.Zone.runTask (zone.js:188)     at drainMicroTaskQueue (zone.js:595)

This is very hard to reproduce, I just checked and deployed a new version,  https://www.majidhajian.com/ngx-barcode-validator, as you see everything is working.
Could you please let me know, what is your Node and NPM version?
and have you cloned/pulled latest changes? I have recently upgraded to Angular 6 and updated all dependencies.

Thanks for your response. Yes, your new demo is working. Unfortunately I still can't reproduce it.
Here's exactly what I did, maybe I miss something:

git clone https://github.com/mhadaily/ngx-barcode-validator.git
npm i
ng serve --port 4300

Node: v8.11.2
NPM: 5.6.0

Hi guys, I actually did the same installation that entspannt and I got the same error:
ERROR Error: Uncaught (in promise): TypeError: undefined is not a function
TypeError: undefined is not a function
at Array.map ()
at webpackAsyncContext ($_lazy_route_resource lazy namespace object:15)
at SystemJsNgModuleLoader.push../node_modules/@angular/core/fesm5/core.js.SystemJsNgModuleLoader.loadAndCompile (core.js:4808) ............
Node: v8.11.2
NPM: 6.1.0
Let me know if you need additional information.

authors: bmundarain entspannt mhadaily

repo: mhadaily/ngx-barcode-validator


text:
Roadmap Inquiry: Options and Futures

Folks -
First - super pumped you guys are working on this.  I'm following it closely - been reading Mike's blog for years.  Thanks for your efforts!  At some point I'll jump in and help... for now I'm working on my coding/unit testing skills as I build a manahl/arctic based data repository to make it easy to do research/etc as well as as a source for backtesting eventually.
I am curious if your roadmap includes equity/futures options and futures capability?  If so, do you have any thoughts or direction on how you'd thread this in?
Ryan

Bit of a discussion going on here for reference :)
https://github.com/mhallsmoore/qstrader/issues/31

Oops! Thanks - I will chime in there!
Sent from my iPhone

On Jul 20, 2016, at 18:57, Ryan Kennedy notifications@github.com wrote:
Bit of a discussion going on here for reference :)
#31
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub, or mute the thread.

You might also have a look at https://github.com/mhallsmoore/qstrader/issues/34

I read with interest recently you were redesigning the framework and wanted to know how it would handle options or at least would it be possible to handle data/instruments/options being added to the algo to trade based on your Strategy/Algo reading an indicator on the underlying for instance?
In options trading you have a huge universe of options to trade but need the underlying and perhaps some other data to make the decision on what to trade. Most OSS engines don't seem to have this ability apart from zipline to some extent with its universe selection/pipelines but its so unbearably slow and only tradeable via Quantopian it's a non starter.
So do you think the framework could handle this strategy execution mode without substantial rewriting of functionality?

Hi guys. Same interest here. I am also looking for a backtest engine with live trading capability with IB that can handle Options (Future and Equity Index options). I am delighted to hear that you are working on a system that goes beyond the typical moving average crossover strategy on US equities. It would be really refreshing and extremely helpful if your new platform would be able to handle a professional options strategy in a way that newly listed options can be traded by the strategy without having to be added manually. So can only second what michaeljohnbennet said.

Hi all,
I realise this is an old issue, however, now that I've released v0.1.0 to PyPI it is probably worth commenting on.
Since the new version is designed for long-only equities strategies the next major task is to integrate a mechanism for short sales along with more sophisticated approaches to portfolio and risk management.
Unfortunately this means that realistic derivatives trading simulation is likely to be some way off.
Regards,
Mike.

authors: SteffenNa canada4663 femtotrader mhallsmoore michaeljohnbennett ryankennedyio

repo: mhallsmoore/qstrader


text:
revertFunc() from eventDrop callback not setting event back to inital column

First, thanks a lot for your plugin. I really like it.
However, I think I found a small bug.
Trying to use the reverFunc() as described here:
http://fullcalendar.io/docs/event_ui/eventDrop/
I noticed, that when an event is dragged to a different column and then tried to be "reverted" to where it came from, the column is not always properly "reverted".
To reproduce the error, check out this fiddle (http://jsfiddle.net/3p7g1zrn/2/) and follows those two steps:

Drag down the event inside the same column and confirm the change (by clicking "ok" twice)
Now drag the event to the other column and click "cancel" on the second alert window
In that case, the event does not jump bach to column 1. However, I also noticed, that if step 1 is not performed and one does immediately try step 2, it seems to work.

Thanks for your help.
Best regards,
Tom

Hi, thanks for the nice words.
I can confirm this bug exists. For me however, it already occurs when I click cancel the first time. The following eventDrop can be used to reproduce it:
eventDrop: function(event, delta, revertFunc) {
    if (!confirm("Are you sure about this change?")) {
        console.log("Reverting...");
        revertFunc();
    }
}

I briefly looked into finding a solution. I'm pretty certain the fix will be a pretty trivial code change. I don't have much time to look into this at the moment, so if you manage to come up with a fix then I'd be happy to accept a pull request. I think one just has to debug a little into FullCalendar/fullcalendar-columns to find a solution.

I just fixed the bug and sent you a pull request. I actually just had to remove one line, although it took me some time to figure it out. ;)
Best regards,
Tom

Great :+1: I've incorporated your change and released a new version. Thanks!

Este me funciono a mi   eventDrop: function(info) {
alert(info.event.title + " was dropped on " + info.event.start.toISOString());
if (!confirm("Are you sure about this change?")) {
  info.revert();
}

}

authors: Robertoart13 mherrmann mitomm

repo: mherrmann/fullcalendar-columns


text:
WIP: Cleanup

update .gitignore for arc and vim
add tools package for tooling dependencies (i.e. the linter)
lock all dependencies in go.mod
fix linter errors
update azure pipeline

Oh, I'm not sure how I feel about adding almost a million lines of code to this repo... (in fact we tried vendoring in Caddy and decided it was a mistake). Go modules can pin the versions for us without all that extra baggage. Otherwise, rest of the bullet points look good.

Drastically speeds up CI/CD times.
I'd like to hear about why you decided that was a mistake with caddy.
That said, once I've got the CI/CD working again I can reset and commit without the -mod=vendor stuff.

It's a long story. And our Caddy CI tests run in under a couple minutes usually without vendoring; I think with a cache of some sort. And that's a larger code base with way more tests. So, let's hold off on vendoring for now (easy to do later, harder to undo); the rest of the cleanup is good :)

... and the Windows CI time is down to 3m. What can I say, I'm a wizard. 🧙♂ 😄

@mholt All changes are complete and tested. This is ready for your review.

repo: mholt/archiver


text:
Minor breaking change in release downloads

So previously we could use the assumption that the filenames were in the structure caddy_<os>_<arch>.tar.gz, but with 0.10 there's the version number in there.
Just pointing it out, not sure if that's something we want to still keep consistent, I think it would make sense, personally. It made scripting pretty easy to get a specific version like this:
https://github.com/mholt/caddy/releases/download/v$VERSION/caddy_linux_amd64.tar.gz

Yep, we added the version to the release filenames. It should be pretty consistent from now on, but this was something I always wanted to do; and finally it became feasible/easy. :)

:+1:
Also FYI https://github.com/abiosoft/caddy-docker/pull/53, v0.10 is built! :smile:
Good luck with the event tonight!

authors: francislavoie mholt

repo: mholt/caddy


text:
New proxy preset for 3 common proxy headers

The proxy directive has the notion of presets which are keywords that tune proxy's configuration a certain, pre-established way that is very common. Right now the only preset is websocket. But we should perhaps add one for these three lines:
proxy_header Host {host}
proxy_header X-Real-IP {remote}
proxy_header X-Forwarded-Proto {scheme}

These are so common that almost every web app requires them to function properly. I just used them to reverse proxy to a Discourse instance.
Should we make this a preset? And what should this preset be called?
And, this is very easy to do, should we agree to do it. Somebody who's new to the project or Go would like to take a stab at it?

Every single time I have used Caddy as a reverse proxy, I used those three lines too.
Why not just call it reverse_proxy ?

I always assumed the proxy directive was a reverse_proxy -- is it a particular kind of reverse proxy? Like a transparent reverse proxy or something?
But reverse_proxy isn't bad, if we can't think of anything else.

I would have preferred reverse_proxy_headers but that will be too long.

Closing since it was fixed in #881.

authors: abiosoft dulldave mholt


text:
Revert "*.txt, *.tpl, *.htm, *.html, *.md should always use eol=lf

This reverts commit d9e6e7ffa58bdd21cd9cef0d41802221a7b2fdc1.
fixes #2348
@mholt @cblecker

 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.You have signed the CLA already but the status is still pending? Let us recheck it.

this PR only reverts the commit, but godep might also be confused for the other .gitattributes entries with eol=XX.

cc @linquize

Hmm, is there a better fix that will not cause confusion? i.e. a way to solve one problem without causing another?

@mholt https://github.com/mholt/caddy/pull/2351 may also fix it, as it appears it's just those three files with bad line endings.

let's merge #2351 and close this one.

authors: CLAassistant cblecker mholt neolit123


text:
Fix for #721

cleaner code :heart:
fixes #721 :hear_no_evil:

P.S.: @mholt sorry, again :cry:

@DenBeke You wanna give this a try? You could even just make the change locally on your computer if that's easier :stuck_out_tongue:
Thanks @elcore! (Haha, and no worries. This happens. If this works, maybe add a test...)

LGTM. Thank you for the fix and test!

Maybe a bit late, but this indeed fixes #721. Thanks!

@DenBeke no problem :smile:

authors: DenBeke elcore mholt


text:
sh: 1: mediainfo: not found

Hello Sir/Medam,
I am getting below error on .3gp file.
sh: 1: mediainfo: not found
Please help me for the same.
Thanks

Hello,
I am facing same issue on behat. Please give your help/suggestion for the same.

Hi @ashishranpara @radhanpuraankit did you install mediainfo as explained here ? php-mediainfo run and parse mediainfo command output if mediainfo is not installed php-mediainfo can't work. You can also specify the command to execute (see here). For an installation on Windows see here.
PS: sorry for response time.

OK, question. I want to have the library off of a relative path vs. a path below the php code.
Meaning, in my case, I want to treat this library as shared library among the website applications.
Exact request.
Instead of PA2/vendor/..
to ../LIB/vendor/..
Can I do that?

For those having trouble, remember that the library is installed as a relative path. What that means is, depending on where your directory structure is, it will install.  If you are in the path:
\bob   it will install \bob\vendor   ....etc
If your website is somewhere else, it will NEVER find that code. Hope that helps.

@davecgs Thanks for your question if your problem is solved I will close this issue as there was no feedback from @radhanpuraankit & @ashishranpara.
If you have question do not hesitate to open another issues.

@mhor I think it would have been nice to configure the path for where the mediainfo binary is in your package, something like FFMPEG / FFPROBE. if there is please let me know.

I found how to configure the path of the binary
(new \Mhor\MediaInfo\MediaInfo)->setConfig('command', '/opt/homebrew/bin/mediainfo');

authors: ashishranpara davecgs hassanazimi mhor radhanpuraankit

repo: mhor/php-mediainfo


text:
Dynamic core reference resolution not working

Commit 65ee1032e9098ec744f69a256388791e38c69b6b made it impossible to build an addin that references Mono.TextEditor.
AddinMaker 0.3.0 works fine.

Got help how to add the implicit reference properly and what name it had (MonoDevelop.SourceEditor2). Closing as invalid.


text:
"msg":"Input should be a valid list"

I totally adapted the OpenAI CURL example as below:
curl https://localhost:7997/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Your text string goes here",
    "model": "BAAI/bge-small-en-v1.5"
  }'

Infinity showed errors as below: {"detail":[{"type":"list_type","loc":["body","input"],"msg":"Input should be a valid list".
I changed to:
curl https://localhost:7997/embeddings \
  -H "Content-Type: application/json" \
  -d '{
    "input": ["Your text string goes here"],
    "model": "BAAI/bge-small-en-v1.5"
  }'

It worked.
How can I modify Infinity codes to totally compatible with OpenAI API? Many thanks!

Should be fixed with 0.0.26!

https://github.com/michaelfeil/infinity/issues/98
https://github.com/michaelfeil/infinity/pull/99

@michaelfeil Michael, would you please also update the docker image? For when run pip install infinity-emb[all], the error still exists.

The docker inage is released. Make sure to NOT pull "latest" but pin your image to a version, e.g. michaelf34/infinity:0.0.26 Thank you.
https://github.com/michaelfeil/infinity/actions/runs/8118848897

@fishfree Also, make sure that the version you have installed via pip is 0.0.26

@michaelfeil Thank you, Michael!

authors: fishfree michaelfeil

repo: michaelfeil/infinity


text:
add mcp-server-playwright

Added the mcp-server-playwright package

Hey there, thanks for the PR. I think you need to set name to  @automatalabs/mcp-server-playwright so it is installable.
I will merge once you fix that.

Hey there, thanks for the PR. I think you need to set name to @automatalabs/mcp-server-playwright so it is installable.
I will merge once you fix that.

Just fixed it up!

authors: VikashLoomba michaellatman

repo: michaellatman/mcp-get


text:
Track manifest list digest instead of image digest

Manifest list digest is useful when supporting multi-arch builds.
Additional information: https://docs.docker.com/registry/spec/manifest-v2-2/

The generate should populate the manifest list digest. Not sure if the manifest list itself should be populated. It might be an extra attribute for future usage.

I was hoping to find a way to reference a parent image with a manifests list digest. Didn't find anything valuable. That mean that during build time the parent image reference should be populated with the image digest for the arch we are building.

Another thought - instead of rewriting the Dockerfile, letting the docker build command to pick up the parent image reference from the lockfile as apposed to the reference in the FROM instruction (Dockerfile).

We were able to get a digest that works for all operating systems and architectures by specifying the header: application/vnd.docker.distribution.manifest.list.v2+json. For those curious, you can read more about this manifest list, aka "fat manifest", here.
This means that docker lock generate will produce fat manifest digests wherever possible, and docker lock rewrite's behavior can remain unchanged.
This behavior is available in version v0.5.1.
Thanks again for the feature request :)

Verified

authors: lioramilbaum michaelperel

repo: michaelperel/docker-lock


text:
SceneActuatorConfGet() needs to be followed up by SceneActuatorConfReport()

Since ST doesn't do any of the heavy lifting for scenes, a driver needs to handle the response for SceneActuatorConfGet(), which is SceneActuatorConfReport().
The switch is asking via SceneActuatorConfGet() about the state following an activation of a scene. The scene controller will, under certain firmware, timeout if it never gets a response ( though eventually many of these scene controller will get hung up and require cutting power to the circuit in order to get them to work again ).

Are you running into trouble with the SmartApp/DTH for the VRCZ4-M0Z?  I have four of them running here -- all working fine

authors: BrianAker michaelphilip

repo: michaelphilip/SmartThings


text:
JSON::ParserError:   765: unexpected token at ''

root@box:~# aquatone-gather --domain pornhub.com --threads 25
                           __
  ____ _____ ___  ______ _/ /_____  ____  ___
 / __ `/ __ `/ / / / __ `/ __/ __ \/ __ \/ _ \
/ /_/ / /_/ / /_/ / /_/ / /_/ /_/ / / / /  __/
\__,_/\__, /\__,_/\__,_/\__/\____/_/ /_/\___/
        /_/  gather v0.1.1 - by @michenriksen

Processing 107 pages...
   Failed: http://31.192.125.235/ (www.google.pornhub.com) - JSON::ParserError:                                                                                765: unexpected token at ''
   Failed: https://31.192.125.235/ (www.google.pornhub.com) - JSON::ParserError:                                                                                765: unexpected token at ''
   Failed: https://31.192.120.36/ (ru.pornhub.com) - JSON::ParserError: 765: une                                                                                xpected token at ''
   Failed: http://94.199.254.41/ (origin.pornhub.com) - JSON::ParserError: 765:

me too

Hey there, please see issue #1 for this problem.

repo: michenriksen/aquatone


text:
Period is getting removed during tokenization?

https://api.michigan.com/v1/article/75995706/
Goaltender Petr Mrazek turned aside 15 Ovechkin shots the last time the teams played (Nov. 10), a 1-0 Red Wings victory.\nThere’s not much of a plan or strategy, said Mrazek, other than being ready for the challenge.
turns into:
Goaltender Petr Mrazek turned aside 15 Ovechkin shots the last time the teams played (Nov. 10), a 1-0 Red Wings victory There’s not much of a plan or strategy, said Mrazek, other than being ready for the challenge.

https://www.youtube.com/watch?v=UtVjRG7PB_4&feature=youtu.be&t=9s

Haven't seen this since, can't replicate in summarizer, going to close issue for now.

authors: migreva neurosnap

repo: michigan-com/summarizer


text:
Add -y flag to update and remove commands.

This allows the confirmation prompts to be skipped (in the same
manner as apt-get's -y flag).
This is desirable if fac is being used from a script where there
is no user input (for example an automatic updater or a new
server installation script).

Good, I had planned to do that but forgot about it


text:
What’s the recommended way to use translations inside components?

I’m wondering what’s the recommended way to use translations inside components. Is there any way to use StaticQuery or should one always use a graphql query inside pages and just use <Trans /> in components?
Thank you!

As stated in the documentation, the graphql query on the page component is all that is necessary to make translations available to all components on that page via either <Trans> or the useTranslation() or useI18Next() hooks.

@stevensacks I think the question was more about "Can you use translations WITHOUT having a query on the page level?". For example, in my case, I have a header and also other elements that are returned from wrapPageElement inside gatsby-browser.js. So then the question is how can the translation be loaded into i18n with a query without a page.

I didn’t build this plugin. You’re welcome to look at the source code, as I did, to see how it works. According to the documentation, the answer appears to be no. You’re using the plugin in a way it was not intended to be used, so you’re going to have to do the extra legwork yourself to figure out how to do that. I’m not saying it can’t work the way you want to use it. I have no idea if it can or not. There may well be a solution for your specific use case that the plugin did not account for.
What I suggest is read the source code, fork the repo, figure out what changes are required to support what you want it to do that do not break the current way it works, and then submit a PR for review.

@ntelkedzhiev at the beginning this plugin was built to load translations without graphql query but it causes multiple problems

no live reload
incremental updates do not work
translations are embedded in the page data and cannot be loaded per page

To summarize: if you really need to load translation without the graphql you need to check the old versions of this plugin, if you want to use translations inside the component, you need to load them into the page where you use this component. For example, if you have a common header component for all pages, you can create a translation namespace common (just a file named common.json and then include it in the translations query on each page.

authors: jimmyn marijoo ntelkedzhiev stevensacks


text:
document which (generated) artifacts should or shouldn't be committed routinely

never commit

anything that you didn't actually edit
project/
examples/output/
JSON or YAML files from nmdc_schema/
src/schema/mixs.yaml

git status  | grep -v project/ | grep -v examples/output/

repo: microbiomedata/nmdc-schema


text:
[API] Prototype API documentation in sidebar

An initial prototype of API documentation in the sidebar that integrates with the editor.
This is early, exploratory work.
Aim to explore:

Where we get the data from (stubs?, via Pyright?, precompute it for quick rendering?)
Whether the stubs contain enough information to do a good job. Is manual curation required?
What documentation improvements are necessary or desirable
How we display it
How we interact with the editor (e.g. drag and drop, other)
Maybe, whether there's any interaction the other way, e.g. focus/goto docs as an alternative to docs on hover

Status comment (I'll edit this as we go):

Branch with API documentation document-style
Branch with API documentation as an accordion (yes, I misspelled the branch 😞 )

Both pretty rough! We could focus on design / layout questions, documentation raising quality, drag-and-drop, progressive disclosure of doc string detail...
If we're generally happy with the direction, and don't seem likely to switch to manually curated docs for this API section, then there's work to be done to tidy up the Pyright side of this too.
Which modules?
I’ve left out modules for now that aren’t in our readthedocs docs: antigravity, builtins, love, this, errno, array, collections, gc, struct, sys.
From discussions with Giles it seems like we might want some docs for the builtins. The stubs currently don't have docs but they exist somewhere...
antigravity and love should probably remain undocumented easter eggs (you can autocomplete to them). The others are probably fine to add but perhaps we'd want to make some modules less prominent as we add more? Though I think a basic/advanced split is harder to justify.
Docstrings
I've truncated them at the moment but that's not really helpful. They're not suitable as-is.
Many don't contain a short summary that we could extract.
There is no systematic approach to parameter documentation.
They contain a lot of restructured text artifacts which we don't yet attempt to format or remove.
It'd be good to discuss what to do here.
Types
I've shown type information. I think it's useful in general but I'm definitely not the target audience. The very few places that use generics look arcane (e.g. random.choose).
More minor

Dunder methods / operations (e.g. adding Images) aren't covered.
Various issues raised against the stubs.

@microbit-matt-hillsdon this looks excellent, thanks! I'm really surprised by how good it looks and how not overwhelming it is even in this early form. Very promising.
Which modules?
I think you've made a good first pass, and consider this ultimately an editorial choice. I think 'filesystem' probably wants to feature, a
I think the accordion doesn't work as well for me because it makes exploration hard, but OTOH the long lists of things like melodies and images slightly confuse the accordionless one. I wonder if there's a version of accordions that is 'auto entering' once one is opened, so you can keep on scrolling as if it's one big list, or contract it?  I( can't tell if in practice this will appear to be just the unexpanded list - I think it depends on when the collapse/re-expand happens, and whether you see the rest of the unexpanded items before )
Some simple things/responses to your questions:

could we use 'returns' instead of '->': I think less notation is good, and I can stand the extra length of 'returns' I think. What do you think @microbit-giles
~As stated on Zoom, class inheritance isn't there, and I think it needs to be especially for buttons... https://microbit-micropython.readthedocs.io/en/v1.0.1/pin.html#classes~

Docstrings
I think, as you say, this needs discussion.
What you've generated looks good for giving us an overview and working out what needs to be done. I think you should probably just do as you suggest and create a good one-line summary for each item in the stubs. We can always refine these and it's good to have a starting point.
Type information
I think on balance this is confusing. Partly because when is shown there is not the actual text you get in the editor, but without types it's way more similar.
Places that are specifically confusing are things like this:

string: str | float | int (💭 a string, that can also be a float or an int? )
choice(seq: Sequence[_T@choice]) -> _T@choice  (💭 whoops, look like the micro:bit Foundation's docs parser broke and there's some mistake in the rendering here)
write(self: NeoPixel) Not sure this is really the fault of the typings, actually.

But, the legitimate followup is do we just show parameters only with their name? The ReadTheDocs docs are more verbose, and are name first: (also note the disparity between our autogenerated name for the first parameter and the name in the docs, string versus value)

I'd be keen to have some kind of ‘expansion’ where you can see the more detailed info, but kinda think maybe type info could go into the docs

Raised https://github.com/microbit-foundation/python-editor-next/issues/301 for code pop-up
Raised https://github.com/microbit-foundation/python-editor-next/issues/302 for drop-down
Closing this initial prototype issue. Doubtless we'll raise more issues in future, e.g. to cover alignment with final designs and to address how we manage the content backing the micro:bit and Python toolkits, but this one isn't really actionable anymore.

authors: jaustin microbit-matt-hillsdon

repo: microbit-foundation/python-editor-next


text:
Update to WF Swarm 2016.12.0, remove unneeded SNAPSHOT repos, cleanup…

… wildfly-swarm profile

@kenfinnigan Looks like Travis failed?

Can't figure out why it can't access some artifacts from maven at the moment

retest this please

authors: karianna kenfinnigan

repo: microprofile/microprofile-samples


text:
Feature computation from the command-line directly into mongo upsert

Currently, the mongo keys are not correct, since they need the screen name prepended. Still, would love to hear your thoughts, @starcalibre!

Coverage increased (+17.64%) to 70.81% when pulling 745d28be88a21efebae081ff39ede9ad734d2e62 on jni:new-features into a45fc542871a33e9959c30c6d4e2fbaab160b0d8 on microscopium:master.

@starcalibre this should be ready for review! Check out that coverage, baby!
The mongo keys are not right, as we discussed, but I think they should be fixed when addressing the new schema, not in this PR.

Well, that rebase was waaaayyyy more painful than expected LOL
If Travis is happy, this is ready for review.
If not, shoot me now. =P

GAAAAH LOL

Ok this should have fixed that last error... Same message as above

Coverage increased (+17.64%) to 70.81% when pulling 07062d909bbb2ee36258d07e164af7517291cd92 on jni:new-features into a45fc542871a33e9959c30c6d4e2fbaab160b0d8 on microscopium:master.

@starcalibre comments addressed! Thank you! =)

Coverage increased (+17.69%) to 70.86% when pulling ebd27baef7a1659817465b1e16d683ad992fbfc2 on jni:new-features into a45fc542871a33e9959c30c6d4e2fbaab160b0d8 on microscopium:master.

authors: coveralls jni

repo: microscopium/microscopium


text:
[Bug]:Dependency Resolution is not working as expected

AL-Go version
5.2
Describe the issue
Al-Go is not able to solve the dependency using Nuget during build process.
Here is the the Log:
Exact match found for AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
- AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
  PackageId: AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
  Get versions using https://nuget.pkg.github.com/TestALGODelivery/download/al-go-4bfc911b-3d4f-4596-9b8d-759f1acfac01/index.json
  1 versions found
  First version is 17.4.22101301
  Last version is 17.4.22101301
  No package found matching version '17.4.22101301.0' for package id AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
  No package found matching package name AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01 Version 17.4.22101301.0

I created and uploaded the Nuget Package manually fo testing purposes.

PS C:\Users\arichter> New-BcNuGetPackage -appfile "C:\Temp\nugettest\Creator_Foundation_17.4.22101301.0.app" -packageId AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01 -packageVersion 17.4.22101301.0
Create NuGet package
AppFile:
C:\Temp\nugettest\Creator_Foundation_17.4.22101301.0.app
NUSPEC file:
<?xml version="1.0" encoding="utf-8"?>
<package xmlns="http://schemas.microsoft.com/packaging/2010/07/nuspec.xsd">
    <metadata>
        <id>AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01</id>
        <version>17.4.22101301.0</version>
        <title>Foundation</title>
        <description>Provides wrapper functions for the UniversalLink ELO OSGI Plugin</description>
        <authors>Creator</authors>
        <dependencies>
            <dependency id="Creator.BaseFoundation.d78da1d4-e45e-479e-9bb3-c368752fb48f" version="17.4.21090102.21090102"/>
            <dependency id="Microsoft.Application.c1335042-3002-4257-bf8a-75c898ccb1b8" version="17.4.0.0" />
            <dependency id="Microsoft.Platform" version="17.0.0.0" />
        </dependencies>
    </metadata>
    <files>
        <file src="Creator_Foundation_17.4.22101301.0.app" target="Creator_Foundation_17.4.221
01301.0.app" />
    </files>
</package>
Successfully created NuGet package (Size: 81Kb)
C:\Users\arichter\AppData\Local\Temp\AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01-17.4.22101301.0.nupkg

PS C:\Users\arichter> Push-BcNuGetPackage -bcNuGetPackage C:\Users\arichter\AppData\Local\Temp\AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01-17.4.22101301.0.nupkg -nuGetServerUrl https://nuget.pkg.github.com/testalgodelivery/index.json -nuGetToken XXXX
Preparing NuGet Package for submission
Submitting NuGet package
Successfully registered nuget package: AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01 (17.4.22101301)

NuGet package successfully submitted

Using BCContailerHelper Find-BCNugetPackage is only successfull when givin exact argument

PS C:\Users\arichter> Find-BcNuGetPackage -packageName AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01 -nuGetServerUrl https://nuget.pkg.github.com/testalgodelivery/index.json -nuGetToken XXX -version 17.4.22101301.0
Search NuGetFeed https://nuget.pkg.github.com/testalgodelivery/index.json
Search package using https://api.github.com/orgs/testalgodelivery/packages?package_type=nuget&per_page=100&page=1
Exact match found for AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
- AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
PackageId: AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
Get versions using https://nuget.pkg.github.com/testalgodelivery/download/al-go-4bfc911b-3d4f-4596-9b8d-759f1acfac01/index.json
1 versions found
First version is 17.4.22101301
Last version is 17.4.22101301
No package found matching version '17.4.22101301.0' for package id AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01

PS C:\Users\arichter> Find-BcNuGetPackage -packageName AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01 -nuGetServerUrl https://nuget.pkg.github.com/testalgodelivery/index.json -nuGetToken XXX -select Exact -version 17.4.22101301.0
Search NuGetFeed https://nuget.pkg.github.com/testalgodelivery/index.json
Search package using https://api.github.com/orgs/testalgodelivery/packages?package_type=nuget&per_page=100&page=1
Exact match found for AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
- AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
PackageId: AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
Get versions using https://nuget.pkg.github.com/testalgodelivery/download/al-go-4bfc911b-3d4f-4596-9b8d-759f1acfac01/index.json
1 versions found
First version is 17.4.22101301
Last version is 17.4.22101301
Exact version matching '17.4.22101301.0' is 17.4.22101301

url                   : https://nuget.pkg.github.com/testalgodelivery/index.json
token                 : XXX
patterns              : {*}
fingerprints          : {}
searchQueryServiceUrl : https://nuget.pkg.github.com/testalgodelivery/query
packagePublishUrl     : https://nuget.pkg.github.com/testalgodelivery
packageBaseAddressUrl : https://nuget.pkg.github.com/testalgodelivery/download

AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
17.4.22101301

PS C:\Users\arichter>

Expected behavior
It looks like there is a problem finding the version if the revision is .0
My expectaion is that it will take the revision into account by default when trying to find teh correct version
Steps to reproduce

Create and upload a nuget package with .0 revision
Create a depend app in Al-go
try to run build process

Additional context (logs, screenshots, etc.)
No response

Will include this bug fix when finalizing the NuGet feature (working on that now)

great, cant wait to have the NuGet Feature up and running :)

i can confirm that this issue is fixed now.
  Missing App dependencies
  - 4bfc911b-3d4f-4596-9b8d-759f1acfac01
  Looking for NuGet package AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01 version 17.4.22101301.0 (Latest match)
  Search NuGetFeed https://nuget.pkg.github.com/TestALGODelivery/index.json
  Search package using https://api.github.com/orgs/TestALGODelivery/packages?package_type=nuget&per_page=100&page=1
  Exact match found for AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
  - AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
  PackageId: AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01
  Get versions using https://nuget.pkg.github.com/TestALGODelivery/download/al-go-4bfc911b-3d4f-4596-9b8d-759f1acfac01/index.json
  1 versions found
  First version is 17.4.22101301
  Last version is 17.4.22101301
  Latest version matching '17.4.22101301.0' is 17.4.22101301
  Best match for package name AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01 Version 17.4.22101301.0: AL-Go-4bfc911b-3d4f-4596-9b8d-759f1acfac01 Version 17.4.22101301 from https://nuget.pkg.github.com/TestALGODelivery/index.json
  Download package using https://nuget.pkg.github.com/TestALGODelivery/download/al-go-4bfc911b-3d4f-4596-9b8d-759f1acfac01/17.4.22101301/al-go-4bfc911b-3d4f-4596-9b8d-759f1acfac01.17.4.22101301.nupkg
  Package successfully downloaded

authors: AlexanderRichter freddydk

repo: microsoft/AL-Go