The Final Task [v0.1]

The Final Task [v0.1]

LINK https://urllio.com/2tlyF6

The desired Python version needs to be added to the tool cache on the self-hosted agent so the task can use it. Normally, the tool cache is located under the _work/_tool directory of the agent; alternatively, the path can be overridden by the environment variable AGENT_TOOLSDIRECTORY. Under that directory, create the following directory structure based off of your Python version:

For the value smi, the percentage traffic split is done at the request level by using a service mesh. A service mesh must be set up by a cluster admin. This task handles orchestration of SMI TrafficSplit objects.

Manifest stability: The rollout status of the deployed Kubernetes objects is checked. The stability checks are incorporated to determine whether the task status is a success or a failure.

Bake manifest: The bake action of the task allows for baking templates into Kubernetes manifest files. The action uses tools such as Helm, Compose, and Kustomize. With baking, these Kubernetes manifest files are usable for deployments to the cluster.

Deployment strategy: Choosing the canary strategy with the deploy action leads to the creation of workload names suffixed with -baseline and -canary. The task supports two methods of traffic splitting:

Service Mesh Interface: Service Mesh Interface (SMI) abstraction allows configuration with service mesh providers like Linkerd and Istio. The Kubernetes Manifest task maps SMI TrafficSplit objects to the stable, baseline, and canary services during the life cycle of the deployment strategy.

Canary deployments that are based on a service mesh and use this task are more accurate. This accuracy is due to how service mesh providers enable the granular percentage-based split of traffic. The service mesh uses the service registry and sidecar containers that are injected into pods. This injection occurs alongside application containers to achieve the granular traffic split.

Compare the baseline and canary workloads by using either a Manual Intervention task in release pipelines or a Delay task in YAML pipelines. Do the comparison before using the promote or reject action of the task.

In the above example, the task tries to find matches for the images foo/demo and bar/demo in the image fields of manifest files. For each match found, the value of either tagVariable1 or tagVariable2 is appended as a tag to the image name. You can also specify digests in the containers input for artifact substitution.

The following YAML code is an example of baking manifest files from Helm charts. Note the usage of a name input in the first task. This name is later referenced from the deploy step for specifying the path to the manifests that were produced by the bake step.

The label selector relationship between pods and services in Kubernetes allows for setting up deployments so that a single service routes requests to both the stable and the canary variants. The Kubernetes manifest task uses this for canary deployments.

If the task includes the inputs of action: deploy and strategy: canary, for each workload (Deployment, ReplicaSet, Pod, ...) defined in the input manifest files, a -baseline and -canary variant of the deployment are created. In this example, there's a deployment sampleapp in the input manifest file and that after completion of run number 22 of the pipeline, the stable variant of this deployment named sampleapp is deployed in the cluster. In the subsequent run (in this case run number 23), Kubernetes manifest task with action: deploy and strategy: canary would result in creation of sampleapp-baseline and sampleapp-canary deployments whose number of replicas are determined by the product of percentage task input with the value of the desired number of replicas for the final stable variant of sampleapp as per the input manifest files.

Theaction: promote and strategy: canary or action: reject and strategy: canary inputs of the Kubernetes manifest tasks can be used to promote or reject the canary changes respectively. Note that in either cases, at the end of this step, only the stable variant of the workloads declared in the input manifest files will be remain deployed in the cluster, while the ephemeral baseline and canary versions are cleaned up.

MatBench is an ImageNet for materials science; aset of 13 supervised, pre-cleaned, ready-to-use ML tasks for benchmarking and fair comparison. The tasks span a wide domain ofinorganic materials science applications.

You can find details and results on the benchmark in our paperBenchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm. Please consider citing this paper if you use Matbench v0.1 for benchmarking, comparison, or prototyping.

These datasets were generated by training an SAC agent for each task, and then using each policy checkpoint saved during training to generate a mixed quality dataset. 300 rollouts were collected for each checkpoint, with 5 checkpoints for the Lift dataset (total of 1500 trajectories), and 13 checkpoints for the Can dataset (total of 3900 trajectories).

Matbench Discovery is an interactive leaderboard and associated PyPI package which together make it easy to benchmark ML energy models on a task designed to closely simulate a high-throughput discovery campaign for new stable inorganic crystals. Matbench-discovery compares ML structure-relaxation methods on the WBM dataset for ranking 250k generated structures according to predicted hull stability (42k stable). Matbench Discovery is developed by Janosh Riebesell.

Matbench is an ImageNet for materials science; acurated set of 13 supervised, pre-cleaned, ready-to-use ML tasks for benchmarking and fair comparison. The tasks span a wide domain ofinorganic materials science applications including electronic, thermodynamic, mechanical, and thermal properties among crystals, 2D materials,disordered metals, and more.

Matbench's 13 tasks can be broken down into various categories; it includes both the small - less than 10,000 samples - datasets that characterizeexperimental materials data as well as larger datasets from computer modelling methods like density functional theory (DFT).

The Matbench Python package provides functions for getting the first two (packaged together for each task as a dataset) as well as running the test procedure. See the How to use documentation page to get started.

You can find details and results on the benchmark in our paper Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm. Please consider citing this paper if you use Matbench v0.1 for benchmarking, comparison, or prototyping.

The authors have adequately addressed the final reviewer comments.# PeerJ Staff Note - this decision was reviewed and approved by Stephen Macknik, a PeerJ Section Editor covering this Section #

Each paragraph of the introduction part became clear and precise now.Comment 1-1Line 80 'Therefore, ...'The idea described here was initially followed a sentence that Vinding et al showed that the size of IB is dependent on the purpose of the action. Now the sentence was revised. The authors need to correct the sentence following the revised sentence. Comment 1-2Line 82This paragraph could be moved to line 69 (after the first sentence of the paragraph). This change will make it easier for readers to follow the flow of the logic.Comment 1-3Line 93I should have commented about this point earlier but are the contributions of predictive process and of volition/planning same Comment 1-4Line 130Does authors hypothesize that the IB disappears after the participant completes motor learning process though many IB studies have shown the presence of IB in simple tasks which do not require motor learning

Comment 3-1The authors plotted the IB values as a function of the actual interval of the stimulus to respond to my suggestion. What I wanted to suggest the authors was to plot the error values of the hand action task as a function of IB (a scatter plot with x (or y): perceived duration - actual duration [ms] and y (or x): error value [px]). If the error in the action was critical to the size of IB, the error values (y) and the IB values (x) should correlate to each other. I would also suggest the authors to plot the IB values as a function of the change in error values of each trial compared to the former trial. Correlation between them will support the discussion that reduction in error facilitated the IB.Comment 3-2Line 325This sentence could be read as if Hon et al. (2013) showed that the sense of agency is reduced when the participants were required to focus on the task very well.Hon et al. (2013) reported that the sense of agency was low in the high cognitive load condition. Their results suggest that the sense of agency is reduced when the attention was taken away from the action and the result of the action.

The authors often use the word 'optimal'. I guess the authors supposed that the sense of agency could be drawn as a U shape model where too easy or too demanding second task (i.e., too low or too high requirement of attention to the second task) reduces the sense of agency. Is this understanding correctLine 282This sentence sounds as if the authors did not design the task optimally to each participant. Line 333It was not clear what the authors thought to be 'optimal'.

1-1Grammar of this manuscript became good now. However, I still felt difficulty in understanding what authors wanted to say very often. Some sentences did not even make sense.For example, Line 18 'How IB may change over the course of a perceptual-motor task, however, has not been explicitly investigated' could be read as if the authors measured how the degrees of IB changes in the time course of each trial. This sentence is grammatically correct but it would be appropriate to say 'perceptual-motor learning' (instead of 'perceptual-motor task') in this context. 1-2 [Line 51-52]The sentence is not precise. First, we feel SoA even when we make an action spontaneously without any explicit goals or purposes. IB has been found in the experiments where participants were required to make actions spontaneously without any goals.Second, motor control is the process to reduce the differences among them. SoA would come up when the differences among them were small. 1-3 [Line 71-73]What Vinding and Pedersen showed was that when the intention to act is formed in advance, it entails a stronger SoA than when the intention is immediately followed by the action. Vinding and Pedersen did not mention that there was difference in the goal between two types of intention in their paradigm.1-4 [Line 76-78]The people who have read Haggard & Clark (2002) will guess what the authors wanted to say by these two sentences. But many readers would not understand them.1-5 [Line 241-242]This sentence does not make sense.Do you mean that the conventional tasks do not consider the possibility that perceptual-motor learning would change the degree of binding1-6Please check the submission guideline. You usually do not use 'et al.' to refer two-author studies.1-7 [Line 263-268]It was hardly possible for me to guess what the authors wanted to say.1-8The discussion about the reward system was a little bit speculative. Reduction of error (i.e., increase of reward) increased SoA in the cluster 2 participants. How is the result of this study consistent with the previous study by Di Costa et al.1-9 [Line 340]To conclude that the postdictive mechanism worked in the IB task, the authors need to test if the IB decreases when the feedback of the perceptual-motor task was given after the estimation of the delay of the tone. 59ce067264

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