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test: update tests/plugin-codeflare/dashboard/inputs/2
Update this test input to have the latest schema
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‎tests/plugin-codeflare/dashboard/inputs/2/choices.json‎

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Original file line numberDiff line numberDiff line change
@@ -1,25 +1,66 @@
11
{
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"name": "default",
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"creationTime": 1657642748915,
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"lastModifiedTime": 1658152672874,
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"choices": {
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"madwizard/apriori/use-gpu": "don't use gpus",
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"madwizard/apriori/arch": "x64",
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"madwizard/apriori/platform": "darwin",
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"madwizard/apriori/mac-installer": "Homebrew",
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"madwizard/apriori/in-terminal": "HTML",
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"Training####Fine Tuning": "Fine Tuning",
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"GLUE": "GLUE",
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"AWS####IBM": "AWS",
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"S3 Bucket for Run Data.expand([ -n \"$MC_CONFIG_DIR\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls s3 | awk '{print substr($NF, 1, length($NF) - 1)}', S3 Buckets)####separator####📁 Create a new bucket": "browsey",
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"Run Locally####Run on a Kubernetes Cluster": "Run on a Kubernetes Cluster",
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"Choose the bucket that contains your model and glue data.madwizard/apriori/platform": "Darwin",
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"expand(kubectl config get-contexts -o name, Kubernetes contexts)": "default/api-codeflare-train-v11-codeflare-openshift-com:6443/kube:admin",
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"expand([ -z ${KUBE_CONTEXT} ] && exit 1 || kubectl --context ${KUBE_CONTEXT} get ns -o name | grep -Ev 'openshift|kube-' | sed 's#namespace/##', Kubernetes namespaces)####Create a namespace": "nvidia-gpu-operator",
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"Number of CPUs####Number of GPUs####Minimum Workers####Maximum Workers####Worker Memory####Head Memory": "{\"Number of CPUs\":\"1\",\"Number of GPUs\":\"1\",\"Minimum Workers\":\"4\",\"Maximum Workers\":\"4\",\"Worker Memory\":\"32Gi\",\"Head Memory\":\"32Gi\"}",
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"Choose the bucket that contains your model and glue data.expand([ -n \"$MC_CONFIG_DIR\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls s3 | awk '{print substr($NF, 1, length($NF) - 1)}', S3 Buckets)####separator####📁 Create a new bucket": "browsey",
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"Choose your Model File.expand([ -n \"$MC_CONFIG_DIR\" ] && [ -n \"$S3_FILEPATH\" ] && [ -n \"$S3_FILEPATH${S3_BUCKET_SUFFIX}\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls \"s3/$S3_FILEPATH${S3_BUCKET_SUFFIX}\" | awk '{print $NF}', S3 Objects)": "roberta-base",
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"Choose your Glue Data File.expand([ -n \"$MC_CONFIG_DIR\" ] && [ -n \"$S3_FILEPATH\" ] && [ -n \"$S3_FILEPATH${S3_BUCKET_SUFFIX}\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls \"s3/$S3_FILEPATH${S3_BUCKET_SUFFIX}\" | awk '{print $NF}', S3 Objects)": "glue_data",
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"BERT": "BERT",
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"Example: Using Ray Tasks to Parallelize a Function####Example: Using Ray Actors to Parallelize a Class####Example: Creating and Transforming Datasets####Example: Training Using PyTorch####Example: Hyperparameter Tuning####Example: Serving a scikit-learn gradient boosting classifier": "Example: Using Ray Tasks to Parallelize a Function",
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"Number of CPUs####Number of GPUs": "{\"Number of CPUs\":4,\"Number of GPUs\":3}",
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"expand(echo ${A-error} ; echo ${B-4} ; echo ${C-5})": "3",
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"XXXXXX.11111####222222": "11111",
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"YYYYYY.11111####222222": "222222",
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"My Cluster is Running Locally####My Cluster is Runing on Kubernetes": "My Cluster is Runing on Kubernetes",
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"expand([ -n \"$RAY_ADDRESS\" ] && ray job list --address $RAY_ADDRESS | tail +2 | awk '{print $1}' | sed \"s/[:{' ]//g\", Ray Runs)": "07a2647f-3656-4e3e-836c-95a2fa841af6",
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"expand([ -n \"$RAY_ADDRESS\" ] && curl $RAY_ADDRESS/api/jobs/ | jq -r 'keys | .[]', Ray Runs)": "d5a0d68f-a675-49ca-bff7-4ae762e6b146",
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"My Cluster is Running Locally####My Cluster is Running on Kubernetes": "My Cluster is Running on Kubernetes",
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"expand([ -n \"$RAY_ADDRESS\" ] && curl $RAY_ADDRESS/api/jobs/ | jq -r 'to_entries | sort_by(.value.start_time) | reverse | .[] | \"\\(.key) \\(.value.status) \\(.value.entrypoint)\"' | sed -E 's/python3 ([^[:space:]])+ //g' | awk '{a=$1;b=$2; $1=\"\";$2=\"\";print \"\\033;1m\" a, \"\\033[0;33m\" b \"\\033[0;2m\" $0 \"\\033[0m\"}', Ray Runs)": "\u001b;1ma88d4632-ab5c-4350-a770-d39a955c42c8 \u001b[0;33mRUNNING\u001b[0;2m -v --datapath /tmp/ --modelpath /tmp/ --logpath /tmp/ --tblogpath s3://browsey/codeflare/a88d4632-ab5c-4350-a770-d39a955c42c8/tensorboard/ --num_workers 1\u001b[0m",
34+
"expand([ -n \"$RAY_ADDRESS\" ] && curl $RAY_ADDRESS/api/jobs/ | jq -r 'to_entries | sort_by(.value.start_time) | reverse | .[] | \"\\(.key) \\(.value.status) \\(.value.entrypoint)\"' | sed -E 's/python3 ([^[:space:]])+ //g' | awk '{a=$1;b=$2; $1=\"\";$2=\"\";print a, \"\\033[33m\" b \"\\033[0;2m\" $0 \"\\033[0m\"}', Ray Runs)": "a88d4632-ab5c-4350-a770-d39a955c42c8 \u001b[33mRUNNING\u001b[0;2m -v --datapath /tmp/ --modelpath /tmp/ --logpath /tmp/ --tblogpath s3://browsey/codeflare/a88d4632-ab5c-4350-a770-d39a955c42c8/tensorboard/ --num_workers 1\u001b[0m",
35+
"expand([ -n \"$RAY_ADDRESS\" ] && curl $RAY_ADDRESS/api/jobs/ | jq -r 'to_entries | sort_by(.value.start_time) | reverse | .[] | \"\\(.key) \\(.value.status) \\(.value.start_time / 1000 | strflocaltime(\"%Y-%m-%dT%H:%M:%S\")) \\(.value.entrypoint)\"' | sed -E 's/python3 ([^[:space:]])+ //g' | awk '{a=$1;b=$2;c=$3; $1=\"\";$2=\"\";$3=\"\"; print a, \"\\033[0;36m\" c, \"\\033[0;1;33m\" b \"\\033[0;2m\" $0 \"\\033[0m\"}', Ray Runs)": "8443709b-74b3-4ae5-ae5a-aa4e1fbe2bb1 \u001b[0;36m2022-07-18T08:06:48 \u001b[0;1;33mPENDING\u001b[0;2m python main.py\u001b[0m",
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"Start a new Run####Connect Dashboard to an existing Run": "Connect Dashboard to an existing Run",
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"Start a new Run####Connect Dashboard to an existing Run####Shut down a Cloud Computer": "Shut down a Cloud Computer",
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"Start a new Run####Connect Dashboard to an existing Run####Boot up a Cloud Computer####Shut down a Cloud Computer": "Start a new Run",
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"SubTab1####SubTab2": "SubTab1",
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"Tab1####Tab2": "Tab2",
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"BERT####BYOT": "BYOT",
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"Location of your working directory": "{\"Location of your working directory\":\"/tmp/qiskit/ray_demo\"}",
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"Provide custom base image, if any": "{\"Provide custom base image, if any\":\"rayproject/ray:1.13.0-py37\"}"
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},
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"madwizard/apriori/use-gpu": "don't use gpus",
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"madwizard/apriori/arch": "x64",
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"madwizard/apriori/platform": "darwin",
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"madwizard/apriori/mac-installer": "Homebrew",
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"madwizard/apriori/in-terminal": "HTML",
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"Run Locally####Run on a Kubernetes Cluster": "Run on a Kubernetes Cluster",
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"Example: Using Ray Tasks to Parallelize a Function####Example: Using Ray Actors to Parallelize a Class####Example: Creating and Transforming Datasets####Example: Training Using PyTorch####Example: Hyperparameter Tuning####Example: Serving a scikit-learn gradient boosting classifier": "Example: Using Ray Tasks to Parallelize a Function",
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"Start a new Run####Connect Dashboard to an existing Run####Boot up a Cloud Computer####Shut down a Cloud Computer": "Start a new Run",
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"Training####Fine Tuning": "Fine Tuning",
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"GLUE": "GLUE",
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"BERT": "BERT",
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"AWS####IBM": "AWS",
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"S3 Bucket for Run Data.expand([ -n \"$MC_CONFIG_DIR\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls s3 | awk '{print substr($NF, 1, length($NF) - 1)}', S3 Buckets)####separator####📁 Create a new bucket": "browsey",
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"Run Locally####Run on a Kubernetes Cluster": "Run on a Kubernetes Cluster",
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"Choose the bucket that contains your model and glue data.madwizard/apriori/platform": "Darwin",
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"expand(kubectl config get-contexts -o name, Kubernetes contexts)": "default/api-codeflare-train-v11-codeflare-openshift-com:6443/kube:admin",
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"expand([ -z ${KUBE_CONTEXT} ] && exit 1 || kubectl --context ${KUBE_CONTEXT} get ns -o name | grep -Ev 'openshift|kube-' | sed 's#namespace/##', Kubernetes namespaces)####Create a namespace": "nvidia-gpu-operator",
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"Number of CPUs####Number of GPUs####Minimum Workers####Maximum Workers####Worker Memory####Head Memory": "{\"Number of CPUs\":\"1\",\"Number of GPUs\":\"1\",\"Minimum Workers\":\"4\",\"Maximum Workers\":\"4\",\"Worker Memory\":\"32Gi\",\"Head Memory\":\"32Gi\"}",
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"Number of CPUs####Number of GPUs####Minimum Workers####Maximum Workers####Worker Memory####Head Memory": "{\"Number of CPUs\":\"1\",\"Number of GPUs\":\"1\",\"Minimum Workers\":\"1\",\"Maximum Workers\":\"1\",\"Worker Memory\":\"32Gi\",\"Head Memory\":\"32Gi\"}",
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"My Cluster is Running Locally####My Cluster is Running on Kubernetes": "My Cluster is Running on Kubernetes",
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"expand([ -n \"$RAY_ADDRESS\" ] && curl $RAY_ADDRESS/api/jobs/ | jq -r 'to_entries | sort_by(.value.start_time) | reverse | .[] | \"\\(.key) \\(.value.status) \\(.value.start_time / 1000 | strflocaltime(\"%Y-%m-%dT%H:%M:%S\")) \\(.value.entrypoint)\"' | sed -E 's/python3 ([^[:space:]])+ //g' | awk '{a=$1;b=$2;c=$3; $1=\"\";$2=\"\";$3=\"\"; print a, \"\\033[0;36m\" c, \"\\033[0;1;33m\" b \"\\033[0;2m\" $0 \"\\033[0m\"}', Ray Runs)": "2c495c48-2202-4be6-a8f7-4cccbb217f34 \u001b[0;36m2022-07-12T13:27:59 \u001b[0;1;33mPENDING\u001b[0;2m -v -b browsey -m roberta-base -g glue_data -t WNLI -M -s 40 41 42 43\u001b[0m",
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"GLUE": "GLUE",
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"Choose the bucket that contains your model and glue data.expand([ -n \"$MC_CONFIG_DIR\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls s3 | awk '{print substr($NF, 1, length($NF) - 1)}', S3 Buckets)####separator####📁 Create a new bucket": "browsey",
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"Choose your Model File.expand([ -n \"$MC_CONFIG_DIR\" ] && [ -n \"$S3_FILEPATH\" ] && [ -n \"$S3_FILEPATH${S3_BUCKET_SUFFIX}\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls \"s3/$S3_FILEPATH${S3_BUCKET_SUFFIX}\" | awk '{print $NF}', S3 Objects)": "roberta-base",
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"Choose your Glue Data File.expand([ -n \"$MC_CONFIG_DIR\" ] && [ -n \"$S3_FILEPATH\" ] && [ -n \"$S3_FILEPATH${S3_BUCKET_SUFFIX}\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls \"s3/$S3_FILEPATH${S3_BUCKET_SUFFIX}\" | awk '{print $NF}', S3 Objects)": "glue_data",
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"BERT": "BERT",
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"Example: Using Ray Tasks to Parallelize a Function####Example: Using Ray Actors to Parallelize a Class####Example: Creating and Transforming Datasets####Example: Training Using PyTorch####Example: Hyperparameter Tuning####Example: Serving a scikit-learn gradient boosting classifier": "Example: Using Ray Tasks to Parallelize a Function",
21-
"Number of CPUs####Number of GPUs": "{\"Number of CPUs\":4,\"Number of GPUs\":3}",
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"expand(echo ${A-error} ; echo ${B-4} ; echo ${C-5})": "3",
23-
"XXXXXX.11111####222222": "11111",
24-
"YYYYYY.11111####222222": "222222"
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"Choose your Glue Data File.expand([ -n \"$MC_CONFIG_DIR\" ] && [ -n \"$S3_FILEPATH\" ] && [ -n \"$S3_FILEPATH${S3_BUCKET_SUFFIX}\" ] && mc -q --config-dir ${MC_CONFIG_DIR} ls \"s3/$S3_FILEPATH${S3_BUCKET_SUFFIX}\" | awk '{print $NF}', S3 Objects)": "glue_data"
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}

‎tests/plugin-codeflare/dashboard/inputs/2/events/kubernetes.txt‎

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LAST SEEN TYPE REASON OBJECT MESSAGE
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3h1m Normal Pulled pod/nvidia-dcgm-exporter-7qs7v Successfully pulled image "nvcr.io/nvidia/cloud-native/gpu-operator-validator@sha256:24d804e8f005d7aeca8343aa13e5f92295d8642a4c47cb24e3ac86a22543bc37" in 1m16.137927284s
3+
3h1m Normal Created pod/nvidia-dcgm-exporter-7qs7v Created container toolkit-validation
4+
3h1m Normal Started pod/nvidia-dcgm-exporter-7qs7v Started container toolkit-validation
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3h1m Normal Pulled pod/nvidia-driver-daemonset-49.84.202201212103-0-zb76w Successfully pulled image "quay.io/openshift-release-dev/ocp-v4.0-art-dev@sha256:89f2c3b7c078a97682d4a375d04d6e45c73099679679dd2e2487ba3632be83d7" in 29.000491244s
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3h1m Normal Created pod/nvidia-driver-daemonset-49.84.202201212103-0-zb76w Created container openshift-driver-toolkit-ctr
7+
3h1m Normal Started pod/nvidia-driver-daemonset-49.84.202201212103-0-zb76w Started container openshift-driver-toolkit-ctr
8+
3h Normal Scheduled pod/mycluster-ray-head-type-jhrqg Successfully assigned nvidia-gpu-operator/mycluster-ray-head-type-jhrqg to ip-10-0-139-77.ec2.internal
9+
3h Normal AddedInterface pod/mycluster-ray-head-type-jhrqg Add eth0 [10.129.84.16/23] from openshift-sdn
10+
3h Normal Pulled pod/ray-operator-56f7b64d7b-wlzcb Successfully pulled image "rayproject/ray:1.13.0-py37" in 40.972650191s
11+
3h Normal Created pod/ray-operator-56f7b64d7b-wlzcb Created container ray
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3h Normal Started pod/ray-operator-56f7b64d7b-wlzcb Started container ray
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3h1m Normal Pulling pod/mycluster-ray-head-type-jhrqg Pulling image "rayproject/ray-ml:1.13.0-py37-gpu"

‎tests/plugin-codeflare/dashboard/inputs/2/job.json‎

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{
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"jobid": "f6c365c4-5fec-41b1-a811-3594c1c0f91d",
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"jobid": "a8998f9b-a4f4-4edd-a440-0340b46ff2bd",
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"cmdline": {
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"appPart": "python3 tmp-65979-Ao7jcCUjajP4 -v -b ${S3_BUCKET} -m ${S3_OBJECTMODEL} -g ${S3_OBJECTGLUEDATA} -t WNLI -M -s 40 41 42 43",
5-
"systemPart": "ray job submit --runtime-env=/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/tmp-65979-l1VQepk5OCSm --job-id ${JOB_ID} --no-wait"
4+
"appPart": "python3 tmp-80978-yaTBykEfMtbc -v -b ${S3_BUCKET} -m ${S3_OBJECTMODEL} -g ${S3_OBJECTGLUEDATA} -t WNLI -M -s 40 41 42 43",
5+
"systemPart": "ray job submit --runtime-env=/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/tmp-80978-9S3zGVjVs2HG --job-id ${JOB_ID} --no-wait"
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},
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"runtimeEnv": {
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"env_vars": {
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"JOB_ID": "f6c365c4-5fec-41b1-a811-3594c1c0f91d",
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"JOB_ID": "a8998f9b-a4f4-4edd-a440-0340b46ff2bd",
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"S3_PROVIDER": "aws",
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"S3_ENDPOINT": "https://s3.amazonaws.com",
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"S3_ACCESS_KEY_ID": "********",
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"AWS_ACCESS_KEY_ID": "********",
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"S3_SECRET_ACCESS_KEY": "********",
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"AWS_SECRET_ACCESS_KEY": "********",
16-
"MC_CONFIG_DIR": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/tmp.kQkTy0d4",
16+
"MC_CONFIG_DIR": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/tmp.eImBHFSt",
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"S3_BUCKETRAYLOGS": "browsey",
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"S3_FILEPATHRAYLOGS": "RAYLOGS",
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"S3_LOGDIR": "browsey/codeflare/f6c365c4-5fec-41b1-a811-3594c1c0f91d",
20-
"LOGDIR_STAGE": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/logdir-stage.TfJpqocD",
21-
"LOGDIR_URI": "s3://browsey/codeflare/f6c365c4-5fec-41b1-a811-3594c1c0f91d",
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"LOGDIR_MC": "s3/browsey/codeflare/f6c365c4-5fec-41b1-a811-3594c1c0f91d",
23-
"STREAMCONSUMER_LOGS": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/logdir-stage.TfJpqocD/logs/",
24-
"STREAMCONSUMER_EVENTS": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/logdir-stage.TfJpqocD/events/",
25-
"STREAMCONSUMER_RESOURCES": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/logdir-stage.TfJpqocD/resources/",
19+
"LOGDIR_STAGE": "/Users/nickm/Library/Application Support/madwizard-nodejs/default/jobs/a8998f9b-a4f4-4edd-a440-0340b46ff2bd",
20+
"STREAMCONSUMER_LOGS": "/Users/nickm/Library/Application Support/madwizard-nodejs/default/jobs/a8998f9b-a4f4-4edd-a440-0340b46ff2bd/logs/",
21+
"STREAMCONSUMER_EVENTS": "/Users/nickm/Library/Application Support/madwizard-nodejs/default/jobs/a8998f9b-a4f4-4edd-a440-0340b46ff2bd/events/",
22+
"STREAMCONSUMER_RESOURCES": "/Users/nickm/Library/Application Support/madwizard-nodejs/default/jobs/a8998f9b-a4f4-4edd-a440-0340b46ff2bd/resources/",
23+
"S3_LOGDIR": "browsey/codeflare/a8998f9b-a4f4-4edd-a440-0340b46ff2bd",
24+
"LOGDIR_URI": "s3://browsey/codeflare/a8998f9b-a4f4-4edd-a440-0340b46ff2bd",
25+
"LOGDIR_MC": "s3/browsey/codeflare/a8998f9b-a4f4-4edd-a440-0340b46ff2bd",
26+
"PATH": "/Users/nickm/.local/bin:/usr/local/opt/python@3.9/Frameworks/Python.framework/Versions/3.9/bin:/Users/nickm/.krew/bin:/Library/Frameworks/Python.framework/Versions/2.7/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Applications/VMware Fusion.app/Contents/Public:/Library/TeX/texbin:/usr/local/go/bin:/Library/Apple/usr/bin:/Users/nickm/bin",
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"KUBE_CONTEXT": "default/api-codeflare-train-v11-codeflare-openshift-com:6443/kube:admin",
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"KUBE_NS": "nvidia-gpu-operator",
29+
"KUBE_POD_LABEL_SELECTOR": "ray-user-node-type=rayWorkerType",
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"KUBE_POD_RAY_HEAD_LABEL_SELECTOR": "ray-user-node-type=rayHeadType",
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"KUBE_PODFULL_LABEL_SELECTOR": "ray-node-type",
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"NUM_CPUS": "1",
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"NUM_GPUS": "1",
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"MIN_WORKERS": "4",
@@ -33,12 +37,10 @@
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"HEAD_MEMORY": "32Gi",
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"RAY_OPERATOR_IMAGE": "rayproject/ray:1.13.0-py37",
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"RAY_IMAGE": "rayproject/ray-ml:1.13.0-py37-gpu",
36-
"HELM_CLONE_TEMPDIR": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/tmp.f0NuCaF1",
40+
"HELM_CLONE_TEMPDIR": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/tmp.3EMkfmLv",
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"RAY_KUBE_CLUSTER_NAME": "mycluster",
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"KUBE_POD_LABEL_SELECTOR": "ray-user-node-type=rayWorkerType",
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"KUBE_PODFULL_LABEL_SELECTOR": "ray-node-type",
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"RAY_KUBE_PORT": "8266",
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"RAY_ADDRESS": "http://127.0.0.1:8266",
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"RAY_KUBE_PORT": "9612",
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"RAY_ADDRESS": "http://127.0.0.1:9612",
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"S3_BUCKET": "browsey",
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"S3_FILEPATH": "browsey",
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"S3_OBJECTMODEL": "roberta-base",
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"WANDB_CONFIG_DIR": "/tmp",
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"WANDB_DISABLED": "true"
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},
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"working_dir": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/tmp-65979-FpQKyyRyOv0T",
53+
"working_dir": "/var/folders/2k/7mgd1tq55gdbghf0xkl2t_l80000gp/T/tmp-80978-UYzNT1wjDJyI",
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"pip": ["boto3", "ray[default]", "ray_lightning", "pytorch_lightning", "torchvision", "transformers==3.0.2"]
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},
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"language": "python",

‎tests/plugin-codeflare/dashboard/inputs/2/jobid.txt‎

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f6c365c4-5fec-41b1-a811-3594c1c0f91d
1+
a8998f9b-a4f4-4edd-a440-0340b46ff2bd

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