remove workflow server branch

Dockerfile

@@ -13,16 +13,20 @@ LABEL \
     org.label-schema.vcs-url="https://github.com/slub/ocrd_controller" \
     org.label-schema.build-date=$BUILD_DATE
 
-# keep PREFIX and VIRTUAL_ENV from ocrd/all
+# keep PREFIX and VIRTUAL_ENV from ocrd/all (i.e. /usr/local)
 # but export them for COPY etc
 ENV PREFIX=$PREFIX
 ENV VIRTUAL_ENV=$VIRTUAL_ENV
 ENV HOME=/
 
 # must mount a host-side directory for ocrd-resources
 VOLUME /models
+# override XDG_DATA_HOME from ocrd/all (i.e. /usr/local/share)
 ENV XDG_DATA_HOME=/models
+# override TESSDATA_PREFIX from ocrd/all
 ENV TESSDATA_PREFIX=$XDG_DATA_HOME/ocrd-resources/ocrd-tesserocr-recognize
+RUN mkdir $TESSDATA_PREFIX
+RUN mv /usr/local/share/tessdata/*.traineddata $TESSDATA_PREFIX
 # must mount a host-side directory for ocrd/resource.yml
 VOLUME /config
 ENV XDG_CONFIG_HOME=/config
@@ -55,16 +59,6 @@ EXPOSE 22
 WORKDIR /build
 
 RUN ln /usr/bin/python3 /usr/bin/python
-# prevent make from updating the git modules automatically
-ENV NO_UPDATE=1
-#
-# update to core#652 (workflow server)
-RUN git -C core fetch origin pull/652/head:workflow-server
-RUN git -C core checkout workflow-server
-RUN for venv in $VIRTUAL_ENV $VIRTUAL_ENV/sub-venv/*; do . $venv/bin/activate && make -C core install PIP_INSTALL="pip install -e"; done
-# update ocrd-import
-RUN git -C workflow-configuration pull origin master
-RUN . $VIRTUAL_ENV/bin/activate && make -C workflow-configuration install
 # configure writing to ocrd.log for profiling
 COPY ocrd_logging.conf /etc
 

README.md

@@ -15,7 +15,6 @@
    * [Starting and mounting](#starting-and-mounting)
    * [General management](#general-management)
    * [Processing](#processing)
-     * [Workflow server](#workflow-server)
    * [Data transfer](#data-transfer)
    * [Parallel options](#parallel-options)
    * [Logging](#logging)
@@ -86,19 +85,6 @@ Subsequently, you can use these models on your `DATA` files:
     # or equivalently:
     ssh -p 8022 ocrd@controller "ocrd-tesserocr-recognize -m some-document/mets.xml -P segmentation_level region -P model Fraktur"
 
-#### Workflow server
-
-Currently, the OCR-D installation hosts an implementation of the [workflow server](https://github.com/OCR-D/core/pull/652),
-which can be used to significantly reduce initialization overhead when running the same workflow repeatedly
-on many workspaces (especially with GPU-bound processors):
-
-    ssh -p 8022 ocrd@controller "ocrd workflow server -j 4 -t 120 'tesserocr-recognize -P segmentation_level region -P model Fraktur'"
-
-And subsequently:
-
-    ssh -p 8022 ocrd@controller "ocrd workflow client process -m some-document/mets.xml"
-    ssh -p 8022 ocrd@controller "ocrd workflow client process -m other-document/mets.xml"
-
 ### Data transfer
 
 If your data files cannot be directly mounted on the host (not even as a network share),
@@ -122,7 +108,6 @@ For parallel processing, you can either
   - issueing parallel commands – 
     * via basic shell scripting
     * via [ocrd-make](https://bertsky.github.io/workflow-configuration) calls
-    * via [`ocrd workflow server --processes`](#workflow-server) concurrency
 - run processes on multiple controllers.
 
 Note: internally, `WORKERS` is implemented as a (GNU parallel-based) semaphore
@@ -137,5 +122,5 @@ All logs are accumulated on standard output, which can be inspected via Docker:
 
 ## See also
 
-- [Meta-repo for integration of Kitodo.Production with OCR-D in Docker](https://github.com/markusweigelt/kitodo_production_ocrd)
-- [Sister component OCR-D Manager](https://github.com/markusweigelt/ocrd_manager)
+- [Meta-repo for integration of Kitodo.Production with OCR-D in Docker](https://github.com/slub/kitodo_production_ocrd)
+- [Sister component OCR-D Manager](https://github.com/slub/ocrd_manager)