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Exquisite time to enjoy the nature before it is too late, but also a good time to finish up leftover tasks and contemplate on our progress and make plans for the future. At the top of this list is this way overdue blog post regarding our work on Galaxy interactive tools, which allow users to visually communicate with software during its execution on Galaxy. + + +## What are interactive tools (IT)? + +For a non-interactive tool execution, the user defines all inputs before the job execution and has access to the results after the job finishes. During the execution phase of the job, the user does not and cannot interact with the system. For majority of the scientific software, this model is actually sufficient, because they are commandline-only, the computation follows a pre-written recipe in the script and a user input during the computation is not needed at all. + +

+ +However, sometimes this model is too restrictive, say when the user wants to manually explore the data. The project might be too new and you do not have a well defined algorithmic approach available yet: you want to try a few ideas out in a rapid succession, visualise it in a few ways and see what works what fails. In theory, one could of course write a tool for each such variation, but it is too cumbersome for creative work. This is where a Galaxy Interactive Tools (IT) come to play. As before, the user can still define some inputs and pass to the interactive environment, and will have access to the results after execution via history. However, user will also be able to communicate with the tool during its execution by clicking, selecting, typing, filtering via a GUI. In essence, this more or less replicates the experience of a remote desktop (or VM) user. This provides a reproducible and resumable computing system which can leverage the existing computing and storage infrastructure of the Galaxy instance. + + +## Adding workflows a "human touch" + +As a case study, consider a user developing a new machine learning model for the first time, a task involving a series of commands executed on some test data sets. The proposed model needs to be debugged, and then optimised with different training parameters. With the integrated Jupyter Notebooks, Galaxy makes such an exploratory research project possible and simple. A local deployment or usage of such an online resource would potentially present technical and security concerns and prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The user would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test her code and the model, which costs researchers time and funds. One way of harnessing training data is by processing big raw data sets via a Galaxy workflows, and the results will be in user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be defined as off limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data within the Galaxy storage system. + +Galaxy ITs can be integrated into a "traditional" workflow, with the possibility to switch back to non-interactive tools at any time, thereby adding a "human touch" to the workflow for the steps that require a manual inspection or intervention. Incorporating ITs into a Galaxy workflow will pause the execution of the downstream steps till the user completes the interactive tasks. Processing will continue asynchronously once the interactive step is complete, potentially making use of the results interactively generated by the user as inputs at various downstream steps. + +![Schematic description of a Galaxy workflow involving IT and non-IT components](./figs/workflow.png) + + +## Updates from usegalaxy.eu + +Within the four-year time window between September 2019 and August 2023, our public instance, [live.usegalaxy.eu](live.usegalaxy.eu), already had served 21 801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of VMs dedicated to IT executions in our cloud, users usually did not need to wait more than 30s after realising their need and spinning up an IT instance. This excludes the technical issues that we experienced in the recent past and now resolved, which meant we were unable to provide the resources within a few days. + +![Histogram of waiting time of all IT job requests as experienced by users, 2019-2023](./figs/waiting_time.png) + + +We think that ITs can provide an opportunity to integrate much more interactive scientific use-cases and visualizations and will increasingly gain popularity in the community. While the popularity of the ITs gradually increased over the past few years, we believe we still have ample room to welcome a higher foot traffic: +![Number of monthly (unique) IT users, 2019-2023](./figs/user_stats.png) + + +In the recent past, we added new ITs to usegalaxy.eu, reaching a now total of 45 ITs available. But adding your own favourite tool is not very different than a typical non-IT integration experience, either. ITs exploit the containerisation technology: adding new ITs require access to a container running the tool, and it suffices to declare the tool as an interactive tool in its wrapper, and define a port through which the tool should be accessed from within the container (see our [quick guide](https://github.com/usegalaxy-eu/operations/blob/main/interactive_tools.md)). + + + From d30252280a58d7117ad17c6f3841e3db282e370f Mon Sep 17 00:00:00 2001 From: Ubuntu <19364610+tuncK@users.noreply.github.com> Date: Tue, 15 Aug 2023 13:20:23 +0000 Subject: [PATCH 2/9] fixed broken link --- content/blog/2023-08-15-ITs/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2023-08-15-ITs/index.md b/content/blog/2023-08-15-ITs/index.md index 94eb6b8e73..9e7874a1d5 100644 --- a/content/blog/2023-08-15-ITs/index.md +++ b/content/blog/2023-08-15-ITs/index.md @@ -45,7 +45,7 @@ Galaxy ITs can be integrated into a "traditional" workflow, with the possibility ## Updates from usegalaxy.eu -Within the four-year time window between September 2019 and August 2023, our public instance, [live.usegalaxy.eu](live.usegalaxy.eu), already had served 21 801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of VMs dedicated to IT executions in our cloud, users usually did not need to wait more than 30s after realising their need and spinning up an IT instance. This excludes the technical issues that we experienced in the recent past and now resolved, which meant we were unable to provide the resources within a few days. +Within the four-year time window between September 2019 and August 2023, our public instance, [live.usegalaxy.eu](https://live.usegalaxy.eu), already had served 21 801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of VMs dedicated to IT executions in our cloud, users usually did not need to wait more than 30s after realising their need and spinning up an IT instance. This excludes the technical issues that we experienced in the recent past and now resolved, which meant we were unable to provide the resources within a few days. ![Histogram of waiting time of all IT job requests as experienced by users, 2019-2023](./figs/waiting_time.png) From e451cdaa940bb5dfb35d794095612ba0ed70af50 Mon Sep 17 00:00:00 2001 From: Tunc Kayikcioglu <19364610+tuncK@users.noreply.github.com> Date: Tue, 15 Aug 2023 16:36:01 +0200 Subject: [PATCH 3/9] Update content/blog/2023-08-15-ITs/index.md Co-authored-by: Sanjay Kumar Srikakulam --- content/blog/2023-08-15-ITs/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2023-08-15-ITs/index.md b/content/blog/2023-08-15-ITs/index.md index 9e7874a1d5..539a1389df 100644 --- a/content/blog/2023-08-15-ITs/index.md +++ b/content/blog/2023-08-15-ITs/index.md @@ -31,7 +31,7 @@ For a non-interactive tool execution, the user defines all inputs before the job IT execution model -However, sometimes this model is too restrictive, say when the user wants to manually explore the data. The project might be too new and you do not have a well defined algorithmic approach available yet: you want to try a few ideas out in a rapid succession, visualise it in a few ways and see what works what fails. In theory, one could of course write a tool for each such variation, but it is too cumbersome for creative work. This is where a Galaxy Interactive Tools (IT) come to play. As before, the user can still define some inputs and pass to the interactive environment, and will have access to the results after execution via history. However, user will also be able to communicate with the tool during its execution by clicking, selecting, typing, filtering via a GUI. In essence, this more or less replicates the experience of a remote desktop (or VM) user. This provides a reproducible and resumable computing system which can leverage the existing computing and storage infrastructure of the Galaxy instance. +However, sometimes this model is too restrictive, say when the user wants to explore the data manually. The project might be too new and you do not have a well-defined algorithmic approach available yet: you want to try a few ideas out in rapid succession, visualise them in a few ways and see what works and what fails. In theory, one could write a tool for each such variation, but it is too cumbersome for creative work. This is where Galaxy Interactive Tools (IT) come into play. As before, the user can still define some inputs and pass them to the interactive environment and will have access to the results after execution via history. However, the user will also be able to communicate with the tool during its execution by clicking, selecting, typing, and filtering via a GUI. In essence, this replicates the experience of a remote desktop (or VM) user. This provides a reproducible and resumable computing system which can leverage the existing computing and storage infrastructure of the Galaxy instance. ## Adding workflows a "human touch" From 6bf2a11d29d739b5d149fcb1ac8e28e1e6782c85 Mon Sep 17 00:00:00 2001 From: Tunc Kayikcioglu <19364610+tuncK@users.noreply.github.com> Date: Tue, 15 Aug 2023 16:56:10 +0200 Subject: [PATCH 4/9] Update content/blog/2023-08-15-ITs/index.md Co-authored-by: Sanjay Kumar Srikakulam --- content/blog/2023-08-15-ITs/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2023-08-15-ITs/index.md b/content/blog/2023-08-15-ITs/index.md index 539a1389df..d9a5e53f0b 100644 --- a/content/blog/2023-08-15-ITs/index.md +++ b/content/blog/2023-08-15-ITs/index.md @@ -36,7 +36,7 @@ However, sometimes this model is too restrictive, say when the user wants to exp ## Adding workflows a "human touch" -As a case study, consider a user developing a new machine learning model for the first time, a task involving a series of commands executed on some test data sets. The proposed model needs to be debugged, and then optimised with different training parameters. With the integrated Jupyter Notebooks, Galaxy makes such an exploratory research project possible and simple. A local deployment or usage of such an online resource would potentially present technical and security concerns and prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The user would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test her code and the model, which costs researchers time and funds. One way of harnessing training data is by processing big raw data sets via a Galaxy workflows, and the results will be in user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be defined as off limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data within the Galaxy storage system. +As a case study, consider a user developing a new machine learning model for the first time, a task involving a series of commands executed on some test data sets. The proposed model needs to be debugged and then optimised with different training parameters. With the integrated Jupyter Notebooks, Galaxy makes such an exploratory research project possible and simple. A local deployment or usage of such an online resource would potentially present technical and security concerns and prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The user would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test their code and the model, which costs researchers time and funds. One way of harnessing training data is by processing big raw data sets via a Galaxy workflow, and the results will be in user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be defined as off-limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data within the Galaxy storage system. Galaxy ITs can be integrated into a "traditional" workflow, with the possibility to switch back to non-interactive tools at any time, thereby adding a "human touch" to the workflow for the steps that require a manual inspection or intervention. Incorporating ITs into a Galaxy workflow will pause the execution of the downstream steps till the user completes the interactive tasks. Processing will continue asynchronously once the interactive step is complete, potentially making use of the results interactively generated by the user as inputs at various downstream steps. From e9a0dc8657163571c482dd43aff371bc53ef6734 Mon Sep 17 00:00:00 2001 From: Ubuntu <19364610+tuncK@users.noreply.github.com> Date: Tue, 15 Aug 2023 15:18:12 +0000 Subject: [PATCH 5/9] comments addressed --- content/blog/2023-08-15-ITs/index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/blog/2023-08-15-ITs/index.md b/content/blog/2023-08-15-ITs/index.md index 9e7874a1d5..7cffc3b3bb 100644 --- a/content/blog/2023-08-15-ITs/index.md +++ b/content/blog/2023-08-15-ITs/index.md @@ -1,7 +1,7 @@ --- title: "Interactive tool collection is growing" date: "2023-08-15" -tease: "New/improved interactive tools are available" +tease: "New and improved interactive tools are available" hide_tease: false authors: 'Tunc Kayikcioglu' authors_structured: @@ -31,12 +31,12 @@ For a non-interactive tool execution, the user defines all inputs before the job IT execution model -However, sometimes this model is too restrictive, say when the user wants to manually explore the data. The project might be too new and you do not have a well defined algorithmic approach available yet: you want to try a few ideas out in a rapid succession, visualise it in a few ways and see what works what fails. In theory, one could of course write a tool for each such variation, but it is too cumbersome for creative work. This is where a Galaxy Interactive Tools (IT) come to play. As before, the user can still define some inputs and pass to the interactive environment, and will have access to the results after execution via history. However, user will also be able to communicate with the tool during its execution by clicking, selecting, typing, filtering via a GUI. In essence, this more or less replicates the experience of a remote desktop (or VM) user. This provides a reproducible and resumable computing system which can leverage the existing computing and storage infrastructure of the Galaxy instance. +However, sometimes this model is too restrictive, say when the user wants to manually explore the data. The project might be too new and you do not have a well defined algorithmic approach available yet: you want to try a few ideas out in a rapid succession, visualise it in a few ways and see what works what fails. In theory, one could of course write a tool for each such variation, but it is too cumbersome for creative work. This is where a Galaxy Interactive Tools (IT) come to play. As before, the user can still define some inputs and pass to the interactive environment, and will have access to the results after execution via history. However, user will also be able to communicate with the tool during its execution by clicking, selecting, typing, filtering via a GUI. In essence, this more or less replicates the experience of a remote desktop (or VM) user. This provides a reproducible and resumable computing system which can leverage the existing computing and storage infrastructure of the Galaxy instance. ## Adding workflows a "human touch" -As a case study, consider a user developing a new machine learning model for the first time, a task involving a series of commands executed on some test data sets. The proposed model needs to be debugged, and then optimised with different training parameters. With the integrated Jupyter Notebooks, Galaxy makes such an exploratory research project possible and simple. A local deployment or usage of such an online resource would potentially present technical and security concerns and prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The user would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test her code and the model, which costs researchers time and funds. One way of harnessing training data is by processing big raw data sets via a Galaxy workflows, and the results will be in user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be defined as off limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data within the Galaxy storage system. +ITs can empower users with interactive coding environments, enabling real-time manipulation of code, visualizations, and data analysis and fostering iterative exploration. This enhances the efficiency and reproducibility within these dynamic research landscapes. As a case study, consider a user developing a new machine learning model for the first time, a task involving a series of commands executed on some test data sets. The proposed model needs to be debugged, and then optimised with different training parameters. With the integrated Jupyter Notebooks, Galaxy makes such an exploratory research project possible and simple. A local deployment or usage of such an online resource would potentially present technical and security concerns and prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The user would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test her code and the model, which costs researchers time and funds. One way of harnessing training data is by processing big raw data sets via a Galaxy workflows, and the results will be in user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be defined as off limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data within the Galaxy storage system. Galaxy ITs can be integrated into a "traditional" workflow, with the possibility to switch back to non-interactive tools at any time, thereby adding a "human touch" to the workflow for the steps that require a manual inspection or intervention. Incorporating ITs into a Galaxy workflow will pause the execution of the downstream steps till the user completes the interactive tasks. Processing will continue asynchronously once the interactive step is complete, potentially making use of the results interactively generated by the user as inputs at various downstream steps. @@ -45,7 +45,7 @@ Galaxy ITs can be integrated into a "traditional" workflow, with the possibility ## Updates from usegalaxy.eu -Within the four-year time window between September 2019 and August 2023, our public instance, [live.usegalaxy.eu](https://live.usegalaxy.eu), already had served 21 801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of VMs dedicated to IT executions in our cloud, users usually did not need to wait more than 30s after realising their need and spinning up an IT instance. This excludes the technical issues that we experienced in the recent past and now resolved, which meant we were unable to provide the resources within a few days. +Within the four-year time window between September 2019 and August 2023, our public instance, [live.usegalaxy.eu](https://live.usegalaxy.eu), already had served 21801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of computing resources dedicated to IT executions in our cloud, users usually did not need to wait more than 30s after realising their need and spinning up an IT instance. This excludes the technical issues that we experienced in the recent past and now resolved, which meant we were unable to provide the resources within a few days. ![Histogram of waiting time of all IT job requests as experienced by users, 2019-2023](./figs/waiting_time.png) From 2a412964bd21d4c534156b25bda7682df7ab0295 Mon Sep 17 00:00:00 2001 From: Ubuntu <19364610+tuncK@users.noreply.github.com> Date: Tue, 15 Aug 2023 15:26:50 +0000 Subject: [PATCH 6/9] relocate to news --- .../2023-08-15-ITs/figs/flowchart.png | Bin .../2023-08-15-ITs/figs/user_stats.png | Bin .../2023-08-15-ITs/figs/waiting_time.png | Bin .../{blog => news}/2023-08-15-ITs/figs/workflow.png | Bin content/{blog => news}/2023-08-15-ITs/index.md | 6 ++++-- 5 files changed, 4 insertions(+), 2 deletions(-) rename content/{blog => news}/2023-08-15-ITs/figs/flowchart.png (100%) rename content/{blog => news}/2023-08-15-ITs/figs/user_stats.png (100%) rename content/{blog => news}/2023-08-15-ITs/figs/waiting_time.png (100%) rename content/{blog => news}/2023-08-15-ITs/figs/workflow.png (100%) rename content/{blog => news}/2023-08-15-ITs/index.md (68%) diff --git a/content/blog/2023-08-15-ITs/figs/flowchart.png b/content/news/2023-08-15-ITs/figs/flowchart.png similarity index 100% rename from content/blog/2023-08-15-ITs/figs/flowchart.png rename to content/news/2023-08-15-ITs/figs/flowchart.png diff --git a/content/blog/2023-08-15-ITs/figs/user_stats.png b/content/news/2023-08-15-ITs/figs/user_stats.png similarity index 100% rename from content/blog/2023-08-15-ITs/figs/user_stats.png rename to content/news/2023-08-15-ITs/figs/user_stats.png diff --git a/content/blog/2023-08-15-ITs/figs/waiting_time.png b/content/news/2023-08-15-ITs/figs/waiting_time.png similarity index 100% rename from content/blog/2023-08-15-ITs/figs/waiting_time.png rename to content/news/2023-08-15-ITs/figs/waiting_time.png diff --git a/content/blog/2023-08-15-ITs/figs/workflow.png b/content/news/2023-08-15-ITs/figs/workflow.png similarity index 100% rename from content/blog/2023-08-15-ITs/figs/workflow.png rename to content/news/2023-08-15-ITs/figs/workflow.png diff --git a/content/blog/2023-08-15-ITs/index.md b/content/news/2023-08-15-ITs/index.md similarity index 68% rename from content/blog/2023-08-15-ITs/index.md rename to content/news/2023-08-15-ITs/index.md index 7cffc3b3bb..5d9ed78fc3 100644 --- a/content/blog/2023-08-15-ITs/index.md +++ b/content/news/2023-08-15-ITs/index.md @@ -31,12 +31,14 @@ For a non-interactive tool execution, the user defines all inputs before the job IT execution model -However, sometimes this model is too restrictive, say when the user wants to manually explore the data. The project might be too new and you do not have a well defined algorithmic approach available yet: you want to try a few ideas out in a rapid succession, visualise it in a few ways and see what works what fails. In theory, one could of course write a tool for each such variation, but it is too cumbersome for creative work. This is where a Galaxy Interactive Tools (IT) come to play. As before, the user can still define some inputs and pass to the interactive environment, and will have access to the results after execution via history. However, user will also be able to communicate with the tool during its execution by clicking, selecting, typing, filtering via a GUI. In essence, this more or less replicates the experience of a remote desktop (or VM) user. This provides a reproducible and resumable computing system which can leverage the existing computing and storage infrastructure of the Galaxy instance. +However, sometimes this model is too restrictive, say when the user wants to explore the data manually. The project might be too new and you do not have a well-defined algorithmic approach available yet: you want to try a few ideas out in rapid succession, visualise them in a few ways and see what works and what fails. In theory, one could write a tool for each such variation, but it is too cumbersome for creative work. This is where Galaxy Interactive Tools (IT) come into play. As before, the user can still define some inputs and pass them to the interactive environment and will have access to the results after execution via history. However, the user will also be able to communicate with the tool during its execution by clicking, selecting, typing, and filtering via a GUI. In essence, this replicates the experience of a remote desktop (or VM) user. This provides a reproducible and resumable computing system which can leverage the existing computing and storage infrastructure of the Galaxy instance. ## Adding workflows a "human touch" -ITs can empower users with interactive coding environments, enabling real-time manipulation of code, visualizations, and data analysis and fostering iterative exploration. This enhances the efficiency and reproducibility within these dynamic research landscapes. As a case study, consider a user developing a new machine learning model for the first time, a task involving a series of commands executed on some test data sets. The proposed model needs to be debugged, and then optimised with different training parameters. With the integrated Jupyter Notebooks, Galaxy makes such an exploratory research project possible and simple. A local deployment or usage of such an online resource would potentially present technical and security concerns and prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The user would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test her code and the model, which costs researchers time and funds. One way of harnessing training data is by processing big raw data sets via a Galaxy workflows, and the results will be in user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be defined as off limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data within the Galaxy storage system. +ITs can empower users with interactive coding environments, enabling real-time manipulation of code, visualizations, and data analysis and fostering iterative exploration. This enhances the efficiency and reproducibility within these dynamic research landscapes. As a case study, consider a user developing a new machine learning model for the first time, a task involving a series of commands executed on some test data sets. The proposed model needs to be debugged and then optimised with different training parameters. With the integrated Jupyter Notebooks or Rstudio, Galaxy makes such an exploratory research project possible and simple. + +A local deployment or usage of such an online resource would potentially present technical and security concerns and prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The user would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test her code and the model, which costs researchers time and funds. One way of harnessing training data is by processing big raw data sets via a Galaxy workflows, and the results will be in user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be defined as off limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data within the Galaxy storage system. Galaxy ITs can be integrated into a "traditional" workflow, with the possibility to switch back to non-interactive tools at any time, thereby adding a "human touch" to the workflow for the steps that require a manual inspection or intervention. Incorporating ITs into a Galaxy workflow will pause the execution of the downstream steps till the user completes the interactive tasks. Processing will continue asynchronously once the interactive step is complete, potentially making use of the results interactively generated by the user as inputs at various downstream steps. 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21801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of computing resources dedicated to IT executions in our cloud, users usually did not need to wait more than 30s after realising their need and spinning up an IT instance. This excludes the technical issues that we experienced in the recent past and now resolved, which meant we were unable to provide the resources within a few days. +Within the four-year time window between September 2019 and August 2023, our public instance, [live.usegalaxy.eu](https://live.usegalaxy.eu), already had served 21801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of computing resources dedicated to IT executions in our cloud, users usually did not need to wait much after realising their need and spinning up an IT instance: half of the requests began within 21s, whereas 83.2% were ready for research use within 100s. To ensure the fair distribution of limited resources, we implement user- and system-wide quotas on the number of simultaneous IT instances, which might delay the provision of requested ITs, as well as occasional system maintenance or outages. + ![Histogram of waiting time of all IT job requests as experienced by users, 2019-2023](./figs/waiting_time.png) From 29095c9ea4108551b2996995fd762588ef709199 Mon Sep 17 00:00:00 2001 From: Ubuntu <19364610+tuncK@users.noreply.github.com> Date: Fri, 15 Sep 2023 13:44:21 +0000 Subject: [PATCH 8/9] sectioning and 4 step launch added --- content/news/2023-08-15-ITs/index.md | 63 --------------- .../figs/flowchart.png | Bin .../figs/how_to_launch.png | Bin 0 -> 149756 bytes .../figs/user_stats.png | Bin .../figs/waiting_time.png | Bin .../figs/workflow.png | Bin .../2023-09-15-interactive-tools/index.md | 76 ++++++++++++++++++ 7 files changed, 76 insertions(+), 63 deletions(-) delete mode 100644 content/news/2023-08-15-ITs/index.md rename content/news/{2023-08-15-ITs => 2023-09-15-interactive-tools}/figs/flowchart.png (100%) create mode 100644 content/news/2023-09-15-interactive-tools/figs/how_to_launch.png rename content/news/{2023-08-15-ITs => 2023-09-15-interactive-tools}/figs/user_stats.png (100%) rename content/news/{2023-08-15-ITs => 2023-09-15-interactive-tools}/figs/waiting_time.png (100%) rename content/news/{2023-08-15-ITs => 2023-09-15-interactive-tools}/figs/workflow.png (100%) create mode 100644 content/news/2023-09-15-interactive-tools/index.md diff --git a/content/news/2023-08-15-ITs/index.md b/content/news/2023-08-15-ITs/index.md deleted file mode 100644 index 8a735a67b4..0000000000 --- a/content/news/2023-08-15-ITs/index.md +++ /dev/null @@ -1,63 +0,0 @@ ---- -title: "Interactive tool collection is growing" -date: "2023-08-15" -tease: "New and improved interactive tools are available" -hide_tease: false -authors: 'Tunc Kayikcioglu' -authors_structured: -- github: tuncK -tags: [EU] -subsites: [all-eu] -main_subsite: eu ---- - - -August more or less marks the end of the summer semester here in southwestern Germany. Exquisite time to enjoy the nature before it is too late, but also a good time to finish up leftover tasks and contemplate on our progress and make plans for the future. At the top of this list is this way overdue blog post regarding our work on Galaxy interactive tools, which allow users to visually communicate with software during its execution on Galaxy. - - -## What are interactive tools (IT)? - -For a non-interactive tool execution, the user defines all inputs before the job execution and has access to the results after the job finishes. During the execution phase of the job, the user does not and cannot interact with the system. For majority of the scientific software, this model is actually sufficient, because they are commandline-only, the computation follows a pre-written recipe in the script and a user input during the computation is not needed at all. - -
- - -Non-IT execution model - -![Schematic comparison of the role in IT and non-IT job executions](./figs/flowchart.png) - -IT execution model -
- -However, sometimes this model is too restrictive, say when the user wants to explore the data manually. The project might be too new and you do not have a well-defined algorithmic approach available yet: you want to try a few ideas out in rapid succession, visualise them in a few ways and see what works and what fails. In theory, one could write a tool for each such variation, but it is too cumbersome for creative work. This is where Galaxy Interactive Tools (IT) come into play. As before, the user can still define some inputs and pass them to the interactive environment and will have access to the results after execution via history. However, the user will also be able to communicate with the tool during its execution by clicking, selecting, typing, and filtering via a GUI. In essence, this replicates the experience of a remote desktop (or VM) user. This provides a reproducible and resumable computing system which can leverage the existing computing and storage infrastructure of the Galaxy instance. - - -## Adding workflows a "human touch" - -ITs can empower users with interactive coding environments, enabling real-time manipulation of code, visualizations, and data analysis and fostering iterative exploration. This enhances the efficiency and reproducibility within these dynamic research landscapes. As a case study, consider a user developing a new machine learning model for the first time, a task involving a series of commands executed on some test data sets. The proposed model needs to be debugged and then optimised with different training parameters. With the integrated Jupyter Notebooks or Rstudio, Galaxy makes such an exploratory research project possible and simple. - -A local deployment or usage of such an online resource would potentially present technical and security concerns and prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The user would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test her code and the model, which costs researchers time and funds. One way of harnessing training data is by processing big raw data sets via a Galaxy workflows, and the results will be in user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be defined as off limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data within the Galaxy storage system. - -Galaxy ITs can be integrated into a "traditional" workflow, with the possibility to switch back to non-interactive tools at any time, thereby adding a "human touch" to the workflow for the steps that require a manual inspection or intervention. Incorporating ITs into a Galaxy workflow will pause the execution of the downstream steps till the user completes the interactive tasks. Processing will continue asynchronously once the interactive step is complete, potentially making use of the results interactively generated by the user as inputs at various downstream steps. - -![Schematic description of a Galaxy workflow involving IT and non-IT components](./figs/workflow.png) - - -## Updates from usegalaxy.eu - -Within the four-year time window between September 2019 and August 2023, our public instance, [live.usegalaxy.eu](https://live.usegalaxy.eu), already had served 21801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of computing resources dedicated to IT executions in our cloud, users usually did not need to wait much after realising their need and spinning up an IT instance: half of the requests began within 21s, whereas 83.2% were ready for research use within 100s. To ensure the fair distribution of limited resources, we implement user- and system-wide quotas on the number of simultaneous IT instances, which might delay the provision of requested ITs, as well as occasional system maintenance or outages. - - -![Histogram of waiting time of all IT job requests as experienced by users, 2019-2023](./figs/waiting_time.png) - - -We think that ITs can provide an opportunity to integrate much more interactive scientific use-cases and visualizations and will increasingly gain popularity in the community. While the popularity of the ITs gradually increased over the past few years, we believe we still have ample room to welcome a higher foot traffic: -![Number of monthly (unique) IT users, 2019-2023](./figs/user_stats.png) - - -In the recent past, we added new ITs to usegalaxy.eu, reaching a now total of 45 ITs available. But adding your own favourite tool is not very different than a typical non-IT integration experience, either. ITs exploit the containerisation technology: adding new ITs require access to a container running the tool, and it suffices to declare the tool as an interactive tool in its wrapper, and define a port through which the tool should be accessed from within the container (see our [quick guide](https://github.com/usegalaxy-eu/operations/blob/main/interactive_tools.md)). - - - diff --git a/content/news/2023-08-15-ITs/figs/flowchart.png b/content/news/2023-09-15-interactive-tools/figs/flowchart.png similarity index 100% rename from content/news/2023-08-15-ITs/figs/flowchart.png rename to content/news/2023-09-15-interactive-tools/figs/flowchart.png diff --git a/content/news/2023-09-15-interactive-tools/figs/how_to_launch.png b/content/news/2023-09-15-interactive-tools/figs/how_to_launch.png new file mode 100644 index 0000000000000000000000000000000000000000..8a4752531aba4489966811dfbf10e2344de4a602 GIT binary patch literal 149756 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The project might be too new and you do not have a well-defined algorithmic approach available yet; you want to try a few ideas out in rapid succession, visualise them in a few ways and see what works, what fails and how the data look. In theory, one could write a tool for each such variation, but it is too cumbersome for creative work. + +This is where Galaxy Interactive Tools (IT) come into play. As before, the user can still define some inputs and pass them to the interactive environment and will have access to the results after execution via history. However, the user will also be able to communicate with the tool during its execution by clicking, selecting, typing, and filtering via a GUI. In essence, this replicates the experience of a remote desktop (or VM) user. They provide a reproducible and resumable computing system, which can leverage the existing computing and storage infrastructure of the Galaxy instance. + + +### Galaxy ITs enhances the efficiency and reproducibility within dynamic research landscapes. +ITs empower users with interactive coding environments, enabling real-time manipulation of code, visualizations, and data analysis and fostering iterative exploration. As a case study, consider a user developing a new machine learning model for the first time, a task involving a execution of a few scripts executed on some (test) datasets, that are modified on the go. The proposed model needs to be debugged and then optimised with different training parameters. With the integrated Virtual Research Environments (VREs) such as Jupyterlab or Rstudio, Galaxy simplifies such exploratory research project ideas. + +### Users only need a browser to access. +A local deployment of such resources could potentially present technical and security concerns, and might even prohibit common usage scenarios due to institutional policies or hardware with restrictive configurations. The users would also likely need to purchase and/or configure a suitable graphics processor to be able to fully test their code and the model, which costs researchers time and funds. As such, ITs lower the barriers to entry. + +### No need for data transfer +One common way of harnessing data for modelling or visualisation is by processing big raw data sets via Galaxy workflows, and the results will already be in the user history. Transfer of training data would also require the user to export this data to a local computing resource, which might trigger high network traffic due to its sheer size or might even be off-limits by local data privacy regulations. ITs alleviate all of these issues as the users would immediately gain access to a pre-configured system with access to their data stored within Galaxy histories or shared libraries. + + +### Launching an IT is very simple + +![Snapshots of steps for user to access an IT instance](./figs/how_to_launch.png) + + +### Adding workflows a "human touch" +Just like any other tool, Galaxy ITs can be integrated into a "traditional" workflow, with the possibility to switch back to non-interactive tools at any time, thereby adding a "human touch" to the workflow for the steps that require a manual inspection or intervention. Incorporating ITs into a Galaxy workflow will pause the execution of the downstream steps till the user completes the interactive tasks. Processing will continue asynchronously once the interactive step is complete, potentially making use of the results interactively generated by the user as inputs at various downstream steps. + +![Schematic description of a Galaxy workflow involving IT and non-IT components](./figs/workflow.png) + + + +## Updates from [usegalaxy.eu](https://usegalaxy.eu) + +Within the four-year time window between September 2019 and August 2023, our public instance, [live.usegalaxy.eu](https://live.usegalaxy.eu), already served 21801 IT jobs executions. A coarse look into the job statistics suggests that the typical availability of the system was not very different than that of a user-owned local system. Thanks to the availability of computing resources dedicated to IT executions in our cloud, users usually did not need to wait much after realising their need and spinning up an IT instance: half of the requests began within 21s, whereas 83.2% were ready for research use within 100s. To ensure the fair distribution of limited resources, we implement user- and system-wide quotas on the number of simultaneous IT instances, which suspend (or postpone) the provision of additional instances if too many were requested at once. + +![Histogram of waiting time of all IT job requests as experienced by users, 2019-2023](./figs/waiting_time.png) + + +We think that ITs provide an opportunity to integrate much more interactive scientific use-cases and visualizations and will increasingly gain popularity in the community. While the popularity of the ITs gradually increased over the past few years, we believe we still have ample room to welcome a higher foot traffic. + +![Number of monthly (unique) IT users, 2019-2023](./figs/user_stats.png) + +### New ITs +In the recent past, we added new ITs to [usegalaxy.eu](usegalaxy.eu), reaching now a total of 46 ITs available. But adding your own favourite tool is not very different than a typical non-IT integration experience, either. ITs exploit the containerisation technology: adding new ITs require access to a container running the tool. It then suffices to declare the tool as an interactive tool in its wrapper, and define a port, through which the tool should be accessed from within the container (see our [quick guide](https://github.com/usegalaxy-eu/operations/blob/main/interactive_tools.md) or a comprehensive [admin tutorial](https://training.galaxyproject.org/training-material/topics/admin/tutorials/interactive-tools/tutorial.html)). + + + From a55da9bf205c76cd387e87f3e9bcb6145320bb10 Mon Sep 17 00:00:00 2001 From: Ubuntu <19364610+tuncK@users.noreply.github.com> Date: Fri, 15 Sep 2023 14:03:02 +0000 Subject: [PATCH 9/9] fix broken URL --- content/news/2023-09-15-interactive-tools/index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/news/2023-09-15-interactive-tools/index.md b/content/news/2023-09-15-interactive-tools/index.md index 8a71d9a101..f53cbc608f 100644 --- a/content/news/2023-09-15-interactive-tools/index.md +++ b/content/news/2023-09-15-interactive-tools/index.md @@ -70,7 +70,7 @@ We think that ITs provide an opportunity to integrate much more interactive scie ![Number of monthly (unique) IT users, 2019-2023](./figs/user_stats.png) ### New ITs -In the recent past, we added new ITs to [usegalaxy.eu](usegalaxy.eu), reaching now a total of 46 ITs available. But adding your own favourite tool is not very different than a typical non-IT integration experience, either. ITs exploit the containerisation technology: adding new ITs require access to a container running the tool. It then suffices to declare the tool as an interactive tool in its wrapper, and define a port, through which the tool should be accessed from within the container (see our [quick guide](https://github.com/usegalaxy-eu/operations/blob/main/interactive_tools.md) or a comprehensive [admin tutorial](https://training.galaxyproject.org/training-material/topics/admin/tutorials/interactive-tools/tutorial.html)). +In the recent past, we added new ITs to [usegalaxy.eu](https://usegalaxy.eu), reaching now a total of 46 ITs available. But adding your own favourite tool is not very different than a typical non-IT integration experience, either. ITs exploit the containerisation technology: adding new ITs require access to a container running the tool. It then suffices to declare the tool as an interactive tool in its wrapper, and define a port, through which the tool should be accessed from within the container (see our [quick guide](https://github.com/usegalaxy-eu/operations/blob/main/interactive_tools.md) or a comprehensive [admin tutorial](https://training.galaxyproject.org/training-material/topics/admin/tutorials/interactive-tools/tutorial.html)).