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1.0.0b3
Signed-off-by: Oleksii Kuchaiev <[email protected]>
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‎Dockerfile

+1-5
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@@ -44,17 +44,13 @@ WORKDIR /tmp/nemo
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COPY requirements .
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RUN for f in $(ls requirements/*.txt); do pip install --disable-pip-version-check --no-cache-dir -r $f; done
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# build CTC beam search decoder
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COPY scripts/install_ctc_decoders.sh .
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RUN ./install_ctc_decoders.sh
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# copy nemo source into a scratch image
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FROM scratch as nemo-src
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COPY . .
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# start building the final container
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FROM nemo-deps as nemo
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ARG NEMO_VERSION=1.0.0b2
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ARG NEMO_VERSION=1.0.0b3
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# Check that NEMO_VERSION is set. Build will fail without this. Expose NEMO and base container
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# version information as runtime environment variable for introspection purposes

‎Jenkinsfile

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@@ -56,8 +56,8 @@ pipeline {
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stage('L0: Unit Tests CPU') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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steps {
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stage('L0: Computer Vision Integration') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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// stage('L0: Integration Tests CPU') {
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// when {
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// anyOf{
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// branch 'v1.0.0b2'
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// changeRequest target: 'v1.0.0b2'
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// branch 'v1.0.0b3'
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// changeRequest target: 'v1.0.0b3'
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// }
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// }
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// steps {
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// when {
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// anyOf{
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// branch 'dev
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// changeRequest target: 'v1.0.0b2'
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// changeRequest target: 'v1.0.0b3'
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// }
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// }
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// steps {
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stage('L2: ASR dev run') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: ASR Multi-dataloader dev run') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Parallel NER with Megatron') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Parallel BERT SQUAD v1.1 / v2.0') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: MegaBERT Token Classification') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: MegaBERT SQUAD v2.0') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Parallel RoBERTa SQUAD v1.1') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Parallel NLP Examples 1') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Text Classification with Model Parallel Size 2 Megatron BERT') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Parallel NLP Examples 2') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Intent and Slot Classification') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Parallel GLUE Examples') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Parallel GLUE-AutoEncoder Examples') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true
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stage('L2: Parallel Pretraining BERT pretraining from Text/Preprocessed') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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stage('L2: Parallel Pretraining BERT using char/word tokenizer') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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stage('L2: Punctuation & Capitalization, 2GPUs with DistilBERT') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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stage('L2: TTS Fast dev runs 1') {
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when {
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anyOf{
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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parallel {
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branch 'v1.0.0b2'
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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stage('L??: Speech Checkpoints tests') {
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when {
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anyOf{
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changeRequest target: 'v1.0.0b2'
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branch 'v1.0.0b3'
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changeRequest target: 'v1.0.0b3'
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}
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}
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failFast true

‎README.rst

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@@ -65,13 +65,13 @@ Docker containers:
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~~~~~~~~~~~~~~~~~~
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The easiest way to start training with NeMo is by using `NeMo's container <https://ngc.nvidia.com/catalog/containers/nvidia:nemo>`_.
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It has all requirements and NeMo 1.0.0b2 already installed.
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It has all requirements and NeMo 1.0.0b3 already installed.
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.. code-block:: bash
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docker run --gpus all -it --rm -v <nemo_github_folder>:/NeMo --shm-size=8g \
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-p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
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stack=67108864 --device=/dev/snd nvcr.io/nvidia/nemo:v1.0.0b2
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stack=67108864 --device=/dev/snd nvcr.io/nvidia/nemo:v1.0.0b3
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If you chose to work with main branch, we recommend using NVIDIA's PyTorch container version 20.09-py3.
@@ -92,7 +92,7 @@ If you are not inside the NVIDIA docker container, please install Cython first.
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Once requirements are satisfied, simply install using pip:
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* ``pip install nemo_toolkit[all]==1.0.0b2`` (latest version)
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* ``pip install nemo_toolkit[all]==1.0.0b3`` (latest version)
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Or if you want the latest (or particular) version from GitHub:
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@@ -188,59 +188,59 @@ To run tutorials:
188188
- GitHub URL
189189
* - NeMo
190190
- Simple Application with NeMo
191-
- `Voice swap app <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/NeMo_voice_swap_app.ipynb>`_
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- `Voice swap app <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/NeMo_voice_swap_app.ipynb>`_
192192
* - NeMo
193193
- Exploring NeMo Fundamentals
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- `NeMo primer <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/00_NeMo_Primer.ipynb>`_
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- `NeMo primer <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/00_NeMo_Primer.ipynb>`_
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* - NeMo Models
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- Exploring NeMo Model Construction
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- `NeMo models <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/01_NeMo_Models.ipynb>`_
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- `NeMo models <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/01_NeMo_Models.ipynb>`_
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* - ASR
199199
- ASR with NeMo
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- `ASR with NeMo <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/asr/01_ASR_with_NeMo.ipynb>`_
200+
- `ASR with NeMo <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/asr/01_ASR_with_NeMo.ipynb>`_
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* - ASR
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- Speech Commands
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- `Speech commands <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/asr/03_Speech_Commands.ipynb>`_
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- `Speech commands <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/asr/03_Speech_Commands.ipynb>`_
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* - ASR
205205
- Speaker Recognition and Verification
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- `Speaker Recognition and Verification <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/speaker_recognition/Speaker_Recognition_Verification.ipynb>`_
206+
- `Speaker Recognition and Verification <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/speaker_recognition/Speaker_Recognition_Verification.ipynb>`_
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* - ASR
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- Online Noise Augmentation
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- `Online noise augmentation <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/asr/05_Online_Noise_Augmentation.ipynb>`_
209+
- `Online noise augmentation <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/asr/05_Online_Noise_Augmentation.ipynb>`_
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* - NLP
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- Using Pretrained Language Models for Downstream Tasks
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- `Pretrained language models for downstream tasks <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/01_Pretrained_Language_Models_for_Downstream_Tasks.ipynb>`_
212+
- `Pretrained language models for downstream tasks <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/01_Pretrained_Language_Models_for_Downstream_Tasks.ipynb>`_
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* - NLP
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- Exploring NeMo NLP Tokenizers
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- `NLP tokenizers <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/02_NLP_Tokenizers.ipynb>`_
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- `NLP tokenizers <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/02_NLP_Tokenizers.ipynb>`_
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* - NLP
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- Text Classification (Sentiment Analysis) with BERT
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- `Text Classification (Sentiment Analysis) <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb>`_
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- `Text Classification (Sentiment Analysis) <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb>`_
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* - NLP
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- Question answering with SQuAD
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- `Question answering Squad <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/Question_Answering_Squad.ipynb>`_
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- `Question answering Squad <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/Question_Answering_Squad.ipynb>`_
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* - NLP
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- Token Classification (Named Entity Recognition)
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- `Token classification: named entity recognition <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb>`_
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- `Token classification: named entity recognition <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/Token_Classification_Named_Entity_Recognition.ipynb>`_
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* - NLP
226226
- Joint Intent Classification and Slot Filling
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- `Joint Intent and Slot Classification <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb>`_
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- `Joint Intent and Slot Classification <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/Joint_Intent_and_Slot_Classification.ipynb>`_
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* - NLP
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- GLUE Benchmark
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- `GLUE benchmark <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/GLUE_Benchmark.ipynb>`_
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- `GLUE benchmark <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/GLUE_Benchmark.ipynb>`_
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* - NLP
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- Punctuation and Capitialization
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- `Punctuation and capitalization <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/Punctuation_and_Capitalization.ipynb>`_
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- `Punctuation and capitalization <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/Punctuation_and_Capitalization.ipynb>`_
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* - NLP
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- Named Entity Recognition - BioMegatron
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- `Named Entity Recognition - BioMegatron <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/Token_Classification-BioMegatron.ipynb>`_
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- `Named Entity Recognition - BioMegatron <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/Token_Classification-BioMegatron.ipynb>`_
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* - NLP
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- Relation Extraction - BioMegatron
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- `Relation Extraction - BioMegatron <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb>`_
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- `Relation Extraction - BioMegatron <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/nlp/Relation_Extraction-BioMegatron.ipynb>`_
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* - TTS
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- Speech Synthesis
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- `TTS inference <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b2/tutorials/tts/1_TTS_inference.ipynb>`_
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- `TTS inference <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0b3/tutorials/tts/1_TTS_inference.ipynb>`_
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Contributing
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------------

‎nemo/package_info.py

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MAJOR = 1
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MINOR = 0
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PATCH = 0
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PRE_RELEASE = 'b2'
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PRE_RELEASE = 'b3'
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# Use the following formatting: (major, minor, patch, pre-release)
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VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)

‎requirements/requirements.txt

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numpy>=1.18.2
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pytorch-lightning>=1.0.5
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pytorch-lightning>=1.0.5,<1.1.0
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python-dateutil
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torch
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wget
@@ -9,4 +9,4 @@ ruamel.yaml
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scikit-learn
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omegaconf>=2.0.2
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hydra-core>=1.0.3
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transformers>=3.1.0
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transformers>=3.1.0,<4.0.0

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