Run on Kubernetes cluster
Analytics Zoo hyperzoo image has been built to easily run applications on Kubernetes cluster. The details of pre-installed packages and usage of the image will be introduced in this page.
- Launch pre-built hyperzoo image
- Run Analytics Zoo examples on k8s
- Run Analytics Zoo Jupyter Notebooks on remote Spark cluster or k8s
- Launch Analytics Zoo cluster serving
Launch pre-built hyperzoo image
Prerequisites
- Runnable docker environment has been set up.
- A running Kubernetes cluster is prepared. Also make sure the permission of
kubectl
to create, list and delete pod.
Launch pre-built hyperzoo k8s image
- Pull an Analytics Zoo hyperzoo image from dockerhub:
sudo docker pull intelanalytics/hyper-zoo:latest
- Speed up pulling image by adding mirrors
To speed up pulling the image from dockerhub in China, add a registry's mirror. For Linux OS (CentOS, Ubuntu etc), if the docker version is higher than 1.12, config the docker daemon. Edit /etc/docker/daemon.json
and add the registry-mirrors key and value:
{
"registry-mirrors": ["https://<my-docker-mirror-host>"]
}
For example, add the ustc mirror in China.
{
"registry-mirrors": ["https://docker.mirrors.ustc.edu.cn"]
}
Flush changes and restart docker：
sudo systemctl daemon-reload
sudo systemctl restart docker
If your docker version is between 1.8 and 1.11, find the docker configuration which location depends on the operation system. Edit and add DOCKER_OPTS="--registry-mirror=https://<my-docker-mirror-host>"
. Restart docker sudo service docker restart
.
If you would like to speed up pulling this image on MacOS or Windows, find the docker setting and config registry-mirrors section by specifying mirror host. Restart docker.
Then pull the image. It will be faster.
sudo docker pull intelanalytics/hyper-zoo:latest
- Launch a k8s client container:
Please note the two different containers: client container is for user to submit zoo jobs from here, since it contains all the required env and libs except hadoop/k8s configs; executor container is not need to create manually, which is scheduled by k8s at runtime.
sudo docker run -itd --net=host \
-v /etc/kubernetes:/etc/kubernetes \
-v /root/.kube:/root/.kube \
intelanalytics/hyper-zoo:latest bash
Note. To launch the client container, -v /etc/kubernetes:/etc/kubernetes:
and -v /root/.kube:/root/.kube
are required to specify the path of kube config and installation.

- NotebookToken value "your-token" is a user specified string.
- http_proxy is to specify http proxy.
- https_proxy is to specify https proxy.
- RUNTIME_SPARK_MASTER is to specify spark master, which should be
k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>
orspark://<spark-master-host>:<spark-master-port>
. - RUNTIME_K8S_SERVICE_ACCOUNT is service account for driver pod. Please refer to k8s RBAC.
- RUNTIME_K8S_SPARK_IMAGE is the k8s image.
- RUNTIME_PERSISTENT_VOLUME_CLAIM is to specify volume mount. We are supposed to use volume mount to store or receive data. Get ready with Kubernetes Volumes.
- RUNTIME_DRIVER_HOST is to specify driver localhost (only required when submit jobs as kubernetes client mode).
- RUNTIME_DRIVER_PORT is to specify port number (only required when submit jobs as kubernetes client mode).
- Other environment variables are for spark configuration setting. The default values in this image are listed above. Replace the values as you need.
Once the container is created, launch the container by:
sudo docker exec -it <containerID> bash
Then you may see it shows:
root@[hostname]:/opt/spark/work-dir#
/opt/spark/work-dir
is the spark work path.
Note: The /opt
directory contains:
- download-analytics-zoo.sh is used for downloading Analytics-Zoo distributions.
- start-notebook-spark.sh is used for starting the jupyter notebook on standard spark cluster.
- start-notebook-k8s.sh is used for starting the jupyter notebook on k8s cluster.
- analytics-zoo-x.x-SNAPSHOT is
ANALYTICS_ZOO_HOME
, which is the home of Analytics Zoo distribution. - analytics-zoo-examples directory contains downloaded python example code.
- jdk is the jdk home.
- spark is the spark home.
- redis is the redis home.
Run Analytics Zoo examples on k8s
Launch an Analytics Zoo python example on k8s
Here is a sample for submitting the python anomalydetection example on cluster mode.
${SPARK_HOME}/bin/spark-submit \
--master ${RUNTIME_SPARK_MASTER} \
--deploy-mode cluster \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=${RUNTIME_K8S_SERVICE_ACCOUNT} \
--name analytics-zoo \
--conf spark.kubernetes.container.image=${RUNTIME_K8S_SPARK_IMAGE} \
--conf spark.executor.instances=${RUNTIME_EXECUTOR_INSTANCES} \
--conf spark.kubernetes.driver.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.options.claimName=${RUNTIME_PERSISTENT_VOLUME_CLAIM} \
--conf spark.kubernetes.driver.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.mount.path=/zoo \
--conf spark.kubernetes.executor.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.options.claimName=${RUNTIME_PERSISTENT_VOLUME_CLAIM} \
--conf spark.kubernetes.executor.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.mount.path=/zoo \
--conf spark.kubernetes.driver.label.<your-label>=true \
--conf spark.kubernetes.executor.label.<your-label>=true \
--executor-cores ${RUNTIME_EXECUTOR_CORES} \
--executor-memory ${RUNTIME_EXECUTOR_MEMORY} \
--total-executor-cores ${RUNTIME_TOTAL_EXECUTOR_CORES} \
--driver-cores ${RUNTIME_DRIVER_CORES} \
--driver-memory ${RUNTIME_DRIVER_MEMORY} \
--properties-file ${ANALYTICS_ZOO_HOME}/conf/spark-analytics-zoo.conf \
--py-files ${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-python-api.zip,/opt/analytics-zoo-examples/python/anomalydetection/anomaly_detection.py \
--conf spark.driver.extraJavaOptions=-Dderby.stream.error.file=/tmp \
--conf spark.sql.catalogImplementation='in-memory' \
--conf spark.driver.extraClassPath=${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-jar-with-dependencies.jar \
--conf spark.executor.extraClassPath=${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-jar-with-dependencies.jar \
file:///opt/analytics-zoo-examples/python/anomalydetection/anomaly_detection.py \
--input_dir /zoo/data/nyc_taxi.csv
Options:
- --master: the spark mater, must be a URL with the format
k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>
. - --deploy-mode: submit application in cluster mode or client mode.
- --name: the Spark application name.
- --conf: require to specify k8s service account, container image to use for the Spark application, driver volumes name and path, label of pods, spark driver and executor configuration, etc. check the argument settings in your environment and refer to the spark configuration page and spark on k8s configuration page for more details.
- --properties-file: the customized conf properties.
- --py-files: the extra python packages is needed.
- file://: local file path of the python example file in the client container.
- --input_dir: input data path of the anomaly detection example. The data path is the mounted filesystem of the host. Refer to more details by Kubernetes Volumes.
Launch an Analytics Zoo scala example on k8s
Here is a sample for submitting the scala anomalydetection example on cluster mode
${SPARK_HOME}/bin/spark-submit \
--master ${RUNTIME_SPARK_MASTER} \
--deploy-mode cluster \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=${RUNTIME_K8S_SERVICE_ACCOUNT} \
--name analytics-zoo \
--conf spark.kubernetes.container.image=${RUNTIME_K8S_SPARK_IMAGE} \
--conf spark.executor.instances=${RUNTIME_EXECUTOR_INSTANCES} \
--conf spark.kubernetes.driver.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.options.claimName=${RUNTIME_PERSISTENT_VOLUME_CLAIM} \
--conf spark.kubernetes.driver.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.mount.path=/zoo \
--conf spark.kubernetes.executor.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.options.claimName=${RUNTIME_PERSISTENT_VOLUME_CLAIM} \
--conf spark.kubernetes.executor.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.mount.path=/zoo \
--conf spark.kubernetes.driver.label.<your-label>=true \
--conf spark.kubernetes.executor.label.<your-label>=true \
--executor-cores ${RUNTIME_EXECUTOR_CORES} \
--executor-memory ${RUNTIME_EXECUTOR_MEMORY} \
--total-executor-cores ${RUNTIME_TOTAL_EXECUTOR_CORES} \
--driver-cores ${RUNTIME_DRIVER_CORES} \
--driver-memory ${RUNTIME_DRIVER_MEMORY} \
--properties-file ${ANALYTICS_ZOO_HOME}/conf/spark-analytics-zoo.conf \
--py-files ${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-python-api.zip \
--conf spark.driver.extraJavaOptions=-Dderby.stream.error.file=/tmp \
--conf spark.sql.catalogImplementation='in-memory' \
--conf spark.driver.extraClassPath=${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-jar-with-dependencies.jar \
--conf spark.executor.extraClassPath=${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-jar-with-dependencies.jar \
--class com.intel.analytics.zoo.examples.anomalydetection.AnomalyDetection \
${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-python-api.zip \
--inputDir /zoo/data
Options:
- --master: the spark mater, must be a URL with the format
k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>
. - --deploy-mode: submit application in cluster mode or client mode.
- --name: the Spark application name.
- --conf: require to specify k8s service account, container image to use for the Spark application, driver volumes name and path, label of pods, spark driver and executor configuration, etc. check the argument settings in your environment and refer to the spark configuration page and spark on k8s configuration page for more details.
- --properties-file: the customized conf properties.
- --py-files: the extra python packages is needed.
- --class: scala example class name.
- --input_dir: input data path of the anomaly detection example. The data path is the mounted filesystem of the host. Refer to more details by Kubernetes Volumes.
Access logs to check result and clear pods
When application is running, it’s possible to stream logs on the driver pod:
$ kubectl logs <spark-driver-pod>
To check pod status or to get some basic information around pod using:
$ kubectl describe pod <spark-driver-pod>
You can also check other pods using the similar way.
After finishing running the application, deleting the driver pod:
$ kubectl delete <spark-driver-pod>
Or clean up the entire spark application by pod label:
$ kubectl delete pod -l <pod label>
Run Analytics Zoo Jupyter Notebooks on remote Spark cluster or k8s
When started a Docker container with specified argument RUNTIME_SPARK_MASTER=k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>
or RUNTIME_SPARK_MASTER=spark://<spark-master-host>:<spark-master-port>
, the container will submit jobs to k8s cluster or spark cluster if you use $RUNTIME_SPARK_MASTER as url of spark master.
You may also need to specify NotebookPort=<your-port>
and NotebookToken=<your-token>
to start Jupyter Notebook on the specified port and bind to <IP_ADDRESS>.
To start the Jupyter notebooks on remote spark cluster, please use RUNTIME_SPARK_MASTER=spark://<spark-master-host>:<spark-master-port>
, and attach the client container with command: “docker exec -it <container-id>
bash”, then run the shell script: “/opt/start-notebook-spark.sh”, this will start a Jupyter notebook instance on local container, and each tutorial in it will be submitted to the specified spark cluster. User can access the notebook with url http://<local-ip>:<your-port>
in a preferred browser, and also need to input required token with <your-token>
to browse and run the tutorials of Analytics Zoo. Each tutorial will run driver part code in local container and run executor part code on spark cluster.
To start the Jupyter notebooks on Kubernetes cluster, please use RUNTIME_SPARK_MASTER=k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>
, and attach the client container with command: “docker exec -it <container-id>
bash”, then run the shell script: “/opt/start-notebook-k8s.sh”, this will start a Jupyter notebook instance on local container, and each tutorial in it will be submitted to the specified kubernetes cluster. User can access the notebook with url http://<local-ip>:<your-port>
in a preferred browser, and also need to input required token with <your-token>
to browse and run the tutorials of Analytics Zoo. Each tutorial will run driver part code in local container and run executor part code in dynamic allocated spark executor pods on k8s cluster.
Launch Analytics Zoo cluster serving
To run Analytics Zoo cluster serving in hyper-zoo client container and submit the streaming job on K8S cluster, you may need to specify arguments RUNTIME_SPARK_MASTER=k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port>
, and you may also need to mount volume from host to container to load model and data files.
You can leverage an existing Redis instance/cluster, or you can start one in the client container:
${REDIS_HOME}/src/redis-server ${REDIS_HOME}/redis.conf > ${REDIS_HOME}/redis.log &
And you can check the running logs of redis:
cat ${REDIS_HOME}/redis.log
Before starting the cluster serving job, please also modify the config.yaml to configure correct path of the model and redis host url, etc.
nano /opt/cluster-serving/config.yaml
After that, you can start the cluster-serving job and submit the streaming job on K8S cluster:
${SPARK_HOME}/bin/spark-submit \
--master ${RUNTIME_SPARK_MASTER} \
--deploy-mode cluster \
--conf spark.kubernetes.authenticate.driver.serviceAccountName=${RUNTIME_K8S_SERVICE_ACCOUNT} \
--name analytics-zoo \
--conf spark.kubernetes.container.image=${RUNTIME_K8S_SPARK_IMAGE} \
--conf spark.executor.instances=${RUNTIME_EXECUTOR_INSTANCES} \
--conf spark.kubernetes.driver.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.options.claimName=${RUNTIME_PERSISTENT_VOLUME_CLAIM} \
--conf spark.kubernetes.driver.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.mount.path=/zoo \
--conf spark.kubernetes.executor.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.options.claimName=${RUNTIME_PERSISTENT_VOLUME_CLAIM} \
--conf spark.kubernetes.executor.volumes.persistentVolumeClaim.${RUNTIME_PERSISTENT_VOLUME_CLAIM}.mount.path=/zoo \
--conf spark.kubernetes.driver.label.<your-label>=true \
--conf spark.kubernetes.executor.label.<your-label>=true \
--executor-cores ${RUNTIME_EXECUTOR_CORES} \
--executor-memory ${RUNTIME_EXECUTOR_MEMORY} \
--total-executor-cores ${RUNTIME_TOTAL_EXECUTOR_CORES} \
--driver-cores ${RUNTIME_DRIVER_CORES} \
--driver-memory ${RUNTIME_DRIVER_MEMORY} \
--properties-file ${ANALYTICS_ZOO_HOME}/conf/spark-analytics-zoo.conf \
--py-files ${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-python-api.zip,/opt/analytics-zoo-examples/python/anomalydetection/anomaly_detection.py \
--conf spark.driver.extraJavaOptions=-Dderby.stream.error.file=/tmp \
--conf spark.sql.catalogImplementation='in-memory' \
--conf spark.driver.extraClassPath=${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-jar-with-dependencies.jar:/opt/cluster-serving/spark-redis-2.4.0-jar-with-dependencies.jar \
--conf spark.executor.extraClassPath=${ANALYTICS_ZOO_HOME}/lib/analytics-zoo-bigdl_${BIGDL_VERSION}-spark_${SPARK_VERSION}-${ANALYTICS_ZOO_VERSION}-jar-with-dependencies.jar:/opt/cluster-serving/spark-redis-2.4.0-jar-with-dependencies.jar \
--conf "spark.executor.extraJavaOptions=-Dbigdl.engineType=mklblas" \
--conf "spark.driver.extraJavaOptions=-Dbigdl.engineType=mklblas" \
--class com.intel.analytics.zoo.serving.ClusterServing \
local:/opt/analytics-zoo-0.8.0-SNAPSHOT/lib/analytics-zoo-bigdl_0.10.0-spark_2.4.3-0.8.0-SNAPSHOT-jar-with-dependencies.jar


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|
0.8.2 API documentation
|
Include to generate mipmaps of textures. More...
Go to the source code of this file.
Namespaces | |
| gli | |
| Namespace where all the classes and functions provided by GLI are exposed. | |
Functions | |
| template<typename texture_type > | |
| texture_type | generate_mipmaps (texture_type const &Texture, filter Minification) |
| Allocate a texture and generate all the mipmaps of the texture using the Minification filter. | |
| texture1d | generate_mipmaps (texture1d const &Texture, texture1d::size_type BaseLevel, texture1d::size_type MaxLevel, filter Minification) |
| Allocate a texture and generate the mipmaps of the texture from the BaseLevel to the MaxLevel included using the Minification filter. | |
| texture1d_array | generate_mipmaps (texture1d_array const &Texture, texture1d_array::size_type BaseLayer, texture1d_array::size_type MaxLayer, texture1d_array::size_type BaseLevel, texture1d_array::size_type MaxLevel, filter Minification) |
| Allocate a texture and generate the mipmaps of the texture from the BaseLayer to the MaxLayer and from the BaseLevel to the MaxLevel included levels using the Minification filter. | |
| texture2d | generate_mipmaps (texture2d const &Texture, texture2d::size_type BaseLevel, texture2d::size_type MaxLevel, filter Minification) |
| Allocate a texture and generate the mipmaps of the texture from the BaseLevel to the MaxLevel included using the Minification filter. | |
| texture2d_array | generate_mipmaps (texture2d_array const &Texture, texture2d_array::size_type BaseLayer, texture2d_array::size_type MaxLayer, texture2d_array::size_type BaseLevel, texture2d_array::size_type MaxLevel, filter Minification) |
| Allocate a texture and generate the mipmaps of the texture from the BaseLayer to the MaxLayer and from the BaseLevel to the MaxLevel included levels using the Minification filter. | |
| texture3d | generate_mipmaps (texture3d const &Texture, texture3d::size_type BaseLevel, texture3d::size_type MaxLevel, filter Minification) |
| Allocate a texture and generate the mipmaps of the texture from the BaseLevel to the MaxLevel included using the Minification filter. | |
| texture_cube | generate_mipmaps (texture_cube const &Texture, texture_cube::size_type BaseFace, texture_cube::size_type MaxFace, texture_cube::size_type BaseLevel, texture_cube::size_type MaxLevel, filter Minification) |
| Allocate a texture and generate the mipmaps of the texture from the BaseLayer to the MaxLayer, from the BaseFace to the MaxFace and from the BaseLevel to the MaxLevel included levels using the Minification filter. | |
| texture_cube_array | generate_mipmaps (texture_cube_array const &Texture, texture_cube_array::size_type BaseLayer, texture_cube_array::size_type MaxLayer, texture_cube_array::size_type BaseFace, texture_cube_array::size_type MaxFace, texture_cube_array::size_type BaseLevel, texture_cube_array::size_type MaxLevel, filter Minification) |
| Allocate a texture and generate the mipmaps of the texture from the BaseLayer to the MaxLayer and from the BaseLevel to the MaxLevel included levels using the Minification filter. | |
Include to generate mipmaps of textures.
Definition in file generate_mipmaps.hpp.


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Telangana state has considered agriculture as its primary goal to improve farmer community wellness, educate on latest technical farming knowledge, train framers to boost the agricultural production and productivity. In three major zones namely, Northern Zone, Central Zone and Southern Zone.Crops that are grown in Telangana are Rice, Maize/ Corn, Red Gram, Green Gram, Jowar, Sesame, Castor, Cotton, Groundnut, Soyabean , Black Gram to mention a few.
Telangana is in the rice bowl of South India, and RICE is cultivated in about 44 lakh acres. In the recent past, climatic changes have greatly influenced the amount rainfall, due to which the area cultivated with RICE has decreased.Read More..
Maize (Corn) is the second major cultivated crop in the state of Telangana in around 14 lakh acres producing annually 16 Lakh tonnes. It is used for human food and animal feed; it is also now widely used in corn starch industry, baby corn production etc.Read More..
Jowar is an important Rabi season crop cultivated in around 1.20 lakh acres of land across the state. Sorghum or corn popularly knowns as “Jowar” in India. It is used for human consumption, fodder animal feed, and for the production of starch, adhesive, paper, etc. Read More..
In India, it is widely cultivated in Maharashtra, Gujarat and Telangana regions and stands in the 3rd position in cotton production. Cotton is popularly called as “White Gold” and is grown in most of the parts of Telangana. Each year cotton is cultivated in about 12.5 lakh hectares with a production of 48 lakh bales. Read More..
Castor is one of the major oil production crops of Telangana. Approximately 80000-85000 hectares of land is cultivated with castor, the average yield harvested is 633 kilograms of castor seeds per hectare. The oil extracted from Castor seed is used widely for various purposes. It is cultivated in Mahbubnagar, Nalgonda, Ranga Reddy, Medak and Karimnagar districts of Telangana.Read More..
Groundnut is cultivated in 2.0 lakh hectares across Telangana region making it one of the major crops of the state. It is widely grown in Mahbubnagar, Warangal, Nalgonda and Karimnagar Districts. Crop rotation is very important in Groundnut farming, this helps to utilize nutrients efficiently utilization and to reduces soil borne diseases. Read More..
Soya beans is one of the most widely cultivated crops in the world as an Oil seed and a Legumes crop. Soya beans contains 36% protein, 30% carbohydrates and 20% oil. Annually, soya bean is cultivated in India in about 110 lakh hectares to produces 147 lakh tonnes. It is mainly grown in Madhya Pradesh, Maharashtra, Rajasthan, Karnataka, Telangana and Chhattisgarh.Read More..
Pigeonpea is popularly called as Red Gram. It is cultivated in 2.75 lakh hectares across Telangana state. It is the major crop for Mahbubnagar, Adilabad, Ranga Reddy, Medak, Nalgonda, Warangal and Khammam districts.Read More..
Green Gram is popularly known as “Moong Dal” and is cultivated in approximately 1.5 lakhs hectares across Telangana. This crop is widely grown in Nalgonda, Medak, Warangal, Mahbubnagar and Khammam districts across the state. This pulses crop is high source of protein (~24%).Read More..
Black Gram popularly known as “Urad Dal” is one of the important pulse crops (protein ~25%). This crop is resistant to adverse climatic conditions and helps to improve the soil fertility by fixing nitrogen in the soil. It is grown around 55,000 hectares of land across Telangana in Nizamabad, Medak and Adilabad. Read More..
Sesame is one of the oldest oilseed crops and popularly known as “Gingelly” or “Til”. This crop is cultivated as Summer, Kharif and semi-Rabi crops in 25,000 – 30,000 acres across the state. Read More..


Dear [Name,fallback=Participant],
Thank you for joining the Governance Workshop yesterday.
The Department requested that we communicate the registration link for the UNISA course.
Click here: https://academyone.co.za/course/unisa-course-in-accounting-for-school-governace
Feel free to call or email me if you have any questions.
Your sincerely,
|


I'm a computer science master's student at Robotic Embedded Systems Lab (RESL) advised by Prof. Gaurav S. Sukhatme at the University of Southern California (USC). My research interests lie in all topics related to robotics. More recently, I have been investigating applications of imitation learning and reinforcement learning in robotics, especially data-efficient robot learning. Previously, I received my Bachelor's degreee from the Zhejiang University (ZJU) under the guidance of Prof. Hui Fang.
Our paper on combining motion predictive control with deep RL is accepted for NIPS 2018 Deep RL Workshop!
Our paper on composing robot skill embeddings is submitted to ICRA 2019!
Our paper on applying deep neural networks to motion planning is submitted to ICRA 2019!
Our paper on robot skill embeddings is accepted for ISER 2018!
|
Simulator-Predictive Control: Using Learned Task Representations and MPC for Zero-Shot Generalization and Sequencing Accepted at NIPS 2018 Deep RL Workshop Z. He*, R. Julian*, E. Heiden, H. Zhang, J. Lim, G. Sukhatme, S. Schaal, K. Hausman |
|---|
|
Auto-conditioned Recurrent Mixture Density Networks for Complex Trajectory Generation Submitted to International Conference on Robotics and Automation (ICRA), 2019 H. Zhang*, E. Heiden*, R. Julian, Z. He, J. Lim, G. Sukhatme |
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Scaling simulation-to-real transfer by learning composable robot skills Accepted at International Symposium on Experimental Robotics (ISER), 2018 R. Julian*, E. Heiden*, Z. He, H. Zhang, S. Schaal, J. Lim, G. Sukhatme, K. Hausman |
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Understanding PILCO
I hope this blog can help more people understand PILCO easier... |
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TRUE CRIME RATING: 5.5 / 10 --> Not good enough to recommend
For more reviews and movie trivia, visit http://www.joblo.com/
Clint Eastwood decides to support some of his own, including his daughter, wife and ex-girlfriend, in a film that he directs, produces and stars in. Based on the novel of the same name, this movie runs approximately 125 minutes.
PLOT: An ex-drunk, wife cheater, bad father and newspaper reporter Everett, gets assigned to a human interest story covering the execution of convicted killer Frank Beachum at midnight. With a nose for the truth, Everett slowly begins to uncover holes in the man's conviction, and sets out to prove his far-fetched theories.
CRITIQUE: No suspense. No tension. Little drama. This film provides us with believable characters, great actors, and another super gruff-and-tumble performance by the charismatic Clint Eastwood, within the confines of a snails-paced plot, overly predictable development, and a hilariously convenient conclusion. If you're looking for an interesting mystery that unfolds with intrigue, obstacles and queries all around, this film is not the one for you. If you're looking for a Clint Eastwood film, featuring him playing another extra-cool character with very little redeemable qualities, and a pace which provides you with extremely limited progress as it moves along, I would suggest this film to you wholeheartedly.
Eastwood is charming, and there is no doubt about that. If it was anyone else in this role, I might've conked out during the early chapters of this floater. James Woods is also effective as the no-nonsense editor, and Isaiah Washington as the poor, convicted sap, with the rest of the cast punching in their time clocks with precision. Ultimately, there just isn't enough happening in this movie, to recommend it as an entertaining piece of cinema. Also, the complete 180 degree "change of heart" from a crucial character in the film seemed falsely created, and left a bad after-taste in my mouth. The whole film felt like a long day at a baseball game where the teams go into extra innings tied at zero-it's that feeling when you know that you've been there long and that time has passed you by, and yet the score is still tied at nothing. In the end, the film downshifts into your typical "race for the clock" scenario, and adds a taste of suspense into a film that could've used much more early on. All in all, a great performance by Eastwood, in a predictable, slow-moving, character study, disguised as an engaging, crime-solving mystery.
Little Known Facts about this film and its stars: Clint Eastwood's real-life daughter plays his cute-as-heck daughter in this film. His ex-lover, and mother of one of his five children, actress Frances Fisher, also has a cameo, as well as his current wife, Dina Ruiz, with a small part. Actor Sidney Poitier's real-life daughter also plays a role in this film. Ironically, her real name is also Sydney Poitier. Clint Eastwood got his first acting role in Rawhide while visiting a friend at the CBS lot when a studio exec spotted him because he "looked like a cowboy." He wore the same poncho, without ever having washed it, in all three of his "man with no name" western movies. MORE COMING SOON on JoBlo's Movie Emporium
Review Date: March 20, 1999 Director: Clint Eastwood Writers: Larry Gross, Paul Brickman and Stephen Schiff Producers: Clint Eastwood, Richard D. Zanuck and Lili Fini Zanuck Actors: Clint Eastwood as Steve Everett, Isaiah Washington as Frank Beachum, James Woods as Alan Mann Genre: Thriller Year of Release: 1999
Visit JoBlo's Movie Emporium http://www.joblo.com
(c) 1999 Berge Garabedian
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
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belongs to the author.
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Related links: index of all rec.arts.movies.reviews reviews


Preparation time
less than 30 mins
Cooking time
over 2 hours
Serves
Serves 4
Allowing the ragú to simmer for a few hours is the essential element here, so don’t be tempted to rush it. If you're short of time you could buy fresh pasta and finish off the ragú in a slow cooker.
Equipment and preparation: for this recipe you will need a pasta machine with a cutting attachment of your choice.
For the fresh pasta, place the flour and eggs into a food processor and pulse until well combined and the mixture comes together as a dough. Place the dough in a bowl, cover with cling film and place in the fridge to rest for 30 minutes.
Cut the pasta dough in half and pass it through a pasta machine at its widest setting. Repeat this process, decreasing the roller setting down grade by grade with each pass until the dough is thin enough to pass through your cutter.
Add your cutter to the pasta machine and pass the pasta through the machine to make your shape. Dust the pasta with a little semolina.
Repeat the process with the remaining dough, then place the shapes onto a plate and chill in the fridge until ready to use.
For the classic ragú sauce, heat a large casserole dish and add the oil. Once hot, add the onion and garlic and gently fry for 2-3 minutes.
Add the carrot, celery and basil and cook for a further two minutes.
Add the meat and cook until brown.
Add the tomato purée cook for a minute, then add the wine. Cook until the liquid is reduced by half, then add the tomatoes and cook for a couple of hours with the lid on.
For the green salad, place the lettuce leaves in a large bowl. Whisk together the oil, lemon juice, salt and pepper. Pour over the salad leaves and toss to coat.
For the pasta, in a large pan of salted boiling water cook the pasta until al dente.
To serve, add the sauce to the cooked pasta and toss until all the pasta is coated.
Serve in a warm bowls topped with the parmesan cheese. Place the salad in a separate bowl and serve alongside.


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Components
MccSpeedDial
This is the main class of the component.
Inputs
| Name |
Description |
open: boolean
|
Set initial open state of the component |
mouseHover: boolean
|
When enabled, handle open/closed state on mouse hover |
spin: boolean
|
Enable/disable spin animation |
direction: DIRECTION
|
Define direction of the actions button |
Outputs
| Name |
Description |
openStateChange: EventEmitter
|
Event emitted when the open state change |
MccSpeedDialActions
This class is used to pass action buttons that will be showed when fab speed dial is opened.
Inputs
| Name |
Description |
animation: ANIMATION
|
Set type of animation that will be executed on open/close |
Types
DIRECTION
|
up | down | left | right |
ANIMATION
|
scale | fling |


 I can remember this fascination began when I was 11 years old, around the time I began smoking pot. I guess this was a little young, but from then until now at 21 I have not been able to enjoy the talkative, chatty side to smoking as often as the introspective, philosophical and somewhat hallucinatory state the weed induces in me. I am known to my friends to shut off into my own little world when stoned. (Inconvenient at parties.)
However, I was well aware that LSD and mushroom trips are on a completely different level to being very stoned, and so have been trying to get my hands on something to make me _really_ trip for 10 years. Coke, speed, ecstacy and ketamine have always been readily available, and I have enjoyed all of em, quite frequently. So I am fairly used to being in some altered state or other. But while all of them have their time and place, and are usually lots of fun, none gave me what I was looking for. None put me in the state I had almost become obsessed with reaching. And for some reason I couldn't find any mushrooms or acid anywhere. Where I lived, it seemed every other drug was for sale but the ones I really wanted.
During my search for hallucinogens I discovered Salvia Divinorum. Mainly because it is legal and therefore easy to obtain. Yes, it is -definitely- a trip, a mind blowing one. I wish to experiment further with it and see what I can learn from it, and I know that it is of some great importance. But the experience is more educational than fun. All other drugs I have tried merely mess with the human brain. Salvia, I believe, taps into powers far beyond, at least for now, the understanding of modern neurology. And all this besides, I found the effects to damn short-lived. I still wanted those mushrooms. I wanted to bounce around, laugh a lot, and see things turn into other things, and not have to think too much. I also wanted more time to explore.
So when my two very good friends G. , D. and I finally got around to planning this trip to Amsterdam we'd been talking about for seven years, the smart shops were first on my sightseeing list. A few beers on the ferry, a few more on the train, we'd had a very pleasant journey. We arrived at Central station and, sick of carrying the bags, checked into the first hotel we saw. Then we headed out and hit the first coffeeshop we came across, and bought a bag of the first weed we saw on the menu.
This weed was stronger than any weed we could ever have hoped to get at home. Despite being economical with it as we had been advised, within an hour of arriving we were ridiculously stoned beyond belief. Though the gear they sold was strong, unluckily, as we were to find out, that first shop was probably one of the worst in Amsterdam. Later on, after visiting one or two other coffeeshops, we regretted even setting foot in that filthy hole. The atmosphere got to us BADLY. For now, we left, paranoid and distressed, back to the hotel. It was a seriously dodgy place.
An hour later back at the hotel we'd all got changed and decided to start again. The journey had be long and my two friends were also still tired from a long drinking session the night before, and we all needed to wake up. So I visited the head shop downstairs and bought a packet of 3 Yellow-Jackets.(Ephedrine capsules) However, my friends were weary of these stimulant concoctions as I had once recommended some to them that had made them quite nauseous. (They had been too polite to tell me at the time!) so I ended up eating them alone.
Nevertheless, D had woken up anyway so now we would look for shrooms, but a small dose of mild ones, to be on the safe side. We purchased a box of Cubensis Mexicana with one and a half large shrooms and a few small caps.  Before eating the Cubensis, I had already begun to feel the yellow jackets kicking in- a sort of tingly, very talkative feeling, and my heart beating faster - more like a jug or two of espresso than anything else. I suddenly didn't feel at all stoned, which was surprising as it usually takes me a long time to wake up from a few fat spliffs. . I'm not sure to what extent the Ephedrine affected my trip, but I am pretty sure I ended up tripping hardest out of the three of us.
We sat there chatting and listening to music. We all ate 1000mg of G.s vitamin C, not for any particular reason other than we didn't want to get ill later on. I ate about 1/4 of my share to begin with. I began to fear that the room we were in was not a suitable environment to trip in - the hotel itself was far from relaxed, (ie, a shithole) and the room itself looked like a cell in a psychiatric prison, no more than 8 feet squared for the three of us. The walls were bare white except for the nicotine stains and dents, with a bright, glaring incandescent bulb fixed dead centre of the ceiling. There were no windows - just a small air-vent in the corner. There were two, steel framed bunk-beds, (military stile) with plain, white bedding. The only distinctive feature was an old, battered ceiling fan that didn't work.
I kept telling myself “you get what you pay for,” but inside, I was very worried about tripping in this room. Until the effects kicked in, when it all went just fantastic.
After about twenty minutes, we all commented on how warm it was. We agreed that this was due to the shrooms, and so, assured they were working, ate some more of them. Then, after about another ten minutes, I looked over at D's bunk, and it occurred to me that it was made of rubber. I felt I could bend the frame with my hands, and so, ever curious, I stood up to try it. D gave me a look as if he was wondering what the hell I was doing. I don't blame him. I knew that the frame was not rubber, but it felt like rubber, and it looked like rubber, it even SMELT like rubber, and I could bend it. I remember saying to him 'It bends! It bends!' G. stood up and said to me 'Kam, I think you're coming up!'
We continued to talk. We hadn't seen each other for a long time and had a lot to talk about. However, it seemed more and more that as time went on, our conversation became more nonsensical. While we were still coherent, we were talking absolute rubbish. All the time, I was watching D's rubber bed sway in time to the music and watching palm trees sprout from the steel tubes. The bare white room began to have a lot more colour to it. I began to see the green of the leaves, the yellowy-brown bark. The music I was listening to was not what was playing on the stereo, although I could still hear the CD. We ate some more of the mushrooms. This was too good to be true. Everything I had hoped for. I for one wanted more of it.
About another half hour must have passed when I noticed G. was tapping on the walls and say 'woosh' a lot.
I lay back against the wall - the wall felt like rubber too and my head began to sink into it. Then I looked up at the ceiling. That old fan was rotating. It was not even wired up, but it was spinning, and suddenly there was another one. And another. Five of them, all spinning, I decided, at precisely 5012rpm. The ceiling began to lift up and move forwards, like the underside of a small airship, with all its propellers. Then it was an airship. Then two airships. Then a whole fleet of them, flying overhead purposefully through the clouds as if on some dangerous mission, dodging gunfire from the artillery below.
Jesus, I thought. I am seeing whatever I am thinking as if it's really there. This is incredible. I was loving it so much I ate the rest of my share, plus the remainder of D.s, as he was feeling a little nauseous and didn't want them.
I can't remember what we were talking about, but while all this was going on I know we were all still managing to talk to each other with fair ease. We were still interacting. Yet I have never felt so in control of my own mind as I did then - I could turn away from the trip to talk to my friends, and turn back to the airships at will. If I became bored of the airships, I would see something else. I saw a million things in those bare white walls that night, but not one I didn't want to. I was in the drivers seat and could take my mind wherever I wanted it to go. It was like my visual imagination had been fitted with power steering, cruise control, and a turbo. Oh, and a DVD player in the back seat. When I closed my eyes I could see in great detail huge landscapes. Mountains, forests, deserts - all populated with animals of every kind. Anything I put there.
Then about two hours into the trip the visuals mellowed a little and the laughter set in. We began chanting 'Fuck da police!' and I began to roll around on the rubber floor in fits of laughter. We heard other guests on the other side of the door, listening in and wondering what the hell was going on. We laughed our asses off for at least 45 minutes saying nothing but 'Fuck da Police' to each other, finding it absolutely hysterical. I could still see the airships, but 'Fuck da police' was funnier at the moment. Eventually we stopped, only because the CD had finished and someone had to change it, 'before the weasels close in', as G. put it.
It was then, getting up to change the CD, that I peaked. And here I got more than I expected from these shrooms, yet everything I had dreamed of at the same time.
I had to sit back down again almost immediately because I began to notice all the little imperfections in the wall, stains and dents, moving around to form many arabic letters. I wanted to read them, and tried for a few minutes, then suddenly remembered I couldn't read arabic. When I realised this, the letters melted away and everything sounded as if we were underwater. I could hear bubbles and whale-song, and waves crashing overhead. As if pushing through cling-film, the most beautiful creature I ever saw pushed its way through the wall opposite me. It was enormous. The room seemed to have grown in size to accommodate it.
It was a huge, tentacled sea creature. It's organs appeared to be outside of its body, pulsating and turning. It shone a brilliant blue light that filled the room. It had seven heads, all of them smiling, all with huge, black eyes that seemed filled with love and calmness. The tentacles waved around the room. It continued to smile as I gaped in awe at it.
An overwhelming feeling of inner peace emanated through my entire body - I felt like this creature was reassuring me of the good in humanity, nature and the universe. It looked like it could have been a boss from one of the Final Fantasy games, but to me, it was the most beautiful thing in the universe. It WAS the universe.
By now, I was aware that they were staring at me with a little concern. And I couldn't blame them for I can now imagine what I must have looked like. I was motionless, open mouthed, wide eyed, pointing at a blank white wall for what must have been well over ten minutes. They were asking me what I could see, and I was describing it to them, but still not taking my eyes off it. I don’t think I blinked in that whole ten minutes. I was still conscious of being in the hotel room, being under the influence of mushrooms, but I did not want ever to stop looking at this. . .this amazing. . .THING. I wanted to communicate with it. It looked friendly. But it had already begun to drift away, back into the wall which had now become a huge, rotating tunnel. I recognised this as the same tunnel I always see when I smoke Salvia.
As the creature moved away it waved to me as if to say 'goodbye for now, we'll meet again someday, come and find me' before it finally disappeared from view, giving a final twirl before vanishing completely. I was very sad to see it go, but the indescribable happiness for having met it far outweighed any sadness.
The tunnel soon closed around it, and the wall became a spiders' web. Spiderman's face appeared in the centre of it, and this was followed by the rest of his body. He began dancing around to the CD playing on the stereo - (I think it was Orbital but it was hardly important) with full disco lights, red green and yellow, with strobes, mirror balls and lasers. I began to laugh - it was a very funny picture. It was almost as if the creature I had met was saying 'Aww, don't be sad that I'm leaving. I know - here's something you'll like!' and throwing the image at me to cheer me up. (I am a big spiderman fan, and somehow it knew.) I got the giggles again, for the first time not being aware of my two friends in the room.
After watching Spiderman dance for a while, the picture faded to white and I was on the floor. All the visuals had disappeared very suddenly and I felt fairly normal. Very refreshed, still filled with the peace and happiness of the sea-creature I had just met, but fairly sober. D was looing at me - 'What do you think, shall we go for a walk?' I looked up - no airships. Just inner calm the likes of which I cannot ever remember feeling in y life
I did not stop smiling for a very long time. I don’t think I stopped smiling until we were back on the boat. We continued to have a great weekend in this wonderful city.
Since then I have not tripped to that extent. I have smoked salvia a few times in the hope that it can shed some light about what I saw, and I have seen that same tunnel, but the Salvia won't tell me anything about the creature. It doesn't believe I need to know just yet. I'm hoping to meet it again some time. Or at least feel the way I felt when I was looking at it. And I have a strong feeling that one day I will.  I cannot wait to repeat it, and yet, I know that there is no rush. It will happen when the time is right.
I was very worried about a lot of things before I ate those mushrooms. I am not very well traveled and was in an unfamiliar place. What if something bad happens to me or my friends? What if we get robbed, ripped off, lost, killed, etc? What if we do TOO many drugs. What, even, if the shrooms wipe us out for the weekend and we waste our long-awaited holiday? Don't they say that you shouldn’t trip if you're worried about things?
Within twenty minutes of eating the mushrooms, all of these worries were wiped away never to return. Along with everything else deep and personal that had been darkening my thoughts. All that was left was complete calm and happiness. I guess cleansed is the word for it. The mushrooms have shown me the inner peace and calm that every human should strive to achieve through life. Buddhism springs to mind, though I know too little of its teachings to draw an accurate parallel. But that creature, in a way, pointed me to a target. When I can feel as I did when looking at the creature, all the time, without the help of the mushrooms, then I know I have achieved my goal as a human being. Enlightenent, if I may say.
Marijuana, Psilocybes and Salvia have been growing on earth before, I am guessing, mankind. They were put there for a purpose - an important one. And they TELL this to me when I use them.
After finally experiencing the wonders of Psilocybin after many years of looking for it, I no longer feel the need for those man-made substances peddled out in the clubs. With them, I have always found that after a while the down far outweighs the up. Maybe I haven't been getting the good stuff, but these ones spring out of the ground, ready for consumption, as nature intended. I have learned that if I give these plants and fungi the respect they deserve, they can teach me a lot about nature, about myself, and others.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Examples¶
For a better understanding of interface to ECLiPSe system read description of C interface.
Simple Example¶
A simple example of unification:
from pyclp import *
init() #Init ECLiPSe engine
myVar=Var() #Create a prolog variable
my_goal=Compound("=",myVar,1) #Create goal
my_goal.post_goal() #Post goal
result,dummy =resume() #Resume execution
#of ECLiPSe engine
if result != SUCCEED:
raise
print(myVar.value +1) #myVar.value get the Var value back.
print(myVar) #print value
cleanup() #Shutdown ECLiPSe engine
Compound class is used to create compound term. First argument is a string representing the functor followed by the argument.
Search Solution¶
A more complex example using ic library and search procedure:
from pyclp import *
init() # Init ECLiPSe engine
Compound("lib",Atom("ic")).post_goal() # Load ic library
A_var=Var() # Create variable A
B_var=Var() # Create variable B
Compound("#::",PList([A_var,B_var]),\
Compound("..",1,10)).post_goal() # [A,B]#::1..10
Compound("#<",A_var,B_var).post_goal() # A#<B
Compound("#=",A_var,5).post_goal() # A#=5
Compound("labeling",\
PList([A_var,B_var])).post_goal() # labeling([A,B])
# Loop on all solution and print them.
while (resume()[0]==SUCCEED):
print(B_var)
Atom("fail").post_goal() # Post fail for backtracking over solutions
cleanup() # Shutdown ECLiPSe engine
Cut Example¶
How to cut solutions:
from pyclp import *
init() # Init ECLiPSe engine
Compound("lib","lists").post_goal() # lib(lists)
A_var=Var() # Create variable A
Compound("member",A_var,PList([1,2,3])).post_goal() # member(A_var,[1,2,3])
# Loop on all solution and print them.
while (resume()[0]==SUCCEED):
if A_var.value()==2: # When value == 2 cut all other solutions.
cut()
break
Atom("fail").post_goal() # Post fail for backtracking over solutions
B_var=Var() # Create variable B
Compound("is",B_var,\
Compound("+",A_var,1)).post_goal() # B_Var is A_var + 1
resume()
print(A_var,B_var)
cleanup() #Shutdown ECLiPSe engine
Input Example¶
Receive data from stdout stream:
from pyclp import *
init() # Init ECLiPSe engine
Compound("writeln","Hello world").post_goal()
result,stream_num=resume()
if result != FLUSHIO: # Check for output data
raise
else:
outStream=Stream(stream_num) # Open output stream
data=outStream.readall() # Return data in a bytes object
print(data)
outStream.close() # Not required but implemented to support RawIO protocol
cleanup() # Shutdown ECLiPSe engine
Input/Output Example¶
Input to stdin stream and output from stdout stream:
from pyclp import *
print ("I/O Example")
init() # Init ECLiPSe engine
stdin=Stream('stdin') # Open stdin stream
stdout=Stream('stdout') # Open stdout stream
A_var=Var() # Variable where to store the input term.
Compound ("read",A_var).post_goal() # read(A)
Compound("writeln",A_var).post_goal() # writeln(A)
while (1):
result,stream=resume()
if result==WAITIO: # ECLiPSe is waiting for data
stdin.write(b'Hello_World')
elif result==FLUSHIO:
print(stdout.readall()) # ECLiPSe send data to stdout stream
else:
break
stdin.close() # Not required but implemented to support RawIO protocol
stdout.close() # Not required but implemented to support RawIO protocol
cleanup() # Shutdown ECLiPSe engine


--- layout: post title: Progressive.net workshops. Register now, save hundreds. date: '2009-03-03T10:00:00.001Z' tags: [alt.net, progressive.net, conferences] modified_time: '2009-03-03T10:00:02.354Z' blogger_id: tag:blogger.com,1999:blog-4015568221071268916.post-3019092160248724200 comments: true blogger_orig_url: http://serialseb.blogspot.com/2009/03/progressivenet-workshops-register-now.html ---
Just a quick reminder. My some mistake of history, I’ve been asked to deliver workshops alongside Ayende, Scott Bellware, Hammet, Mike Hadlow, Ian Cooper, David Laribee and Robert Pickering. I have to admit I’m very anxious to be surrounded by such people! Anyway, it’s going to be an absolute blast and extreme experience for anyone taking part.
I’ve just been reminded that if you register before the 4th of March, the 3 days of training will cost you £525, and before the end of may £700.
So if you care about all things progressive and the great speakers that will be presenting (and me), make your way to SkillsMatter and book now!


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PMTC was created by @amosbastian to make the Reddit post-match thread creation for League of Legends, Counter-Strike: Global Offensive and Overwatch much more easy and quick. Currently it only outputs the scoreboard of each match, but hopefully in the future it will be able to create the entire post-match thread automatically!


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My grade opinion
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This is my first paragraph.
This is my second paragraph.
This is my third paragraph which also contains a line break to mix things up a little.


There will be an introduction of my name and 3-4 keywords to describe who I am on the left side. The text will be overlaid on a large photo.
My about me will be a short and sweet overview of my background. While adding details about my passion for all things design and web development. I want to give glimpses into who I am outside of work, like a dog lover with two creative businesses. I'd like to add icons that pertain to my hobbies and likes.I want to set a fun and personable tone with this section because it fits my personality and overall environment I would love to work in. I will add another photo of myself to the right of the text.
I would like for this to be more of a resume section where I include my education, certifications, and experience with content creation. As a new web developer, my experience is limited but I would like to add our capstone project with a photo and link inviting users to check it out! There will be sections for my work exerience, projects, etc. and anytime you hover over the text, a text bubble with more details will pop up and disappear when you move your mouse away. For the mobile site, you will have to click on an arrow to the right of the sections and the details for each section will appear.
This section will have a contact form with a witty or sweet, "get in touch" with me message. The footer will include links to my github, linkedin, twitter, and instagram page, and possibly my professional address.


clusterset116
Modules / mean expression (click the image to zoom in)
Relationship tree
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GENE LIST
ENRICHMENT TABLE


Experience winter beauty on a guided snowshoe walk with a park
ranger. Snowshoes can be rented at the Bear Den ski shop next to the
Mammoth Hotel, or bring your own. Come prepared with warm, layered
clothes, sunglasses, and water. Meet at the Upper Terrace Drive
entrance parking area two miles south of Mammoth. Note: If there is
a lack of snow, this will become a walk without snowshoes. Moderately
strenuous. Beginners welcome! 2 hours.
Ecology of Yellowstone's Northern Range
(December 21–February 24)
The Northern Range of Yellowstone has always been a premier place
to watch wildlife. Learn how all forms of life interact here and how this
ecosystem has changed since the restoration of wolves. Meet in front of
the Albright Visitor Center. Wheelchair Accessible. 30 minutes.
Stars Over Yellowstone
(January 3 to February 14)
Enjoy a short walk to a view of Yellowstone’s pristine night sky. Dress
warmly and bring a flashlight. Meet in front of Albright Visitor
Center. Wheelchair Accessible. 30 minutes. Weather Permitting.
Evening Program
(December 24–February 27)
Join a park ranger for an illustrated program that highlights a fascinating
aspect of Yellowstone’s natural, cultural, or scenic wonders. Program
descriptions will be posted in the Mammoth Hotel and Albright Visitor
Center. Meet in the Mammoth Hotel Map Room. Wheelchair Accessible. 1 hour.
Geysers Galore!
(December 15–March 15)
Discover more about Yellowstone geysers. Meet a park ranger at the
benches in front of Old Faithful. Wheelchair Accessible. 20 minutes.
Old Faithful Evening Program
(December 20–February 28)
Join a park ranger for an illustrated program that highlights a fascinating
aspect of Yellowstone’s natural, cultural, or scenic wonders. Meet in
the Old Faithful Visitor Education Center. Doors open at 7 pm. Enter
via the main doors on the parking lot side. Program titles and descriptions
are posted at the Old Faithful Visitor Education Center and the
Old Faithful Snow Lodge. Wheelchair Accessible. 45 minutes.
Snowshoe Walk
(December 19–March 6)
Discover Yellowstone’s fascinating winter ecology while wearing
snowshoes. Join a ranger for a 2-mile walk into Yellowstone National
Park along the Riverside Trail. Meet at the West Yellowstone Visitor
Information Center at the corner of Yellowstone Avenue and
Canyon Street in West Yellowstone, Montana. Bring snowshoes, water,
and a snack. No experience necessary. For information, please call
307-344-2876. 2 hours.
Afternoon Talk in West Yellowstone
(December 19–March 6)
Join a ranger for a program highlighting a fascinating aspect of
Yellowstone’s natural, cultural, or scenic wonders. Program descriptions
will be posted in the West Yellowstone Visitor Information
Center (corner of Yellowstone Ave. and Canyon St.), and at the
Grizzly and Wolf Discovery Center at 201 South Canyon Street.
Talks are free and open to the public. The program is free. However,
there is a fee to visit the rest of the Grizzly and Wolf Discovery Center.
Wheelchair Accessible. 45 minutes.
FOLLOW US!


Some of my favorite meals to cook includes pasta
and chicken dishes. I love to cook for my
roommates and parents.
When I'm not cooking, I'm most likley listening to music.
I don't really listen to a specific genre or artist,
just whatver comes up on the radio or spotify!
Me and my friends are always watching movies. Some
of my favorites are classic Disney, horror films,
and comedies!