V2.0.7

CHANGES.txt

@@ -1,7 +1,8 @@
-* V2.0.6.1
+* V2.0.7
   * fixed problem in module:program to return error when pre/post processing fails
   * added more tests to catch bugs in program workflows
   * fixed a new bug in the program workflow
+  * updated docs
 
 * V2.0.6
   * improved module:program meta to be able to use global CMD run_time parameters

README.md

@@ -78,7 +78,7 @@ with any compatible data set on any compatible platform:
 ```bash
 python3 -m pip install ck
 
-ck pull repo --url=https://github.com/ctuning/ck-crowdtuning
+ck pull repo:octoml@mlops
 
 ck ls program:*susan*
 
@@ -89,19 +89,22 @@ ck detect soft --tags=compiler,llvm
 
 ck show env --tags=compiler
 
-ck compile program:cbench-automotive-susan --speed
+ck compile program:image-corner-detection --speed
 
-ck run program:cbench-automotive-susan --cmd_key=corners --repeat=1 --env.MY_ENV=123 --env.TEST=xyz
+ck run program:image-corner-detection --repeat=1 --env.MY_ENV=123 --env.TEST=xyz
 ```
 
 You can check output of this program in the following directory:
 ```bash
-cd `ck find program:cbench-automotive-susan`/tmp
-ls -l
+cd `ck find program:image-corner-detection`/tmp
+ls
 
-tmp-output.tmp - image with detected corners (rename to ppm to view it)
+processed-image.pgm
 ```
 
+You can now view this image with detected corners.
+
+
 Check [CK docs](https://ck.readthedocs.io/en/latest/src/introduction.html) for further details.
 
 ### MLPerf benchmark workflows

ck/kernel.py

@@ -27,7 +27,7 @@
 
 
 # We use 3 digits for the main (released) version and 4th digit for development revision
-__version__ = "2.0.6.1"
+__version__ = "2.0.7"
 # Do not use characters (to detect outdated version)!
 
 # Import packages that are global for the whole kernel

docs/mlperf-automation/README.md

@@ -8,7 +8,7 @@ using the open-source [CK workflow framework](https://github.com/ctuning/ck).
 * [Install CK framework](tools/ck.md)
   * [Install CK virtual environment (optional)](tools/ck-venv.md)
   * [Use adaptive CK container](tools/ck-docker.md)
-* [Prepare and run native MLPerf](tasks/README.md)
+* [**Prepare and run native MLPerf**](tasks/README.md)
 * [Analyze MLPerf inference results](results/README.md)
   * [Example of CK dashboards for ML Systems DSE](results/ck-dashboard.md)
 * [Reproduce MLPerf results and DSE](reproduce/README.md)

docs/src/first-steps.md

@@ -32,7 +32,7 @@ if you encounter any problem or have questions.
 Now you can pull CK repo with the universal program workflow. 
 
 ```bash
-ck pull repo --url=https://github.com/ctuning/ck-crowdtuning
+ck pull repo:octoml@mlops
 ```
 
 CK will automatically pull all required CK repositories with different automation actions, benchmarks, and datasets in the CK format.
@@ -53,34 +53,34 @@ You can now see all shared program workflows in the CK format:
 ck ls program
 ```
 
-You can find and investigate the CK format for a given program (such as *cbench-automotive-susan*) as follows:
+You can find and investigate the CK format for a given program (such as *image-corner-detection*) as follows:
 
 ```bash
-ck find program:cbench-automotive-susan
+ck search program --tags=demo,image,corner-detection
 ```
 
 You can see the CK meta description of this program from the command line as follows:
 ```bash
-ck load program:cbench-automotive-susan
-ck load program:cbench-automotive-susan --min
+ck load program:image-corner-detection
+ck load program:image-corner-detection --min
 ```
 
-It may be more convenient to check the structure of this entry at [GitHub](https://github.com/ctuning/ctuning-programs/tree/master/program/cbench-automotive-susan) with all the sources and meta-descriptions.
+It may be more convenient to check the structure of this entry at [GitHub](https://github.com/ctuning/ck-ml/tree/master/program/image-corner-detection) with all the sources and meta-descriptions.
 
-You can also see the CK JSON meta description for this CK program entry [here](https://github.com/ctuning/ctuning-programs/blob/master/program/cbench-automotive-susan/.cm/meta.json).
+You can also see the CK JSON meta description for this CK program entry [here](https://github.com/ctuning/ck-ml/blob/master/program/image-corner-detection/.cm/meta.json).
 When you invoke automation actions in the CK module *program*, the automation code will read this meta description and perform actions for different programs accordingly.
 
 ## Invoke CK automation actions
 
 You can now try to compile this program on your platform:
 
 ```bash
-ck compile program:cbench-automotive-susan --speed
+ck compile program:image-corner-detection --speed
 ```
 
-CK will invoke the function "compile" in the module "program" (you can see it at [GitHub](https://github.com/ctuning/ck-autotuning/blob/master/module/program/module.py#L3551)
+CK will invoke the function "compile" in the module "program" (you can see it at [GitHub](https://github.com/ctuning/ck/blob/master/ck/repo/module/program/module.py#L3594)
 or you can find the source code of this CK module locally using "ck find module:program"),
-read the JSON meta of *cbench-automotive-susan*, and perform a given action.
+read the JSON meta of *image-corner-detection*, and perform a given action.
 
 Note, that you can obtain all flags for a given action as follows:
 ```bash
@@ -122,7 +122,7 @@ Such approach allows us to separate CK workflows from hardwired dependencies and
 
 You can now run this program as follows:
 ```bash
-ck run program:cbench-automotive-susan
+ck run program:image-corner-detection
 ```
 
 While running the program, CK will collect and unify various characteristics (execution time, code size, etc).
@@ -131,7 +131,7 @@ Furthermore, we can continue improving this universal program workflow to monito
 performing statistical analysis of collected characteristics, validating outputs, etc:
 
 ```bash
-ck benchmark program:cbench-automotive-susan --repetitions=4 --record --record_uoa=ck_entry_to_record_my_experiment
+ck benchmark program:image-corner-detection --repetitions=4 --record --record_uoa=ck_entry_to_record_my_experiment
 ck replay experiment:ck_entry_to_record_my_experiment
 ```
 
@@ -216,7 +216,7 @@ You can even participate in [crowd-tuning](https://cKnowledge.org/rpi-crowd-tuni
 of multiple programs and data sets across diverse platforms:.
 
 ```
-ck crowdtune program:cbench-automotive-susan
+ck crowdtune program:image-corner-detection
 ck crowdtune program
 ```
 
@@ -230,15 +230,15 @@ You can also run CK automation actions directly from any Python (2.7+ or 3.3+) u
 ```python
 import ck.kernel as ck
 
-# Equivalent of "ck compile program:cbench-automotive-susan --speed"
-r=ck.access({'action':'compile', 'module_uoa':'program', 'data_uoa':'cbench-automotive-susan', 
+# Equivalent of "ck compile program:image-corner-detection --speed"
+r=ck.access({'action':'compile', 'module_uoa':'program', 'data_uoa':'image-corner-detection', 
              'speed':'yes'})
 if r['return']>0: return r # unified error handling 
 
 print (r)
 
-# Equivalent of "ck run program:cbench-automotive-susan --env.OMP_NUM_THREADS=4
-r=ck.access({'action':'run', 'module_uoa':'program', 'data_uoa':'cbench-automotive-susan', 
+# Equivalent of "ck run program:image-corner-detection --env.OMP_NUM_THREADS=4
+r=ck.access({'action':'run', 'module_uoa':'program', 'data_uoa':'image-corner-detection', 
              'env':{'OMP_NUM_THREADS':4}})
 if r['return']>0: return r # unified error handling 
 
@@ -247,58 +247,11 @@ print (r)
 ```
 
 
-## Try the CK ML workflow
+## Try the CK MLPerf workflow
 
-You can now try a more complex example with TensorFlow.
-You should pull the related CK repository and install the prebuilt version of TensorFlow CPU via CK:
-
-```bash
-ck pull repo:ck-tensorflow
-ck install package --tags=lib,tensorflow,vcpu,vprebuilt
-```
-
-Check that it was successfully installed:
-
-```bash
-ck show env --tags=lib,tensorflow
-```
-
-You can find a path to a given entry describing this TF installation as follows:
-```bash
-ck find env:{env UID from above list}
-```
-
-Run the CK virtual environment and test TF:
-```bash
-ck virtual env --tags=lib,tensorflow
-ipython
-> import tensorflow as tf
->
-```
-
-You can try to run the CK image classification workflow example using the installed TF:
-
-```bash
-ck run program:tensorflow --cmd_key=classify
-```
-
-You can even try to rebuild TensorFlow via CK for your platform with CUDA:
-
-```bash
-ck install package:lib-tensorflow-1.7.0-cuda
-```
-
-CK will attempt detect your CUDA compiler and related libraries and tools
-including Java, Basel, and will then try to rebuild TF. 
-Note that you may still need to install some extra dependencies yourself
-as described in this [readme](https://github.com/ctuning/ck-tensorflow#prerequisites-for-ubuntu).
-
-
-You can also try to run ML workflows from the [MLPerf benchmarking initiative](https://mlperf.org)
-using this [CK MLPerf repository](https://github.com/ctuning/ck-mlperf).
-
-Finally, you can try our recent [MLPerf automation demo](https://cKnowledge.io/test)
-to automate submissions and validations of MLPerf results.
+Feel free to try more complex CK MLPerf workflows to benchmark ML Systems 
+across different models, data sets, frameworks and hardware
+as described [here](https://github.com/ctuning/ck/blob/master/docs/mlperf-automation/README.md).
 
 
 ## Further information
@@ -308,17 +261,8 @@ of reusable components with common automation actions and unified meta descripti
 The goal is to promote artifact sharing and reuse while gradually substituting and unifying 
 all tedious and repetitive research tasks!
 
-You can find shared CK repositories, components, automation actions, and live scoreboards 
-at the open [cKnowledge.io platform](https://cKnowledge.io).
-
-You can also check how the universal CK program workflow was successfully reused in 
-[different projects](https://doi.org/10.5281/zenodo.4005588)
-including the [ACM REQUEST tournaments](http://cKnowledge.org/request) to collaboratively co-design SW/HW stack for deep learning
-([Report about results of the 1st ReQuEST-ASPLOS'18 tournament and next steps](https://portalparts.acm.org/3230000/3229762/fm/frontmatter.pdf)
-and [ACM ReQuEST-ASPLOS'18 proceedings with artifact descriptions](https://doi.org/10.1145/3229762))
-and [reproducible quantum tournaments](https://cKnowledge.org/quantum).
 
-Finally, check this [guide](how-to-contribute.md) to learn how to add your own repositories, workflows, and components!
+Please check this [guide](how-to-contribute.md) to learn how to add your own repositories, workflows, and components!
 
 
 ## Contact the CK community

docs/src/introduction.md

@@ -120,24 +120,24 @@ on any platform, and then create a copy of the ***CK program component***:
 ```bash
 pip install ck
 
-ck pull repo --url=https://github.com/ctuning/ck-crowdtuning
+ck pull repo:octoml@mlops
 
 ck search dataset --tags=jpeg
 
-ck search program:cbench-automotive-*
+ck search program:image*
 
-ck find program:cbench-automotive-susan
+ck find program:image-corner-detection
 
-ck load program:cbench-automotive-susan
+ck load program:image-corner-detection --min
 
 ck help program
 
-ck compile program:cbench-automotive-susan --speed
-ck run program:cbench-automotive-susan --env.OMP_NUM_THREADS=4
+ck compile program:image-corner-detection --speed
+ck run program:image-corner-detection --env.OMP_NUM_THREADS=4
 
 ck run program --help
 
-ck cp program:cbench-automotive-susan local:program:new-program-workflow
+ck cp program:image-corner-detection local:program:new-program-workflow
 
 ck find program:new-program-workflow
 
@@ -148,7 +148,7 @@ ck replay experiment:my-test
 ```
 
 The [CK program module](https://cKnowledge.io/c/module/program) describes dependencies on software detection plugins 
-and meta packages usin simple tags with version ranges that the community has to agree on:
+and meta packages using simple tags with version ranges that the community has agreed on:
 
 ```json
  {
@@ -173,15 +173,15 @@ CK also provides a Python library with a simple API that can be easily used in w
 ```python
 import ck.kernel as ck
 
-# Equivalent of "ck compile program:cbench-automotive-susan --speed"
-r=ck.access({'action':'compile', 'module_uoa':'program', 'data_uoa':'cbench-automotive-susan', 
+# Equivalent of "ck compile program:image-corner-detection --speed"
+r=ck.access({'action':'compile', 'module_uoa':'program', 'data_uoa':'image-corner-detection', 
              'speed':'yes'})
 if r['return']>0: return r # unified error handling 
 
 print (r)
 
-# Equivalent of "ck run program:cbench-automotive-susan --env.OMP_NUM_THREADS=4
-r=ck.access({'action':'run', 'module_uoa':'program', 'data_uoa':'cbench-automotive-susan', 
+# Equivalent of "ck run program:image-corner-detection --env.OMP_NUM_THREADS=4
+r=ck.access({'action':'run', 'module_uoa':'program', 'data_uoa':'image-corner-detection', 
              'env':{'OMP_NUM_THREADS':4}})
 if r['return']>0: return r # unified error handling 
 
@@ -270,15 +270,6 @@ and [ACM artifact review and badging](https://www.acm.org/publications/policies/
 ![cKnowledge platform concept](../static/platform.png)
 
 
-You can learn more about the CK project from [CK presentations and white papers](https://github.com/ctuning/ck/wiki/Publications).
-
-*Even though the CK technology is used [in production](https://cKnowledge.org/partners.html) for more than 5 years,
-it is still a proof-of-concept prototype requiring further improvements
-and standardization. Depending on the available resources, we plan
-to develop a new, backward-compatible and more user-friendly version - please [get in touch](https://cKnowledge.org/contacts.html) if you are
-interested to know more!*
-
-
 
 
 

docs/src/portable-workflows.md

@@ -123,10 +123,10 @@ A user describes dependencies on CK packages in the CK program meta as well as c
 
 Examples:
 ```bash
-ck pull repo:ck-crowdtuning
+ck pull repo:octoml@mlops
 
-ck compile program:cbench-automotive-susan --speed
-ck run program:cbench-automotive-susan --repeat=1 --env.OMP_NUM_THREADS=4
+ck compile program:image-corner-detection --speed
+ck run program:image-corner-detection --repeat=1 --env.OMP_NUM_THREADS=4
 
 ```
 
@@ -140,7 +140,7 @@ This module records all resolved dependencies, inputs and outputs when running a
 thus allowing to preserve experiments with all the provenance and replay them later on the same or different machine:
 
 ```bash
-ck benchmark program:cbench-automotive-susan --record --record_uoa=my_experiment
+ck benchmark program:image-corner-detection --record --record_uoa=my_experiment
 
 ck find experiment:my_experiment
 

docs/src/typical-usage.md

@@ -127,26 +127,26 @@ Since CK concept is about reusing and extending existing components with a commo
 we suggest you to look at [this index]( https://cKnowledge.io/programs ) of shared CK programs
 in case someone have already shared a CK workflows for the same or similar program!
 
-If you found a similar program, for example "cbench-automotive-susan" 
+If you found a similar program, for example "image-corner-detection" 
 you can create a working copy of this program in your new CK repository
 for further editing as follows:
 
 ```bash
 ck pull repo:ctuning-programs 
-ck cp program:cbench-automotive-susan my-new-repo:program:my-copy-of-cbench-automotive-susan
+ck cp program:image-corner-detection my-new-repo:program:my-copy-of-image-corner-detection
 ```
 
-You now have a working copy of the CK "cbench-automotive-susan" program entry in your new repository 
+You now have a working copy of the CK "image-corner-detection" program entry in your new repository 
 that contains sources and the CK meta information about how to compile and run this program:
 
 ```bash
-ck compile program:my-copy-of-cbench-automotive-susan --speed
-ck run program:my-copy-of-cbench-automotive-susan
+ck compile program:my-copy-of-image-corner-detection --speed
+ck run program:my-copy-of-image-corner-detection
 ```
 
 You can find and explore the new CK entry from command line as follows:
 ```bash
-ck find program:my-copy-of-cbench-automotive-susan
+ck find program:my-copy-of-image-corner-detection
 ```
 
 You will see the following files in this directory: